thesis big data supply chain

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Impact of big data analytics on supply chain performance: an analysis of influencing factors

• Original Research
• Open access
• Published: 27 May 2022
• Volume 333 , pages 769–797, ( 2024 )

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• P. R. C. Gopal 1 ,
• Nripendra P. Rana   ORCID: orcid.org/0000-0003-1105-8729 2 ,
• Thota Vamsi Krishna 1 &
• M. Ramkumar 3  

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This paper aims to understand the impact of big data analytics on the retail supply chain. For doing so, we set our context to select the best big data practices amongst the available alternatives based on retail supply chain performance. We have applied TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) for the selection of the best big data analytics tools among the identified nine practices (data science, neural networks, enterprise resource planning, cloud computing, machine learning, data mining, RFID, Blockchain and IoT and Business intelligence) based on seven supply chain performance criteria (supplier integration, customer integration, cost, capacity utilization, flexibility, demand management, and time and value). One of the intriguing understandings from this paper is that most of the retail firms are in a dilemma between customer loyalty and cost while implementing the big data practices in their organization. This study analyses the dominance of the big data practices at the retail supply chain level. This helps the newly emerging retail firms in evaluating the best big data practice based on the importance and dominance of supply chain performance measures.

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1 Introduction

Huge competition and fluctuating demand patterns increase the data generation in the supply chain (SC) (Arunachalam et al., 2018 ). This pushes the companies to adopt the supply chain analytics (SCA) so as to gain competitive advantage (Davenport and O’dwyer, 2011 ; Shafiq et al., 2020 ). Adopting the SCA practices improves the accuracy and overall performance of the SC. As firms are already using statistical methods for decision making in one way or another, some practitioners are against the implementation of SCA because of the time and cost incurred in adoption of these solutions (Kusiak, 2006 ; Trkman et al., 2010 ). Implementing SCA in businesses is a tricky task because of the uncertainty associated with the data and the changing requirements of the customer (Handfield and Nicholas 2004 ; Liberatore & Luo, 2010 ; Huner et al., 2011 ; LaValle et al., 2011 ; Manyika et al., 2011 ).

Industries are facing high pressure to improve the overall performance of the SC to gain competitive advantage over rivals (Aloysius et al., 2018 ; Arunachalam et al., 2018 ; Hazen et al., 2018 ). Designing and developing data-driven innovations (DDI) is the way that the companies live, breathe, strive, and sustain their competitive advantage in a competitive data-driven environment (Sultana et al., 2021 ). Data plays a vital role mainly for retail and e-commerce industries to make a decision in the selection of strategies and to increase the performance (Mishra et al., 2018 ; Rai et al., 2006 ). Sultana et al. ( 2022 ) foregrounded the significance of DDI in strategic competitive performance of the companies. These days, the information or data generated by the industries are increasing exponentially because of the usage of technology solutions such as enterprise resource planning (ERP), radio frequency identification (RFID), Internet of Things (IoT), etc. (McAfee and Brynjolfsson, 2012 , Wamba et al., 2018 ). So, as time increases, information (data) is also rapidly increasing. The exponential data growth leads to certain challenges such as an organization’s data literacy, privacy and security regarding the usage of data (Sultana et al., 2021 ). This is more of a central concern in SC. Because of the rich and abundant data in the SC, retail firms are facing issues like wasting time in analysing the irrelevant and inaccurate data, in addition to this, challenges such as how to store and access massive amounts of data, etc., which in turn decreases the overall performance and efficiency of the SC and also profits of the organizations. Tiwari et. al. ( 2018 ) also highlighted that there is a nascent focus on addressing the challenges of data driven companies in the retail sector. To overcome this, big data analytics (BDA) largely helps in retail and e-commerce industries to optimize and store the data in a purposeful way.

BDA, a part of SCA, is used for the analysis and storage of the data associated with the SC. Big data can be defined by following five characteristics: (1) Velocity (how fast the data is produced), (2) Volume (how much data is generated), (3) Value (the data should be reliable), (4) Variety (types of data collection) and (5) Veracity (quality and accuracy of the data). The BDA practices are implemented in the SC so that large data can be easily accessible and stored (Gupta et al., 2019 ; Katal, et al., 2013 ). Some of the practices like Business Intelligence, RFID, IoT, etc., can be implemented and integrated into the SC so that there will be a gain in competitive advantage where the retail firm can withstand market competition (Waller & Fawcett, 2013 ). The benefits of the big data practices are data quality, data integration, data visualization, data storage, etc. But organizations need supporting factors for implementing these BDA practices in the SC. Organizations will also face some of the challenges like capital investment, the skill of employers, scalability of the database, etc., in order to implement the BDA practices in the SC. By implementing specific practices for the corresponding SCs, the big data can be easily accessed and quality, reliability, consistency, scalability, and flexibility of data can be achieved (Katal, Wazid et al., 2013 ; Chae et al., 2014 ). It also addresses the critical challenges which influence the poor performance of the SC. These challenges can be eliminated by properly implementing the specific BDA practices in the SC. We can also achieve long term economic benefits of an organization by implementing BDA practices. It refines the big data into small, precise, and accurate data so that the data can be easily used and make decisions. Akter et al., ( 2020 ) highlighted that digital transformation helped the companies to improve performance. Consumer packaged goods industry has achieved improvements through digital transformation by becoming closer to their customers and forming longer-lasting relationships that equate to repeat business and higher satisfaction (Kumar et al., 2020 ; Zhu, et al., 2010 ). Wamba et al. ( 2018 ) has emphasized that one side has huge benefits through big data practices and the other side huge challenges to handle to achieve breakthrough performance. In this research, our motivation is to implement the practices in retail and e- commerce SC because of the abundance of big data and changing needs of the customer which is uncertain. Raman et al. ( 2018 ) highlighted that BDA in industries increases the operational excellence, productivity, real-time visibility, low-cost operations, etc., which gives value added and monetary gain for the supply chain. State-of-the-art literature in the field of operations management, marketing and information systems neither provides a consolidated list of criteria that can be used to select the best BDA practices in retail SC and prioritize the criteria involved in the selection process nor suggests a comprehensive methodology that allows the decision makers to combine these criteria to select the best BDA for retail SC in an unbiased manner. So, the research questions for this paper remain as follows:

RQ1 Which are the various big data practices that are more suitable for retail SC?

RQ2 On what criteria, we can prioritize and select the best BDA for retail SC?

This paper ameliorates the domain of big data analytics research by making the following four contributions. This study is the first to analyse the dominance of the big data practices in retail supply chain level and thus helps the newly emerging retail firms. Secondly, this study takes a first step to identify a unique set of seven retail supply chain performance measures (supplier integration, customer integration, cost, capacity utilization, flexibility, demand management, and time and value) that the selection of the best big data practices. This was done by reviewing the extant literature, and then by having a discussion with the industry respondents. Thirdly, this study uses the TODIM (Portuguese acronym for interactive and multi criteria decision-making) methodology to select the best big data technology based on the seven-retail supply chain performance measures. The proposed methodology has the following benefits over other MCDM methods. (1) TODIM is more enticing to the decision makers because of no need in optimizing the criterion within the constraints. (2) It is a simpler and faster method to solve any MCDM problem because it does not need an optimization tool. (3) It is even simple to configure the TODIM method by altering the weights and adding new criteria as per the requirement of the decision maker. Lastly, this study acknowledges the necessity of a diversified pool of experts with technical and domain expertise to make the right decision as well as to achieve consensus.

The rest of the paper is structured as follows: In the next section, we present a brief background of big data practices in the retail supply chain with a specific focus on retail supply chain performance measures. Then, we provide the TODIM approach, followed by its application to a case study in India. We close with a discussion of our results, implications for research and practice, followed by conclusion with the limitations of the study.

2 Literature review

The number of published articles using the keyword search “Big Data Analytics” from the Web of Science database revealed 5,203 relevant journal papers from more than 100 multi-disciplinary journals from 2010 to 2021. Out of these 5203 articles, the big data papers related to manufacturing supply chain are 480, service supply chain are 265 and retail supply chain are 226 respectively. This trend clearly shows the number of research articles in “Big Data Analytics” are found to be growing after 2010 as shown in Fig.  1 . This pattern also indicates that different researchers especially in operations and supply chain domains all around the world are working in this area because of its ability to disrupt the supply chain positively. Despite the growing interest in this research arena, no explicit research has been done so far to select the best BDA technologies (data science, neural networks, enterprise resource planning, cloud computing, machine learning, data mining, RFID, Blockchain and IoT and Business intelligence) based on retail supply chain performance measures (supplier integration, customer integration, cost, capacity utilization, flexibility, demand management, and time and value). This motivates us to analyse the dominance of the big data practices at the retail supply chain by applying the MCDM based TODIM approach. The purpose of each big data practice at enterprise and supply chain level are presented in Table 1 .

Number of papers published in three different supply chains and big data practices from 2010–2020

2.1 Retail supply chain in Indian scenario

Since the last decade, the retail sector in India is active because of increasing power of buyers, product variety, economies of scale, and usage of modern supply and distribution management. Consumer literature reveals that most of the 71% of the retail sales in India are from FMCG goods (Srivastava, 2007 ). But there is a serious concern on FMCG retail because of improper data management, inventory inaccuracy, and vendor management which needs to be addressed promptly. So, to overcome this issue, BDAs are introduced in the retail SC in areas like strategic sourcing, supply chain network design, product design and development, demand planning, procurement, production, inventory, logistics and distribution and supply chain agility and sustainability (Tiwari et al., 2018 ). The introduction of BDA in retail SCs at the aforementioned areas has the capability to transform the conventional SC to data driven retail SCs or ‘Retail 4.0’. BDA in retail SC has the benefit of offering better-quality products and services to the consumer and to improve their shopping experience. The Indian retail sector is highly competitive enough to transform itself to ‘Retail 4.0’ environment. But to implement the best BDA in retail SC we need to focus on those criteria that enhance SC performance.

2.2 The Big data practices

2.2.1 data science.

In a retail supply chain, the raw data is increasing drastically due to more customers, advancement in the technology and collection techniques. By combining different fields like statistics and computation with data science, we can extract, improve, store, and monitor the meaningful data from the huge raw data for decision making. This is a complicated method which requires more time for industries to find meaningful data. This requires the usage of machine learning, data mining and artificial intelligence to get more accuracy from the data collected by RFID, sensors, etc. Though it was a costlier proposition (Ruehle, 2020 ), we can gain more benefits by implementing data science in the retail supply chain.

2.2.2 Neural networks

As there are a larger number of customers in the retail and e-commerce supply chain with human wants, there arises a problem in prediction of demand and supply. To overcome this problem, neural networks can be used. Neural networks consist of a series of algorithms which are being used to recognize the relations in a data set by forward bias and backward bias similarly to how the human brain operates. We can predict the sales by changing the input data and it is used for market forecasting (Loureiro et al., 2018 ). So, this practice can be used effectively in predicting the sales, demand, and behavior of the customers. This can be used in optimizing the information regarding the customer data and used widely in many industries for prediction purposes. But while using this technique we require more data and to develop the algorithm, we need more time and also it is computationally expensive than traditional algorithms. This also will not give the clue of why and how this happens which reduces the trust on this technique.

2.2.3 Enterprise resource planning

As the number of customers is increasing in the retail industry, the variety of products must increase according to customers' taste and preferences. This further tends to expand retailers’ size. But, at the same time, there will be difficulties for retailers to sell the right product at the right time to the right customers at the right price due to the problems related to demand forecasting. To handle this situation, every front end and back end worker of each functional department must be connected tightly. So, in this case, retailers will use ERP to connect the system from every department. For mixture and allocation of products, the retailers use the Distribution Requirements Planning (DRP) module which helps in outbound and inbound logistics and also inventories (Helms & Cengage, 2006 ; Martin, 1995 ; Ngare, 2007 ). ERP helps retailers in better understanding of information like inventories, order status, production rate, etc., of one department, to other departments but research in this content is limited. This may be due to the fact that installation of ERP is more costly and to get completed and fully functional, it may take years and success depends on skills of the workforce.

2.2.4 Cloud computing

Cloud computing is the trending resource system that is available today. This is mainly because it operates without the direct involvement of the user and it has the capacity to store a large amount of data. It has some important aspects like on-demand service, broad network access, resource pooling, rapid elasticity, and finally measured service. These aspects simplify the supply chain in terms of operating and performance which in turn reduces the market entry cost, so that small enterprises can easily enter the market (Ozu et al., 2020 ). Cloud computing offers more benefits to the retailers and e-commerce-based industries. But there are limitations like bandwidth issues, and uncontrollable data stored in the internet is not secure. Encompassing security in cloud computing requires additional investment. But, in the long run, cloud computing improves the financial performance of the organizations.

2.2.5 Machine learning

Machine Learning is mainly used as a problem-solving technique with the help of machines which act like the human brain by combining big data and algorithms without externally programming. So that, it can learn the patterns in the data by performing iterations and gives the result that what the customer needs. We can also predict the sales of the products according to the customer tastes and preferences by simplifying the complex data into easily accessible ones (Sharma et al., 2020 ). In retail and e-commerce industries, the prediction of customer needs and sales of the product are very difficult manually due to the increasing number of customers and increase in volume of the data. So, to understand the data patterns and future sales of the products can be predicted easily which in turns the cost of products can be optimized. But in this technique, there are some limitations in importing quality data.

