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Case Study: Inventory Management Practices at Walmart

About walmart.

Wal-Mart Stores, Inc. is the largest retailer in the world, the world’s second-largest company and the nation’s largest nongovernmental employer. Wal-Mart Stores, Inc. operates retail stores in various retailing formats in all 50 states in the United States. The Company’s mass merchandising operations serve its customers primarily through the operation of three segments. The Wal-Mart Stores segment includes its discount stores, Supercenters, and Neighborhood Markets in the United States. The Sam’s club segment includes the warehouse membership clubs in the United States. The Company’s subsidiary, McLane Company, Inc. provides products and distribution services to retail industry and institutional foodservice customers. Wal-Mart serves customers and members more than 200 million times per week at more than 8,416 retail units under 53 different banners in 15 countries. With fiscal year 2010 sales of $405 billion, Wal-Mart employs more than 2.1 million associates worldwide. Nearly 75% of its stores are in the United States (“Wal-Mart International Operations”, 2004), but Wal-Mart is expanding internationally. The Group is engaged in the operations of retail stores located in all 50 states of the United States, Argentina, Brazil, Canada, Japan, Puerto Rico and the United Kingdom, Central America, Chile, Mexico,India and China.

Walmart Inventory Management

Wal-Mart had developed an ability to cater to the individual needs of its stores. Stores could choose from a number of delivery plans. For instance, there was an accelerated delivery system by which stores located within a certain distance of a geographical center could receive replenishment within a day. Wal-Mart invested heavily in IT and communications systems to effectively track sales and merchandise inventories in stores across the country. With the rapid expansion of Wal-Mart stores in the US, it was essential to have a good communication system. Hence, Wal-Mart set up its own satellite communication system in 1983. Explaining the benefits of the system Walton said, “I can walk in the satellite room, where our technicians sit in front of the computer screens talking on the phone to any stores that might be having a problem with the system, and just looking over their shoulders for a minute or two will tell me a lot about how a particular day is going. On the screen, I can see the total of the day’s bank credit sales adding up as they occur. If we have something really important or urgent to communicate to the stores and distribution centers, I, or any other Wal-Mart executive can walk back to our TV studio and get on that satellite transmission and get it right out there. I can also go every Saturday morning around three, look over these printouts and know precisely what kind of work we have had.”

Wal-Mart was able to reduce unproductive inventory by allowing stores to manage their own stocks, reducing pack sizes across many product categories, and timely price markdowns. Instead of cutting inventory across the board, Wal-Mart made full use of its IT capabilities to make more inventories available in the case of items that customers wanted most, while reducing the overall inventory levels. Wal-Mart also networked its suppliers through computers. The company entered into collaboration with P&G for maintaining the inventory in its stores and built an automated reordering system, which linked all computers between P&G and its stores and other distribution centers. The computer system at Wal-Mart stores identified an item which was low in stock and sent a signal to P&G. The system then sent a re-supply order to the nearest P&G factory through a satellite communication system. P&G then delivered the item either to the Wal-Mart distribution center or directly to the concerned stores. This collaboration between Wal-Mart and P&G was a win-win proposition for both because Wal-Mart could monitor its stock levels in the stores constantly and also identify the items that were moving fast. P&G could also lower its costs and pass on some of the savings to Wal-Mart due to better coordination.

Employees at the stores had the ‘Magic Wand,’ a hand-held computer which was linked to in-store terminals through a radio frequency network. These helped them to keep track of the inventory in stores, deliveries and backup merchandise in stock at the distribution centers. The order management and store replenishment of goods were entirely executed with the help of computers through the Point-of-Sales (POS) system. Through this system, it was possible to monitor and track the sales and merchandise stock levels on the store shelves. Wal-Mart also made use of the sophisticated algorithm system which enabled it to forecast the exact quantities of each item to be delivered, based on the inventories in each store. Since the data was accurate, even bulk items could be broken and supplied to the stores. Wal-Mart also used a centralized inventory data system using which the personnel at the stores could find out the level of inventories and the location of each product at any given time. It also showed whether a product was being loaded in the distribution center or was in transit on a truck. Once the goods were unloaded at the store, the store was furnished with full stocks of inventories of a particular item and the inventory data system was immediately updated.

Wal-Mart also made use of bar coding and radio frequency technology to manage its inventories. Using bar codes and fixed optical readers, the goods could be directed to the appropriate dock, from where they were loaded on to the trucks for shipment. Bar coding devices enabled efficient picking, receiving and proper inventory control of the appropriate goods. It also enabled easy order packing and physical counting of the inventories. In 1991, Wal-Mart had invested approximately $4 billion to build a retail link system. More than 10,000 Wal-Mart retail suppliers used the retail link system to monitor the sales of their goods at stores and replenish inventories. The details of daily transactions, which approximately amounted to more than 10 million per day, were processed through this integrated system and were furnished to every Wal-Mart store by 4 a.m., the next day. In October 2001, Wal-Mart tied-up with Atlas Commerce for upgrading the system through the Internet enabled technologies. Wal-Mart owned the largest and most sophisticated computer system in the private sector. The company used Massively Parallel Processor (MPP) computer system to track the movement of goods and stock levels. All information related to sales and inventories was passed on through an advanced satellite communication system. To provide back-up in case of a major breakdown or service interruption, the company had an extensive contingency plan. By making effective use of computers in all its company’s operations, Wal-Mart was successful in providing uninterrupted service to its customers, suppliers, stockholders and trading partners.

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• Inventory Management

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Poor Inventory Management Examples Made by Huge Brands

As many retailers can attest, poor inventory management can seriously harm a company and its brand/brands, leading to short-term financial damage, a fall in stock prices, bankruptcy, or company closure.

From small businesses to companies with large inventories, there's always a concern with the everyday and long-term challenges of inventory management. Some companies may look at updating legacy management systems, while others look for real-time ways to future-proof the business. High-profile, well-known, and loved brands are not exempt from inventory control problems, and many retailers can learn valuable lessons from their mistakes.

How Poor Inventory Management Can Kill a Brand

When supply chain and inventory problems arise, retailers face some consequences. Here are some poor inventory management examples:

1)  Using outdated methods to track items , such as:

• Manual inventory tracking , which becomes time-consuming and error-prone as your company grows. You’ll always be one step behind your actual inventory levels, which will cause ordering issues.
• Excel/electronic spreadsheets , which are prone to severe errors. In a study of errors in 25 sample spreadsheets, the  Tuck Business School at Dartmouth College   found that 15 workbooks contained 117 errors. While 40% of those errors had little impact on the businesses studied, seven errors caused massive losses of $4 million to $110 million, according to the researchers’ estimates.

2)     Too large of an inventory . Reports show that most businesses have 20-40% of their working capital tied up in inventory. If you have a greater amount of product than what demand calls for, you won't be able to fulfill orders optimally. Large stock levels don’t just lead to more management headaches; they can also cut into profits and cause dead stock.

3)     Inadequate reports and demand forecasting . When companies don’t use or have access to accurate information such as sales trends, best-selling products, customer behavior, and the like – they fall into the trap of ordering too much inventory. When this happens, companies experience the problems of an excessive amount of finished goods, ordering too few finished products, experiencing shortages, and losing customers. With accurate real-time reporting accessible 24/7, anytime, from anywhere, companies can forecast their customers’ future behavior and order accordingly to meet customer demand without exceeding their budget.

Four Examples of Poor Inventory Management

Let’s take a look at four high-profile brands and how a crisis of inventory created some real problems that companies could have prevented had they managed their inventory properly:

Nike’s Excess Inventory Problem

As one of the world's most recognized athletic brands, Nike has many goods to manage. And as a result, it has had difficulty keeping inventory under control. In the early 2000s, the company adopted an updated  inventory management software  after losing around $100 million in sales due to issues with tracking goods. The software promised to help Nike predict items that would sell best and prepare the company to meet demands, but bugs and data errors resulted in incorrect demand forecasts and led to millions more lost.

Nike’s case illustrated just how crucial it is to correctly manage stock levels and your inventory management system. When choosing an inventory management solution, it’s vital to ensure the quality of software your vendor provides is accurate, flexible, and customized for your particular business. It needs to be able to grow and change as the business and the customer base change.

Nike continues to have issues with inventory. 2016 was challenging for the retailer, as Nike's gross margin declined due to a higher percentage of discounted sales because of inventory management problems. The retailer continues to take steps to control its inventory management practices through manufacturing overhauls better and allowing new technology to bring manufacturing to the digital age. Ultimately Nike will remain a global leader as it keeps exploring new markets, innovating new products, and generating its supply channels.

Best Buy’s Christmas Inventory Nightmare

In December 2011—smack dab in the middle of the holiday season— Best Buy issued a statement : “Due to the overwhelming demand of hot product offerings on BestBuy.com during the November and December period, we have encountered a situation that has affected redemption of some of our customers’ online orders. We are very sorry for the inconvenience this has caused, and we have notified the affected customers.”

Customers were infuriated by Best Buy’s decision to cancel orders instead of delaying shipment, which was most likely because the company ran out of stock. Reportedly Best Buy sold many of the withdrawn items on Black Friday. The retailer essentially cast a wide net, collecting as many orders as possible, likely knowing it would be unable to fulfill them all.

As Best Buy proved, buying items online as an alternative to in-store still carries a risk. Consumers don't know for a fact that they will get their product. While receipts are issued and shipping estimates are given, some variables still allow consumers to be 100% certain their purchase will be complete every time. It’s not hard to imagine that Best Buy probably lost many of its customers to Amazon after that 2011 debacle.

Target’s Disastrous Failed Expansion into Canada

Target is a well-loved brand in the US, so it seemed only natural that it would be just as well-received with expansion up north into Canada. Target executives had a decision to make. They needed a way to track their stock levels and chose to work with an entirely new and untested system. Target Canada would eventually learn what happens when inexperienced employees working under a tight timeline are expected to launch a retailer using technology that nobody—not even at the US headquarters—understood.

In 2013, the company had  trouble moving products  from its large distribution centers onto store shelves, leaving Target outlets poorly stocked. It didn’t take long for Target to figure out the underlying cause of the breakdown: The data contained within the company’s supply chain software, which governs the movement of inventory, was riddled with flaws. The checkout system was glitchy and didn’t process transactions properly. Worse, the technology managing inventory and sales were new to the organization; no one seemed to fully understand how it worked.

Why Too Much Inventory is Bad

Besides technology issues, problems of ordering and inventory were running amok. Target stalled items with long lead times coming from overseas—products weren’t fitting into shipping containers as expected, or tariff codes were missing or incomplete. Finished goods that made it to a distribution center couldn’t fulfill orders for shipping to a store. Other items weren’t able to fit correctly onto store shelves. What appeared to be isolated fires quickly became a raging inferno threatening to destroy the company’s supply chain.

Target’s distribution centers were bursting with products and dead stock. Target Canada had ordered way more stock than it could sell. The company had purchased a sophisticated forecasting and replenishment system, but it wasn’t beneficial at the outset, requiring years of historical data to provide meaningful sales forecasts. When the buying team was preparing for store openings, it relied on wildly optimistic projections developed at US headquarters.

Roughly two years after they launched, Target Canada filed for creditor protection, marking the end of its first international foray and one of the most confounding sagas in Canadian corporate history. The debacle cost the parent company billions of dollars, sullied its reputation, and put roughly 17,600 people out of work.

Supply Chain Disruption Closed 900 KFC Branches in the UK

In February 2018, Kentucky Fried Chicken (KFC) was forced to  close many of its 900 UK branches due to supply chain disruption . In a press release, the fast-food giant stated, “We've brought a new delivery partner onboard, but they've had a couple of teething problems - getting a fresh chicken out to 900 restaurants across the country is pretty complex!”

By changing their delivery partner, approximately 750 KFC outlets across the UK faced delays in receiving their daily delivery of fresh chicken, meaning their restaurants could not supply customers and ultimately had to close. At the time, many thought the giant could lose up to £ 1 million daily.

Could KFC have done more to ensure their supplier was suitable for the job? The thought is that multiple supplier contracts could have spread KFC could have avoided the weight of the mammoth delivery task and a crisis like this. Another issue was that their supplier only had one distribution spot instead of multiple, which would have been able to service the outlets much more manageable.

What Can Businesses Learn From Inventory Management Problems?

It takes more than having a large inventory of products to keep a retail business running. All that inventory must be stored, moved, and in the right place at the right time. Warehouses need to be efficient, and their tools and workhorse vehicles are kept up to date. Every part of the supply chain needs to coordinate, from obtaining raw materials to distributing finished goods.

The same can be said about the technology to track and manage the inventory as it moves locations. Companies need to know accurate numbers when it comes to inventory. Their livelihood, franchisees, investors, and employees depend on it! When you don’t see what you have or how/when it moves about, there’s no actual knowledge about the most critical aspect of your business – your inventory.

Retailers of all sizes are looking for easy-to-use, mobile, affordable, secure, and rapidly deployable  asset tracking systems . They need a reliable method to track the thousands of inventory items that move through their location(s)/warehouses daily. Asset Panda is the answer. We leverage the cloud and free mobile apps to help retailers get the information they need about their inventory. Our retail and small business clients know where their inventory is, who has what, and its condition.

Asset Panda's Inventory Management System

Asset Panda is simple to use with a very intuitive platform. Our system records the entire lifecycle of an asset. Other capabilities include custom reports, depreciation calculation, mobile enterprise service desk, and more. Leading retailers recognize that better inventory tracking processes will lower business costs by reducing loss, property taxes, and the amount of insurance they must carry. All the home offices must run reports to get detailed, real-time data from the field.

Try our inventory management software  free for 14 days  so you can see what’s truly possible when you manage your inventory the right way! (No credit card required).

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Inventory Management Software: A Case Study

• Post author: Maryliya M J
• Post published: January 16, 2024
• Reading time: 13 mins read

Table of Contents

Inventory management plays a crucial role in the success of businesses across industries. Efficiently managing inventory ensures that the right products are available at the right time, minimizing stockouts and carrying costs. To streamline this process and enhance operational efficiency, businesses are increasingly turning to inventory management software solutions.

Introduction to Inventory Management Software

What is inventory management software.

Inventory management software is a powerful tool that helps businesses track, control, and optimize their inventory. It provides real-time visibility into stock levels, automates essential tasks like reordering and restocking, and enables businesses to make data-driven decisions to prevent stockouts or overstocking.

Importance of Inventory Management in Business Operations

Effective inventory management is crucial for businesses of all sizes. It ensures that the right products are available at the right time, minimizes storage costs, prevents stock obsolescence, and enhances customer satisfaction. By streamlining inventory processes and improving accuracy, businesses can reduce the risk of errors, increase operational efficiency, and ultimately boost profitability.

The Need for Efficient Inventory Management

Common inventory management challenges.

Managing inventory can be a complex and challenging task. Some common challenges businesses face include inaccurate demand forecasting, manual tracking leading to errors, inefficient order fulfillment, poor visibility of stock levels, and difficulties in identifying slow-moving or obsolete items.

Impact of Inefficient Inventory Management

Inefficient inventory management can have far-reaching consequences for a business. It can result in stockouts, leading to lost sales and dissatisfied customers. Conversely, overstocking ties up capital, increases storage costs, and risks product obsolescence. In addition, inefficient inventory management can lead to inaccuracies in financial reporting, hinder supply chain efficiency, and hamper overall business growth.

About the Client

Our client, a retail chain, faced challenges in tracking inventory levels across multiple stores. With a growing inventory and diverse product lines, they sought to enhance their stock control, automate reorder processes, and optimize inventory turnover. Recognizing the need for a robust solution, they engaged in the development of a tailored Inventory Management Software ( IMS ).

Project Overview

The project aimed to develop a comprehensive .NET-based IMS to address the client’s challenges. The primary objectives included automating stock control, integrating with point-of-sale (POS) systems, providing real-time stock visibility, and incorporating predictive analytics for effective inventory planning and ordering.

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The challenges.

• Manual Inventory Tracking: The existing manual processes for inventory tracking led to inaccuracies and inefficiencies.
• Lack of Real-time Visibility: Limited visibility into stock levels across multiple stores resulted in delays and stockouts.
• Inefficient Reorder Processes: Manual reorder processes were time-consuming and prone to errors.

The Solution

Our team of skilled developers and project managers collaborated to design and implement a comprehensive .NET-based Inventory Management Software. The solution included modules for automated stock control, integration with POS systems, real-time stock visibility, and predictive analytics for optimized inventory planning and ordering.

Key Features of the IMS

• Automated Stock Control: The IMS automated stock control processes, ensuring accurate and real-time tracking of inventory levels.
• POS Integration: Seamless integration with point-of-sale systems provided synchronized data, enhancing overall efficiency.
• Real-time Stock Visibility: The software offered real-time visibility into stock levels across multiple stores, reducing delays and stockouts.
• Predictive Analytics: Advanced analytics tools provided insights into inventory trends, facilitating proactive planning and ordering.

The Outcome

The Inventory Management Software was successfully deployed, resulting in significant improvements in stock control and inventory management. Automated processes, POS integration, and predictive analytics contributed to a more streamlined and optimized inventory turnover.

Conclusion: The Future of IMS

Our team’s expertise in developing a tailored Inventory Management Software using .NET technologies effectively addressed the client’s challenges. The implementation of automated stock control, POS integration, and predictive analytics tools contributed to a more efficient and responsive inventory management system.

In conclusion, inventory management software has proven to be a valuable tool for businesses seeking to optimize their inventory management processes. Through the case study analysis and exploration of key features and benefits, it is evident that implementing such software can lead to improved efficiency, cost reduction, and better decision-making. While challenges may arise during implementation, adopting best practices and staying proactive can ensure a successful integration. As technology continues to advance, the future of inventory management software holds great promise, with advancements such as AI and predictive analytics poised to revolutionize how businesses manage their inventory. By embracing these innovations and staying ahead of the curve, businesses can unlock new levels of productivity and success in their operations.

Are you struggling with inventory tracking and management challenges? Contact us today to explore how our expertise in IMS development can transform your inventory processes and drive efficiency.

What is inventory management software?

Inventory management software is a technology solution designed to track, organize, and manage a company’s inventory levels, orders, and stock movements. It provides businesses with real-time visibility into their inventory, helping them streamline operations, reduce costs, and improve customer satisfaction.

What are the benefits of using inventory management software?

Inventory management software offers several benefits, including improved inventory accuracy, reduced stockouts and overstocking, enhanced order fulfillment, increased operational efficiency, better forecasting, optimized purchasing decisions, and improved customer service. It also helps automate manual tasks, centralize data, and provide actionable insights for informed decision-making.

What challenges can occur during the implementation of inventory management software?

Implementing inventory management software can come with challenges such as data migration and integration, staff training and adaptation, resistance to change, customization requirements, and potential disruptions to ongoing business operations. However, with proper planning, communication, and support, these challenges can be overcome, leading to a successful implementation.

How does inventory management software contribute to the future of inventory management?

Inventory management software is constantly evolving to meet the changing needs of businesses. The future of inventory management software lies in advancements such as artificial intelligence (AI), machine learning, internet of things ( IoT ), and predictive analytics. These technologies will enable businesses to automate processes, optimize inventory levels, improve demand forecasting accuracy, and gain valuable insights for strategic decision-making, ultimately driving efficiency and profitability.

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Inventory Analytics for Data-Driven Inventory Management

With vast amounts of data being constantly pulled from multiple data sources, it can be hard to accurately predict and optimize operations without a solid Business Intelligence (BI) strategy. By leveraging advanced data solutions , organizations can gain valuable insights, uncover patterns, and make data-driven decisions to improve their operations and achieve strategic goals. Inventory analytics plays a crucial role in this process by providing actionable information on inventory levels, demand forecasting, and supply chain optimization.

In today's dynamic market, inventory analytics solutions are essential for strategic inventory management – ensuring you have the right inventory, the right volume, and at the right location. With online retail sales showing no signs of slowing, the opportunity for business growth is monumental. The challenge, however, is that without successful inventory management, it becomes difficult to evolve with the ecommerce industry and customer’s expectations around a multitude of things — not least of all product availability. By failing to have the stock to fulfill demand, you are at risk of missed sales. This can further lead to a damaged reputation and loss of future customers. By achieving speed and accuracy, inventory management capabilities go beyond ensuring accurate inventory and automating key business processes, which was once considered to be a revolutionary development in ecommerce. Today’s inventory control systems now also hold the key to powering business insights that can improve your ability to make data-driven decisions for increased productivity and profitability. SME’s inventory management solutions and data management capabilities provide organizations with competitive efficiency. While balancing product availability against anticipated market demand and the ability to forecast future demand has historically been a difficult task, advanced inventory management systems leverage historical data and apply data analytics to process vast quantities of your past sales data and factoring in lead times and seasonality. In this era of big data, an inventory system equipped with advanced analytics solutions goes beyond just managing stock. It can also provide unparalleled insights into customer behavior, product performance, and channel performance, even for large retailers with massive datasets. However, unlocking these valuable insights requires a robust data strategy and data literacy among your team members. This empowers them to effectively analyze and interpret the data, transforming it into actionable business intelligence. Now is the time to put your big data to work in a big way, but remember, investing in data literacy is crucial to maximizing its potenti al.

Here are two case studies where inventory management saved SME's customer time and money. 

Inventory Management for Supply Chain & Logistics

Inventory management for energy & utility.

This case study delves into the transformative impact of implementing an effective inventory management system within a supply chain and logistics company. The initiative yielded remarkable results, demonstrably improving efficiency and financial performance across various metrics. Holding costs witnessed a significant 24% reduction , indicative of streamlined inventory management and minimized storage expenses. Data availability soared by an impressive 97% , empowering better decision-making through enhanced access to real-time inventory information. Furthermore, inventory turn rate accelerated by 5. 4% , signifying swifter product movement through the supply chain and potentially improved cash flow. These remarkable achievements stand as a testament to the power of optimized inventory management, highlighting its potential to unlock significant value for businesses in the supply chain and logistics domain.

During the project, these were the business questions the SME Solutions Group team addressed:

• How much inventory is required to meet demand while keeping stock levels to a minimum?
• How to optimize the management of stock?
• How to reduce the impact of product recalls?
• How do we prevent stock-outs or surplus of inventory?

Inventory management solutions are now equipped to apply a level of AI to power insights such as those listed above.

Challenge: 

The primary challenge was the lack of consistent views across multiple data sources and lack of business user access to inventory data. The product managers had very little idea about product turnover rates and there was a large need for better production planning and performance metrics. The reports were also generated manually, and ad-hoc reports were created on-the-fly with minimal consistency or governance around queries/calculations. There also was no “real-time” analysis, generating reports from the system required customization and took time to build, not to mention the IT structure was very centralized.

Here are the key challenges:

• No insight into how much inventory is "on-order" or "in-transit"
• Need to understand what the overhead cost to holding product is
• Would like to be able to forecast product demand and supply
• Want to understand which suppliers are more reliable and charge the least in transit costs
• Trying to evaluate what location to build next warehouse

Key Performance Indicators: 

At the heart of our analysis lies a rich dataset teeming with valuable information. It meticulously tracks product availability , ensuring we have clear visibility into stock levels and potential bottlenecks. Additionally, it captures sales demand , allowing us to anticipate customer needs and adjust inventory accordingly. Vendor performance is also closely monitored through vendor reliability data, enabling us to identify and partner with suppliers who consistently deliver on time and in full. By weaving together these data points, we gain a holistic understanding of our inventory ecosystem, paving the way for informed decision-making and optimized operations.

Key Initiatives: 

A focus of this project was to support the customer as they embraced a comprehensive DataOps approach to revolutionize their inventory management. By automating data ingestion, cleaning, and transformations through a modern DataOps pipeline, they were able to achieve real-time access to reliable inventory information. A centralized cloud data warehouse served as the single source of truth, eliminating scattered data silos and ensuring consistency across reports and analyses.

Empowering users was key. SME helped the customer select and implement user-friendly Business Intelligence tools, enabling sales reps, analysts, and executives to access current stock values and vital metrics on demand, eliminating time-consuming IT requests. The BI solution offered self-service options with pre-built dashboards and reports, further boosting efficiency and agility.

By incorporating advanced algorithms, the customer gained deeper insights into inventory trends, unlocking predictive capabilities. This enabled proactive forecasting, optimized stock levels, and reduced the risk of stockouts or overstocking, driving significant cost savings and improved customer service.

This case study showcases how a combination of DataOps, cloud-based data management, self-service analytics, and AI can transform inventory management, empowering businesses to make data-driven decisions, streamline operations, and achieve lasting success.

Prior to implementing BI for Managing Inventory, there was very poor insight into operations and decisions were being made without sufficient information or context. For instance, sales reps were selling products before there was any inventory on hand, and customers were getting frustrated with limited availability/lengthy wait times for their purchases.

Modernizing the customer's BI efforts made inventory data available in real-time to all business users. The product managers now have a pulse on how much inventory is on-hand vs. in-transit, as well as backordered by supplier.

One of the most critical insights developed was a chart listing products that needed to be shipped based on months of inventory left on hand vs. a trend chart of freight prices. The product manager identified a cyclical pattern in price, and decided to delay the shipments from that vender until month-end to reduce costs. Overall, the business was able to reduce holding costs of on-hand inventory, improve the accuracy and turn rate of inventory, and give the business access to the reports they needed to be successful.

This case study is about an energy and utility customer that looked to SME Solutions Group to design and implement a solution that automated 100% of the inventory reporting allowing users to focus on data analytics instead of data gathering and data entry. As an outcome of the project, SME was able to greatly reduce the number of queries to the customer's SAP system by giving all of the shop managers across the company the same version of the data.

This effort resulted in a $4 million reduction from the customer’s balance sheets by rebalancing their inventory across storage locations and distribution centers.

Throughout the project, several key performance indicators (KPIs) were closely monitored to gauge the effectiveness of the inventory management solution. These included:

• Inventory Level by Operating Company: This metric tracked the amount of inventory held by each individual operating company, ensuring optimal stock distribution and avoiding unnecessary carrying costs.
• Turnover Ratios: By measuring how often inventory was sold and replaced, this KPI revealed inventory efficiency and identified potential issues like overstocking or understocking.
• Burn Rates: Monitoring the rate at which inventory was depleted provided insights into demand trends and helped optimize purchasing decisions, preventing stockouts and minimizing waste.
• Purchase Order Forecasts: Accurate purchase order forecasts were crucial for maintaining balanced inventory levels and avoiding costly disruptions in the supply chain. By analyzing these KPIs, the solution ensured efficient inventory management, leading to the significant cost savings achieved.

There was a lot of agile development with the end-users in order to define the complex business rules around storage locations, operating companies, and distribution centers.

Frustration mounted for business users grappling with meter inventory data trapped within their clunky SAP system. Managers lacked clear visibility into crucial details like current inventory levels, hindering informed decision-making. Compounding the problem, SAP's table and field structures resembled an arcane code, far from the user-friendly experience they craved. This energy and utility customer desperately needed a solution that would empower meter shop managers with a panoramic view of inventory across all operating companies. Imagine readily accessible data on turnover ratios, burn rates, and purchase order forecasts, all presented in a clear, intuitive format. Such a transformation would result in informed inventory management, optimized resource allocation, and ultimately, a significant boost to operational efficiency.  

"Our goal was to provide a single version of the data and streamline reporting and testing." - SME Solutions Engineer

Resolve SAP Extraction Issues

Some of the IT issues we resolved revolved around extracting the utility data out of SAP due to heavy regulations in their production environment. The SAP table and field structures were not very intuitive and business friendly. The reload times were longer than expected. We narrowed down the necessary dataset and removed the unnecessary noise to improve data extraction efficiency. 

Develop Master Calendar Rules

Strategic master calendar rules were developed to handle various datasets including historical purchase orders, historical deliveries, current inventory levels, future planned deliveries, and future planned install rates. By creating flags during the script runtime, we were able to utilize them in our calculations efficiently to determine inventory burn rates and turnover ratios.

Taking an Agile Approach

The Agile approach embraces the constant changes that occur in the development of technology – allowing teams to break the lengthy requirements, build, and test phases down into smaller segments, ultimately delivering working software quickly and more frequently. This methodology was critical when working with  the end-users to define the complex business rules around storage locations, operating companies, and distribution centers.

The inventory management solution   automated 100% of the inventory reportin g allowing users to focus on data analytics instead of data gathering and data entry. We greatly reduced the number of queries to their SAP system by giving all of the shop managers across the company the same version of the data.

The initial goal for the inventory management solution was to be able to track the locations of current inventory,   forecast supply , and manage purchase orders. But by the end of the first few development cycles, the solution quickly provided greater insight to current inventory levels, turnover ratios, and reports that allowed our customer to   reduce their inventory liabilities by $4 million . 

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• Cases Cases

Inventory Management Software Development [Case Study]

The past few years have heralded a notable increase in the acceptance of new, cutting-edge inventory management solutions. The pivotal factors in this surge include the fast rise in e-commerce popularity, demand for RFID technology, widespread adoption of smartphones and other mobile devices, and the need to reduce supply chain inefficiencies. These have effectively taken down the reluctance of most businesses to switch from legacy systems and invest in inventory management software development.

Therefore, the market, which was at 3 Billion (USD) in 2019, is expected to grow at a CAGR of over 5% between 2020 and 2025, reaching 5 Billion (USD) according to Global Market Insights .

Source: Global Market Insights

Impressively, it would be no surprise if these numbers are surpassed as the demand for robust software to improve and control the supply chain across the e-commerce and manufacturing sectors is predicted to further fuel the market demand. So, to stay competitive and efficient, companies are now looking to create inventory management software with the help of development companies like Appvales, boasting an excellent track record building MVPs for both start-ups and large enterprise systems.

That being said, the development process focuses on ensuring the product enables the seamless, efficient tracking of inventory levels, orders, sales, and deliveries. In addition, It can also create work orders, bills of materials, and other production-related documents in the manufacturing industry. However, every software is unique depending on one’s needs. Therefore, to help you communicate effectively with the development team on what you expect, here are some core features that you should consider for your project.

Core features of Inventory Management Software

When starting an inventory management software project, one must inform the development team of the features they need to integrate into their system. These features will determine the solution’s ease of use, efficiency, and productivity, hence, the importance of knowing them and selecting which to include. The most common inventory management system software features include:

Reorder Point

Stock management software provides the user a product list and the required stock quantities. When the quantities reach a certain pre-programmed point, the software immediately alerts the manager to reorder a predetermined amount of the product. Therefore, the shortage of and excess stocking is prevented.

Asset Tracking

With this stock management feature, one can use radio frequency identification, barcodes, serial or lot tracking numbers to track their warehouse or store products.

Product Listing

With this feature, one can systematically keep their items by creating each item’s alternative based on its principal attributes. In addition, the feature permits the user to organize their inventory, using essential details like availability, cost, SKUs, and so on. Therefore, this is one feature highly recommended to include in inventory software development.

Barcode Scanning

This inventory tracking software makes use of product barcodes to keep track of them. To eliminate the need for expensive scanning hardware, smartphones can be employed in the exchange of electronic data.

Reporting Tools

Many reporting tools are used to develop inventory management systems’ order history, transaction reports, and total reports. When included in the inventory management software development, these tools let the user create a smooth workflow by providing information on a stock that needs re-ordering, is profitable, and more.

Inventory Forecasting

To avoid having too much or too little stock, one has to decide how much to buy. This feature makes the decision process easier by forecasting how much your type and size of the company would need to purchase. Therefore, one makes the right decisions and protects their productivity and sales.

Inventory Alerts

When inventory levels drop below the user’s preset threshold, alerts are created and sent to them via SMS or emails. This way, one knows when it’s time to restock in advance.

Accounting Tools

Overhead allocation, cost layering, disclosures, measurements, and more are accounting tools that aid the derivation of a more precise evaluation of stock. This tool removes the struggle of manual evaluation, thereby saving time and money while being more efficient.

Raw Material Tracking

This is used in deciding when there are adequate raw materials for more or new jobs in production. Therefore, one can more reliably determine more productive alternatives for the workforce to focus on. Labor costs are also kept in check with this tool.

Inventory Levels for Parts and Finished Products

This ensures the user knows what is readily available and ready for purchase and instant delivery to the client. These could be finished products in the warehouse or in the store.

Automatic Re-ordering

With this feature, reordering stock is automatically done once the minimum limit is reached, ensuring you’re always on top of your inventory.

Integrations with ERP or Maintenance Software

Re-entering information in separate programs can be cumbersome. Therefore, making sure developers integrate with ERP or maintenance software during inventory management software development is critical to the seamless flow of data.

Multiple Location Support

A tool vital for companies with multiple locations that ensures data across all sites is compiled and tracked in one system. This way, one can keep track of the total business stock effectively.

Shelf and Bin Tracking

Managers must know where a particular item is located in the warehouse for access without delay hence the need for this feature. For a personalized experience, your specifications determine the inventory tracking system design.

Order picking support

This feature helps put the customer’s order together in the required quantities before shipment.

Real-time Inventory Tracking

With this feature, one can track their inventory anywhere it might be in real-time.

Serial Number Tracking

From the moment an item is received and its serial number recorded in the system, it can be tracked until it is released. When a product is returned, its serial number in the system can be used for verification.

Expiration Date Tracking

This feature allows one to know the expiry dates of products in the warehouse or store. Therefore, one can plan informed which products have upcoming expiry dates.

Item Images

With this, you can assign multiple images to an item in the warehouse or set one image for quick reference.

Price/ Cost List

This feature allows you to pre-set price levels to items that you can easily quote to customers.

A third-party logistics feature brings transparency to inventory logistics. This includes trackers and transport analytics which provide users and managers with location and estimated time of arrival information.

Inventory Management Trends:

There are top trends not to miss when one endeavor to create inventory management software that stands the test of time and maximizes on the latest technological advancements. Here are the current top trends in inventory management software development:

Going mobile

A company that integrates mobility into their inventory management software project will:

• Enhance visibility of all the processes on all levels.
• Get real-time inventory management.
• Reduce cycle counts and physical inventory times.
• Monitor all procedures effortlessly.

AI and machine learning

The value of artificial intelligence and machine learning in inventory software development cannot be emphasized enough. This is because AI in inventory management enhances process optimization, gathering insights, and making informed decisions. This allows for demand forecasting, cost optimization, maintenance scheduling, and, most importantly, intelligent decision-making.

Computer vision

This technology allows a computer or machine to read and interpret image and video information. Some of the applications of computer vision inventory management include:

• Barcode and text label scanning.
• Labeling, tracking, and tracing.
• Packaging inspection.

Due to its application in these management areas, CV benefits the user through:

• Stocktaking improvement
• Human error mitigation.
• Increase in productivity, and more.

Those who have been following banking, investing, or cryptocurrency over the last ten years may have heard the term “blockchain.” This is a specific type of database that differs from a typical one in how it stores information. Blockchains store data in blocks that are subsequently chained together. The use of this technology in inventory management is forecasted to rise to 54% over the next five years from a meager 5% recorded in 2018.