2.2.6 Data mining

Data mining is a process where a large amount of data can be refined so that patterns are identified for problem-solving and forecasting. Firstly, it refines the big data into simple data and understands the trend of the data patterns. Then, it shares the information to all the departments in the industry which means it eliminates the unwanted data and uses the data which is more important for resources and operational performance of the industry (Senar et al., 2019 ). So, in retail and e-commerce industries the data which is collected from the customers can be refined by data mining techniques. But there are limitations like violation of user’s privacy which is not safe and secure and also provides data accuracy in its own limits.

RFID (Radio Frequency Identification) is the best technique used to collect data in the industry. It tracks the inventories, production rates, movements of products from one department to another department and also stores and shares that information and protects them from theft. Due to this, some benefits like warehouse management, transportation management, production scheduling, order management, inventory management, and asset management and also increment in labor productivity because the asset visibility reduces the loss in the stock and thus states the labor workload for stock-keeping units (Angeles, 2005 ; Tsai et al., 2010 ). At the store level, it reduces out of stock by improving inventory control and product availability. So, in retail and e-commerce industries these applications are used to improve the overall performance of the supply chain by scanning multiple items with greater security but there are limitations like not accurate or reliable as barcodes, and ten times more expensive than barcodes and installation requires more time and need to check whether it is fit to industry or not.

2.2.8 Blockchain and IoT

Blockchain in the supply chain is mainly used to track the functions of each department and monitor every action which is non-traceable with high authentication by eliminating the auditors (Manupati et al., 2020 , 2022 ; Raj et al., 2022 ). In the supply chain, we need to know every aspect so that we can modify the process in the supply chain to improve the performance and optimize the cost. In the retail and e-commerce industry, the supply chain must be very effective to control the defects and to increase the performance. So, every partner can track the products, defects and monitor the quality of the products but this is done with the help of IoT to get more efficiency. But it will not change the critical activities in the network of the supply chain (Helo, & Hao, 2019 ; Kshetri, 2018 ). In retail and e-commerce industries to maintain the effective supply chains, we need to implement blockchain technology along with IoT but there are also limitations like storage, private keys, network security, over dependence on the internet and loss of jobs for the workers.

2.2.9 Business intelligence

In this competitive world, the industries are facing problems like data management, market place, and business operations to finally achieve a competitive advantage. To achieve this industry has to get accurate and timely information where markets are getting competitive in which product life cycles are getting shortening. To achieve this, industries need access to data sets and data management systems. So, Business Intelligence (BI) facilitates the industry by analyzing, consolidating and gathering the data by using analytical software and solutions. Due to an increase in the number of customers, it requires BI to understand the reports to monitor the trends, market potentials and customer behavior changes (Ranjan, 2009 ; Gangadharan & Swamy, 2004 ; Banerjee & Mishra, 2017 ). In this aspect, BI helps in decision making so that the industry changes to the reactive-to-data model. But there are limitations like expensive for SMEs, creating rigid techniques where it throttles the business, time consuming like it takes almost 18–20 months to completely implement the data warehouse. Finally and mainly piling of historical data like it creates models from historical data of the industry but nowadays industries are not concentrating on the historical data due to frequent alteration of the market so it became a constraint.

2.3 Criteria for supply chain performance

2.3.1 supplier and customer integration.

Integration enhances the degree of partnership with both customers and suppliers, thus forming the strategies at firm-level, practices and processes synchronized to achieve inter-organizational information sharing. By doing this integration through collaborative relationships we can know the necessary technological and managerial resources that has to be implemented and achieves the sharing of information by combining the core elements from heterogeneous sources of data into a single platform which is common to all which increases the visibility of the data (Li et al., 2018 ; Shafiq et al., 2020 ). Due to a single platform, there will be transparency in the information sharing through suppliers, customers and intra- organizations. Thus it decreases the variability in the information from supplier to end user, therefore, increases the supply chain performance by reduced lead time, improved inventory, reliable delivery, etc., but we need to know the extent to which supplier and customer should be involved in the process i.e., degree of integration. Hence, integration improves the supply chain performance by sharing real time, accurate and reliable information across the partners externally and within the organization.

Cost is one of the major criteria in supply chain and managing it among supply chain processes is a major difficulty that organizations are facing (Manupati et al., 2019 , 2021 ; Tarei et al., 2022 ). Due to lack of information sharing among the organizations the wastage is increasing which in turns cost. The best supply chain is with optimized cost but not with minimum cost (Whicker et al., 2009 ). So, we need to choose the implementation of practice with optimized cost which gives more benefits like decreasing the defect products, decreasing the late deliveries, etc. If we choose the best practice with optimized cost we can reduce the wastage from supplier end to end user. Hence, optimization of cost in the supply chain improves the performance in terms of cost of the supply chain.

2.3.3 Capacity utilization

By sharing information through suppliers, customers and within organizations we can manage the inventory inaccuracy like damaged, out-of-date, seasonal and incorrect incoming and outgoing deliveries and finally misplaced items (Hollinger & Davis, 2001 ; Raman et al., 2001 ). Due to this supply chain performance will decrease, so, to overcome this information sharing has to happen transparently all over the organization. By sharing information each department will come to know the exact production rate, inventory rate, etc., so that the wastage of the products will decrease which in turn increases the supply chain performance.

2.3.4 Flexibility

Flexibility is used to measure the ability of the supply chain in terms of volume and schedule variations from suppliers to end users and potential behaviour of collaborative supply chain. This depends on the market environment and practice that the supply chain is using in which it operates (Angerhofer et al., 2006 ). Due to flexibility there will be increase or decrease aggregate production with response to customer demand which directly impacts the supply chain performance. We need to know the perfect practice which increases the flexibility so that supply chain performance increases.

2.3.5 Demand management

For supply chain performance demand management is one of the criteria. Demand management refers to demand forecasting and production planning which sense the demand signal, optimal prices and tracing of customer loyalty. This helps to find out new market trends and also root causes of failures and defects (Tiwari et al., 2018 ). So we can analyse the requirements from the customer’s point of view and proceed according to the needs of customers. To overcome this we need a best practice to analyse the demand so that performance of the supply chain increases.

2.3.6 Time and value

Time here represents every minute in the supply chain process to reduce lead time, cycle time, delivery time, etc., where it plays a major role in supply chain performance. Value the property of a product or service that customer is willing to pay. By reducing time and also optimizing the value of the product we can increase the performance of the supply chain (Whicker et al., 2009 ). To achieve this we need exact information which is transparent through the supply chain process. For this we need data driven practices like big data practices. The above mentioned are the criteria for improvement in the supply chain performance and also we need big data practices as alternatives to find out the performance of the supply chain. By implementing these practices, we can achieve data quality, data security, data visibility, etc., where the information is circulated among the suppliers to end users and also intra-organizations to achieve performance and competitive advantages. As described above, the authors selected the nine big data practices and seven supply chain performance evaluation criteria to identify the most suitable big data practices in the context of Indian retail and ecommerce supply chain.

3 Research methodology

3.1 the todim method.

TODIM is a discrete multi criteria decision making method which was coined in the early nineties based on Prospect Theory. In Portuguese, it is named as Interactive and Multicriteria Decision Making (Kahmenan & Tversky, 1979). In 2002, this method was awarded a Nobel Prize for Economics under the subject psychological theory (Roux, 2002 ). Thus, the decision makers always have an assumption as the solution is maximum of some global measure value for all multicriteria methods—for example, the highest value among the global value is the solution of a multi-attribute utility function in case of MAUT (Keeney & Raiffa, 1993 )—but the TODIM method uses the paradigm of prospect theory to get the global value. Thus, we can say how people are going to make decisions which are effective in the face of risk based on description and empirical evidence. The use of TODIM depends upon the value function of multi-attribute which is a combination of few mathematical descriptions which in turn reproduces the gain and loss function and finally aggregates them with over all criteria. Therefore, the value function shape in the TODIM method is the same as the gain/loss function of prospect theory but not all multicriteria problems deal with risk.

In calculations of the TODIM method, each specific form of loss and gain functions are tested based on the value of one single parameter. Once the forms are empirically validated, it will serve people to develop the additive difference function which is indeed a global multi-attribute function and gives the measurements of dominance of each alternative over the other (Tversky, 1969 ). In this way, the TODIM method is similar to outranking methods like PROMETHEE (Brans & Mareschal, 1990 ) because the final global value of each alternative is relative to its dominance over the other. Even though it appears that the validation process is complicated, which turns decision analysts to implement other gain or loss functions, in reality it is not so. Since, the two mathematical forms which were used in the past nineties have practical uses and are empirically validated in different applications (Gomes & Lina, 1992a , b ; Trotta et al., 1999 ).

From the above mentioned TODIM additive difference function which is similar to multi-attribute value function will have its use and it has to be validated by doing the verification of condition of the mutual preferential independence which leads to the ordering of the global values of the alternatives (Clemen & Reilly, 2013 ; Keeney & Raiffa, 1993 ). In the TODIM method it can be seen that the multi-attribute value function or additive difference function is defined as the differences between the values of any two alternatives to the referential or reference criterion which was perceived in relation to each criterion.

Technically, the TODIM method utilizes the simple resources to eradicate the inconsistencies from the pairwise comparisons between the decision criteria. Using a criteria hierarchy process, fuzzy value judgments and also interdependence relationships among alternatives, this method allows value judgements to be carried out in a verbal scale. In this sense, the trade-offs will not occur due to non-compensatory methods (Bouyssou, 1986 ).

About the TODIM method, Roy and Bouyssou ( 1993 ) stated that it is “a method based on the French School and the American School. It combines aspects of the Multi-attribute Utility Theory, of the AHP method and the ELECTRE methods”. BarbaRomero & Pomerol ( 2000 ) also stated in the respect of TODIM method “it is based on a notion extremely similar to a net flow, in the PROMETHEE sense”, because the formulation of the expressions of gains and losses in multi-attribute function are similar to the expressions in the PROMETHEE methods, which make the use of net outranking flow.

Let us consider n number of alternatives to be ordered based on m number of qualitative and quantitative criteria and also let us assume that one criterion is considered as reference criterion. After this, experts are asked to estimate the values of each criteria c and also the value of each alternative i with respect to the objective of the criterion. Then the estimated values of each alternative in relation to criteria has to be numerical, so, it has to be normalized, in the same way the values of the criteria are also transformed from the verbal scale to the cardinal scale. The values of the criteria are obtained from the performance of the alternatives with respect to the criteria, such as, the level of noise will be measured in decibels, the power if the engine is measured in horsepower, etc. Therefore, the TODIM method is used for both qualitative and quantitative criteria and later on criteria of verbal scales are converted into cardinal scales and both are normalized. For each pair of alternatives, the relative measure of dominance of one alternative over another is calculated and computed as the sum over all criteria for both relative gain or loss function values for all alternatives. The sum will be either gains or losses or zeros which depends on the performance of each alternative with respect to each criterion.

The computation of the alternatives with the relation to all the criteria gives a numerical matrix. Again, the normalization is performed by the division of the matrix value of one alternative by the sum of all the alternatives for each criterion and obtains a matrix of values between zero and one. This matrix is known as the matrix of normalized alternatives’ scores against criteria and denoted as P  = [ P nm], where n indicates the number of alternatives and m indicates the number of criteria, as shown in Table 2 .

Now after the evaluation of normalized alternative scores, the partial matrices of dominance and final matrix of dominance must be determined. According to the relative importance assigned to each criteria the decision makers have to indicate the reference criteria r for further calculations. The reference criteria is chosen from the all criteria which has the highest value according to its importance. The decision makers give the values of each criteria on a numerical scale (e.g., from 1 to 5) and then it has to be normalized. Therefore, w rc is defined as the weight of the criterion c divided by the weight of the reference criteria r . This helps us to allow all pairs of differences between performance measurements to be in reference criterion dimension. The dominance measurement of each alternative A i over each alternative A j which is incorporated to Prospect Theory, is given by the expression

here \(\delta \left( {A_{i} ,A_{j} } \right)\) denotes the measurement of dominance of alternative A i over alternative A j ; m is the number of criteria; c is any criterion, for c  = 1, …, m ; w rc is equal to w c divided by w r , where r is the reference criterion; P ic and P jc are, respectively, the performances of the alternatives A i and A j in relation to c ; \(\theta\) is the attenuation factor of the losses; different choices of \(\theta\) lead to different shapes of the prospect theoretical value function in the negative quadrant.

The expression \(\Phi_{c} \left( {A_{i} ,A_{j} } \right) \) denotes the contribution of criterion c to function \( \delta \left( {A_{i} ,A_{j} } \right)\) , when comparing alternative i with alternative j . If the value of \(\left( {P_{ic} - P_{jc} } \right)\) is positive, it will represent a gain function of \(\delta \left( {A_{i} ,A_{j} } \right)\) and, therefore the Eq. (2) is used. If \(\left( {P_{ic} - P_{jc} } \right)\) is nil, the value zero will be assigned by applying Eq. (3). If \(\left( {P_{ic} - P_{jc} } \right)\) is negative, it will represent a loss function and Eq. (4) is used. Therefore, the expression \(\Phi_{c} \left( {A_{i} ,A_{j} } \right)\) permits an adjustment of the data of the problem to the value function of Prospect Theory and it explains the aversion and the propensity to risk. This function is in ‘‘ S ’’ shape and represented in Fig.  2 . There are two curves one is concave which is above the horizontal axis and represents the gains for the analysis and also reflects the aversion to risk and below the horizontal axis there is a convex curve, which represents the losses for the analysis and also reflects propensity to risk.