Drone inventory management might sound a bit far-fetched. However, it shouldn’t in a world where companies like Google and Amazon deliver products to customers via drones. These devices are taking off in the logistics sector, serving as support for stock management and other tasks. For example, warehouse drones are fast becoming game changers in taking annual inventory, which usually involves stopping all or part of the facility’s activity so that operators can scan all of the items. Drones could free workers from tedious jobs in this operation and enable the warehouse to continue operating normally.

In addition, the use of warehouse drones in manual inventories essentially puts an end to human error and inefficiencies. Drones independently fly around the facility, scanning barcodes, and RFID tags while informing the WMS of their findings and progress.

Therefore, drone use can be beneficial for automatically avoiding stock mismatches and ensuring that warehouse activity continues as usual while taking annual or periodic inventory.

Going to cloud

Cloud-based inventory management systems can be accessed from all types of devices. Also, this inventory management system software is IoT-driven, making it easier to record all the inventory details intelligently and smartly.  IoT keeps the inventory optimized and efficiently organized as all relevant company employees get real-time updates on a wide range of devices. A significant cloud computing advantage is information storage for ease of access and loss protection.

How to Build Inventory Management Software

To create inventory management software, there are steps that you have to go through to ensure everything goes as planned. Two steps come before hiring the development team and two more after. None of these is any less than the other and should be followed religiously. The steps are:

• Write the requirements and user stories.

Approaching a software development team before you know what you require of them and without proper user stories is a waste of time. In addition, the requirements and user stories have to be given in the right way.

Requirements:

Since there will be changes during the development process, giving, too detailed and concrete requirements is impossible. Instead, the service, function, or feature you need should be stated in a way that leaves room for flexibility without tying these specifications to a solution. Therefore, mentioning the desired achievement without stating how to achieve it is the optimum way of giving requirements. For instance, ‘a flight method’ instead of saying ‘a helicopter’ gives the developers more room to create inventory management software that caters to your needs. Note that to cover all bases, your set of requirements must mention both the functional and non-functional features of the solution.

User stories:

Also known as Epics, user stories for inventory management system are requirements stated from the end-user goal context. Epics are meant to facilitate communication between the client and the developer by expressing requirements more understandably.

• Plan time and budget.

Reasonable time estimates based on past projects with similar requirements are essential. However, always remember that meticulous application development takes time. As such, patience is vital in the development process. In addition, create a budget to cover the creation of management software that will stand the test of time.

• Hire a development team or outsource

Depending on your timeframe, budget, and other factors, you could choose to either hire a development team or outsource. However, fully employing a team of developers would require a long process with HR accepting applications, interviewing, and testing potential employees. This would not be time and cost-effective and also draws your focus from the main drive of your company. Therefore, hiring a team that is already familiar with each other and knows how to create an inventory system is advised. Outsourcing is another effective way to get this done without all the hustle of employing in-house developers.

• Test the system.

Once the team is done, the system should not be hurried to full use without testing. This helps you familiarize yourself with the software and is essential to see if it functions expectedly and if all features you required were included.

Including a maintenance agreement in the development project is vital. It ensures you will not experience severe drawbacks if the system needs a fix, an upgrade, or even an overhaul, depending on your maintenance agreement terms.

Inventory Management Software Development Cost

No inventory management software project is the same. Therefore, to get an accurate quote, the developers need to have all the precise requirements first. However, the average cost of the inventory management system is an estimated $30K – $50K for an MVP, while a full-featured version should cost you no less than $100K.

How We Built Store Management Web Application for a Client

We made a flawless Tobacco control and reports system for our client. In this section, we shall go into detail on how to create an inventory system in python.

• Problem + timeframe

USA governmental institutions and private agencies needed a store management web application for tobacco control and reports. Building this system took about two weeks in the engagement process phase and two for the formal commitment to proceed. The formal commencement of the project, including the design specification, solution documentation; prototype; and system test plan and cases, took three months, while the post-launch commencement period was two weeks. We then started the formal maintenance and support agreement as required of us by the client. Therefore the whole project was done in less than five months.

• Features, User stories

The features included were asset tracking, multiple location support, real-time inventory tracking, reorder point, product listing, to name a few. In addition, the detailed yet precise user stories were vital in developing a system with all client requirements.

• Solution + tech stack

Finally, the solution delivered was a high quality, efficient system built by a dedicated software development team using the following stack:

Technological stack:

The store management application development saw Appvales using different technologies, including:

• Redis/RabbitMQ

The Back-end developers developed the stock management application using Python as the development language. First, the API Framework was done using the Django REST Framework. Then the Database was created by Postgres + PostGIS. Finally, for Background task processing (geoprocessing, import process, etc.),  Celery + Redis/RabbitMQ were used.

The Front-end developers used JavaScript as the development language. The framework was created using React + Redux. DevOps: AWS: EC2 – as API and front-end instance; RDS – database storage, S3 – file storage.

The Application was also dockerized to deploy and run any app on any infrastructure quickly and reliably.

How to Improve Inventory Management – 15 Proven Ways with Case Studies

Demand forecasting.

Predicting product demand enables businesses to have the right amount of inventory on hand. Demand forecasting is a key strategy for inventory management as it predicts consumer demand for products or services, allowing businesses to manage inventory more effectively and efficiently.

Demand forecasting uses historical sales data, market research, and statistical methods to predict future demand. The fundamental theories behind demand forecasting include time series analysis, causal models, and machine learning models.

Time Series Analysis

This involves examining historical data and identifying patterns like seasonality, trends, and cycles. These patterns are then used to project future demand.

Causal Models

These models analyze the relationship between demand and various external factors, such as economic indicators, marketing efforts, and price changes.

Machine Learning Models

Machine learning models use algorithms to analyze large datasets and identify complex patterns. These patterns are then used to predict future demand.

One case study showcasing the importance of demand forecasting is IBM’s use of demand forecasting models powered by AI. The multinational technology company has been able to achieve a reduction in forecasting errors by up to 28% through their AI-enabled models.

IBM applied machine learning to time-series forecasting, which allowed them to generate short-term and long-term sales forecasts at scale across various product categories. They used different AI models such as Auto-Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Decision Trees to make their predictions.

Furthermore, IBM utilized a combination of structured (e.g., sales numbers, stock levels) and unstructured data (e.g., text reviews, social media sentiment) in their forecasting models. By combining different types of data and using machine learning to process it, IBM could create more accurate and sophisticated demand forecasts.

The accuracy of demand forecasting can have a significant impact on business performance. According to a report by the Global Journal of Management and Business Research, a 1% improvement in forecast accuracy can result in a 2% decrease in inventory costs. Thus, demand forecasting can lead to substantial cost savings and increased profitability for businesses.

Despite these benefits, demand forecasting also has some challenges. It requires quality data, sophisticated analytical capabilities, and the ability to adjust forecasts based on changing market conditions. Therefore, businesses need to invest in technology, data management, and analytics capabilities to leverage demand forecasting effectively.

Safety Stock

Having a safety stock can mitigate the risk of stockouts. Safety stock is a fundamental inventory management strategy where companies keep extra inventory on hand to protect against variability in market demand or supply disruptions. It acts as an insurance against stockouts, which can lead to lost sales, disappointed customers, and potential harm to a company’s reputation

The theoretical underpinning for safety stock calculations often revolves around lead time, demand variability, and service level expectations. The classic safety stock formula is:

Safety Stock = (Max Lead Time – Average Lead Time) * Average Demand

Here, the Max Lead Time and Average Lead Time represent the longest and average time taken to replenish stock, and the Average Demand is the average units sold during that lead time.

Many companies modify this formula to consider the standard deviation of lead time and demand, as well as the desired service level (probability of not having a stockout).

Implementing safety stock requires a delicate balance. While having a high level of safety stock can prevent stockouts, it also increases inventory holding costs. The inventory holding cost is critical facet of any inventory management technique on the other hand, keeping a low level of safety stock reduces holding costs but increases the risk of stockouts.

A case study highlighting the effective use of safety stock is Amazon . The e-commerce giant uses advanced algorithms and machine learning techniques to determine optimal safety stock levels for millions of products. Amazon’s sophisticated inventory management system takes into account various factors, such as historical sales data, product life cycle, seasonal trends, and supplier reliability. This approach has reportedly helped Amazon maintain a high in-stock rate of 97.8% in 2020, reducing the risk of lost sales and improving customer satisfaction.

Moreover, a study by the International Journal of Production Economics found that a well-managed safety stock can lead to a 10-20% reduction in total inventory costs. This is achieved by maintaining a balance between holding costs and the cost of stockouts.

Despite its benefits, managing safety stock comes with challenges. It requires accurate data, sophisticated algorithms, and the ability to respond swiftly to changing market conditions. Businesses also need to periodically review and adjust their safety stock levels as demand patterns, lead times, and business goals change.

Batch Tracking

Batch tracking can mitigate the risks of product recalls. Batch tracking, also known as lot tracking, is a quality control inventory management technique that allows businesses to track goods along the distribution chain. It is especially important in industries where products need to be closely monitored for reasons of safety, compliance, or quality control, such as food, pharmaceuticals, and electronics.

In essence, batch tracking records the journey of a batch or lot of products or materials from their origin, through the manufacturing process, to the end consumer. This allows companies to manage recalls effectively, ensure regulatory compliance, and improve product quality.

The concept of batch tracking relies on the following principles:

Traceability

Every batch or lot of products should have a unique identifier that allows it to be tracked throughout the supply chain.

Transparency

Information about each batch, such as its origin, processing history, and distribution, should be recorded and readily available.

Accountability

Companies should take responsibility for the quality and safety of their products and have procedures in place for managing recalls or quality issues.

Case Studies

A case study demonstrating the importance of batch tracking is in the pharmaceutical industry, particularly in the case of the pharmaceutical company XYZ (hypothetical for the sake of the explanation). The company implemented batch tracking to improve its inventory management and product quality control. Inventory management is this highly critical of any product which is sensible to time.

The company assigned unique identifiers to each batch of drugs manufactured, enabling it to trace the journey of each batch from the raw materials used, through the manufacturing process, to the distribution to pharmacies. This allowed XYZ to quickly identify and isolate any batches that were associated with quality issues, reducing the scope and cost of recalls.

Furthermore, the implementation of batch tracking led to a decrease in carrying costs by about 27%, as the company was able to manage its inventory more effectively and reduce waste.

Additionally, a study by the Aberdeen Group found that companies using batch tracking had a 26% higher successful product completion rate compared to those not using batch tracking. This demonstrates how batch tracking can contribute to operational efficiency and product quality.

However, batch tracking can be challenging to implement. It requires sophisticated tracking systems, accurate data, and strong cooperation from all stakeholders in the supply chain. Therefore, businesses need to invest in the right technologies and processes to implement batch tracking effectively.

Vendor-Managed Inventory (VMI)

In a VMI arrangement, suppliers manage inventory levels.  Vendor -Managed Inventory (VMI) is a supply chain practice where the supplier or vendor is responsible for maintaining the customer’s inventory levels. Under this model, the supplier has access to the customer’s inventory data and is responsible for generating purchase orders.

This practice aims to improve inventory turnover and reduce stockouts or overstock situations. Aligning the manufacturer’s production with the retailer’s sales cycle enhances the efficiency of the supply chain. The Collaborative Process of Inventory Management is becoming very popular in lean and agile systems now a days.

The VMI model is based on several principles:

Information Sharing

In a VMI relationship, the customer shares real-time data on stock levels, sales, and forecasts with the supplier. This transparency allows the supplier to better plan production and deliveries.

Inventory Ownership

The supplier retains ownership of the inventory until it’s sold, effectively transferring the risks associated with inventory management from the customer to the supplier.

Performance Metrics

The supplier’s performance is usually evaluated based on the level of customer service they provide, such as their ability to avoid stockouts and maintain optimal inventory levels.

A successful case study of VMI implementation comes from Barilla, an Italian pasta manufacturer. Before implementing VMI, Barilla suffered from significant demand variability, leading to stockouts and excess inventory. By adopting a VMI strategy, Barilla transferred the responsibility of inventory management to its suppliers.

With access to real-time sales data, suppliers were able to better forecast demand, optimize production schedules, and improve delivery performance. This resulted in a 30% reduction in stockout instances and a significant improvement in Barilla’s customer service levels.

The benefits of VMI are supported by numerous studies. According to research by the Journal of Operations Management, VMI can lead to an average inventory reduction of 31% and an increase in service levels by up to 6%.

However, implementing a VMI strategy requires a high degree of collaboration and trust between the customer and the supplier. Both parties need to invest in compatible IT systems, adopt standardized processes, and agree on performance metrics. The benefits of VMI also depend on the nature of the products, the stability of demand, and the capabilities of the supplier.

ABC Analysis

This involves categorizing inventory based on its importance and value.  ABC analysis is a method of categorizing inventory into three categories based on their importance and value to the business. The system gets its name from the classes it involves: ‘A’ items are very important, ‘B’ items are important, and ‘C’ items are marginally important.

‘A’ Items: These are the high-priority items, often representing a small percentage of total items but a large portion of the inventory cost. They require close inventory control and rigorous demand forecasting.

‘B’ Items: These are the intermediate items, making up a larger percentage of total items but representing a lower portion of inventory cost than ‘A’ items. They require a moderate level of inventory control.

‘C’ Items: These are the low-priority items, which constitute the majority of the total items but contribute the least to the inventory cost. They require less stringent control and can be ordered in larger quantities less frequently.

The basic principle of ABC analysis is the Pareto principle, or the 80/20 rule, which suggests that 80% of the effects come from 20% of the causes. In the context of inventory management, it often happens that 80% of a company’s inventory value is made up of only 20% of its items. The categorization is a basic for any inventory management process

Example and Case Study

An example of the application of ABC analysis is in a pharmaceutical company, XYZ (hypothetical for the sake of the explanation). The company categorized its inventory based on the annual consumption value of each product (calculated as the annual demand multiplied by the cost per unit).

‘A’ items were the top 20% of items that accounted for about 70% of the company’s total inventory value. ‘B’ items were the next 30% of items, contributing around 25% of the total inventory value. The remaining 50% of items, classified as ‘C’ items, contributed only 5% to the inventory value.

By focusing its inventory management efforts on ‘A’ items, XYZ was able to manage its inventory more effectively, leading to a decrease in carrying costs by about 27%.

Several studies have validated the benefits of ABC analysis. For instance, a study published in the Journal of Operations Management found that companies using ABC analysis achieved a 14% reduction in inventory costs compared to those not using it.

However, implementing ABC analysis requires a good understanding of the company’s products and market dynamics. It also requires the collection and analysis of accurate demand and cost data. Therefore, companies need to invest in data management and analytical capabilities to apply ABC analysis effectively.

Inventory Turnover Ratio

This measures how often inventory is sold and replaced within a specific period.

The inventory turnover ratio is a key performance indicator that measures the efficiency of inventory management. It represents how many times a company has sold and replaced its inventory during a specific period, usually a year. The ratio provides insights into a company’s operational efficiency, liquidity, and overall financial health.

The formula to calculate the inventory turnover ratio is:

Inventory Turnover Ratio = Cost of Goods Sold (COGS) / Average Inventory

Cost of Goods Sold (COGS) is the total cost of all goods sold during a specific time period.

Average Inventory is the mean value of inventory during the same time period, usually calculated as the average of the inventory levels at the start and end of the period.

A higher inventory turnover ratio indicates that a company sells its inventory quickly, implying efficient inventory management and high demand for its products. Conversely, a lower ratio could suggest overstocking, slow sales, or obsolete inventory.

Consider a hypothetical example: a retail company, XYZ, which had a COGS of $2 million and an average inventory of $500,000 for the year. By applying the formula, XYZ’s inventory turnover ratio would be 4. This means that XYZ sold and replaced its inventory four times during the year.

The optimal inventory turnover ratio can vary significantly across different industries. For instance, in fast-moving industries like fashion or perishable goods, a high turnover ratio is desirable. In contrast, industries with slower-moving goods, like furniture or appliances, may have a lower turnover ratio. Having a solid understanding of a turnover ratio is key to inventory management

According to a study published in the Journal of Business Logistics, companies with higher inventory turnover ratios tend to have higher profit margins. The research found that a 10% increase in the inventory turnover ratio could lead to a 1% increase in the profit margin.

However, interpreting the inventory turnover ratio requires caution. While a high turnover ratio can suggest efficiency, it might also indicate inadequate inventory levels, leading to stockouts and lost sales. On the other hand, a low turnover ratio could signal overstocking or weak sales, but it might also reflect a strategic decision to maintain higher inventory levels to guard against supply chain disruptions.

Just-in-Time (JIT) Inventory

This method reduces inventory carrying costs.  Just-in-Time (JIT) inventory management is a strategy aimed at reducing in-process inventory and its associated carrying costs. The approach is based on producing goods to meet demand precisely when needed in the production process, not before, thereby minimizing inventory levels.

The underlying principles of JIT are:

Pull System

Production is driven by customer demand rather than forecasts. Each stage of the production process only produces what the next stage needs, and the process starts when the final customer places an order.

Zero Inventory

The aim is to eliminate inventory, both raw materials and finished goods, as much as possible. Inventory is seen as a sign of inefficiency, indicating overproduction and waste.

Continuous Improvement (Kaizen)

JIT is closely associated with the principle of Kaizen, which focuses on continuous improvement in all aspects of the business, including reducing waste, improving efficiency, and enhancing quality.

Toyota is famously known for implementing JIT in its production system, known as the Toyota Production System. Before JIT, Toyota, like many other companies, produced more than necessary and stored surplus goods in warehouses, leading to high inventory costs and waste.

Implementing JIT allowed Toyota to dramatically reduce its raw material, work-in-process, and finished goods inventories. By synchronizing the production rate with consumer demand, Toyota reduced its inventory levels by more than 50%, leading to significant cost savings. It also led to an improvement in quality, as it was easier to detect defects in a system with minimal inventory.

Several studies have demonstrated the benefits of JIT. For instance, a study published in the Journal of Operations Management found that implementing JIT can lead to an improvement in return on assets (ROA) by up to 70%.

However, successful implementation of JIT requires a stable and reliable supply chain, efficient production processes, and accurate demand forecasting. Any disruption in the supply chain, such as supplier failure or transportation delays, can halt production and lead to stockouts. Thus, while JIT can bring significant cost savings and efficiency improvements, it also comes with its own set of risks.

Real-time Tracking

This involves monitoring inventory in real-time. Real-time tracking in inventory management refers to the continuous and instantaneous tracking of inventory items, from the moment they enter the warehouse until they are sold and dispatched. It involves the use of advanced technologies, such as RFID tags, barcodes, IoT devices, and cloud-based software, to monitor and update inventory levels in real-time. With advanced data analytics, real-time inventory management is becoming more economical.

The primary principles of real-time tracking are:

Instantaneous Updates

Every movement of inventory, from receiving and storing to picking and shipping, is immediately recorded and reflected in the inventory levels.

Real-time tracking provides a clear and accurate view of the current inventory levels, location of items, and status of orders at any given time.

By eliminating manual entry and the delay in updating inventory records, real-time tracking significantly improves the accuracy of inventory data.

A case in point is Amazon, which uses real-time tracking extensively in its fulfillment centers. Amazon utilizes RFID tags, automated guided vehicles (AGVs), and sophisticated inventory management systems to track each item in its vast warehouses in real time. This allows Amazon to maintain accurate inventory records, streamline order fulfillment, and provide real-time updates to customers.

Implementing real-time tracking resulted in a significant reduction in order fulfillment time, an increase in warehouse efficiency, and improved customer satisfaction due to the visibility into order status. In terms of numbers, Amazon was reportedly able to reduce its “click to ship” time, that is, the time from when a customer places an order to when it’s shipped, from 60-75 minutes to under 15 minutes.

Several studies have shown the benefits of real-time tracking. According to a report by Zebra Technologies, businesses that implemented real-time tracking reported an average improvement of 32% in inventory accuracy and a 27% acceleration in order cycle times.

However, implementing real-time tracking requires a significant investment in technology and the development of standardized processes. It also involves a shift in mindset from periodic to continuous inventory management. Therefore, businesses need to carefully assess their needs, capabilities, and resources before implementing real-time tracking.

Dropshipping

This involves selling products without stocking them. Dropshipping is a retail fulfillment model in which the retailer does not keep goods in stock. Instead, when a retailer sells a product using the dropshipping model, it purchases the item from a third party—usually a wholesaler or manufacturer—and has it shipped directly to the customer. This eliminates the need for the retailer to handle the product directly, reducing inventory and warehousing requirements. Inventory management with dropshipping needs heavy optimization though.

The fundamental principles of dropshipping are:

In a dropshipping model, the retailer does not own inventory. Instead, inventory is held by the suppliers until it’s sold.

Order Fulfillment

When a customer places an order, the retailer transfers the customer’s order details and shipping information to the supplier, who then fulfills the order directly to the customer.

Product Assortment

As retailers do not need to pre-purchase the items they sell, they can offer a wider variety of products to customers.

One successful case study of dropshipping is Wayfair, a popular online furniture retailer. The company holds virtually no inventory and relies extensively on dropshipping. When a customer places an order on Wayfair’s platform, the order is sent to the manufacturer, who ships the item directly to the customer. This model allows Wayfair to offer a vast selection of products without the need to manage complex inventory or large warehouses.

According to data from a report by Market Research Future, the global dropshipping market size was projected to reach approximately $557.9 billion by 2025, growing at a compound annual growth rate (CAGR) of 28.8% from 2018.

However, the dropshipping model also has its challenges. It can lead to lower profit margins, as suppliers also take their share of profits. Also, because retailers don’t control the entire supply chain, they can face difficulties with product quality control, order fulfillment, and customer service.

Cross-docking

This involves transferring incoming shipments directly to outgoing trucks, reducing warehouse storage needs.

Cross-docking is a logistics strategy in which products from a supplier or manufacturing plant are distributed directly to customers with minimal to no handling or storage time. The term “cross-docking” comes from the process of receiving products through an inbound dock and then transferring them across the dock to the outbound transportation dock. Cross docking is becoming more popular for large organizations with decentralized inventory management

The core principles of cross-docking include:

In cross-docking, speed is key. The objective is to unload materials from an incoming semi-trailer truck or rail car and then directly load these materials onto outbound trucks or trailers with little to no storage in between.

Synchronization

The success of cross-docking depends on the synchronization of inbound and outbound transport. The goal is to ensure that the incoming goods arrive just in time to be loaded onto the outbound transport.

Cross-docking requires a central site where the routing of products is handled. At this site, products are received from multiple sources and then sorted onto outbound trucks going to different destinations.

An effective example of cross-docking is Walmart’s distribution model. Walmart’s suppliers send full truckloads of products to a Walmart distribution center, where they’re then distributed to individual stores in less-than-truckload (LTL) quantities. Walmart uses cross-docking efficiently to get products from suppliers to their stores without holding inventory at the distribution centers. It helps them to reduce inventory holding costs, minimize storage requirements, and get products into stores faster, which is crucial in retail industries.

According to a study published in the International Journal of Retail & Distribution Management, the implementation of cross-docking can lead to a reduction in order cycle time by 33%, and inventory reduction by up to 50%.

However, to successfully implement cross-docking, companies require significant planning, investment in a central routing facility, and sophisticated logistics software to coordinate and synchronize transport schedules. It’s also crucial to have reliable suppliers that can adhere to strict delivery schedules. If these conditions aren’t met, cross-docking can lead to increased transportation costs, delivery delays, and customer dissatisfaction.

Inventory Management Software

Companies like Oracle, SAP, and Microsoft offer advanced inventory management software that can automate various processes, resulting in reduced errors and increased efficiency.

Inventory management software is a tool that helps businesses track and manage their inventory levels, sales, orders, and deliveries. It can also be used to create purchase orders, back orders, and invoices. The software’s primary purpose is to avoid product overstock and outages, ensuring that the right amount of stock is maintained at all times. Using these software are essential for building a strong inventory management system

The key features of inventory management software include:

Inventory Tracking

This feature allows businesses to track their inventory levels in real time. The software can provide updates when stock is low or when it’s time to reorder.

Barcode Scanning

Many inventory management systems come with barcode scanning capabilities. This allows businesses to quickly input and track products, reducing manual errors.

Reporting and Analytics

These features provide businesses with insights into their inventory levels, sales trends, and order history. This can help businesses make more informed decisions about stock management, pricing, and sales strategies.

Integration Capabilities: Inventory management software can often be integrated with other business systems, such as accounting software, e-commerce platforms, and CRM systems, to streamline operations.

One example of a company leveraging inventory management software is Zara, the Spanish clothing retailer. Zara uses sophisticated inventory management software to track each item in real-time, from when it’s manufactured to when it’s sold. This allows Zara to keep track of its fast-moving inventory, respond quickly to changes in demand, and minimize stockouts and overstock.

According to a report by Mordor Intelligence, the global inventory management software market was valued at USD 2.42 billion in 2020 and is expected to reach USD 5.6 billion by 2026, growing at a CAGR of 15% during the forecast period (2021-2026).

However, implementing inventory management software requires a significant investment in technology and may require staff training. It also requires the collection and analysis of accurate data. Therefore, businesses need to carefully evaluate their needs and resources before deciding to invest in inventory management software.

Consignment Inventory

Here, payment to suppliers is made only when their goods are sold.

Consignment inventory is a business model in which a consignee (retailer) agrees to receive and store products from a consignor (supplier or manufacturer), but the consignor retains ownership of the products until they are sold. Once the product is sold, the consignee pays the consignor for the inventory. It is very similar yet different from dropshipping model of inventory management.

The core principles of consignment inventory include:

In a consignment inventory agreement, the consignor retains the ownership of the goods until they are sold. This implies that the consignor bears the financial risk of the inventory until the point of sale.

The consignee pays for the inventory only after it is sold to the end consumer. The unsold inventory can be returned to the consignor, reducing the financial risk for the consignee.

Inventory Management

Typically, the consignor is responsible for managing the inventory, which includes replenishing stock when levels are low and removing outdated or unsold merchandise.

A great example of the consignment model is the relationship between book authors (consignors) and bookstores (consignees). Many bookstores will display books, particularly from unknown or independent authors, on a consignment basis. This allows the bookstore to offer a wide range of titles without the risk of investing in inventory that may not sell.

According to data from Stitch Labs, using a consignment model can increase revenue by consignment model can increase revenue by as much as 20%, as retailers can offer a wider variety of products without the risk of unsold inventory.

However, there are potential downsides to the consignment model. For consignors, delayed revenue recognition and the risk of not selling the inventory can be significant. For consignees, consignment inventory can take up valuable retail space without providing immediate revenue. Also, managing consignment inventory can be complex, requiring careful tracking and accounting to ensure accurate payment upon sale.

Cycle Counting

This involves regularly counting a subset of inventory.

Cycle counting is an inventory auditing technique where a small subset of inventory, in a specific location, is counted on a specified day. It is a method used by businesses to count their inventory continuously and cyclically throughout the year, rather than counting all inventory at once during a full physical inventory count. Cycle counts contrast with traditional physical inventory counts, where operations are halted once or twice a year to count all inventory items. To bring discipline into inventory management this process is necessary.

The essential principles of cycle counting are:

Consistent Counting

In a cycle counting system, a small, specific subset of inventory is counted at regular intervals, ensuring ongoing accuracy.

Inventory items are not all counted at the same frequency. High-value items, fast-moving items, or items critical to business operations are often counted more frequently.

Division of Labor

By breaking down the task of inventory counting into smaller parts, the job can be completed without disrupting normal operations.

A successful implementation of cycle counting can be found at Apple Inc. Apple uses cycle counting to maintain an accurate inventory record. This technique helps them to identify and correct potential problems early, reducing discrepancies between actual and recorded inventory.

A study by the Association for Supply Chain Management (APICS) found that companies using cycle counting systems could achieve inventory accuracy levels of 97% or higher. This contrasts with a traditional annual physical inventory system, which often results in lower overall accuracy due to a lack of frequent validation.

While cycle counting can significantly improve inventory accuracy, its implementation is not without challenges. It requires a consistent and ongoing effort and requires businesses to invest in proper training for staff to conduct the counts effectively and efficiently. If not appropriately managed, cycle counting can lead to discrepancies and errors.

Inventory Shrinkage Control

Implementing measures to control shrinkage can reduce inventory losses. Inventory management thus takes central stage.

Inventory shrinkage refers to the loss of products between the point of manufacture or purchase from suppliers and the point of sale. It is a significant issue that can impact a company’s bottom line. The main causes of inventory shrinkage include theft, damages, miscounting, and supplier fraud. Instilling discipline is required for any type of inventory management.

Here are the key principles to control inventory shrinkage:

Regular Audits

Regular inventory audits, including cycle counting, can help identify shrinkage early and mitigate its impact. It allows you to identify discrepancies between your physical inventory and your inventory records, indicating potential shrinkage.

Security Measures

Implementing security measures, such as surveillance cameras, electronic article surveillance (EAS) systems, and security personnel, can deter theft, one of the significant contributors to inventory shrinkage.

Employee Training

Well-trained employees are more likely to handle inventory properly, reducing losses due to damage or miscounting. Also, educating them about the consequences of theft, including job loss and legal action, can deter internal theft.

Vendor Management

Establishing strong relationships with vendors and carefully monitoring their activities can help prevent supplier fraud. This can include checking shipments for accuracy and quality.

One successful case study of shrinkage control is at Target Corporation. The company employs advanced analytics to identify patterns in theft and uses electronic surveillance systems throughout their stores. They also have a rigorous vendor vetting process to prevent vendor fraud. These measures have reportedly resulted in a substantial reduction in inventory shrinkage.

According to the National Retail Federation’s 2020 National Retail Security Survey, the average inventory shrink rate in the U.S. retail industry is around 1.62% of sales. This may seem like a small percentage, but given the volume of sales in the retail industry, it can amount to billions of dollars.

Centralized Inventory

Centralizing inventory can reduce costs and improve efficiency.

A centralized inventory management system is a method where a company maintains its entire inventory from one central location or few select locations. Rather than keeping stock in various places such as individual stores or warehouses scattered across different regions, all products are kept in one central warehouse from which they’re distributed to individual sale locations or directly to consumers. Organizations are looking forward to centralized inventory management after Covid disruptions.

The core principles of centralized inventory management include:

Central Location

In centralized inventory management, all inventory is managed from one central location. This could be a central warehouse, distribution center, or a fulfillment center.

Streamlined Supply Chain

Centralizing inventory allows for a more streamlined supply chain as all goods come in and go out of the same place. This can make it easier to manage and keep track of inventory.

Consolidated Management

With centralized inventory, inventory management is consolidated. This means that one team (or sometimes one person) can oversee the entire inventory, which can lead to more effective management and decision-making.

One notable example of successful centralized inventory management is Amazon. Amazon keeps its inventory in large, strategically located fulfillment centers from where they dispatch products directly to customers. This centralized system allows Amazon to manage and control its inventory effectively and deliver products rapidly.

According to a report from Accenture, centralizing inventory management can lead to a 10% – 20% reduction in inventory carrying costs. It also can help companies enhance customer service and increase sales by ensuring the right products are available at the right time.

However, there are potential drawbacks to centralized inventory management. One of the main risks is that if the central warehouse encounters a problem (like a natural disaster or a major system failure), it can disrupt the entire supply chain. Also, centralized inventory may not always provide the speed needed for rapid delivery to distant locations.

Samrat is a Delhi-based MBA from the Indian Institute of Management. He is a Strategy, AI, and Marketing Enthusiast and passionately writes about core and emerging topics in Management studies. Reach out to his LinkedIn for a discussion or follow his Quora Page

Center for Regional Food Systems

Small Potatoes, Big Impact: Michigan Farm to School Potato Processing Partnership Case Study

June 4, 2024 - Mariel Borgman <[email protected]> and Garrett Ziegler <[email protected]>

Potatoes are a highly accepted vegetable among students and have a high-volume potential. This resource examines the results of potato processing trials at a Michigan school district. Dan Gorman, farm to school innovator and food service director for Montague Area Public Schools, wanted to explore the possibility of processing Michigan produce in-house along with a more nutritious implementation as roasted potatoes, in place of french fries.  

As recipients of a sub-award from the Michigan Department of Education’s Specialty Crop Block Grant Program, through the Michigan Department of Agriculture and Rural Development, MSU Extension supported the development of this potato processing program at Montague Area Public Schools. This aligns with the award’s goal to fund farm to school supply chain development in support of the 10 Cents a Meal program.  

This resource can be used to explore the possibility of in-school processing for Michigan grown produce, as well as the supply chain challenges that may arise between farmers and institutions.  

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Tags: agriculture , beginning farmer , center for regional food systems , community , community food systems , farm management , farm marketing , field crops , food hubs , food processing , michigan farm to institution , msu extension , organic agriculture , product center , vegetables

Mariel Borgman [email protected]

Garrett Ziegler [email protected]

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Accessibility questions:.

For questions about accessibility and/or if you need additional accommodations for a specific document, please send an email to ANR Communications & Marketing at [email protected] .

• agriculture,
• beginning farmer,
• center for regional food systems,
• community food systems,
• farm management,
• farm marketing,
• field crops,
• food processing,
• michigan farm to institution,
• msu extension,
• organic agriculture,
• product center,

Food System Infrastructure, Planning and Policy

• Michigan Food Hub Learning and Innovation Network
• Michigan Food System Workforce Assessment
• Michigan Local Food Council Network
• National Food Hub Survey
• Racial Equity in the Food System Workgroup

Michigan Good Food

• Michigan Good Food Charter
• Michigan Good Food Charter Shared Measurement Project
• Michigan Good Food Fund Technical Assistance Providers

Farm to Institution/School

• 10 Cents a Meal
• Michigan Farm to ECE
• Michigan Farm to ECE Network
• Michigan Farm to Institution Network
• Michigan Farm to School

• Open access
• Published: 03 June 2024

Continue nursing education: an action research study on the implementation of a nursing training program using the Holton Learning Transfer System Inventory

• MingYan Shen 1 , 2 &
• ZhiXian Feng 1 , 2  

BMC Medical Education volume  24 , Article number:  610 ( 2024 ) Cite this article

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Metrics details

To address the gap in effective nursing training for quality management, this study aims to implement and assess a nursing training program based on the Holton Learning Transfer System Inventory, utilizing action research to enhance the practicality and effectiveness of training outcomes.

The study involved the formation of a dedicated training team, with program development informed by an extensive situation analysis and literature review. Key focus areas included motivation to transfer, learning environment, and transfer design. The program was implemented in a structured four-step process: plan, action, observation, reflection.

Over a 11-month period, 22 nurses completed 14 h of theoretical training and 18 h of practical training with a 100% attendance rate and 97.75% satisfaction rate. The nursing team successfully led and completed 22 quality improvement projects, attaining a practical level of application. Quality management implementation difficulties, literature review, current situation analysis, cause analysis, formulation of plans, implementation plans, and report writing showed significant improvement and statistical significance after training.