Value function of the TODIM method (Gomes & Lina, 1992a )

After the calculations of diverse partial matrices of dominance, one for each criterion, the final dominance matrix of the general element \(\delta \left( {A_{i} ,A_{j} } \right)\) is determined, through the sum of the elements of the diverse matrices. Expression ( 5 ) is used to find out the overall value of alternative i through normalization of the corresponding dominance measurements. The respective values are ordered by giving ranks to every alternative.

Therefore, the global values obtained from the computation by Eq.  5 which permits the complete rank ordering of all alternatives. To know the stability on the decision makers’ preferences a sensitivity analysis should be carried out on \(\theta \) , the attenuation factor. We can also conduct sensitivity analysis on the criteria weights to know the performance evaluations.

3.2 Proposed methodology

The TODIM is used to address the issue that small scale retail industries are facing these days. To compete with the other companies in the market, small scale retail industries are worried about big data. To solve the volume, access, etc. There are many practices but to implement these big data practices we need huge investment. Small scale retail industries which are enlarging slowly can’t afford the huge investment so these companies are in a dilemma about which big data practices can be used to reduce the cost according to the company requirements. So, we identified a few big data practices which the industry wants to control over big data and there are nine big data practices which are mentioned as alternatives. And also identified seven criteria that a company requires to increase the overall performance of the supply chain. The main reasons to choose this TODIM method even though there are many multi criteria decision making methods:

To combine both qualitative and quantitative data in order to provide a new path towards selection for decision makers in the companies.

No other MCDM approaches deal with the risk but this method deals with the gain or loss function using prospect theory.

Figure  3 represents the block diagram of the proposed methodology, which shows the step by step process. Tables 3 and 4 present the representation of criteria and alternatives respectively. The following shows the notations of the big data practices and criteria which are required for the analysis.

Flowchart for the methodology

3.3 Data collection

The data was conducted through a structured questionnaire. We have selected five experts who have knowledge of all the practices aforementioned in this study. As the adoption and implementation of the BDA practices are at a nascent stage in India. Hence, the majority of the practitioners do not have complete awareness about the practices. To avoid the nescience bias of the experts authors interviewed the experts and selected five experts for the final responses. The respondent profile is mentioned in Table 5 .

Here the level of technology implementation represents the integration of big data practices with criteria to improve the supply chain performance. In Table 5 , The level of technology implementation column refers that the amount of capability that the big data practices are implemented in the supply chain: Rank 1–Rank 5 shows the integration rank between big data practices and supply chain criteria which gives the ranks through pairwise comparison based on the scale of 1 to 5, where ‘1’ is ‘very unimportant’ and ‘5’ is ‘very important’ and also ranks for alternatives based on the same scale, where ‘1’ is ‘very poor’ and ‘5’ is ‘very good’.

From Fig.  2 we can say that \(\theta > 0\) and \(0 < \theta < 1\) indicates the losses are amplified while \( \theta > 1\) indicates the losses are attenuated means the effect of loss will be reduced. So the value of \(\theta\) is taken as 1 and according to our company requirement we can change the values of \(\theta\) to get different values of loss functions and can be used for sensitivity analysis. In order to find out the weights of each criteria and also find out the reference criteria the ranks are collected from the experts and normalized. Table 6 presents the weights of criteria.

Here the criteria with highest weight is taken as reference criteria and based on reference criteria the values of W rc of each criteria is calculated. After calculating these weights (see Table 7 ) we need to find out the normalized matrix (see Table 8 ) of alternatives with respect to criteria from the ranks given by the experts.

After calculating the normalized matrix, we need to find out the partial dominance of every alternative using Eqs. 2, 3 and 4.

Similarly, the partial dominance values for other alternatives are also calculated as presented in Table 9 . From the partial dominance values, we need to calculate the final dominance values for all the alternatives using Eq.  1 , so that we are eliminating the criteria.

After finding out the final dominance values (see Table 10 ) of all the alternatives, we need to calculate the global values (see Table 11 ). We then find out normalized global values with ranks according to their values.

According to the ranks of each alternative, the alternative A 7 i.e., RFID has got the first rank and it is the best choice that satisfies the optimal conditions and criteria of the companies to improve overall performance of the supply chain.

3.4 Sensitivity analysis

After the results by the TODIM method, a sensitivity analysis was carried out by varying the value of \(\theta \) the attenuation factor of losses. Previously, this value was taken as 1 but now we varied this to 2.5 and 5 as shown in Tables 12 and 13 . The global values of all the alternatives are changed and are as follows:

Now the alternative A 9 i.e., business intelligence has the rank 1 and no losses in the alternatives.

Now the alternative A 4 i.e., cloud computing has the rank 1 and losses in the alternatives A2, A5, A6 and A8 i.e., neural networks, machine learning data mining and block chain and IoT.

Here, we have done sensitivity analysis by varying the value of \(\theta \) to know the consistency of the data. The values of \(\theta \) are 1, 2.5 and 5 (see Table 14 ), the global values of all the alternatives are plotted in graph (see Fig.  2 ) and R 2 values are also calculated.

From the above Table 15 , we can say that the data is consistent in nature because there are less losses even though the values of \(\theta \) are changing. Only the alternatives A 2 , A 5 , A 6 and A 8 are the loss functions that too when \(\theta =5\) .

4 Discussion

The Indian retail industry has seen phenomenal growth in the last one decade. Despite this, adoption of big data practices is in the nascent stage (Gawankar et al., 2020 ). Hence, in this study, we integrated key big data practices with FMCG supply chain and obtained the dominance of each big data practice over the other. The calculated final global values of each practice are normalized and then ranked according to the order. The results show that RFID is a high dominant practice over the other practices. In brief, RFID plays a key role in the FMCG supply chain which highlights that supply chain managers need to make more emphasis on RFID to realise the benefits of big data practices. This result is in line with the Reyes et al. ( 2016 ) study on RFID practices, where they highlighted the benefits of RFID practice in supply chain such as reducing operational costs, high level of integrity, increased efficiency and cut-down reworks by assessing the tracking of shipping and receiving products. This evidenced that it controls the uncertainty in the data for the retail supply chain (Gawankar et al., 2020 ). Further, RFID practice helps to realise the benefits in cost, supplier and customer integration, demand management, time and value, capacity utilization and flexibility to improve the overall performance of the supply chain. Cloud computing is the dominance practice after RFID because cloud computing scalability, efficiency and integration increases by decreasing redundancy and also making supply chain networks a cost effective one. Later, machine learning ranks in the order and gives the benefits like informed decisions, greater contextual intelligence and finally asset and inventory management decisions to improve the performance of the retail supply chain. Blockchain and IoT are ranked in the top five. These practices help to improve the retail supply chain performance by eliminating the middleman, increasing the speed of transactions, lowering the costs and improving security to the data. According to Shah ( 2016 ), pilferage loss in Indian retail supply chain approximately 1.5 percent of the total sales value. This shows Blockchain and IoT are the most priority adoptive big data practices for Indian retail supply chain to overcome the challenges such as pilferage or breakage losses. Following this, neural networks are in the next order which has the ability to adapt and easily combine with other technologies which can learn from each other and make up for their own deficiencies. Finally, data mining, data science, business intelligence and ERP come in a row to improve the performance of the Indian retail supply chain. The results obtained from this study are discussed with the respondents . The majority of the respondents agreed with the results obtained from the study. Three of the respondents expressed that cloud computing and RFID plays a vital role with cost criteria performance in retail and ecommerce supply chain. The use of BDA in industries will increase the operational excellence, productivity, real-time visibility, low-cost operations, etc., which gives value added and monetary gain for the industries.

This paper also gives an idea and model to small and medium industries on how to choose a best big data practice and how much it has to be integrated to overcome the issue of data uncertainty to increase the performance of the supply chain. To know how the dominance value changes with the attenuation factor of losses sensitivity analysis is performed (refer Table 14 ). Initially, the attenuation factor (uncertainty) was taken as ‘1’ assuming that there is no uncertainty we obtained different dominant values for the practices. In this scenario, RFID has placed the highest priority for the ecommerce and retail supply chain. Further, when we increased the attenuation factor to 2.5, now the dominance values changes and business intelligence ranks first among the BDA alternatives. Further, we increased the attenuation factor to 5, analysis shows that cloud computing plays a highest priority and it plays a major role in retail SC performance (Fig. 4 ). It concludes that cloud computing practice is vital for retail and ecommerce supply chains to address the high data uncertainty.

Value function graph

4.1 Theoretical implications

This study brings the following research contributions. Though the literature confesses the necessity for development for a comprehensive methodology for understanding the impact of the big data analytics tools in retail supply chain performance, no conscious effort has been made till date in this direction (Gawankar, 2020 ). This study is the first to analyse the dominance of the big data practices (data science, neural networks, enterprise resource planning, cloud computing, machine learning, data mining, RFID, Blockchain and IoT and Business intelligence) in retail supply chain level and thus helps the newly emerging retail firms. Secondly, this study takes a first step to identify a unique set of seven retail supply chain performance measures (supplier integration, customer integration, cost, capacity utilization, flexibility, demand management, and time and value) that the selection of the best big data practices. This was done by reviewing the extant literature, and then by having a discussion with the industry respondents. Thirdly, this study uses the TODIM (Portuguese acronym for interactive and multi criteria decision-making) methodology to select the best big data technology based on the seven-retail supply chain performance measures. Cloud computing, RFID and data science are the top three practices with the cost criteria which are in-lined to similar studies conducted in retail and ecommerce supply chain.

4.2 Managerial implications

From the results, we found that RFID placed a top priority among all other selected big data practices. It is evident that one of the major challenges for supply chain managers in India is pilferage. According to the global retail theft barometer, 2.38 percent of sales are considered as a pilferage (SDM, 2015 ). To overcome this RFID will help supply chain managers across the supply chain. RFID also bids real time inventory visibility in the retail shop. This characteristic of RFID aids the inventory managers so as to monitor and control the inventory supply at all times. Moreover, in retail business, identification of the exact location of the item is challenging because of the inventory of hundreds of thousands of stock keeping units. This particular problem related to identification of the exact location of the item can be solved by implementing RFID in the retail sector. Cloud computing is placed as a second priority, which helps supply chain managers to understand customers in a better way and to bring more innovative products using the enormous data obtained from the customers. Integrating cloud computing services in the retail sector not only significantly decreases the IT costs but also streamlines the workflow, advances efficiency and end-user experience. Prediction of demand and maintenance of inventory across the stages of the supply chain is a key challenging issue in the retail industry. With the AI and automated machine learning tools, the retail outlet can plan for optimizing purchases, inventory and sales. AI and automated machine learning tools helps the retail outlets in assortment planning according to customer demand. The AI and ML tools further checks whether the amounts are set appropriately, and which outlets require the supply of certain goods in certain quantities by considering both the local, regional and other features. AI and automated machine learning tools can also help the retail outlet in envisaging the number of goods required on a given day in retort to customers’ demand, thereby saving money and time. In the same line, blockchain technology will help supply chain managers to manage inventories across the chain and will overcome the shortages of the products. However, during our data collection stage we observed that blockchain technology is still at a very nascent stage in the Indian retail industry. Neural networks will help the supply chain managers to manage the data and segment the customer database as required.

4.3 Limitations and future research directions

This study elevates numerous vital matters that require further research investigation. For instance, the same research issue can be solved by using fuzzy or grey TODIM techniques to overcome the nebulousness of decisions by the experts. Furthermore, the result of the multi criteria decision making models is purely reliant on the inputs provided by the respondents of the case organization. Effort should be taken to select the right expert pool. If we select a single expert, then there will be problem with expert bias. Therefore, it is worthwhile to select experts from different functional groups with relevant knowledge and expertise. Otherwise, it will become a garbage in, garbage out problem. Also, effort should be taken to see the consensus of feedback from the effort. Group decision making techniques like geometric mean will solve this purpose (Ramkumar & Jenamani, 2012 ; Ramkumar et al., 2016 ). Further improvement of the model can also be done by performing additional field surveys. Further refinement of the model can also be made based on some widely used theoretical perspectives instead of developing the model purely based on literature. It is also worthwhile to study the impact of each criterion on the final selection.

5 Conclusions

The MCDM methods support the decisions given in the business, which contains many criteria that have to be satisfied to make any decision. The uniqueness of this study for the best big data practice to improve the overall performance of the supply chain is that, the qualitative and quantitative factors with different scales are combined in the same technique, also we need to know the risk concept in the analysis and last, TODIM method is applied to this problem. Although there are many decision-making methods that are based on complex calculations, the proposed framework can be applicable easily by the companies in various industries in order to make decisions based on many criteria. To get more accurate data and efficient decisions in the decision-making process, we can implement fuzzy logic to the TODIM method and Pythagorean Fuzzy TODIM so that we can reduce uncertainty and ambiguity in the decisions taken by the companies. The criteria chosen in this study are relevant to the Indian retail supply chain. These criteria can vary across the industries. Hence, the dominance values change with criteria which are different for different industries like for some industry cost may be the main criteria but for others demand management is the main criteria, so it varies from industry to industry. Hence, obtained priorities may not be generalized.