The study confirms the efficacy of action research guided by Holton’s model in significantly enhancing the capabilities of nursing staff in executing quality improvement projects, thereby improving the overall quality of nursing training. Future research should focus on refining the training program through long-term observation, developing a multidimensional evaluation index system, exploring training experiences qualitatively, and investigating the personality characteristics of nurses to enhance training transfer effects.

Peer Review reports

Introduction

The “Medical Quality Management Measures“ [ 1 ] and “Accreditation Standards for Tertiary Hospitals (2020 Edition)” [ 2 ] both emphasize the importance of using quality management tools in medical institutions to carry out effective quality management [ 3 ]. However, there is a notable gap in translating theoretical training into effective, practical application in clinical settings [ 4 ]. This gap is further highlighted in the context of healthcare quality management, as evidenced in studies [ 5 ] which demonstrate the universality of these challenges across healthcare systems worldwide.

Addressing this issue, contemporary literature calls for innovative and effective training methods that transition from passive knowledge acquisition to active skill application [ 6 ]. The Holton Learning Transfer System Inventory [ 7 ] provides a framework focusing on key factors such as motivation, learning environment, and transfer design [ 7 , 8 , 9 ]. This study aims to implement a nursing training program based on the Holton model, using an action research methodology to bridge the theoretical-practical gap in nursing education.

Quality management training for clinical nurses has predominantly been characterized by short-term theoretical lectures, a format that often fails to foster deep engagement and lasting awareness among nursing personnel [ 10 ]. The Quality Indicator Project in Taiwan’s nursing sector, operational for over a decade, demonstrates the effective use of collective intelligence and scientific methodologies to address these challenges [ 11 ]. The proposed study responds to the need for training programs that not only impart knowledge but also ensure the practical application of skills in real-world nursing settings, thereby contributing to transformative changes within the healthcare system [ 12 ].

In April 2021, the Nursing Education Department of our hospital launched a quality improvement project training program for nurses. The initiation of this study is underpinned by the evident disconnect between theoretical training and the practical challenges nurses face in implementing quality management initiatives, a gap also identified in the work [ 13 ]. By exploring the efficacy of the Holton Learning Transfer System Inventory, this study seeks to enhance the practical application of training and significantly contribute to the field of nursing education and quality management in healthcare.

Developing a nursing training program with the Holton Learning Transfer System Inventory

Establishing a research team and assigning roles.

There are 10 members in the group who serve as both researchers and participants, aiming to investigate training process issues and solutions. The roles within the group are as follows: the deputy dean in charge of nursing is responsible for program review and organizational support, integrating learning transfer principles in different settings [ 14 ]; the deputy director of the Nursing Education Department handles the design and implementation of the training program, utilizing double-loop learning for training transfer [ 15 ]; the deputy director of the Nursing Department oversees quality control and project evaluation, ensuring integration of evidence-based practices and technology [ 16 ] and the deputy director of the Quality Management Office provides methodological guidance. The remaining members consist of 4 faculty members possessing significant university teaching experience and practical expertise in quality control projects, and 2 additional members who are jointly responsible for educational affairs, data collection, and analysis. Additionally, to ensure comprehensive pedagogical guidance in this training, professors specializing in nursing pedagogy have been specifically invited to provide expertise on educational methodology.

Current situation survey

Based on the Holton Learning Transfer System Inventory (refer to Fig.  1 ), the appropriate levels of Motivation to Improve Work Through Learning (MTIWL), learning environment, and transfer design are crucial in facilitating changes in individual performance, thereby influencing organizational outcomes [ 17 , 18 ]. Motivation to Improve Work Through Learning (MTIWL) is closely linked to expectation theory, fairness theory, and goal-setting theory, significantly impacting the positive transfer of training [ 19 ]. Learning environment encompasses environmental factors that either hinder or promote the application of learned knowledge in actual work settings [ 20 ]. Transfer design, as a pivotal component, includes training program design and organizational planning.

To conduct the survey, the research team retrieved 26 quality improvement reports from the nursing quality information management system, which were generated by nursing units in 2020. A checklist was formulated, and a retrospective evaluation was conducted across eight aspects, namely, team participation, topic selection feasibility, method accuracy, indicator scientificity, program implementation rate, effect maintenance, and promotion and application. Methods employed in the evaluation process included report analysis, on-site tracking, personnel interviews, and data review within the quality information management system [ 21 ]. From the perspective of motivation [ 22 ], learning environment [ 23 ], and transfer design, a total of 14 influencing factors were identified. These factors serve as a reference for designing the training plan and encompass the following aspects: lack of awareness regarding importance, low willingness to participate in training, unclear understanding of individual career development, absence of incentive mechanisms, absence of a scientific training organization model, lack of a training quality management model, inadequate literature retrieval skills and support, insufficient availability of practical training materials and resources, incomplete mastery of post-training methods, lack of cultural construction plans, suboptimal communication methods and venues, weak internal organizational atmosphere, inadequate leadership support, and absence of platforms and mechanisms for promoting and applying learned knowledge.

Learning Transfer System Inventory

Development of the training program using the 4W1H approach

Drawing upon Holton’s Learning Transfer System Inventory and the hospital training transfer model diagram, a comprehensive training outline was formulated for the training program [ 24 , 25 ]. The following components were considered:

(1) Training Participants (Who): The training is open for voluntary registration to individuals with an undergraduate degree or above, specifically targeting head nurses, responsible team leaders, and core members of the hospital-level nursing quality control team. Former members who have participated in quality improvement projects such as Plan-Do-Check-Act Circle (PDCA) or Quality control circle (QCC) are also eligible.

(2) Training Objectives (Why): At the individual level, the objectives include enhancing the understanding of quality management concepts, improving the cognitive level and application abilities of project improvement methods, and acquiring the necessary skills for nursing quality improvement project. At the team level, the aim is to enhance effective communication among team members and elevate the overall quality of communication. Moreover, the training seeks to facilitate collaborative efforts in improving the existing nursing quality management system and processes. At the operational level, participants are expected to gain the competence to design, implement, and manage nursing quality improvement project initiatives. Following the training, participants will lead and successfully complete a nursing quality improvement project, which will undergo a rigorous audit.

(3) Training Duration (When): The training program spans a duration of 11 months.

(4) Training Content (What): The program consists of 14 h of theoretical courses and 18 h of practical training sessions, as detailed in Table  1 .

(5) Quality Management Approach (How): To ensure quality throughout the training process, two team members are assigned to monitor the entire training journey. This encompasses evaluating whether quality awareness education, quality management knowledge, and professional skills training are adequately covered. Additionally, attention is given to participants’ learning motivation, the emphasis placed on active participation in training methods, support from hospital management and relevant departments, as well as participants’ satisfaction and assessment results. Please refer to Fig.  2 for a visual representation.

In-house training model from Holton Learning Transfer

Implementation of the nursing project training program using the action research method

The first cycle (april 2021).

In the initial cycle, a total of 22 nurses were included as training participants after a self-registration process and qualification review. The criteria used to select these participants, elaborated in Section Development of the training program using the 4W1H approach, ‘Development of the Training Program,’ were meticulously crafted to capture a broad spectrum of experience, expertise, and functional roles within our hospital’s nursing staff. The primary focus was to investigate their learning motivation. The cycle comprised the following key activities:

(1) Training Objectives: The focus was on understanding the learning motivation of the participating nurses.

(2) Theoretical Training Sessions: A total of 7 theoretical training sessions, spanning 14 class hours, were completed. The contents covered various aspects, including an overview of nursing quality improvement projects, methods for selecting project topics, common tools used in nursing quality improvement projects, effective leadership strategies to promote project practices, literature retrieval and evaluation methods, formulation and promotion of project plans, and writing project reports. Detailed course information, including the title, content, and class hours, is listed in Table  1 . At the end of each training session, a course satisfaction survey was conducted.

(3) Assessment and Reporting: Following the completion of the 7 training sessions, a theoretical assessment on quality management knowledge was conducted. Additionally, nurses were organized to present their plans for special projects to be carried out during the training. Several issues were identified during this cycle:

Incomplete Literature Review Skills: Compared to other quality control tools, nursing quality improvement project places more emphasis on the scientific construction of project plans. The theoretical evaluation and interviews with nurses highlighted the incomplete and challenging nature of their literature review skills.

Insufficient Leadership: Among the participants, 6 individuals were not head nurses, which resulted in a lack of adequate leadership for their respective projects.

Learning environment and Support: The learning environment, as well as the support from hospital management and relevant departments, needed to be strengthened.

Second cycle (may-october 2021)

In response to the issues identified during the first cycle, our approach in the second cycle was both corrective and adaptive, focusing on immediate issues while also setting the stage for addressing any emerging challenges. The team members actively implemented improvements during the second cycle. The key actions taken were as follows:

(1) Establishing an Enabling Organizational Environment: The quality management department took the lead, and multiple departments collaborated in conducting the “Hospital Safety and Quality Red May” activity. This initiative aimed to enhance the overall quality improvement atmosphere within the hospital. Themed articles were also shared through the hospital’s WeChat public account.

(2) Salon-style Training Format: The training sessions were conducted in the form of salons, held in a meeting room specifically prepared for this purpose. The room was arranged with a round table, warm yellow lighting, green plants, and a coffee bar, creating a conducive environment for free, democratic, and equal communication among the participants. The salon topics included revising project topic selection, conducting current situation investigations, facilitating communication and guidance for literature reviews, formulating improvement plans, implementing those plans, and writing project reports. After the projects were presented, quality management experts provided comments and analysis, promoting the transformation of training outcomes from mere memory and understanding to higher-level abilities such as application, analysis, and creativity.

(3) Continuous Support Services: Various support services were provided to ensure ongoing assistance. This included assigning nursing postgraduates to aid in literature retrieval and evaluation. Project team members also provided on-site guidance and support, actively engaging in the project improvement process to facilitate training transfer.

(4) Emphasis on Spiritual Encouragement: The Vice President of Nursing Department actively participated in the salons and provided feedback on each occasion. Moreover, the President of the hospital consistently commended the training efforts during the weekly hospital meetings.

Issues identified in this cycle

(1) Inconsistent Ability to Write Project Documents: The proficiency in writing project documents for project improvement varied among participants, and there was a lack of standardized evaluation criteria. This issue had the potential to impact the quality of project dissemination.

(2) Lack of Clarity Regarding the Platform and Mechanism for Training Result Transfer: The platform and mechanisms for transferring training results were not clearly defined, posing a challenge in effectively sharing and disseminating the outcomes of the training.

The third cycle (November 2021-march 2022)

During the third cycle, the following initiatives were undertaken.

(1) Utilizing the “Reporting Standards for Quality Improvement Research (SQUIRE)”, as issued by the US Health Care Promotion Research, to provide guidance for students in writing nursing project improvement reports.

(2) Organizing a hospital-level nursing quality improvement project report meeting to acknowledge and commend outstanding projects.

(3) Compiling the “Compilation of Nursing Quality Improvement Projects” for dissemination and exchange among nurses both within and outside the hospital.

(4) Addressing the issue of inadequate management of indicator monitoring data, a hospital-level quality index management platform was developed. The main evaluation data from the 22 projects were entered into this platform, allowing for continuous monitoring and timely intervention.

Effect evaluation

To assess the efficacy of the training, a diverse set of evaluation metrics, encompassing both outcome and process measures [ 26 ]. These measures can be structured around the four-level training evaluation framework proposed by Donald Kirkpatrick [ 27 ].

Process evaluation

Evaluation method.

To assess the commitment and support within the organization, the process evaluation involved recording the proportion of nurses’ classroom participation time and the presence of leaders during each training session. Additionally, a satisfaction survey was conducted after the training to assess various aspects such as venue layout, time arrangement, training methods, lecturer professionalism, content practicality, and interaction. On-site recycling statistics were also collected for project evaluation purposes.

Evaluation results the results of the process evaluation are as follows

Nurse training participation rate: 100%.

Training satisfaction rate (average): 97.75%.

Proportion of nurses’ participation time in theoretical training sessions (average): 36.88%.

Proportion of nurses’ participation time in salon training sessions (average): 74.23%.

Attendance rate of school-level leaders: 100%.

Results evaluation

Assessment of theoretical knowledge of quality management.

To evaluate the effectiveness in enhancing the trainees’ theoretical knowledge of quality management, the research team conducted assessments before the training, after the first round of implementation, and after the third round of implementation. Assessments to evaluate the effectiveness of the training program were conducted immediately following the first round of implementation, and after the third round of implementation. This dual-timing approach was designed to evaluate both the immediate impact of the training and its sustained effects over time, addressing potential influences of memory decay on the study results. The assessment consisted of a 60-minute examination with different question types, including 30 multiple-choice questions (2 points each), 2 short-answer questions (10 points each), and 1 comprehensive analysis question (20 points). The maximum score achievable was 100 points.

The assessment results are as follows:

Before training (average): 75.05 points.

After the first round of implementation (average): 82.18 points.

After the third round of implementation (average): 90.82 points.

Assessment of difficulty in quality management project implementation

To assess the difficulty of implementing quality management projects, the trainees completed the “Quality Management Project Implementation Difficulty Assessment Form” before and after the training. They self-evaluated 10 aspects using a 5-point scale, with 5 indicating the most difficult and 1 indicating no difficulty. The evaluation results before and after implementation are presented in Table  2 .

Statistically significant differences were found in the following items: literature review, current situation analysis, cause analysis, plan formulation, implementation plan, and report writing. This indicates that the training significantly enhanced the nurses’ confidence and ability to tackle practical challenges.

Evaluation of transfer effect

To assess how effectively the training translated into practical applications. The implementation of the 22 quality improvement projects was evaluated using the application hierarchy analysis table. The specific results are presented in Table  3 .

In addition, the “Nursing Project Guidance Manual” and “Compilation of Nursing Project Improvement Projects” were compiled and distributed to the hospital’s management staff, nurses, and four collaborating hospitals, receiving positive feedback. The lecture titled “Improving Nurses’ Project Improvement Ability Based on the Training Transfer Theory Model” shared experiences with colleagues both within and outside the province in national and provincial teaching sessions in 2022. Furthermore, four papers were published on the subject.

The effectiveness of the training program based on the Holton Learning transfer System Inventory

The level of refined management in hospitals is closely tied to the quality management awareness and skills of frontline medical staff. Quality management training plays a crucial role in improving patient safety management and fostering a culture of quality and safety. Continuous quality improvement is an integral part of nursing management, ensuring that patients receive high-quality and safe nursing care. Compared to the focus of existing literature on the individual performance improvements following nursing training programs [ 28 , 29 , 30 ], our study expands the evaluation framework to include organizational performance metrics. Our research underscores a significantly higher level of organizational engagement as evidenced by the 100% attendance rate of school-level leaders. The publication of four papers related to this study highlights not only individual performance achievements but also significantly broadens the hospital organization’s impact on quality management, leading to meaningful organizational outcomes.

Moreover, our initiative to incorporate indicators of quality projects into a hospital-level evaluation index system post-training signifies a pivotal move towards integrating quality improvement practices into the very fabric of organizational operations. In training programs, it is essential not only to achieve near-transfer, but also to ensure that nurses continuously apply the acquired management skills to their clinical work, thereby enhancing quality, developing their professional value, and improving organizational performance. The Holton learning Transfer System Inventory provides valuable guidance on how to implement training programs and evaluate their training effect.

This study adopts the training transfer model as a framework to explore the mechanisms of “how training works” rather than simply assessing “whether training works [ 31 ].” By examining factors such as Motivation to Improve Work Through Learning (MTIWL), learning environment, and transfer design, the current situation is analyzed, underlying reasons are identified, and relevant literature is reviewed to develop and implement training programs based on the results of a needs survey. While individual transfer motivation originates from within the individual, it is influenced by the transfer atmosphere and design. By revising the nurse promotion system and performance management system and aligning them with career development, nurses’ motivation to participate and engage in active learning has significantly increased [ 32 ]. At the learning environment level, enhancing the training effect involves improving factors such as stimulation and response that correspond to the actual work environment [ 33 ]. This project has garnered attention and support from hospital-level leaders, particularly the nursing dean who regularly visits the training site to provide guidance, which serves as invaluable recognition. Timely publicity and recognition of exemplary project improvement initiatives have also increased awareness and understanding of project knowledge among doctors and nurses, fostering a stronger quality improvement atmosphere within the team.

Transfer design, the most critical component for systematic learning and mastery of quality management tools, is achieved through theoretical lectures, salon exchanges, and project-based training. These approaches allow nurses to gain hands-on experience in project improvement under the guidance of instructors. Throughout the project, nurses connect project management knowledge and skills with practical application, enabling personal growth and organizational development through problem-solving in real work scenarios. Finally, a comprehensive evaluation of the training program was conducted, including assessments of theoretical knowledge, perception of management challenges, and project quality. The results showed high satisfaction among nurses, with a satisfaction rate of 97.75%. The proportion of nurses’ participation time in theoretical and practical training classes was 36.88% and 74.23%, respectively. The average score for theoretical knowledge of quality management increased from 75.05 to 90.82. There was also a significant improvement in the evaluation of the implementation difficulties of quality management projects. Moreover, 22 nurses successfully led the completion of one project improvement project, with six projects focusing on preventing the COVID-19 pandemic, demonstrating valuable crisis response practices.

Action research helps to ensure the quality of organizational management of training

Well-organized training is the basis for ensuring the scientific and standardized development of nursing project improvement activities. According to the survey results of the current situation, there is a lot of room for improvement in the training quality; since it is the first time to apply the Holton training transfer model to the improvement training process of nurses in the hospital, in order to allow the nurses to have sufficient time to implement and evaluate the improvement project, the total training time Set at 11 months, a strong methodology is required to ensure training management during this period. Action research is a research method that closely combines research with solving practical problems in work. It is a research method aimed at solving practical problems through self-reflective exploration in realistic situations, emphasizing the participation of researchers and researchees. Practice, find problems in practice, and adjust the plan in a timely manner. According to the implementation of the first round, it was found that nurses had insufficient literature review skills, insufficient leadership, and lack of support from hospital management and related departments [ 32 ]. In the second round, the training courses were carried out in the form of salons. The project team members went deep into the project to improve on-site guidance, arranged graduate students to assist in document retrieval and evaluation, and promoted the transfer of training; the “Hospital Safety and Quality Red May” activity was carried out, and the vice president of nursing Regularly participate in the salon and make comments. The problems exposed after this round of implementation are the low quality of the project improvement project document, and the unclear platform and mechanism for the transfer of training results. In the third round, the “Reporting Standards for Quality Improvement Research (SQUIRE)” was used to standardize the writing of the report [ 33 ], and the “Compilation of Nursing Project Improvement Projects” was completed, and the main evaluation data of 22 projects were entered into the hospital-level quality index management platform for continuous monitoring and intervention. As of May 2022, the effect maintenance data of each project has reached the target value. It can not only produce useful improvement projects, but also help to promote the dissemination and penetration of quality awareness.

Future research directions

Drawing on the Holton training evaluation model, this study implemented nurse quality improvement project training using action research methodology, resulting in a successful exploration practice, and achieving positive transfer effects. To further advance this research area, the following future research directions are recommended:

Summarize the experiences gained from this action research training and continue to refine and enhance the training program. Through ongoing practice, reflection, and refinement in subsequent training sessions, long-term observation of the transfer effects can be conducted to establish an effective experiential model that can serve as a reference for future initiatives.

Develop a multidimensional evaluation index system for assessing transfer effects. A comprehensive framework that captures various dimensions of transfer, such as knowledge application, skill utilization, and behavior change, should be established. This will enable a more holistic and accurate assessment of the training program’s impact on the participants and the organization.

Conduct qualitative research to explore the training experiences of nurses. By gathering in-depth insights through interviews or focus group discussions, a deeper understanding of the nurses’ perceptions, challenges, and facilitators of training transfer can be obtained. This qualitative exploration will provide valuable information to further refine and tailor the training program to meet the specific needs and preferences of the nurses.

Investigate the personality characteristics of nurses who actively engage in training transfer and consider developing them as internal trainers. By identifying and cultivating nurses with a proactive attitude and a strong inclination towards knowledge application and skill development, the organization can enhance employee participation and initiative. These internal trainers can play a crucial role in motivating their colleagues and driving the transfer of training outcomes into daily practice.

By pursuing these future research directions, the field of healthcare and nursing care can continue to advance in optimizing training programs, enhancing transfer effects, and ultimately improving the quality of care and patient outcomes.

Limitations

The research was conducted with a cohort of 22 nurses and a 10-member research team from Grade 3, Class A hospitals in China Southeast. This specific composition and the relatively small sample size may affect the generalizability of our findings. The experiences and outcomes observed in this study might not fully encapsulate the diverse challenges and environments encountered by nursing professionals in varying healthcare settings. The significant improvements noted in the capabilities of the participating nursing staff underscore the potential impact of the training program. However, the study’s focus on a specific demographic—nurses from high-grade hospitals in a developed urban center—may limit the external validity of the findings.

Conclusions

This study affirms the efficacy of the Holton Learning Transfer System Inventory-based training program, coupled with action research, in significantly advancing nursing quality management practices. The strategic incorporation of motivation to improve work through learning, an enriched learning environment, and thoughtful transfer design significantly boosted the nurses’ engagement, knowledge acquisition, and practical application of quality management tools in their clinical work.

It highlights the importance of continuous learning, organizational support, and methodological flexibility in achieving sustainable improvements in healthcare quality and safety. Future endeavors should aim to expand the scope of this training model to diverse nursing contexts and evaluate its long-term impact on organizational performance and patient care outcomes.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to hospital policy but are available from the corresponding author on reasonable request.

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This study was funded by Department of Education of Zhejiang Province, Grant Number jg20220475.

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The following statements specify the individual contributions of each author to the manuscript titled “Continue Nursing Education: An Action Research Study on the Implementation of a Nursing Training Program Using the Holton Learning Transfer System Inventory”:ZhiXian Feng conceived and designed the analysis; led the research team and coordinated the project; critically reviewed and revised the manuscript for important intellectual content; oversaw the implementation of the training program; MingYan Shen conducted the research; collected and organized the data; analyzed and interpreted the data; contributed to the statistical analysis; wrote the initial draft of the manuscript; managed logistics and operational aspects of the study.

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Approval of this study was granted by the Research Ethics Committee of Shulan Hospital (Approval no. KY2021042).

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Shen, M., Feng, Z. Continue nursing education: an action research study on the implementation of a nursing training program using the Holton Learning Transfer System Inventory. BMC Med Educ 24 , 610 (2024). https://doi.org/10.1186/s12909-024-05552-6

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Canadian scientists launch early warning system to spot traces of H5N1 bird flu in milk

Pan-canadian milk network hopes to conduct ongoing tests alongside government surveillance.

Social Sharing

It all started with a few text messages in late April.

Several well-known Canadian scientists — Toronto-based infectious diseases specialist Dr. Isaac Bogoch, Saskatoon-based virologist Angela Rasmussen and Winnipeg-based microbiologist Jason Kindrachuk — were all chatting about the unprecedented outbreak of H5N1 bird flu in U.S. dairy cows.

By then, American officials had tracked cow cases for roughly a month, and harmless viral particles were showing up in processed, pasteurized milk. 

But on this side of the border, the Canadian Food Inspection Agency (CFIA) was clear its team wasn't yet undertaking milk testing .

The trio of Canadian academic researchers saw a missed opportunity.

"I think we all were thinking independently: Why aren't we doing milk testing?" recalled Kindrachuk, an associate professor at the University of Manitoba. "If we can simply get milk off the shelves and [run tests], this would seem like a great initiative for us to undertake."

• Second Opinion Bird flu in U.S. cows caught scientists by surprise. Canadian research has seen it coming since 1953
• Canadian dairy farmers urged to consider goggles, gloves and other bird flu protections

Rather than waiting for the government to launch that kind of surveillance, the scientists spearheaded a coast-to-coast initiative to watch for H5N1 in Canadian milk. 

"Within the span of about two minutes … we had a flurry of emails out to partners and collaborators across all the provinces," Kindrachuk said.

The result, unveiled through an unpublished preprint paper shared online on Wednesday , is what the team dubbed the Pan-Canadian Milk Network. Eighteen scientists in total worked on the findings, from universities throughout Canada. The researchers say the goal is to continue to test milk to spot any fragments of this virus showing up in the Canadian milk supply. 

That outcome wouldn't pose a risk to consumers — as testing shows pasteurization ensures milk is safe to drink — but it would signal infections in Canadian dairy cows. (Cases of bird flu in U.S. cows have led to high fevers, severe dehydration, aborted calves and a substantial drop in milk production , while exposing a rising number of farm workers to this virus.

No viral fragments found to date

"Our network and testing will act as an early warning system which will enable rapid responses necessary to contain an outbreak should any samples test positive," the team wrote.

As of May 24, the researchers have tested 18 retail milk samples from five provinces (Newfoundland, New Brunswick, Quebec, Manitoba and Alberta) and all came back negative for influenza A, the virus family of which H5N1 is a member.

Though government officials initially opted not to pursue milk testing, the CFIA announced its inclusion in surveillance efforts in early May, as CBC News previously reported .

• Federal tests find no signs of bird flu virus in Canadian retail milk

The CFIA also beat the independent team to public results, releasing their preliminary data in mid-May. Full results of their first testing round — looking at roughly 300 retail milk samples from across Canada — were published online a week later. 

None found any H5N1 viral fragments.

Milk testing top priority during migration

What's not clear yet is whether the CFIA will keep up that work. Next steps will be decided "as part of further discussions with partners," the agency said.

A member of Kindrachuk's lab team, virologist and postdoctoral candidate Hannah Wallace, was the lead author on the Pan-Canadian Milk Network's first preprint paper. She stressed that ongoing milk testing has to be a top priority. 

"Especially because there will be another big bird migration in the fall, and that's when influenza testing and influenza rates in wild birds are at their highest levels," Wallace explained.

"So if there was a time for something to happen, it would probably be then."

CFIA testing more of the milk supply over avian flu concerns

More samples from other provinces are expected soon, Wallace noted.

The team is scanning milk first for the influenza virus more broadly, but has the necessary equipment to spot H5N1 specifically as well. Any positive finds would then be reported to the CFIA.

• Second Opinion Bird flu vaccine candidates already exist. But if H5N1 sparks a pandemic, making enough doses won't be easy
• Scientists watching wastewater for signs of H5N1 as U.S. bird flu outbreak in dairy cattle grows

The team's next step is looking for anti-influenza antibodies in cows' milk to see if any Canadian dairy cows were previously exposed to influenza, suggesting prior infections that may have flown under-the-radar, Wallace said.

The CFIA also has additional surveillance efforts underway, including requiring negative test results for lactating dairy cattle being imported from the United States to Canada and helping to facilitate voluntary testing of cows that aren't presenting with any symptoms.

Cows across 9 U.S. states have confirmed infections

Those scaled-up efforts, both from the Canadian government and independent researchers, come as the U.S. dairy cow outbreak keeps growing.

The virus has shown up in close to 70 herds across nine states so far. There have also been two human infections linked to the outbreak, high rates of death among farm cats exposed to contaminated raw milk, and yet another jump to a new species — alpaca on a farm in Idaho .

• Canada expanding surveillance, testing milk for H5N1 avian flu amid U.S. dairy cattle outbreak
• Second Opinion Why dangerous bird flu is spreading faster and farther than first thought in U.S. cattle

Veterinarian and researcher Dr. Scott Weese, with the University of Guelph, stressed that at this point, "more eyes on the problem is good."

He said milk testing needs to remain part of a multifaceted surveillance program including testing on both sick and healthy cows, farm cats, and continued tests on imported cattle.

"It's just one piece of the puzzle as far as surveillance is concerned," echoed Dr. Samira Mubareka, a microbiologist and clinical scientist at the Sunnybrook Health Sciences Centre in Toronto. Looking at various sample types, over time, across a wide geographic range, are all factors research teams need to consider, she said.

Bird flu is spreading in cows. Are humans at risk? | About That

When it comes to milk, "it would be ideal to have a centralized and co-ordinated approach as there are ways to much more efficiently get a large number of milk samples, through milk processors," Weese said in an email exchange with CBC News. "Retail milk, though, is a simple and relevant indicator."

It also sidesteps any hesitation that may emerge at the farm level, since it doesn't require sampling on any individual premises — and offers an anonymous way to get results.

• Analysis Now that bird flu is spreading among cows, scientists worry where H5N1 will jump next

"That can help with some of the concerns that farmers have," Weese said. It isn't yet clear what kind of regulations would be needed on farms if tests came back positive, he noted, so there would "likely be barriers to on-farm sampling."

While the new network's data is rather general for privacy reasons, Wallace said the team does have the ability to trace samples back to specific sources. "We would be able to provide CFIA with all of the information like the lot number, the batch, where the milk was bought, what company it was from."

The hope now, Kindrachuk says, is that Canada will have a leg up on a dangerous virus that keeps catching the world off guard.

"We can't be behind the eight ball," he said.

ABOUT THE AUTHOR

Senior Health & Medical Reporter

Lauren Pelley covers health and medical science for CBC News, including the global spread of infectious diseases, Canadian health policy, pandemic preparedness, and the crucial intersection between human health and climate change. Two-time RNAO Media Award winner for in-depth health reporting in 2020 and 2022. Contact her at: [email protected]

• @LaurenPelley

Applications of Artificial Intelligence in Inventory Management: A Systematic Review of the Literature

• Review Article
• Published: 07 February 2023
• Volume 30 , pages 2605–2625, ( 2023 )

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• Özge Albayrak Ünal   ORCID: orcid.org/0000-0001-7798-8799 1 ,
• Burak Erkayman   ORCID: orcid.org/0000-0002-9551-2679 1 &
• Bilal Usanmaz   ORCID: orcid.org/0000-0003-0531-4618 2  

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Today, companies that want to keep up with technological development and globalization must be able to effectively manage their supply chains to achieve high quality, increased efficiency, and low costs. Diversified customer needs, global competitors, and market competition have led companies to pay more attention to inventory management. This article provides a comprehensive and up-to-date review of Artificial Intelligence (AI) applications used in inventory management through a systematic literature review. As a result of this analysis, which focused on research articles in two scientific databases published between 2012 and 2022 for detailed study, 59 articles were identified. Furthermore, the current situation is summarized and possible future aspects of inventory management are identified. The results show that the interest in AI methods has increased in recent years and machine learning algorithms are the most commonly used methods. This study is meticulously and comprehensively conducted so it will probably make significant contributions to the further studies in this field.

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Albayrak Ünal, Ö., Erkayman, B. & Usanmaz, B. Applications of Artificial Intelligence in Inventory Management: A Systematic Review of the Literature. Arch Computat Methods Eng 30 , 2605–2625 (2023). https://doi.org/10.1007/s11831-022-09879-5

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Decarbonization potential of floating solar photovoltaics on lakes worldwide

• R. Iestyn Woolway   ORCID: orcid.org/0000-0003-0498-7968 1 ,
• Gang Zhao   ORCID: orcid.org/0000-0003-2737-0530 2 ,
• Sofia Midauar Gondim Rocha   ORCID: orcid.org/0000-0001-5296-5355 3 ,
• Stephen J. Thackeray   ORCID: orcid.org/0000-0003-3274-2706 4 &
• Alona Armstrong 3 , 5  

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• Solar energy

As climate change progresses, there is increasing emphasis on net zero and energy system decarbonization. Several technologies are contributing to this agenda, but among these, the growth of solar photovoltaics has consistently exceeded all projections. With increasing land-use pressures, and the expense of building-mounted photovoltaics, water surfaces are increasingly being exploited to host these technologies. However, to date, we lack an understanding of the global potential of floating solar photovoltaics and, as such, we do not yet have sufficient insight to inform decisions on (in)appropriate areas for future deployment. Here we quantify the energy generation potential of floating solar photovoltaics on over 1 million water bodies worldwide (14,906 TWh). Our analysis suggests that with a conservative 10% surface area coverage, floating solar photovoltaics could produce sufficient energy to contribute a considerable fraction (16%, on average) of the electricity demand of some countries, thus playing an important role in decarbonizing national economies.

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The impact of floating photovoltaic power plants on lake water temperature and stratification

Decarbonization of the global economy has become increasingly urgent as anthropogenic climate change progresses. Low carbon energy generation is fundamental to this decarbonization, and within this sphere, the growth of solar photovoltaics (PVs) has consistently exceeded all projections. Indeed, solar energy is predicted to be the dominant renewable energy source by 2050 1 . This rapid growth is attributed to cost effectiveness, the global nature of the resource and flexibility in deployment. Deployment flexibility has enabled the installation of ground- or building-, and more recently, water-mounted or floating systems 2 . Floating solar photovoltaics (FPVs), known colloquially as ‘floatovoltaics’, typically consist of an array of PV modules mounted upon a series of floats, moored into position on the surface of a water body. A growing body of evidence suggests that FPVs have several advantages over conventionally deployed PVs. For example, FPVs avert the need for land-use change where the alternative is a ground-mounted system; this is particularly beneficial in land-scarce countries and regions with high land prices 3 . Moreover, FPVs have also been shown to reduce evaporative losses, potentially providing vital water savings for drought-stricken areas 4 . Finally, FPV systems have lower temperatures, and thus higher efficiencies, compared with land-based systems 5 , 6 . These advantages have driven rapid deployment of FPVs around the world in recent years, particularly on artificial water bodies 7 , 8 . This growth is anticipated to continue, capitalizing on the estimated 5 million km 2 of Earth’s surface area that is covered by lakes and reservoirs 9 . Such expansion could provide the potential for FPVs to meet a considerable proportion of current and future energy demands at local to regional scales 7 . However, to date, we lack an understanding of global FPV potential that moves beyond considerations of theoretical maximum capacity to take into account the impact of global variation in several important determining factors such as water body size and suitability, solar energy receipts, climatic conditions and energy demand. As such, we do not yet have sufficient insight to inform decisions on (in)appropriate areas for future deployment.

In this study, we quantify the energy generation potential of FPVs on over 1 million water bodies (>0.1 km 2 in surface area) worldwide, including both natural and artificial lakes and reservoirs (Supplementary Fig. 1 ). Assuming a 1 kW FPV system, we simulated daily electricity outputs for each of the ~1 million water bodies using the Global Solar Energy Estimator (GSEE) tool 10 , based on climate input data from ERA5, the European Centre for Medium-Range Weather Forecasts’ fifth generation atmospheric reanalysis of the global climate 11 ( Methods ). We then calculated the total annual power output for each water body by multiplying the 1 kW FPV annual power output by an area equivalent to 10% of the median surface area of each water body during the study period (1991–2020) ( Methods ). Although the percent surface coverage of FPVs can vary widely across host water bodies, with literature values ranging from less than 1% to more than 90% (ref. 12 ), we follow a conservative approach and consider 10% coverage (up to 30 km 2 ; Methods ) for all sites 7 .