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Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities

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Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. In this survey, we investigate the predictive BDA applications in supply chain demand forecasting to propose a classification of these applications, identify the gaps, and provide insights for future research. We classify these algorithms and their applications in supply chain management into time-series forecasting, clustering, K-nearest-neighbors, neural networks, regression analysis, support vector machines, and support vector regression. This survey also points to the fact that the literature is particularly lacking on the applications of BDA for demand forecasting in the case of closed-loop supply chains (CLSCs) and accordingly highlights avenues for future research.

Introduction

Nowadays, businesses adopt ever-increasing precision marketing efforts to remain competitive and to maintain or grow their margin of profit. As such, forecasting models have been widely applied in precision marketing to understand and fulfill customer needs and expectations [ 1 ]. In doing so, there is a growing attention to analysis of consumption behavior and preferences using forecasts obtained from customer data and transaction records in order to manage products supply chains (SC) accordingly [ 2 , 3 ].

Supply chain management (SCM) focuses on flow of goods, services, and information from points of origin to customers through a chain of entities and activities that are connected to one another [ 4 ]. In typical SCM problems, it is assumed that capacity, demand, and cost are known parameters [ 5 ]. However, this is not the case in reality, as there are uncertainties arising from variations in customers’ demand, supplies transportation, organizational risks and lead times. Demand uncertainties, in particular, has the greatest influence on SC performance with widespread effects on production scheduling, inventory planning, and transportation [ 6 ]. In this sense, demand forecasting is a key approach in addressing uncertainties in supply chains [ 7 , 8 , 9 ].

A variety of statistical analysis techniques have been used for demand forecasting in SCM including time-series analysis and regression analysis [ 10 ]. With the advancements in information technologies and improved computational efficiencies, big data analytics (BDA) has emerged as a means of arriving at more precise predictions that better reflect customer needs, facilitate assessment of SC performance, improve the efficiency of SC, reduce reaction time, and support SC risk assessment [ 11 ].

The focus of this meta-research (literature review) paper is on “demand forecasting” in supply chains. The characteristics of demand data in today’s ever expanding and sporadic global supply chains makes the adoption of big data analytics (and machine learning) approaches a necessity for demand forecasting. The digitization of supply chains [ 12 ] and incoporporation Blockchain technologies [ 13 ] for better tracking of supply chains further highlights the role of big data analytics. Supply chain data is high dimensional generated across many points in the chain for varied purposes (products, supplier capacities, orders, shipments, customers, retailers, etc.) in high volumes due to plurality of suppliers, products, and customers and in high velocity reflected by many transactions continuously processed across supply chain networks. In the sense of such complexities, there has been a departure from conventional (statistical) demand forecasting approaches that work based on identifying statistically meannignful trends (characterized by mean and variance attributes) across historical data [ 14 ], towards intelligent forecasts that can learn from the historical data and intelligently evolve to adjust to predict the ever changing demand in supply chains [ 15 ]. This capability is established using big data analytics techniques that extract forecasting rules through discovering the underlying relationships among demand data across supply chain networks [ 16 ]. These techniques are computationally intensive to process and require complex machine-programmed algorithms [ 17 ].

With SCM efforts aiming at satisfying customer demand while minimizing the total cost of supply, applying machine-learning/data analytics algorithms could facilitate precise (data-driven) demand forecasts and align supply chain activities with these predictions to improve efficiency and satisfaction. Reflecting on these opportunities, in this paper, first a taxonmy of data sources in SCM is proposed. Then, the importance of demand management in SCs is investigated. A meta-research (literature review) on BDA applications in SC demand forecasting is explored according to categories of the algorithms utilized. This review paves the path to a critical discussion of BDA applications in SCM highlighting a number of key findings and summarizing the existing challenges and gaps in BDA applications for demand forecasting in SCs. On that basis, the paper concludes by presenting a number of avenues for future research.

Data in supply chains

Data in the context of supply chains can be categorized into customer, shipping, delivery, order, sale, store, and product data [ 18 ]. Figure  1 provides the taxonomy of supply chain data. As such, SC data originates from different (and segmented) sources such as sales, inventory, manufacturing, warehousing, and transportation. In this sense, competition, price volatilities, technological development, and varying customer commitments could lead to underestimation or overestimation of demand in established forecasts [ 19 ]. Therefore, to increase the precision of demand forecast, supply chain data shall be carefully analyzed to enhance knowledge about market trends, customer behavior, suppliers and technologies. Extracting trends and patterns from such data and using them to improve accuracy of future predictions can help minimize supply chain costs [ 20 , 21 ].

Taxonomy of supply chain data

Analysis of supply chain data has become a complex task due to (1) increasing multiplicity of SC entities, (2) growing diversity of SC configurations depending on the homogeneity or heterogeneity of products, (3) interdependencies among these entities (4) uncertainties in dynamical behavior of these components, (5) lack of information as relate to SC entities; [ 11 ], (6) networked manufacturing/production entities due to their increasing coordination and cooperation to achieve a high level customization and adaptaion to varying customers’ needs [ 22 ], and finally (7) the increasing adoption of supply chain digitization practices (and use of Blockchain technologies) to track the acitivities across supply chains [ 12 , 13 ].

Big data analytics (BDA) has been increasingly applied in management of SCs [ 23 ], for procurement management (e.g., supplier selection [ 24 ], sourcing cost improvement [ 25 ], sourcing risk management [ 26 ], product research and development [ 27 ], production planning and control [ 28 ], quality management [ 29 ], maintenance, and diagnosis [ 30 ], warehousing [ 31 ], order picking [ 32 ], inventory control [ 33 ], logistics/transportation (e.g., intelligent transportation systems [ 34 ], logistics planning [ 35 ], in-transit inventory management [ 36 ], demand management (e.g., demand forecasting [ 37 ], demand sensing [ 38 ], and demand shaping [ 39 ]. A key application of BDA in SCM is to provide accurate forecasting, especially demand forecasting, with the aim of reducing the bullwhip effect [ 14 , 40 , 41 , 42 ].

Big data is defined as high-volume, high-velocity, high-variety, high value, and high veracity data requiring innovative forms of information processing that enable enhanced insights, decision making, and process automation [ 43 ]. Volume refers to the extensive size of data collected from multiple sources (spatial dimension) and over an extended period of time (temporal dimension) in SCs. For example, in case of freight data, we have ERP/WMS order and item-level data, tracking, and freight invoice data. These data are generated from sensors, bar codes, Enterprise resource planning (ERP), and database technologies. Velocity can be defined as the rate of generation and delivery of specific data; in other words, it refers to the speed of data collection, reliability of data transferring, efficiency of data storage, and excavation speed of discovering useful knowledge as relate to decision-making models and algorithms. Variety refers to generating varied types of data from diverse sources such as the Internet of Things (IoT), mobile devices, online social networks, and so on. For instance, the vast data from SCM are usually variable due to the diverse sources and heterogeneous formats, particularly resulted from using various sensors in manufacturing sites, highways, retailer shops, and facilitated warehouses. Value refers to the nature of the data that must be discovered to support decision-making. It is the most important yet the most elusive, of the 5 Vs. Veracity refers to the quality of data, which must be accurate and trustworthy, with the knowledge that uncertainty and unreliability may exist in many data sources. Veracity deals with conformity and accuracy of data. Data should be integrated from disparate sources and formats, filtered and validated [ 23 , 44 , 45 ]. In summary, big data analytics techniques can deal with a collection of large and complex datasets that are difficult to process and analyze using traditional techniques [ 46 ].

The literature points to multiple sources of big data across the supply chains with varied trade-offs among volume, velocity, variety, value, and veracity attributes [ 47 ]. We have summarized these sources and trade-offs in Table  1 . Although, the demand forecasts in supply chains belong to the lower bounds of volume, velocity, and variety, however, these forecasts can use data from all sources across the supply chains from low volume/variety/velocity on-the-shelf inventory reports to high volume/variety/velocity supply chain tracking information provided through IoT. This combination of data sources used in SC demand forecasts, with their diverse temporal and spatial attributes, places a greater emphasis on use of big data analytics in supply chains, in general, and demand forecasting efforts, in particular.

The big data analytics applications in supply chain demand forecasting have been reported in both categories of supervised and unsupervised learning. In supervised learning, data will be associated with labels, meaning that the inputs and outputs are known. The supervised learning algorithms identify the underlying relationships between the inputs and outputs in an effort to map the inputs to corresponding outputs given a new unlabeled dataset [ 48 ]. For example, in case of a supervised learning model for demand forecasting, future demand can be predicted based on the historical data on product demand [ 41 ]. In unsupervised learning, data are unlabeled (i.e. unknown output), and the BDA algorithms try to find the underlying patterns among unlabeled data [ 48 ] by analyzing the inputs and their interrelationships. Customer segmentation is an example of unsupervised learning in supply chains that clusters different groups of customers based on their similarity [ 49 ]. Many machine-learning/data analytics algorithms can facilitate both supervised learning (extracting the input–output relationships) and unsupervised learning (extracting inputs, outputs and their relationships) [ 41 ].

Demand management in supply chains

The term “demand management” emerged in practice in the late 1980s and early 1990s. Traditionally, there are two approaches for demand management. A forward approach which looks at potential demand over the next several years and a backward approach that relies on past or ongoing capabilities in responding to demand [ 50 ].

In forward demand management, the focus will be on demand forecasting and planning, data management, and marketing strategies. Demand forecasting and planning refer to predicting the quantities and timings of customers’ requests. Such predictions aim at achieving customers’ satisfaction by meeting their needs in a timely manner [ 51 ]. Accurate demand forecasting could improve the efficiency and robustness of production processes (and the associated supply chains) as the resources will be aligned with requirements leading to reduction of inventories and wastes [ 52 , 53 ].

In the light of the above facts, there are many approaches proposed in the literature and practice for demand forecasting and planning. Spreadsheet models, statistical methods (like moving averages), and benchmark-based judgments are among these approaches. Today, the most widely used demand forecasting and planning tool is Excel. The most widespread problem with spreadsheet models used for demand forecasting is that they are not scalable for large-scale data. In addition, the complexities and uncertainties in SCM (with multiplicity and variability of demand and supply) cannot be extracted, analyzed, and addressed through simple statistical methods such as moving averages or exponential smoothing [ 50 ]. During the past decade, traditional solutions for SC demand forecasting and planning have faced many difficulties in driving the costs down and reducing inventories [ 50 ]. Although, in some cases, the suggested solutions have improved the day’s payable, they have pushed up the SC costs as a burden to suppliers.

The era of big data and high computing analytics has enabled data processing at a large scale that is efficient, fast, easy, and with reduced concerns about data storage and collection due to cloud services. The emergence of new technologies in data storage and analytics and the abundance of quality data have created new opportunities for data-driven demand forecasting and planning. Demand forecast accuracy can be significantly improved with data-mining algorithms and tools that can sift through data, analyze the results, and learn about the relationships involved. This could lead to highly accurate demand forecasting models that learn from data and are scalable for application in SCM. In the following section, a review of BDA applications in SCM is presented. These applications are categorized based on the employed techniques in establishing the data-drive demand forecasts.

BDA for demand forecasting in SCM

This survey aims at reviewing the articles published in the area of demand and sales forecasting in SC in the presence of big data to provide a classification of the literature based on algorithms utilized as well as a survey of applications. To the best of our knowledge, no comprehensive review of the literature specifically on SC demand forecasting has been conducted with a focus on classification of techniques of data analytics and machine learning. In doing so, we performed a thorough search of the existing literature, through Scopus, Google Scholar, and Elsevier, with publication dates ranging from 2005 to 2019. The keywords used for the search were supply chain, demand forecasting, sales forecasting, big data analytics, and machine learning.

Figure  2 shows the trend analysis of publications in demand forecasting for SC appeared from 2005 to 2019. There is a steadily increasing trend in the number of publications from 2005 to 2019. It is expected that such growth continues in 2020. Reviewing the past 15 years of research on big data analysis/machine learning applications in SC demand forecasting, we identified 64 research papers (excluding books, book chapters, and review papers) and categorized them with respect to the methodologies adopted for demand forecasting. The five most frequently used techniques are listed in Table  2 that includes “Neural Network,” “Regression”, “Time-series forecasting (ARIMA)”, “Support Vector Machine”, and “Decision Tree” methods. This table implies the growing use of big data analysis techniques in SC demand forecasting. It shall be mentioned that there were a few articles using multiple of these techniques.

Distribution of literature in supply chain demand forecasting from 2005 to 2019

It shall be mentioned that there are literature review papers exploring the use of big data analytics in SCM [ 10 , 16 , 23 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 ]. However, this study focuses on the specific topic of “demand forecasting” in SCM to explore BDA applications in line with this particular subtopic in SCM.

As Hofmann and Rutschmann [ 58 ] indicated in their literature review, the key questions to answer are why, what and how big data analytics/machine-learning algorithms could enhance forecasts’ accuracy in comparison to conventional statistical forecasting approaches.

Conventional methods have faced a number of limitations for demand forecasting in the context of SCs. There are a lot of parameters influencing the demand in supply chains, however, many of them were not captured in studies using conventional methods for the sake of simplicity. In this regard, the forecasts could only provide a partial understanding of demand variations in supply chains. In addition, the unexplained demand variations could be simply considered as statistical noise. Conventional approaches could provide shorter processing times in exchange for a compromise on robustness and accuracy of predictions. Conventional SC demand forecasting approaches are mostly done manually with high reliance on the planner’s skills and domain knowledge. It would be worthwhile to fully automate the forecasting process to reduce such a dependency [ 58 ]. Finally, data-driven techniques could learn to incorporate non-linear behaviors and could thus provide better approximations in demand forecasting compared to conventional methods that are mostly derived based on linear models. There is a significant level of non-linearity in demand behavior in SC particularly due to competition among suppliers, the bullwhip effect, and mismatch between supply and demand [ 40 ].

To extract valuable knowledge from a vast amount of data, BDA is used as an advanced analytics technique to obtain the data needed for decision-making. Reduced operational costs, improved SC agility, and increased customer satisfaction are mentioned among the benefits of applying BDA in SCM [ 68 ]. Researchers used various BDA techniques and algorithms in SCM context, such as classification, scenario analysis, and optimization [ 23 ]. Machine-learning techniques have been used to forecast demand in SCs, subject to uncertainties in prices, markets, competitors, and customer behaviors, in order to manage SCs in a more efficient and profitable manner [ 40 ].

BDA has been applied in all stages of supply chains, including procurement, warehousing, logistics/transportation, manufacturing, and sales management. BDA consists of descriptive analytics, predictive analytics, and prescriptive analytics. Descriptive analysis is defined as describing and categorizing what happened in the past. Predictive analytics are used to predict future events and discover predictive patterns within data by using mathematical algorithms such as data mining, web mining, and text mining. Prescriptive analytics apply data and mathematical algorithms for decision-making. Multi-criteria decision-making, optimization, and simulation are among the prescriptive analytics tools that help to improve the accuracy of forecasting [ 10 ].

Predictive analytics are the ones mostly utilized in SC demand and procurement forecasting [ 23 ]. In this sense, in the following subsections, we will review various predictive big data analytics approaches, presented in the literature for demand forecasting in SCM, categorized based on the employed data analytics/machine learning technique/algorithm, with elaborations of their purpose and applications (summarized in Table  3 ).

Time-series forecasting

Time series are methodologies for mining complex and sequential data types. In time-series data, sequence data, consisting of long sequences of numeric data, recorded at equal time intervals (e.g., per minute, per hour, or per day). Many natural and human-made processes, such as stock markets, medical diagnosis, or natural phenomenon, can generate time-series data. [ 48 ].

In case of demand forecasting using time-series, demand is recorded over time at equal size intervals [ 69 , 70 ]. Combinations of time-series methods with product or market features have attracted much attention in demand forecasting with BDA. Ma et al. [ 71 ] proposed and developed a demand trend-mining algorithm for predictive life cycle design. In their method, they combined three models (a) a decision tree model for large-scale historical data classification, (b) a discrete choice analysis for present and past demand modeling, and (c) an automated time-series forecasting model for future trend analysis. They tested and applied their 3-level approach in smartphone design, manufacturing and remanufacturing.

Time-series approach was used for forecasting of search traffic (service demand) subject to changes in consumer attitudes [ 37 ]. Demand forecasting has been achieved through time-series models using exponential smoothing with covariates (ESCov) to provide predictions for short-term, mid-term, and long-term demand trends in the chemical industry SCs [ 7 ]. In addition, Hamiche et al. [ 72 ] used a customer-responsive time-series approach for SC demand forecasting.

In case of perishable products, with short life cycles, having appropriate (short-term) forecasting is extremely critical. Da Veiga et al. [ 73 ] forecasted the demand for a group of perishable dairy products using Autoregressive Integrated Moving Average (ARIMA) and Holt-Winters (HW) models. The results were compared based on mean absolute percentage error (MAPE) and Theil inequality index (U-Theil). The HW model showed a better goodness-of-fit based on both performance metrics.

In case of ARIMA, the accuracy of predictions could diminish where there exists a high level of uncertainty in future patterns of parameters [ 42 , 74 , 75 , 76 ]. HW model forecasting can yield better accuracy in comparison to ARIMA [ 73 ]. HW is simple and easy to use. However, data horizon could not be larger than a seasonal cycle; otherwise, the accuracy of forecasts will decrease sharply. This is due to the fact that inputs of an HW model are themselves predicted values subject to longer-term potential inaccuracies and uncertainties [ 45 , 73 ].

Clustering analysis

Clustering analysis is a data analysis approach that partitions a group of data objects into subgroups based on their similarities. Several applications of clustering analysis has been reported in business analytics, pattern recognition, and web development [ 48 ]. Han et al. [ 48 ] have emphasized the fact that using clustering customers can be organized into groups (clusters), such that customers within a group present similar characteristic.

A key target of demand forecasting is to identify demand behavior of customers. Extraction of similar behavior from historical data leads to recognition of customer clusters or segments. Clustering algorithms such as K-means, self-organizing maps (SOMs), and fuzzy clustering have been used to segment similar customers with respect to their behavior. The clustering enhances the accuracy of SC demand forecasting as the predictions are established for each segment comprised of similar customers. As a limitation, the clustering methods have the tendency to identify the customers, that do not follow a pattern, as outliers [ 74 , 77 ].

Hierarchical forecasts of sales data are performed by clustering and categorization of sales patterns. Multivariate ARIMA models have been used in demand forecasting based on point-of-sales data in industrial bakery chains [ 19 ]. These bakery goods are ordered and clustered daily with a continuous need to demand forecasts in order to avoid both shortage or waste [ 19 ]. Fuel demand forecasting in thermal power plants is another domain with applications of clustering methods. Electricity consumption patterns are derived using a clustering of consumers, and on that basis, demand for the required fuel is established [ 77 ].

K-nearest-neighbor (KNN)

KNN is a method of classification that has been widely used for pattern recognition. KNN algorithm identifies the similarity of a given object to the surrounding objects (called tuples) by generating a similarity index. These tuples are described by n attributes. Thus, each tuple corresponds to a point in an n-dimensional space. The KNN algorithm searches for k tuples that are closest to a given tuple [ 48 ]. These similarity-based classifications will lead to formation of clusters containing similar objects. KNN can also be integrated into regression analysis problems [ 78 ] for dimensionality reduction of the data [ 79 ]. In the realm of demand forecasting in SC, Nikolopoulos et al. [ 80 ] applied KNN for forecasting sporadic demand in an automotive spare parts supply chain. In another study, KNN is used to forecast future trends of demand for Walmart’s supply chain planning [ 81 ].

Artificial neural networks

In artificial neural networks, a set of neurons (input/output units) are connected to one another in different layers in order to establish mapping of the inputs to outputs by finding the underlying correlations between them. The configuration of such networks could become a complex problem, due to a high number of layers and neurons, as well as variability of their types (linear or nonlinear), which needs to follow a data-driven learning process to be established. In doing so, each unit (neuron) will correspond to a weight, that is tuned through a training step [ 48 ]. At the end, a weighted network with minimum number of neurons, that could map the inputs to outputs with a minimum fitting error (deviation), is identified.

As the literature reveals, artificial neural networks (ANN) are widely applied for demand forecasting [ 82 , 83 , 84 , 85 ]. To improve the accuracy of ANN-based demand predictions, Liu et al. [ 86 ] proposed a combination of a grey model and a stacked auto encoder applied to a case study of predicting demand in a Brazilian logistics company subject to transportation disruption [ 87 ]. Amirkolaii et al. [ 88 ] applied neural networks in forecasting spare parts demand to minimize supply chain shortages. In this case of spare parts supply chain, although there were multiple suppliers to satisfy demand for a variety of spare parts, the demand was subject to high variability due to a varying number of customers and their varying needs. Their proposed ANN-based forecasting approach included (1) 1 input demand feature with 1 Stock-Keeping Unit (SKU), (2) 1 input demand feature with all SKUs, (3) 16 input demand features with 1 SKU, and (4) 16 input demand features with all SKUs. They applied neural networks with back propagation and compared the results with a number of benchmarks reporting a Mean Square Error (MSE) for each configuration scenario.

Huang et al. [ 89 ] compared a backpropagation (BP) neural network and a linear regression analysis for forecasting of e-logistics demand in urban and rural areas in China using data from 1997 to 2015. By comparing mean absolute error (MAE) and the average relative errors of backpropagation neural network and linear regression, they showed that backpropagation neural networks could reach higher accuracy (reflecting lower differences between predicted and actual data). This is due to the fact that a Sigmoid function was used as the transfer function in the hidden layer of BP, which is differentiable for nonlinear problems such as the one presented in their case study, whereas the linear regression works well with linear problems.

ANNs have also been applied in demand forecasting for server models with one-week demand prediction ahead of order arrivals. In this regard, Saha et al. [ 90 ] proposed an ANN-based forecasting model using a 52-week time-series data fitted through both BP and Radial Basis Function (RBF) networks. A RBF network is similar to a BP network except for the activation/transfer function in RBF that follows a feed-forward process using a radial basis function. RBF results in faster training and convergence to ANN weights in comparison with BP networks without compromising the forecasting precision.

Researchers have combined ANN-based machine-learning algorithms with optimization models to draw optimal courses of actions, strategies, or decisions for future. Chang et al. [ 91 ] employed a genetic algorithm in the training phase of a neural network using sales/supply chain data in the printed circuit board industry in Taiwan and presented an evolving neural network-forecasting model. They proposed use of a Genetic Algorithms (GA)-based cost function optimization to arrive at the best configuration of the corresponding neural network for sales forecast with respect to prediction precision. The proposed model was then compared to back-propagation and linear regression approaches using three performance indices of MAPE, Mean Absolute Deviation (MAD), and Total Cost Deviation (TCD), presenting its superior prediction precision.

Regression analysis

Regression models are used to generate continuous-valued functions utilized for prediction. These methods are used to predict the value of a response (dependent) variable with respect to one or more predictor (independent) variables. There are various forms of regression analysis, such as linear, multiple, weighted, symbolic (random), polynomial, nonparametric, and robust. The latter approach is useful when errors fail to satisfy normalcy conditions or when we deal with big data that could contain significant number of outliers [ 48 ].

Merkuryeva et al. [ 92 ] analyzed three prediction approaches for demand forecasting in the pharmaceutical industry: a simple moving average model, multiple linear regressions, and a symbolic regression with searches conducted through an evolutionary genetic programming. In this experiment, symbolic regression exhibited the best fit with the lowest error.

As perishable products must be sold due to a very short preservation time, demand forecasting for this type of products has drawn increasing attention. Yang and Sutrisno [ 93 ] applied and compared regression analysis and neural network techniques to derive demand forecasts for perishable goods. They concluded that accurate daily forecasts are achievable with knowledge of sales numbers in the first few hours of the day using either of the above methods.

Support vector machine (SVM)

SVM is an algorithm that uses a nonlinear mapping to transform a set of training data into a higher dimension (data classes). SVM searches for an optimal separating hyper-plane that can separate the resulting class from another) [ 48 ]. Villegas et al. [ 94 ] tested the applicability of SVMs for demand forecasting in household and personal care SCs with a dataset comprised of 229 weekly demand series in the UK. Wu [ 95 ] applied an SVM, using a particle swarm optimization (PSO) to search for the best separating hyper-plane, classifying the data related to car sales and forecasting the demand in each cluster.

Support vector regression (SVR)

Continuous variable classification problems can be solved by support vector regression (SVR), which is a regression implementation of SVM. The main idea behind SVR regression is the computation of a linear regression function within a high-dimensional feature space. SVR has been applied in financial/cost prediction problems, handwritten digit recognition, and speaker identification, object recognition, etc. [ 48 ].

Guanghui [ 96 ] used the SVR method for SC needs prediction. The use of SVR in demand forecasting can yield a lower mean square error than RBF neural networks due to the fact that the optimization (cost) function in SVR does not consider the points beyond a margin of distance from the training set. Therefore, this method leads to higher forecast accuracy, although, similar to SVM, it is only applicable to a two-class problem (such as normal versus anomaly detection/estimation problems). Sarhani and El Afia [ 97 ] sought to forecast SC demand using SVR and applied Particle swarm optimization (PSO) and GA to optimize SVR parameters. SVR-PSO and SVR-GA approaches were compared with respect to accuracy of predictions using MAPE. The results showed a superior performance by PSO in terms time intensity and MAPE when configuring the SVR parameters.

Mixed approaches

Some works in the literature have used a combination of the aforementioned techniques. In these studies, the data flow into a sequence of algorithms and the outputs of one stage become inputs of the next step. The outputs are explanatory in the form of qualitative and quantitative information with a sequence of useful information extracted out of each algorithm. Examples of such studies include [ 15 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 ].

In more complex supply chains with several points of supply, different warehouses, varied customers, and several products, the demand forecasting becomes a high dimensional problem. To address this issue, Islek and Oguducu [ 100 ] applied a clustering technique, called bipartite graph clustering, to analyze the patterns of sales for different products. Then, they combined a moving average model and a Bayesian belief network approaches to improve the accuracy of demand forecasting for each cluster. Kilimci et al. [ 101 ] developed an intelligent demand forecasting system by applying time-series and regression methods, a support vector regression algorithm, and a deep learning model in a sequence. They dealt with a case involving big amount of data accounting for 155 features over 875 million records. First, they used a principal component analysis for dimension reduction. Then, data clustering was performed. This is followed by demand forecasting for each cluster using a novel decision integration strategy called boosting ensemble. They concluded that the combination of a deep neural network with a boosting strategy yielded the best accuracy, minimizing the prediction error for demand forecasting.