The theoretical global potential for FPVs

Considering a 1 kW FPV, we calculated a total global annual power output of 1,301 GWh (Fig. 1a ) across the ~1 million water bodies. There were clear spatial patterns in power output, at both annual and monthly timescales (Fig. 1 and Supplementary Fig. 2 , respectively), largely reflecting geographical variations in solar irradiance due to differences in latitude, altitude and cloud cover (Supplementary Fig. 3 ). The simulated annual power output varied, per water body, from less than 1,250 kWh in, for example, northern Europe, to more than 2,500 kWh in regions such as the western United States, the Andean Mountains in South America and the Qinghai–Tibet Plateau in Asia (Fig. 1 ). Moreover, the simulated annual capacity factor, which is the proportion of maximum capacity per year from a FPV and an indicator of economic viability, varied from a minimum of 10–15% in many European countries situated at latitudes above 48° N (for example, 12% in the United Kingdom) to more than 30% in the regions mentioned above with the highest power output (Supplementary Fig. 4 ). Our calculations suggest clear seasonal patterns in the simulated capacity factor, for example, varying from less than 7.5% to more than 20% across the Northern Hemisphere during boreal winter (December–February) and summer (June–August), respectively (Supplementary Fig. 5 ).

a , b , Shown, for each of the >1 million water bodies considered in this study, are the calculated annual power output of a 1 kW FPV system, calculated from GSEE ( Methods ) ( a ) and the median surface area (km 2 ) ( b ). The power output from a 1 kW FPV was initially calculated at hourly intervals and then summed annually. These summaries represent the average annual sum over the period 1991–2020. The median surface area for each water body was calculated from monthly reconstructions that were based on a satellite-derived water surface area time series.

Globally, we calculated a total theoretical annual power output of 14,906 TWh across the ~1 million water bodies when considering a 10% surface coverage of FPV (up to 30 km 2 ). In addition to being influenced by geographical variations in solar irradiance, the total power output from each water body will be influenced by the area of water available to host FPVs. Intuitively, larger water bodies can host larger FPV arrays given a specific percent area coverage. Following the methods outlined in ref. 13 , here we reconstructed the monthly water surface area time series for each of the ~1 million water bodies from 1991 to 2020 ( Methods ). Our analysis demonstrates clear geographical variations in water body surface area during the study period (Fig. 1b ). We demonstrate that some parts of the world are dominated by the presence of small water bodies (<0.5 km 2 ), such as some regions in northwestern Canada, the western United States and eastern South America, whereas others are dominated by the presence of larger water bodies (>20 km 2 ), such as the northeastern United States (Fig. 1b ). By multiplying the power output of a single FPV with 10% of the water body surface area (up to 30 km 2 ), we estimate a theoretical total power output for each of the ~1 million water bodies considered in this study (Fig. 2 ). Our simulations demonstrate considerable differences in the total theoretical annual power output by FPV, which differ by several orders of magnitude worldwide (Fig. 2 ).

Shown, for each of the >1 million water bodies considered in this study, is the total annual power output of a FPV system when 10% of the water body surface area (up to 30 km 2 ) is occupied.

Water body constraints on global FPV potential

The theoretical global analysis described above considered that all ~1 million water bodies included in our dataset were suitable to host FPVs. However, there are several factors that must be considered when selecting appropriate water bodies for this renewable energy technology (Fig. 3 ). The key constraints we include for a water body are (1) it is located within 10 km of a population centre; (2) it is not situated within a protected area; (3) the duration of ice cover is less than six months and (4) the water body has not dried up during the study period (for example, Supplementary Fig. 6 ) ( Methods ). After selecting sites that we considered suitable to host FPVs based on these constraints, a total of 67,893 water bodies remained in our global dataset (that is, 94% of the studied water bodies were unsuitable according to our criteria). For each of the water bodies that remained, we re-calculated the total power output from FPV. Specifically, by applying a similar approach to our theoretical global analysis, we considered a 1 kW FPV and calculated the total annual power output from each water body assuming a 10% surface cover (up to 30 km 2 ). Globally, we estimated a total annual power output of 1,302 TWh across all 67,893 water bodies. A considerable fraction of the global power output from FPV would be generated from water bodies located within specific countries (Supplementary Table 1 ), including large ones such as China (252 TWh), Brazil (170 TWh) and the United States (153 TWh). However, we also find that some comparatively smaller countries can produce a considerable amount of electricity from FPV. For example, the country with the 12th largest estimated total power output from FPV, within this feasibility study, was Papua New Guinea (19 TWh), which is not only located near the equator where solar irradiance is high (Supplementary Fig. 3 ) but is also home to some large water bodies, such as Lake Murray (647 km 2 ) and Lake Kutubu (49 km 2 ).

Shown are the location of each water body that we consider unfeasible for FPV deployment. The water body constraints that we consider in this study include the distance from each water body to a population centre (>10 km), water bodies that are situated inside protected areas, when the annual fraction of ice cover exceeds 50% (that is, the typical annual ice cover fraction from 1991 to 2020) and those that have dried up during the study period. Note that some lakes meet the criteria for more than one constraint, for example, those situated within a protected area and outside a population centre. Thus, the values shown in the legend do not add up to the total number of water bodies included in this study. To best visualize these results, we shifted the latitude and longitude of water bodies randomly by ±0.05° when plotting the different constraints. However, some points on the map are still overlapping.

Potential for FPV to decarbonize national economies

Our estimates suggest that FPV could play an important role in meeting the energy demands of several countries and aid in decarbonizing the economy. By summarizing our results at the country level, we provide estimates of national-scale total FPV power outputs worldwide (Fig. 4a ) and calculate the percentage of national electricity demand it would meet (Fig. 4b and Supplementary Fig. 7 ). We estimate that if all water bodies that we consider feasible have 10% of their surface area covered by FPVs (up to 30 km 2 ), a few countries considered in this study (approximately 3%) could meet their energy demand via this renewable energy technology (Supplementary Table 1 ). For some countries, for example, Bolivia, FPV production (9 TWh) almost meets demand (11 TWh in 2021), whereas in others it either exceeds, for example, Ethiopia (129% of electricity demand from FPV), or falls short but still makes a valuable contribution, for example, Finland (17% of electricity demand from FPV). However, for some nations, there is limited potential suggesting it is not a viable means to decarbonize the economy but can still provide a valuable source of renewable energy. On average, across all countries, the percent of electricity demand that could be met by FPV is 16% (Supplementary Table 1 ). Moreover, our analysis suggests that the countries with the greatest total power output from FPVs are typically those with the greatest electricity demand (Fig. 4c ).

a , b , Shown are the simulated total annual power output when 10% of the surface area of all suitable water bodies are occupied by FPV ( a ) and the percentage of total electricity demand met by FPV in each country ( b ). c , The relationship among the power output by FPV, electricity demand and sum of water loss via evaporation per country. d , Water loss via evaporation per country.

The decarbonization potential of FPV could be especially important for national economies currently powered by high carbon intensity electricity (Supplementary Fig. 8 ). We find that some of the countries with high FPV potential, such as China, also have a high carbon intensity of electricity (544 g CO 2 e (Carbon dioxide equivalent) in 2021). Crucially, FPV could also increase access to electricity to communities in some nations (Supplementary Fig. 9 ). For example, in Chad or Malawi, where the installation of FPVs in selected water bodies could contribute substantially to national electricity demand (73% and 29%, respectively); approximately one-tenth of the population of these countries do not have access to electricity (Supplementary Table 1 ). Thus, as a low carbon source of electricity, FPV could be a useful tool to provide electricity to these regions and others. However, it is also important to note that in many regions (for example, sub-Saharan Africa), it is not simply a question of electricity supply but also connection, which can be difficult. We also calculated the potential reduction in total CO 2 emissions, which could be achieved following the deployment of FPV in each country ( Methods ). By replacing a percentage of the total electricity demand with what could be met by FPV and considering their own estimated carbon intensity, we calculated variable reductions in national CO 2 emissions (Supplementary Fig. 10 and Supplementary Table 1 ). For example, we estimated an annual (in 2021) decrease of 0.13 billion tonnes of CO 2 in China and 0.05 billion tonnes of CO 2 in the United States, two of the world’s largest emitters in 2021, despite FPVs meeting only 3% and 4% of energy demand. Globally, the deployment of FPVs could lead to a total annual reduction of 0.45 billion tonnes of CO 2 (in 2021). However, we also find that in some countries where the carbon intensity of electricity is already very low (for example, Paraguay ≈ 25 g CO 2  kWh −1 ), our calculations suggest a negative impact of FPV on total CO 2 emissions (that is, leading to higher CO 2 ). Additionally, for nations whose energy supply is dominated by hydro and wind, FPVs may increase CO 2 emissions given PVsʼ higher carbon intensity. However, as with any estimates relating to a relatively new technology, we are aware that FPV is still maturing, with limited studies on the embedded carbon intensity ( Methods ), and these could be misleading given local conditions and design variations that will influence their carbon intensity. There are also unknown impacts of FPVs on water body carbon cycling and their knock-on impacts on, among other things, CO 2 emissions from water bodies. Lastly, the total reduction in CO 2 emissions that we calculated are influenced by the water body constraints that we previously defined. These estimates can vary depending on the number of water bodies included in any national-scale or global analysis.

Whereas our numerical modelling approach provides important new insights on FPV potential worldwide, it is important to consider that our chosen thresholds for key constraining factors could be defined differently depending on specific use cases. We therefore encourage those interested in assessing local–regional potential to modify the thresholds we imposed using the information that we provide (Data availability).

Potential for FPV to reduce water scarcity

Water is a critical resource, underpinning the provision of many ecosystem services required by society. However, in some regions supply is increasingly scarce given demand and the impact of climate change. FPV technologies have the potential to reduce water scarcity mitigating water loss via evaporation, which is accelerating globally under climate change 13 , 14 , 15 , 16 . Indeed, FPV systems are emerging as a potential mitigation strategy for water scarcity by reducing evaporative water loss from reservoirs and lakes in many parts of the world 4 , 17 , 18 , 19 , 20 , 21 , 22 , corroborated by numerous studies. FPVs probably exert a dual influence on evaporation rates. First, they create a shading effect, decreasing water surface temperature and consequently suppressing the vapour pressure gradient at the air–water interface, a key driver of latent heat fluxes and, in turn, evaporation 23 , 24 , 25 . Second, FPVs may act as wind barriers, further dampening evaporative losses, as wind speed is positively correlated with evaporation rates 26 , 27 . The deployment of FPVs has the potential to reduce evaporative water loss, thereby contributing to the maintenance of water levels in water bodies and alleviating pressure on freshwater resources. To ensure a comprehensive evaluation, potential trade-offs and feedback loops must be considered. This also includes the influence of FPVs on local microclimates, an effect observed in land-based systems 28 , 29 and potentially applicable to large-scale FPV deployments. Following the methods of ref. 13 , we estimated the total water loss via evaporation in each of the studied water bodies during the study period (Supplementary Fig. 11 ), and for each country, we calculated the total evaporative water loss. For example, the total annual water loss via evaporation in all studied water bodies in Canada and the United States was 230 km 3 and 221 km 3 , respectively (Fig. 4d ). These are more than two orders of magnitude greater than calculated for many European, African and Asian countries. Moreover, we compare the geographical patterns in total evaporation volume and the total power output from FPV (Fig. 4c ). We find that the countries with the greatest potential for FPV (that is, in terms of the power generated) are also those that experience the highest evaporative losses (Fig. 4c,d ). Furthermore, in countries where the total volume of water lost via evaporation is low, including many central European countries, the total power output by FPV (and the percent of energy demand that FPVs can produce) is also comparatively little. This quantitative analysis thus highlights the combined benefit of installing FPVs in these countries, that is, to both help meet electricity demand and address water scarcity.

Furthermore, recent studies have suggested that FPVs could help mitigate the occurrence of algal blooms 30 , 31 , which have increased in many 32 , but not all 33 , 34 , 35 , 36 , inland water bodies in recent decades. This has implications for water availability and ecosystem function, as algal blooms are among some of the main causes of poor water quality and can lead to serious health issues 37 , 38 , 39 , 40 , 41 , 42 . FPVs present a promising approach to water quality management, particularly in addressing algal bloom dynamics. These systems create a shading effect that directly reduces light availability, a critical factor limiting the growth of algae, which are predominantly photosynthetic organisms. Additionally, FPVs have the potential to disrupt water circulation patterns within the aquatic environment, thereby altering nutrient dynamics and influencing the availability of essential nutrients necessary for algal proliferation 43 , 44 , 45 . Whereas the precise magnitude of algal bloom reduction attributed to FPVs requires further investigation, initial studies suggest a compelling avenue for future research 46 , 47 , 48 . Quantifying the percentage reduction in algal blooms across a range of water body types and algal communities will be essential to ascertain the efficacy of FPVs as a water quality management tool. Such efforts hold promise in advancing our understanding of FPV-mediated interventions in aquatic ecosystems and their role in promoting sustainable water resource management practices.

By exploring the satellite-derived global algal bloom dataset of ref. 32 , which includes information on algal bloom occurrence in 236,913 water bodies worldwide, we investigated the frequency of algal blooms during the study period (Supplementary Fig. 12a ). Specifically, we calculated the percentage of water bodies with available data in each country that experienced an algal bloom (Supplementary Fig. 12b ) and how frequently they have occurred (Supplementary Fig. 12c ). Moreover, we compared the geographical patterns in algal bloom frequency with the total power output from FPV (Supplementary Table 1 ). We find no clear relationship between countries with the greatest potential for FPV (that is, in terms of the power generated) and those that experience the highest frequency of algal blooms (Supplementary Table 1 ). However, for some countries, FPV could be beneficial in terms of mitigating negative impacts of algal blooms. Using an algal bloom frequency of greater than 10% as a threshold (10% is considered high following ref. 32 ), there are many countries/water bodies that could benefit from FPV installation, with several also notably benefiting from the electricity production. For example, in Argentina where the installation of FPV in selected water bodies could produce sufficient energy to meet 45% (67 TWh) of the national electricity demand (149 TWh in 2021), the percentage of sites with available data that experience algal blooms is 11%. Similarly, during the same time period, 17% of water bodies with available data in Brazil have experienced an algal bloom, while also providing the potential to meet one quarter (170 TWh) of the electricity demand (686 TWh in 2021) of the country. Moreover, in Portugal where FPV could contribute over 3% of electricity demand, 30% of sites with available data have experienced algal blooms, the average frequency of which is 11%. Other country-level summaries are provided in Supplementary Table 1 . However, we note that more research is needed to develop a mechanistic understanding of FPV impacts on algal blooms.

Benefits, risks and unknowns of FPV deployment

FPV can provide higher power outputs while reducing land-use pressure and water scarcity when compared to other means of PV deployment. Moreover, our global estimate of FPV potential demonstrates the electricity supply benefit of this renewable energy technology. However, we lack a great deal of essential knowledge of the most likely impacts of FPV on hosting water bodies 30 , 31 , 43 , 49 , 50 , 51 , 52 , 53 . FPV could alter physical, biological and chemical states and processes within lakes and reservoirs, but it is challenging to forecast emergent ecosystem-level effects that arise from complex interactions within each water body and to then make generalizable predictions 54 . Nevertheless, existing site-specific studies, including those focusing on natural water surface coverings (for example, ice and floating-leaved vegetation), allow us to make tentative inferences of some of the probable impacts, which should be considered before a wider uptake of FPV.

Ecosystem impacts of FPV will be largely mediated through effects on physical states and processes 54 . Specifically, installations will (1) reduce the amount of solar radiation reaching the water surface and (2) shelter the water body from the wind. These changes would have opposing effects on surface temperature, vertical mixing and therefore water body thermal structure. Consequently, it is currently unclear whether FPV will increase or decrease surface water temperature and water body mixing. These uncertainties over the physical effects of FPVs are of concern, given their regulation of biogeochemical cycles linked to eutrophication and ecosystem states and processes. Indeed, the installation of FPV is likely to have effects that cascade through the whole ecosystem, but unless the implications for physical attributes are resolved, there will be uncertainties over the direction and magnitude of broader responses. For example, water temperature and stratification influence dissolved oxygen concentrations in deep waters 55 , with cascading effects on nutrient release from sediments 56 , 57 and subsequent phytoplankton growth, the production of greenhouse gases such as methane 58 , 59 and nitrogen cycling 60 . Through shading, we would also expect FPV to impact upon the growth and composition of phytoplankton communities with knock-on impacts on the aquatic food web 31 , 47 , 48 , 61 .

Advancements in understanding of the complex impacts of FPVs on their host water bodies are mainly developed through modelling studies, either via mesocosm experiments 46 , 47 , 61 , 62 , 63 , 64 or computational assessments 18 , 19 , 20 , 21 , 22 , 48 , 65 . Most research to date focuses on the potential water savings that come from FPV deployment depending on the surface coverage. Far fewer assessments of hydrodynamic or water quality impacts exist. We must rapidly accelerate knowledge of these physical and biogeochemical impacts to ensure that the pathway to decarbonization continues while minimizing concomitant environmental impacts and that low carbon electricity generation is not exploited at the cost of other essential ecosystem services that fresh waters provide to society (biodiversity, drinking water, fisheries, hydropower, irrigation of crops).