Chen and Lu [ 98 ] combined clustering algorithms of SOM, a growing hierarchical self-organizing mapping (GHSOM), and K-means, with two machine-learning techniques of SVR and extreme learning machine (ELM) in sales forecasting of computers. The authors found that the combination of GHSOM and ELM yielded better accuracy and performance in demand forecasts for their computer retailing case study. Difficulties in forecasting also occur in cases with high product variety. For these types of products in an SC, patterns of sales can be extracted for clustered products. Then, for each cluster, a machine-learning technique, such as SVR, can be employed to further improve the prediction accuracy [ 104 ].

Brentan et al. [ 106 ] used and analyzed various BDA techniques for demand prediction; including support vector machines (SVM), and adaptive neural fuzzy inference systems (ANFIS). They combined the predicted values derived from each machine learning techniques, using a linear regression process to arrive at an average prediction value adopted as the benchmark forecast. The performance (accuracy) of each technique is then analyzed with respect to their mean square root error (RMSE) and MAE values obtained through comparing the target values and the predicted ones.

In summary, Table  3 provides an overview of the recent literature on the application of Predictive BDA in demand forecasting.

Discussions

The data produced in SCs contain a great deal of useful knowledge. Analysis of such massive data can help us to forecast trends of customer behavior, markets, prices, and so on. This can help organizations better adapt to competitive environments. To forecast demand in an SC, with the presences of big data, different predictive BDA algorithms have been used. These algorithms could provide predictive analytics using time-series approaches, auto-regressive methods, and associative forecasting methods [ 10 ]. The demand forecasts from these BDA methods could be integrated with product design attributes as well as with online search traffic mapping to incorporate customer and price information [ 37 , 71 ].

Predictive BDA algorithms

Most of the studies examined, developed and used a certain data-mining algorithm for their case studies. However, there are very few comparative studies available in the literature to provide a benchmark for understanding of the advantages and disadvantages of these methodologies. Additionally, as depicted by Table  3 , there is no clear trend between the choice of the BDA algorithm/method and the application domain or category.

Predictive BDA applicability

Most data-driven models used in the literature consider historical data. Such a backward-looking forecasting ignores the new trends and highs and lows in different economic environments. Also, organizational factors, such as reputation and marketing strategies, as well as internal risks (related to availability of SCM resources), could greatly influence the demand [ 107 ] and thus contribute to inaccuracy of BDA-based demand predictions using historical data. Incorporating existing driving factors outside the historical data, such as economic instability, inflation, and purchasing power, could help adjust the predictions with respect to unseen future scenarios of demand. Combining predictive algorithms with optimization or simulation can equip the models with prescriptive capabilities in response to future scenarios and expectations.

Predictive BDA in closed-loop supply chains (CLSC)

The combination of forward and reverse flow of material in a SC is referred to as a closed-loop supply chain (CLSC). A CLSC is a more complex system than a traditional SC because it consists of the forward and reverse SC simultaneously [ 108 ]. Economic impact, environmental impact, and social responsibility are three significant factors in designing a CLSC network with inclusion of product recycling, remanufacturing, and refurbishment functions. The complexity of a CLSC, compared to a common SC, results from the coordination between backward and forward flows. For example, transportation cost, holding cost, and forecasting demand are challenging issues because of uncertainties in the information flows from the forward chain to the reverse one. In addition, the uncertainties about the rate of returned products and efficiencies of recycling, remanufacturing, and refurbishment functions are some of the main barriers in establishing predictions for the reverse flow [ 5 , 6 , 109 ]. As such, one key finding from this literature survey is that CLSCs particularly deal with the lack of quality data for remanufacturing. Remanufacturing refers to the disassembly of products, cleaning, inspection, storage, reconditioning, replacement, and reassembling. As a result of deficiencies in data, optimal scheduling of remanufacturing functions is cumbersome due to uncertainties in the quality and quantity of used products as well as timing of returns and delivery delays.

IoT-based approaches can overcome the difficulties of collecting data in a CLSC. In an IoT environment, objects are monitored and controlled remotely across existing network infrastructures. This enables more direct integration between the physical world and computer-based systems. The results include improved efficiency, accuracy, and economic benefit across SCs [ 50 , 54 , 110 ].

Radio frequency identification (RFID) is another technology that has become very popular in SCs. RFID can be used for automation of processes in an SC, and it is useful for coordination of forecasts in CLSCs with dispersed points of return and varied quantities and qualities of returned used products [ 10 , 111 , 112 , 113 , 114 ].

Conclusions

The growing need to customer behavior analysis and demand forecasting is deriven by globalization and increasing market competitions as well as the surge in supply chain digitization practices. In this study, we performed a thorough review for applications of predictive big data analytics (BDA) in SC demand forecasting. The survey overviewed the BDA methods applied to supply chain demand forecasting and provided a comparative categorization of them. We collected and analyzed these studies with respect to methods and techniques used in demand prediction. Seven mainstream techniques were identified and studied with their pros and cons. The neural networks and regression analysis are observed as the two mostly employed techniques, among others. The review also pointed to the fact that optimization models or simulation can be used to improve the accuracy of forecasting through formulating and optimizing a cost function for the fitting of the predictions to data.

One key finding from reviewing the existing literature was that there is a very limited research conducted on the applications of BDA in CLSC and reverse logistics. There are key benefits in adopting a data-driven approach for design and management of CLSCs. Due to increasing environmental awareness and incentives from the government, nowadays a vast quantity of returned (used) products are collected, which are of various types and conditions, received and sorted in many collection points. These uncertainties have a direct impact on the cost-efficiency of remanufacturing processes, the final price of the refurbished products and the demand for these products [ 115 ]. As such, design and operation of CLSCs present a case for big data analytics from both supply and demand forecasting perspectives.

Availability of data and materials

The paper presents a review of the literature extracted from main scientific databases without presenting data.

Abbreviations

Adaptive neural fuzzy inference systems

Auto regressive integrated moving average

Artificial neural network

• Big data analytics

Backpropagation

Closed-loop supply chain

Extreme learning machine

Enterprise resource planning

Genetic algorithms

Growing hierarchical self-organizing map

Holt-winters

Internet of things

K-nearest-neighbor

Mean absolute deviation

Mean absolute error

Mean absolute percentage error

Mean square error

Mean square root error

Radial basis function

Particle swarm optimization

Self-organizing maps

Stock-keeping unit

Supply chain analytics

Supply chain

• Supply chain management

Support vector machine

Support vector regression

Total cost deviation

Theil inequality index

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Seyedan, M., Mafakheri, F. Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. J Big Data 7 , 53 (2020). https://doi.org/10.1186/s40537-020-00329-2

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Big Data Application in Supply Chain Management: Scopus Based Literature Review

18 Pages Posted: 9 Jun 2020 Publication Status: Preprint

Baha Mohsen

Wayne State University - Industrial & System Engineering

For modern industry, supply chain optimization is becoming very critical. To win the competitive edge, companies should be able to optimize its supply chain. Customers are expecting quick order fulfillment and fast delivery in addition to options, styles and features of products. It is expected that companies who meets these expectations will be recording success stories. Big data plays a key role in several areas of supply chain management such as demand predictions, product development, supplying decisions, distribution, and customer feedback. The increasing amount of data exchange by supply chains in manufacturing and service sectors justify the use of big data in supply chain management. This paper outlines research activities review in the field of big data in supply chain management. In addition, it investigates applications of big data in supply chain management, its opportunities, challenges and future trends.

Keywords: Supply chain management, Big data, Analytics, Data Management

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Baha Mohsen (Contact Author)

Wayne state university - industrial & system engineering ( email ).

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Big data and the supply chain: The big-supply-chain analytics landscape (Part 1)

Big data and the era of digital means a big analytics landscape for supply chain to work with.

Your supply chains generate big data. Big supply-chain analytics turn that data into real insights.

The explosive impact of e-commerce on traditional brick and mortar retailers is just one notable example of the data-driven revolution that is sweeping many industries and business functions today. Few companies, however, have been able to apply to the same degree the "big analytics" techniques that could transform the way they define and manage their supply chains.

In our view, the full impact of big data in the supply chain is restrained by two major challenges. First, there is a lack of capabilities. Supply chain managers—even those with a high degree of technical skill—have little or no experience with the data analysis techniques used by data scientists. As a result, they often lack the vision to see what might be possible with big data analytics. Second (and perhaps more significantly), most companies lack a structured process to explore, evaluate and capture big data opportunities in their supply chains.

In the second part of this article series, we will show how companies can take control of the big data opportunity with a systematic approach. Here, we will look at the nature of that opportunity and at how some companies have managed to embed data driven methodologies into their DNA. Exhibit 1 provides an overview of the landscape of supply chain analytics opportunities.

What is big supply-chain analytics?

Big supply chain analytics uses data and quantitative methods to improve decision making for all activities across the supply chain. In particular, it does two new things. First, it expands the dataset for analysis beyond the traditional internal data held on Enterprise Resource Planning (ERP) and supply chain management (SCM) systems. Second, it applies powerful statistical methods to both new and existing data sources. This creates new insights that help improve supply chain decision-making, all the way from the improvement of front-line operations, to strategic choices, such as the selection of the right supply chain operating models.

Let's look at each main area in turn.

A. Sales, Inventory and Operations Planning

Typically, planning is already the most data-driven process in the supply chain, using a wide range of inputs from Enterprise Resource Planning (ERP) and SCM planning tools. There is now significant potential to truly redefine the planning process, however, using new internal and external data sources to make real-time demand and supply shaping a reality.

We can think about managing inventory in a supply chain similar to the way electricity is managed: Storing electricity is expensive and difficult; power companies bring in additional consumers or start and stop plants to ensure a balanced power grid. Retailers now have the opportunity to use a similar approach. Visibility of point of sale (POS) data, inventory data, and production volumes can be analyzed in real time to identify mismatches between supply and demand. These can then drive actions, like price changes, the timing of promotions or the addition of new lines, to realign things.

Retailers can also use new data sources to improve planning processes and their demand-sensing capabilities. For example, Blue Yonder has developed data intensive forecasting methods now deployed into retailing where 130,000 SKUs and 200 influencing variables generate 150,000,000 probability distributions every day. This has dramatically increased forecast accuracy; enabled a better view of the company's logistics capacity needs; and reduced obsolescence, inventory levels, and stockouts. The recent growth of third party cloud-based services like Blue Yonder is making such activities more accessible for other retailers, too.

Similarly, IBM has helped develop links between production planning and weather forecasts for bakeries. By incorporating temperature and sunshine data, baking companies are able to more accurately predict demand for different product categories based on factors that influence consumer preferences. Amazon, meanwhile, has patented an "anticipatory shipping" approach, in which orders are packaged and pushed into the delivery network before customers have actually ordered them.

Having truly mastered big-data forecasting, the next level of sophistication is to start actively shaping demand. Leading online retailers, for example, use big data analytics, inventory data, and forecasting to change the products recommended to customers. This effectively steers demand towards items that are available in stock.

B. Sourcing

In many companies, data on procurement volumes and suppliers are only gathered for few activities in the sourcing process. However, supply data goes beyond the classic spend analysis and annual supplier performance review. On a transactional basis, supply processes can be sensed in real time to identify deviations from normal delivery patterns. Firms are also finding opportunities for predictive risk management. By mapping its supply chains and using "Google trend"-style information and social data about strikes, fires, or bankruptcies, a firm can monitor supply disruptions in transportation, or at 2nd or 3rd tier suppliers, and take decisive actions before its competitors.

Data analysis can also drive strategic decisions. In recent years, one pharmaceutical company has created a database with all bids submitted for packaging. The data has been evaluated to fully understand the cost structure of those suppliers and to create detailed cost models for different types of packaging. Using updated information on commodity prices, factor costs, and plant utilization, these models can be used to aid the selection of the most appropriate suppliers for new packaging projects. Similarly, Caterpillar has initiated a contest on the crowd-data science website Kaggle to model quoted prices for industrial tube assemblies.

These "clean sheet costing" bottom-up calculations can also be applied in the purchase of transportation and warehousing. By exploiting data on the cost breakdown of operations of trucks and warehouses across the globe, companies do create a powerful fact base to challenge carriers and Logistics Solution Providers (LSPs), and provide real insight into "should cost" during negotiations.

C. Manufacturing

Big data and analytics can already help improve manufacturing. For example, energy-intensive production runs can be scheduled to take advantage of fluctuating electricity prices. Data on manufacturing parameters, like the forces used in assembly operations or dimensional differences between parts, can be archived and analyzed to support the root-cause analysis of defects, even if they occur years later. Agricultural seed processors and manufacturers analyze the quality of their products with different types of cameras in real-time to get the quality assessments for each individual seed.

The Internet of Things, with its networks of cameras and sensors on millions of devices, may enable other manufacturing opportunities in the future. Ultimately, live information on a machine's condition could trigger production of a 3D-printed spare part that is then shipped by a drone to the plant to meet an engineer, who may use augmented reality glasses for guidance while replacing the part.

D. Warehousing

Logistics has traditionally been very cost-focused, and companies have happily invested in technologies that provide competitive advantage. Warehousing in particular has seen many advances using available ERP data. One example are "chaotic" storage approaches that enable the efficient use of warehouse space and minimize travel distances for personnel. Another are high-rack bay warehouses that can automatically reshuffle pallets at night to optimize schedules for the next day. Companies can track the performance of pickers in different picking areas to optimize future staff allocation.

New technologies, data sources and analytical techniques are also creating new opportunities in warehousing. A leading forklift provider is looking into how the forklift truck can act as a big data hub that collects all sorts of data in real time, which can then be blended with ERP and Warehouse Management System (WMS) data to identify additional waste in the warehouse process. For example, the analysis of video images collected by automated guided vehicles, along with sensor inputs including temperature, shelf weight, and the weight on the forklift, can be used to monitor picking accuracy, warehouse productivity and inventory accuracy in real time. Similarly forklift driving behavior and route choices can be assessed and dynamically optimized to drive picking productivity. The data can also be used to conduct root-cause analysis of picking errors by shape, color, or weight, to help to make processes more robust.

New 3D modelling technologies can also help to optimize warehouse design and simulate new configurations of existing warehouse space to further improve storage efficiency and picking productivity. German company Logivations, for example, offers a cloud-based 3D warehouse layout planning and optimization tool.

E. Transportation

Truck companies already make use of analytics to improve their operations. For example, they use fuel consumption analytics to improve driving efficiency; and they use GPS technologies to reduce waiting times by allocating warehouse bays in real time.

Courier companies have started real-time routing of deliveries to customers based on their truck's geo-location and traffic data. UPS, for example has spent ten years developing its On-Road Integrated Optimization and Navigation system (Orion) to optimize the 55,000 routes in the network. The company's CEO David Abney says the new system will save the company $300 million to $400 million a year.

Big analytics will also enable logistics providers to deliver parcels with fewer delivery attempts, by allowing them to mine their data to predict when a particular customer is more likely to be at home. On a more strategic basis, companies can cut costs and carbon emissions by selecting the right transport modes. A major CPG player is investing in analytics that will help it to understand when goods need be shipped rapidly by truck or when there is time for slower barge or train delivery.

F. Point of Sale

Brick and mortar retailers—often under heavy pressure from online competitors that have mastered analytics—have understood how datadriven optimization can provide them with competitive advantages. These techniques are being used today for activities like shelf-space optimization and mark-down pricing. Advanced analytics can also help retailers decide which products to put in high value locations, like aisle ends, and how long to keep them there. It can also enable them to explore the sales benefits achieved by clustering related products together.

Search engine giant Google has acquired Skybox, a provider of highresolution satellite imagery, that can be used to track cars in the car park in order to anticipate in-store demand. Others have explored the use of drones equipped with cameras to monitor on-shelf inventory levels.

A topic that is still a challenge for many retailers is out-of-stock detection and prevention. In developed markets, manual inspections are expensive, while RFID tags still cost too much to be applied to individual grocery items. Instead, retailers are now monitoring sales activity for out of stock indicators. If an item that usually sold every few minutes does not appear at the tills, an alert is triggered to have person check if the item is out of stock at the shelf. Other innovative technologies are also being tested, including the installation of light or weight sensors on shelves as well as the use of in-store cameras to monitor on-shelf stock levels.

Similar technologies can be applied directly at the point of use. Amazon's Dash service, for example provides consumers with wireless buttons that can be used to reorder domestic products with a single push, like washing powder or razor blades. Ultimately, stores may be able to link to data gathered from consumer's Internet-connected refrigerators to forecast demand in real time.

As the examples in this article show, big data is already helping leading organizations transform the performance of their supply chains. Today, such approaches are the exception rather than the norm, however. Lack of capabilities and the lack of a structured approach to supply chain big data is holding many companies back. For big data and advanced analytical tools to deliver greater benefits for more companies, those organizations need a more systematic approach to their adoption. Part 2 of this series will address that topic in detail.

About the authors: Knut Alicke is a master expert in the Stuttgart office, Christoph Glatzel is a director in the Cologne office, and Per-Magnus Karlsson is a consultant in the Stockholm office. Kai Hoberg is an associate professor of supply chain and operations strategy at Kühne Logistics University, Germany.

Using Big Data for Decisions in Agricultural Supply Chain

• Forecasting
• Product Development

This project explores whether the readily available data in agriculture, published by various sources such as USDA, universities and weather stations, can be used in conjunction with the manufacturer’s private data to better forecast the demand of agricultural products (chemicals). We analyzed certain variables such as corn price, acres harvested etc., using three regression models to quantify their significance in predicting demand over various time and geographical horizons. We found out that corn price and fertilizer price are significant in predicting annual demand.

Authors: Derik Smith and Satya Dhavala Advisor: Dr. Bruce Arntzen

Prologis: Still On Track If Deglobalization Continues

• Prologis stock has underperformed the S&P 500 in the past year.
• Deglobalization could lead to increased demand for warehouse and production capacity, benefiting Prologis.
• Current valuation suggests Prologis is fairly valued with potential for future gains.
• If you're looking for a safe and growing dividend, you should consider buying Prologis.

Introduction

I wrote an article about Prologis ( NYSE: PLD ) a bit more than a year ago in July 2022: " Prologis: Deglobalization Could Be The Next Dominant Tailwind For This REIT ." I rated Prologis as a "buy" due to "deglobalization" being a major tailwind for the stock. Since then, Prologis' stock has had a price return of -4.58% and a total return of -1.74% against the S&P 500's return of 18.62%.

In my first article, I explained how deglobalization could unfold in the coming years and how that could affect Prologis

Increased domestic production and a shift away from JIT manufacturing will inevitably lead to a greater demand for warehouse and production capacity. Prologis would profit from this as more demand for warehouse and production capacity/logistic centers would increase rents and more units could be bought and rented out. Analyzed for itself, I like Prologis a lot. I believe it's one of the world's greatest REITs and has a wide moat, therefore making it a relatively safe bet. Nevertheless, I would say its growth prospects are relatively weak going forward, considering the size this REIT has already achieved. In my opinion, this could and likely will change due to a growing need for warehouse, production, and logistics center capacity, driven by past events and the potential for future trade/tariff wars, which may compel countries and companies to reconsider offshoring and place a greater emphasis on onshoring.

In this article, I want to explain why Prologis performed so poorly and why it's a good thing to start or extend a position in Prologis.

A quick analysis of deglobalization

My thesis about deglobalization hasn't changed. We still see damage due to supply chain disruptions and changes in companies. A few recent examples:

• Toyota now uses higher inventory to prevent production disruptions due to supply chain complications (source: SCMR )
• Amazon is continuously expanding its storage capacity by opening more decentralized warehouses. These warehouses are strategically located near metropolitan areas. (source: KPMG )
• Maersk has diversified its logistics services to become more resilient to global supply chain disruptions. (source: Maersk )
• Pfizer has invested in expanding its cold chain infrastructure to ensure that vaccines and other temperature-sensitive products can be safely transported and stored (source: KPMG )

These changes should benefit PLD directly. The expansion of inventory increases the number of needed warehouses, which gives PLD the possibility to expand its portfolio. Furthermore, rising demand enhances PLD’s pricing power over its existing portfolio.

Global tensions also haven't cooled off within the last year. This is what I wrote in my last article about this:

Rising global tensions could also contribute to deglobalization. The US-China trade conflict, starting in 2018, resulted in significant tariffs and reduced trade between both nations. Despite a phase-one trade deal in 2020, relations remain strained, suggesting further tariff wars are plausible. Moreover, the longstanding China-Taiwan conflict continues to intensify. It appears China regards Taiwan as its territory and has threatened military action to enforce control. Observers view the Russia-Ukraine conflict as a potential model for a China-Taiwan confrontation, escalating global tensions and prompting further trade restrictions. While other conflicts exist, the China-Taiwan issue remains a primary concern.

A side effect of global tensions could be that companies and governments will work more within their country, limiting the control of other countries or the logistics other countries have over their operations. This should also increase demand for more warehouses and therefore support PLD's growth.

In summary, deglobalization remains on track, as I analyzed and predicted in my last article .

Fundamental performance within the last year

As I said in the introduction, the stock performance was rather poor, especially compared to the overall market.

The funds from operations (FFO) per share have decreased slightly since July 2023 and have been flat since 2021.

The tangible book value (TBV) stayed pretty much the same since 2023, which isn't uncommon for the stock, as you can see in the chart.

As I said in my last article, I don't see accelerating growth for PLD as long as deglobalization isn't going to continue and expand to greater measures. This fundamental performance fits the poor performance of the stock in the last year. Nevertheless, the current fundamental performance isn't all that bad and should get better.

Prologis' Q2 2024 earnings

Let's look a bit deeper at the fundamentals with the last earnings.

In its Q2 earnings, PLD reported net income per diluted share of $0.92 compared to $1.31 in Q2 2023. This decrease is due to the inclusion of $0.58 per share of net promote income in the 2023 figures. Without the net promote income in Q2 2023, net income per diluted share would have been $0.73. Compared to this, the net income per diluted share in Q2 2024 looks quite good. But what's net promote income, and why did it disappear in Q2 2024?

Net Promote Income is the bonus that Prologis receives when its co-investment ventures perform well and exceed financial benchmarks. Net Promote Expenses arise when performance is lower or when Prologis must account for prior share compensation linked to previous bonuses. The shift from income in 2023 to expenses in 2024 is likely due to weaker performance, market conditions or necessary accounting adjustments.

Core FFO per diluted share was $1.34 in Q2 2024, compared to $1.83 in Q2 2023. However, Core FFO, excluding Net Promote Income (Expense), slightly increased to $1.36 per share in Q2 2024 compared to $1.25 in Q2 2023.

Cash Same Store Net Operating Income (NOI), a critical metric for REITs like PLD, increased by 7.2%, which is solid growth but below the rate seen in recent years:

• Q2 2023: The growth was 7.7% compared to the prior year.
• Q2 2022: The increase was 8.7% compared to 2021

PLD gave guidance for 66% growth in acquisitions for 2024. Considering this, the $279 million in Q2 seems modest, but in the past, PLD's management has rarely missed guidance, so there should be more to come in the second half of the year. Overall, the 66% growth rate in acquisitions would indicate robust growth and favorable market conditions. In Q2, PLD initiated development starts worth $300 million, with estimated margins of 19.4% which reflects a solid investment into projects with high expected returns.

From a balance sheet perspective, PLD maintained significant liquidity of nearly $6.5 billion and a debt-to-EBITDA ratio below 5.0x. During the quarter, Prologis raised $1.2 billion of debt at a weighted average interest rate of 4.4% and an average term of 10.9 years.

2024 Guidance:

• The guidance for net earnings per share was revised upward to $3.25 to $3.45, while the guidance for Core FFO remained at $5.39 to $5.47.

Prologis compared to other REITs

One of PLD's greatest advantages is its size.

Total assets of PLD and its peers (Seekingalpha.com)

As the biggest REIT according to total assets and market cap, this, for example, gives PLD cheap access to cash, which is crucial for a REIT that usually works with a lot of debt. According to gurufocus.com, PLD has a cost of debt of 2.54%. Compared to 3.65%, which is the average cost of debt of PLD's peers: Rexford Industrial Realty, Inc. (REXR) , EastGroup Properties, Inc. ( EGP ), Americold Realty Trust, Inc. ( COLD ), First Industrial Realty Trust, Inc. ( FR ), and STAG Industrial, Inc. ( STAG ). Furthermore, PLD has a good structure when it comes to its interest coverage. PLD covers its interest expenses with its FFO by 6.85x. Compared to the sector median of 2.88x, this is phenomenal.

PLD FFO interest coverage ratio (Seekingalpha.com)

Looking at the return on assets, PLD does not significantly outperform its closest peers.

Return on assets, PLD vs peers (Seekingalpha.com)

Currently, PLD's ROA is in the middle of its peers while being quite volatile in the last five years.

Yield cuts as a tailwind for real estate stocks

For the majority of the year, the market accepted that the Fed wouldn't cut rates as fast as thought last year. After the recession fears came to mind and started the biggest correction since 2022 last week, there are a lot of voices advocating for faster rate cuts than the expected rate cut in September. Looking a bit further in the future, the yield curves look better than last year when I wrote my article:

Yield curves (Koyfin.com)

In blue, you can see the yield curve projections from July 14, 2023, when I wrote my first article on PLD. In red, you can see the current yield curve projections. Pretty obvious is, that for the next 5–7 years, the rates will be lower than expected last year, while we can see higher rates in the long run. In any case, I think it's pretty safe to say that in the shorter term, yields should be a tailwind for PLD's stock.

Looking at FAST Graphs, we get a good impression of how PLD is valued right now:

PLD current valuation vs historical valuation (Fastgraphs.com)

PLD is sitting right under the average adjusted FFO of the last 10 years. You could say PLD is fairly valued right now. Looking ahead, by using the average adjusted FFO of the past 10 years in combination with analysts' predictions, we can see the potential for gains:

PLD forecasting (Fastgraphs.com)

If PLD returns to its mean of the last ten years and analysts' predictions are correct, PLD could generate a total return of 14.64% by the end of 2026. 4% of these returns are from dividends.