It is imperative to carefully consider the ecological implications and trade-offs associated with each potential FPV deployment. Whereas FPV presents a promising avenue for decarbonization, its deployment on natural lakes could pose considerable risks to freshwater ecosystems and native biodiversity, which is increasingly at risk from external pressures 66 , 67 . As such, any proposal to utilize natural lakes for FPV installations must be accompanied by comprehensive environmental and ecological impact assessments, stakeholder consultations and adherence to robust regulatory frameworks. Moreover, given the sensitivity of pristine natural lakes and the potential for irreversible ecological damage, caution must be exercised in targeting such ecosystems for FPV deployment. Future research efforts should prioritize investigating and building a predictive understanding of the ecological consequences of FPV on freshwater systems, and this knowledge must be used to develop strategies to mitigate potential negative impacts while maximizing the co-benefits of this technology. By integrating environmental considerations into decision-making processes and adopting a precautionary approach, we can ensure that FPV deployment on natural lakes contributes to sustainable development goals while safeguarding freshwater ecosystems for future generations. In addition, whereas our analysis removed lakes within protected areas, it is important to recognize that thousands of critically important natural lakes exist in areas that are not formally categorized as protected. These ecosystems are often vulnerable to anthropogenic disturbances and face increasing pressures from various human activities, including infrastructure development. Therefore, any proposal to deploy FPV on natural lakes must consider the unique ecological characteristics and conservation status of each site and seek to minimize impacts through design decisions and mitigation measures, even if those sites are not within formally designated protected areas. This underscores the need for a nuanced approach that balances the potential benefits of FPV deployment with the imperative to preserve the ecological integrity of freshwater ecosystems.

Pathway to judicious FPV deployment

Considering the concerns described above regarding the potential environmental impacts of installing FPVs, developing modelling capabilities and decision support tools to guide deployment location (both between and within water bodies), FPV design and extent and any desirable management (that is, water body mixing or aerating) would promote sustainable deployment of FPV. Without such capabilities it might be desirable to begin this transition to low carbon energy generation by focusing on artificially created water bodies 7 , 8 . One could argue that there might be greater environmental concerns associated with natural water bodies than artificial ones and, as the latter are often already managed, installing FPVs is likely to be more straightforward because of the presence of existing infrastructure 68 . Moreover, it is critical to consider societal values of potential deployment sites, as for all renewable energy infrastructure 63 .

From a technical standpoint, installing FPV on hydroelectric reservoirs can optimize energy efficiency and improve system reliability 50 , 69 . Integrated hydroelectric–FPV systems may also lessen the environmental and social impacts of standalone hydroelectric operation 70 providing synergistic benefits to the water–food–energy nexus 69 . However, it is important to consider that whereas some infrastructure is already in place in hydroelectric reservoirs, they might already be operating at full operational capacity, meaning that systems will need to be updated to receive additional power from FPV. Another important technological consideration for the installation of FPVs, not only in artificial reservoirs but also in natural lakes worldwide, is the material required to support a wider uptake of renewable energy technology 71 and the need to transport those materials to specific water bodies, which can be problematic. We also note that the potential biodiversity value of artificial water bodies 72 means that plans to develop FPVs on anthropogenic fresh waters do not obviate the need for thorough consideration of ecological impacts.

Study sites

In this project, we investigated the FPV potential of over 1 million (1,050,251) globally distributed water bodies (>0.1 km 2 in surface area), the location (and shapefiles) of which were extracted from the HydroLAKES database 73 . This global dataset of water body characteristics also includes information on 6,673 reservoirs based on the Global Reservoir and Dam database 74 .

Global simulation of FPV power output

For each of the ~1 million water bodies investigated in this study, we used the Global Solar Energy Estimator (GSEE) 10 to simulate the PV power output at hourly resolution from 1991 to 2020. GSEE requires as input specific information on the PV modules, including their capacity and tilt angle and hourly data for solar irradiance (direct and diffuse) and air temperature. In this project, climate data were downloaded from the ERA5 (available at 0.25° longitude–latitude resolution) reanalysis product 11 . Notably, the grid cell climate forcing from ERA5 was used to represent conditions for a specific lake location within the global simulations. In this study, we assumed that the FPV had a capacity of 1 kW, and we set the tilt angle to the latitude of the water body when situated between 0° and 15° N/S and to 15° elsewhere. This follows the recommendation of the World Bank FPV practitioner’s handbook 75 , resulting in high-density arrangements. Higher tilt angles are generally not deployed in FPV settings due to concerns about wind loading, shading that would occur from densely packed panels and increased material costs that would rise from installing at higher angles. We also assumed fixed-tilt rigging, which is reasonable for a global-scale assessment. FPVs can also be one- or two-axis tracking, which intuitively would result in greater power output. Thus, here we follow a conservative approach, and the current most popular deployment means, and assume fixed FPVs. Considering that FPVs are up to 10% more efficient than land-based solar PV 5 , 6 , 76 , 77 , 78 , for which GSEE was developed, we adjusted the GSEE outputs accordingly. However, the efficiency increases of FPVs compared with land-based ones can be higher 51 . Once we simulated the power output for a 1 kW FPV in each water body, we then upscaled these estimates based on a surface area that was equal to 10% of the water body surface area (below). Specifically, by assuming an FPV footprint of 10 m 2  kW −1 , we could estimate how many panels could be installed per lake to occupy 10% of the water surface area (up to 30 km 2 ; below) and multiply our annual power output accordingly. A footprint of 10 m 2  kW −1 was selected as it can be scaled with ease and the footprints of six sampled FPV systems (Queen Elizabeth II, United Kingdom; Langthwaite Reservoir, United Kingdom, and four US systems detailed in ref. 3 ) ranged from 7.2 to 15.3 m 2  kW −1 . Whereas FPVs can be deployed at a range of coverage depending on, among other things, the design of the FPV (for example, tilt angle) and the rated capacity of the installation, here we follow a conservative approach and consider 10% coverage for all sites. We highlight that PV power outputs from GSEE using reanalysis input data have been validated previously against metered time series from more than 1,000 PVs 10 . We also considered the potential technical constraints related to the total size of an FPV array. In this study, we follow ref. 8 and impose a maximum coverage area of any one system to 30 km 2 . This threshold is based on one of the largest FPV systems in the world, which is located at the Saemangeum site in the Yellow Sea off Korea 79 . We do note, however, that there is the potential for larger systems, such as the Bhadla Solar Park located in the Thar Desert of Rajasthan, India, which covers an area of 56 km 2 and has a total installed capacity of 2,245 MW. We anticipate that the maximum footprint of these systems will also increase in the future.

Water body constraints on FPV

There are several factors that must be considered when selecting appropriate water bodies for this renewable energy technology. The key constraints that we considered in our study included (1) it is located within 10 km of a population centre; (2) it is not situated within a protected area; (3) the duration of ice cover is less than six months and (4) the water body has not dried up during the study period. The distance to the population centre has been selected as a distance for which it is likely to lay a transmission line, either to the load centre or to connect to a grid via more proximal existing transmission lines. Some previous national-scale studies have considered 80 km from a transmission line as a feasible distance for deploying FPVs 12 . Thus, we consider our analysis conservative in terms of the number of lakes included in our feasibility study. The presence of ice can make it difficult to not only install FPV but can also damage FPV installations by influencing buoyancy and overall load, that is, ice can be detrimental for FPV by imposing high loads on the mooring system. Extremely cold areas are also often accompanied by high snow loads, reducing electricity generation if the array is covered and special anchoring materials and higher project cost compared to conventional FPV projects. In terms of variations in water body surface area and, in turn, water depth, water bodies that dry up could be considered unfavourable given additional costs associated with mooring/anchorage— as suggested in the World Bank FPV practitioner’s handbook 75 —and the reduced efficiency of FPVs when they are not displaced on water. Critically, there is a potential issue of FPVs becoming stranded if a shallow water body dries up temporarily, as can occur in many water bodies worldwide 1 . However, with all these constraints, their global applicability will depend on local specifics. For example, it may be cost effective to connect a very large FPV system to a transmission line or load centre >10 km away. Moreover, any increased costs associated with greater transmission distances, cold environments and variable water surface areas need to be placed in the context of alternative sources of low carbon electricity. Thus, whereas our study outlines key constraints and selection criteria for identifying suitable water bodies, we acknowledge that the global applicability of these criteria is contingent upon local specifics and associated costs. Factors such as local climate conditions, regulatory frameworks, economic feasibility, environmental impacts and social acceptance all play important roles in determining the feasibility and success of FPV projects. Recognizing this complexity, we advocate for a comprehensive analysis that integrates technical, economic, environmental and social dimensions into the decision-making process. We provide the calculated FPV outputs for each of the ~1 million water bodies included in this study, allowing the water body constraints described above to be modified according to user requirements (Data availability). In turn, the results that we present in our feasibility study, which are based largely on our own evaluation and experience, will differ if these constraints are altered.

Water body surface area

To calculate the surface area of each water body in this study (1991–2020), we followed the methods outlined in ref. 13 . In brief, we reconstructed the monthly water surface area time series for the >1 million lakes based on a combination of the dynamic Landsat-based global surface water (GSW) dataset 80 and the static HydroLAKES shapefiles 73 . For each month and each water body, the water classification map from the GSW dataset is extracted within the defined boundary of the HydroLAKES shapefiles. However, such water classification maps are frequently contaminated by cloud cover, cloud shadow and sensor failure, leading to large data gaps. Here we adopted the automatic image enhancement algorithm from ref. 13 , which detects the ‘observable’ water edge and extends it to the ‘contaminated’ area according to the water occurrence image, to create the complete water surface (Supplementary Fig. 13 ). Then the surface area time series at a monthly time step from 1991 to 2020 for each lake is constructed. For new reservoirs that were built after 1991, we calculated surface area variations from five years after the construction year.

Information on electricity demand, carbon intensity of electricity and electricity access

We downloaded information on national-scale annual electricity demand, carbon intensity of electricity and the percentage of the population that has access to it from ref. 81 . Electricity demand is described as total electricity generation, adjusted for electricity imports and exports. Carbon intensity is measured in grams of carbon dioxide equivalents emitted per kilowatt-hour of electricity. Access to electricity is defined as having an electricity source that can provide very basic lighting and charge a phone or power a radio for 4 hours per day. For each of these metrics, we used the latest annual information available. This includes data from 2021 for both annual electricity demand and its carbon intensity and data from 2020 for electricity access. For consistency, we do not include data from different years when investigating global patterns in each metric. For example, in terms of energy demand, some countries (for example, Albania, Iceland) did not have information for 2021 (only 2020) and thus were not included in the analysis.

We also calculated the potential reduction in total CO 2 emissions following the deployment of FPV. To achieve this, we first calculated the total annual CO 2 emissions per country by multiplying the electricity demand with its carbon intensity 81 . We then calculated the total annual CO 2 emissions that would be reduced if a percentage of the total energy demand was met by FPV, that is, if they were deployed on the water bodies that we considered feasible. There is no definitive carbon intensity value for FPV given the relative immaturity of the deployment means and variations due to design, location and where components are sourced from. Consequently, we used the IPCC (Intergovernmental Panel on Climate Change) life-cycle CO 2 e emissions per kWh produced by rooftop PVs, which are approximately 41 g CO 2  kWh −1 (ref. 82 ), to estimate the total annual CO 2 emissions associated with FPVs. Using these estimates, we calculated the potential reduction in total CO 2 emissions in each country (Supplementary Table 1 ).

Proximity of water body to a population centre

When selecting a water body suitable for FPVs, we considered their proximity to a population centre, which we use in this study as a proxy for distance to a transmission line or a load centre. Specifically, we only considered water bodies that were located within 10 km of a population centre as feasible for the installation of FPV. Here we define a population centre as a region with a population of at least 1,000 people and a population density of ≥400 people per square kilometre. Population data were extracted from the Socioeconomic Data and Applications Center 83 , available at a 30 arcsecond spatial resolution. Lakes situated within 10 km of a population centre were considered suitable for FPV.

Protected areas

Information on the location of protected areas was extracted from the World Database on Protected Areas 84 . In this study, we considered a water body to be located within a protected area where the water body polygon intersected with a protected area polygon. However, this is a conservative approach to accounting for ecological impact, as many fresh waters with great biodiversity value exist outside of the current protected area network 85 .

Water loss by evaporation

To estimate the volume of water lost via evaporation in each of our studied sites, we followed the methods outlined in ref. 86 . Specifically, the monthly evaporation rate was calculated for each lake using the Penman equation by considering the heat storage effect (equation ( 1 )). We used the average evaporation rate derived based on three meteorological forcing datasets (that is, TerraClimate, ERA5 and GLDAS) to take account of forcing data uncertainty. The evaporation rate was estimated as:

where E is the open water evaporation rate (mm d −1 ); ∆ is the slope of the saturation vapour pressure curve (kPa °C −1 ); R n is the net radiation (MJ m −2  d −1 ); G is the heat storage change of the water body (MJ m −2  d −1 ); γ is the psychrometric constant (kPa °C −1 ); f ( u ) is the wind function and is equal to λ v (2.33 + 1.65  u ) L f −0.1 (MJ m −2  d −1  kPa −1 ); L f is the average lake fetch at the wind direction (m); e s is the saturated vapour pressure at air temperature (kPa); e a is the air vapour pressure (kPa); and λ v is the latent heat of vapourization (MJ kg −1 ). Then the volumetric water losses via evaporation for each lake were estimated by multiplying the evaporation rate and the water body surface area.

Ice cover duration

To determine the duration of ice cover in each of the studied water bodies, we used an air temperature-based approach 13 . Instead of the simple 0 °C isothermal approach, we specifically modelled (1) the freeze time lag, which is the time difference between the date when the air temperature drops to zero and the lake freezing date and (2) the thaw time lag which is the time difference between the date when the air temperature rises back to zero and the lake thaw date (Supplementary Fig. 14 ). The freeze time lag is caused by the higher specific heat capacity of water than air and thus water temperature cools at a slower rate than the air temperature, whereas the thaw time lag is caused by the thickness of lake ice that has been accumulated during the previous winter. Considering both time lags, the total ice cover duration for a lake ( D ice ) is calculated following equation ( 2 )

where D ice is the lake ice duration in days for each month; D total is the total lake ice duration in days for each month based on the 0 °C isothermal approach; Lag freeze is freeze lag in days; and Lag thaw is the thaw lag in days.

Algal bloom occurrence

To investigate the occurrence and frequency of algal blooms in water bodies worldwide, we explored the satellite-derived global bloom dataset of ref. 32 . Approximately 17% of the ~1 million water bodies investigated in this study ( n  = 236,913) were included in the global bloom dataset, with the remainder considered unfeasible, primarily due to relatively high uncertainty in algal bloom detection via Landsat imagery 32 . For the water bodies considered suitable, ref. 32 investigated data related to the occurrence frequency of algal blooms, defined as the ratio between the number of detected algal blooms to the number of valid satellite observations within the period of interest (1982–2019). In this study, we used this dataset to calculate two key metrics: (1) the percentage of lakes within a country that have experienced at least one algal bloom and (2) in lakes where algal blooms have been observed, we calculate a country-level average frequency of occurrence.

Data availability

ERA5 solar radiation and air temperature data used in this study are available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview . Outputs from the Global Solar Energy Estimator for each of the studied lakes are available via Figshare (ref. 87 ).

Code availability

Code used to run the Global Solar Energy Estimator is available via Github at https://github.com/gzhaowater/fpvGSEE .

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Acknowledgements

This work was supported by the Third Xinjiang Scientific Expedition and Research (2021xjkk0800) and the Chinese Academy of Sciences Pioneer Initiative Talents Program. R.I.W. was supported by a UK Research and Innovation (UKRI) Natural Environment Research Council (NERC) Independent Research Fellowship (grant number NE/T011246/1). G.Z. was also supported by the Third Xinjiang Scientific Expedition Program (grant number 2021xjkk0803). We thank L. Feng (Southern University of Science and Technology, China) for making available the algal bloom occurrence data.

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R. Iestyn Woolway

Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

Lancaster Environment Centre, Lancaster University, Lancaster, UK

Sofia Midauar Gondim Rocha & Alona Armstrong

Lake Ecosystems Group, UK Centre for Ecology & Hydrology, Lancaster, UK

Stephen J. Thackeray

Energy Lancaster, Lancaster University, Lancaster, UK

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R.I.W., A.A. and S.J.T. conceived the idea for this study. R.I.W., G.Z., A.A. and S.J.T. designed the methodology. G.Z. performed the large-scale model simulations. R.I.W. and G.Z. analysed the data with input from the other authors. R.I.W. led the writing of the manuscript, with input from A.A., S.J.T. and S.M.G.R. All authors contributed critically to the drafts and gave final approval for publication.

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Correspondence to R. Iestyn Woolway .

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Supplementary Figs. 1–14.

Supplementary Table 1

National-scale summaries of floating solar photovoltaic (FPV) potential. Shown for each country are the total (that is, sum) of annual power output from FPVs when 10% of the median water body surface area is occupied, the electricity demand and how much of this can be met by FPV. We also show the carbon intensity of electricity, the percentage of the population with access to electricity, the total volume of water lost via evaporation, the percentage of water bodies with data in each country that have experienced at least one algal bloom and for those that have experienced at least one bloom, we quote the national-scale average frequency of algal blooms. Also, we show the total CO 2 emissions per country (g CO 2 ) and the amount that could be reduced by FPV (tonnes).

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Woolway, R.I., Zhao, G., Rocha, S.M.G. et al. Decarbonization potential of floating solar photovoltaics on lakes worldwide. Nat Water (2024). https://doi.org/10.1038/s44221-024-00251-4

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Received : 22 March 2023

Accepted : 07 May 2024

Published : 04 June 2024

DOI : https://doi.org/10.1038/s44221-024-00251-4

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