PLD has a very strong record of paying and increasing dividends.

SA dividend score (Seekingalpha.com)

Seeking Alpha's scorecard is a bit too negative in my opinion. Looking at dividend safety, I think of PLD's dividend as safe but it depends on the specific timing.

As you can see in the chart, in the last quarter, PLD paid out 58% of its FFO. This is a good ratio considering PLD is a REIT. At one point in 2023, the payout ratio rose to nearly 80%, which enters a less healthy territory. Overall, however, the payout ratio remains healthy and sustainable.

The dividend growth is very good, considering PLD has way greater growth than the sector it belongs to and the growth is 12.6% in the last five years and is supposed to be 8.84% in the coming year:

PLD dividend growth (Seekingalpha.com)

SeekingAlpha rates the dividend yield as a D-.

PLD's dividend yield (Seekingalpha.com)

Considering that PLD's dividend yield is below the sector median and currently sits at 3.15%, a negative rating is justified. However, at present, you can secure a dividend yield above the average of the last 10 years:

PLD's dividend yield average (Koyfin.com)

The dividend consistency is very good with 13 consecutive years of dividend payments and 10 consecutive years of dividend increases.

Risks to my thesis

The risk to my thesis is that either deglobalization has no or only little impact on the operations of governments and companies in the US and Europe or PLD fails to utilize the increasing demand.

• Deglobalization: I could be wrong and deglobalization doesn't continue to expand. Or maybe deglobalization continues, but doesn't have the desired effect. Considering the number of companies that already restructure their warehouse and logistics operations, and the sentiment in governments toward a more "self-centric" approach, I don't think that this is likely. However, it's still a possible outcome.
• PLD doesn't utilize the increasing demand. Provided that deglobalization happens as expected, there's the possibility that PLD won't be able to profit off it. PLD's peers (mentioned above) could capture some market share. Considering that PLD did a fantastic job in the past, I'm sure that this scenario isn't very likely.

If one of the above-mentioned risks happens, I wouldn't expect much upside in the PLD stock. Due to its already massive market cap and rather low growth prospects, I don't expect PLD to outperform the market without a new growth catalyst like deglobalization.

Prologis stock performed poorly last year. This gives you a second chance to buy Prologis before a potential increase in share price due to yield cuts, better sentiment in the real estate industry, and potential tailwinds due to deglobalization. If you think deglobalization will continue and expand, Prologis could be worth buying. If you don't foresee this happening or positively impacting the stock, I believe there are better opportunities than Prologis. If you don't care as much for price appreciation and solely care for a safe dividend over 3% with a good history of payments and growth, Prologis is worth taking a look at.

This article was written by

Analyst’s Disclosure: I/we have a beneficial long position in the shares of PLD either through stock ownership, options, or other derivatives. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

Seeking Alpha's Disclosure: Past performance is no guarantee of future results. No recommendation or advice is being given as to whether any investment is suitable for a particular investor. Any views or opinions expressed above may not reflect those of Seeking Alpha as a whole. Seeking Alpha is not a licensed securities dealer, broker or US investment adviser or investment bank. Our analysts are third party authors that include both professional investors and individual investors who may not be licensed or certified by any institute or regulatory body.

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XRP exchange supply dwindles, paves way for 10% gains in Ripple price

• XRP supply on exchanges is on a steady decline, signaling a decline in selling pressure per Santiment data. 
• Ripple started testing its stablecoin in private beta, while traders digest SEC lawsuit ruling. 
• XRP whales drop their tokens, while retail traders accumulate since last week’s crypto crash.
• XRP extends gains to $0.57 and eyes 10% rally to $0.63 in a bullish scenario. 

Ripple (XRP) shared positive developments with traders in the past week, following the end of the Securities & Exchange Commission (SEC) lawsuit against the firm. XRP erased its losses and held steady above key support at $0.57 on Tuesday. 

On-chain metrics show bullish potential in the native token of the XRP Ledger amid other positive developments in the ecosystem. 

Daily digest market movers: XRP on-chain metrics support bullish thesis

• Santiment data shows that XRP supply on exchanges is on a steady decline. As XRP tokens held on exchange wallets reduce, there is less availability of the token to sell, reducing the selling pressure on the asset. 
• Dwindling exchange supply is, therefore, considered a sign of potential gains in the altcoin price. 

XRP supply on exchanges vs. price

• XRP large wallet investors have shed their token holdings, while retail holders accumulate the asset, even as Ripple extends gains. Santiment data shows XRP supply distribution in the last week. 

XRP supply distribution vs. price

• Ripple started its private beta testing for stablecoin RippleUSD (RUSD). The firm informed traders that the token is not available for trading or sale since regulatory approval awaits. 

Technical analysis: XRP eyes 20% gains

Ripple is in a multi-month downward trend as seen in the XRP/USDT daily chart. XRP extends gains on Tuesday, up to $0.5744 at the time of writing. XRP could extend gains by 10.21% and rally toward the $0.63 target. 

The $0.63 level is key to XRP, and the altcoin faces resistance at the psychologically important $0.60 level. The Relative Strength Index (RSI) reads 52, signaling positive momentum in XRP price trend. 

XRP/USDT daily chart 

XRP could find support in the Fair Value Gap (FVG) between $0.5888 and $0.5785 in a bearish scenario.

SEC vs Ripple lawsuit FAQs

Is xrp a security.

It depends on the transaction, according to a court ruling released on July 14: For institutional investors or over-the-counter sales, XRP is a security. For retail investors who bought the token via programmatic sales on exchanges, on-demand liquidity services and other platforms, XRP is not a security.

How does the ruling affect Ripple in its legal battle against the SEC?

The United States Securities & Exchange Commission (SEC) accused Ripple and its executives of raising more than $1.3 billion through an unregistered asset offering of the XRP token. While the judge ruled that programmatic sales aren’t considered securities, sales of XRP tokens to institutional investors are indeed investment contracts. In this last case, Ripple did breach the US securities law and will need to keep litigating over the around $729 million it received under written contracts.

What are the implications of the ruling for the overall crypto industry?

The ruling offers a partial win for both Ripple and the SEC, depending on what one looks at. Ripple gets a big win over the fact that programmatic sales aren’t considered securities, and this could bode well for the broader crypto sector as most of the assets eyed by the SEC’s crackdown are handled by decentralized entities that sold their tokens mostly to retail investors via exchange platforms, experts say. Still, the ruling doesn’t help much to answer the key question of what makes a digital asset a security, so it isn’t clear yet if this lawsuit will set precedent for other open cases that affect dozens of digital assets. Topics such as which is the right degree of decentralization to avoid the “security” label or where to draw the line between institutional and programmatic sales are likely to persist.

Is the SEC stance toward crypto assets likely to change after the ruling?

The SEC has stepped up its enforcement actions toward the blockchain and digital assets industry, filing charges against platforms such as Coinbase or Binance for allegedly violating the US Securities law. The SEC claims that the majority of crypto assets are securities and thus subject to strict regulation. While defendants can use parts of Ripple’s ruling in their favor, the SEC can also find reasons in it to keep its current strategy of regulation by enforcement.

Can the court ruling be overturned?

The court decision is a partial summary judgment. The ruling can be appealed once a final judgment is issued or if the judge allows it before then. The case is in a pretrial phase, in which both Ripple and the SEC still have the chance to settle.

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EY announces alliance with o9 Solutions to help modernize supply chain operations through business planning and analytics solutions

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The EY organization today announces an alliance between o9 Solutions, a pioneer in advanced planning and supply chain management software solutions, and Ernst & Young LLP (EY US) to provide advanced supply chain planning solutions backed by artificial intelligence (AI), machine learning (ML), big data and cloud computing.

With today's rapid technological advancements, organizations must reinvent how they operate and do business. the ey–o9 solutions alliance helps next-generation global enterprises in adopting integrated business planning solutions and modernizing their core supply chain, commercial and profit and loss functions., the alliance combines o9 solutions' ai- and ml-supported planning, analytics and data platform, known as the digital brain, with the deep supply chain knowledge, system integration and data science capabilities of ey us. the o9 solutions’ enterprise software platform unifies business processes, transforming traditionally slow and siloed planning functions into integrated and “intelligent” planning., together, ey us and o9 solutions support clients with system integration, sprint execution, program management, governance and change management. through this alliance, ey us and o9 solutions will help clients detect early risks across the supply chain and make intelligent business decisions., rajesh rao, ey–o9 solutions alliance leader, ernst & young llp, says:, “we are thrilled to be cultivating this relationship with o9 solutions, which will help us to transform our supply chain client engagements. this alliance provides a fit-for-purpose, configurable technology platform designed for quick deployment and agile implementation via the cloud. with this alliance, clients can have faster, more intelligent planning and business decision-making to help improve business outcomes.”, tanguy caillet, executive vice president, growth markets and global alliances, o9 solutions, says:, “we’re pleased to build a collaborative relationship with ey us, a trusted collaborator for businesses worldwide, to transform and digitalize the planning organizations and decision-making processes of our respective customers using the o9 digital brain platform. we look forward to providing, with ey us, robust business planning transformation that helps organizations gain the business insights and decision-making tools necessary to move their business forward.”, for more information, visit  ey.com/alliances., ey exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets., enabled by data and technology, diverse ey teams in over 150 countries provide trust through assurance and help clients grow, transform and operate., working across assurance, consulting, law, strategy, tax and transactions, ey teams ask better questions to find new answers for the complex issues facing our world today., ey refers to the global organization, and may refer to one or more, of the member firms of ernst & young global limited, each of which is a separate legal entity. ernst & young global limited, a uk company limited by guarantee, does not provide services to clients. information about how ey collects and uses personal data and a description of the rights individuals have under data protection legislation are available via ey.com/privacy. ey member firms do not practice law where prohibited by local laws. for more information about our organization, please visit ey.com., this news release has been issued by eygm limited, a member of the global ey organization that also does not provide any services to clients., related news.

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Exclusive: Redpoint-backed VoyceMe has raised $10 million in seed funding to tap the $100 billion-plus market for anime, manga, and webtoons 

One-Punch Man is a manga-turned-anime about existential dread, and the stilted, life-sucking agony of being unchallenged. 

“I love One-Punch Man, particularly because the creator at the start actually had no idea how to create good art,” said Dylan Telano, founder and CEO of VoyceMe, a platform for publishing and reading webcomics. “The original One-Punch Man was drawn on a napkin and that ended up getting published, and then it became the series that we all love. It’s honestly one of the inspirations behind why I started VoyceMe.”

The series follows a superhero who, no matter what, beats all his foes with a single punch. And that gets pretty depressing after a while. The scale of the anime superhero’s problem may be unrelatable (who on Earth can solve all their problems with a single punch?), but his search for meaning in an impossible world is universal. And Telano is doing something many would consider impossible—or at the very least, far from being venture-backable: With VoyceMe, Telano is hoping to solve core issues for artists working in manga and webcomics, while meeting the demand of a growing and interconnected media market.

VoyceMe has raised new seed capital, bringing its total funding so far to $10 million, Fortune can exclusively report. This latest round was led by Redpoint Ventures, with participation from Torch Capital, Red Sea Ventures, and Clara Vista Partners, all existing investors. The company did not disclose a valuation. 

The bear case for VoyceMe is pretty obvious: If monetization never takes off at scale or if it never reaches that scale in the first place because the market isn’t big enough, that’s that. The bull case, I’d argue, is a lot more interesting. That case expressly includes the idea that not only is the marketplace for anime and webtoons big enough—it’s growing. 

“We actually have three markets that all hit our demographics, the anime market, the manga market and the webtoon market,” said Telano. “What’s interesting about all those markets is that they’re rapidly growing.”

There’s certainly evidence this marketplace is maturing—for example, consider the IPO of OG comics publishing platform Webtoon this summer. And the numbers on where the marketplace is headed are compelling, especially when considered in one fell swoop: By 2032, the manga market is projected to hit $53 billion, the anime market is expected to reach $60 billion, and the webtoon market’s set to make it to $40 billion, according to various market researchers. 

For VoyceMe, the goal is to make money in a multi-pronged plan that includes licensing its proprietary AI tools, producing original content (for which VoyceMe owns the intellectual property), and monetizing its platform and its relationships with creators (carefully) over time. 

VoyceMe declined to disclose any current revenue numbers, but did say the platform receives more than eight million monthly visits, with readership since last year spiking by 1,000%. The company has a scattered world of semi-competitors, from newly public Webtoon to the nontraditional comics-sharing that happens on Reddit. 

But Telano’s thesis is this: The North American market, considered to be “prized” but nascent compared to Asia, is set to grow. And that means that there are going to be more manga, anime, and webcomic fans in a marketplace that’s not ready to meet the demand .

“There is, in my opinion, a massive misalignment between supply and demand, and ultimately the creators are suffering from the consequences of a hard life, with low pay,” said Telano. “Creators live such crappy lives, and in this space, the issue runs really deep…People read content in this space very quickly. It takes five artists working 80 hours a week to make one chapter, then it gets consumed in 30 seconds.”

Telano connected me with artist Matthew Johnson, who was one of the top 10 creators on the platform before he started working with VoyceMe. (His work includes Thrash: The Rise of Shidou , a moody but luminous comic about a world of dying gods.) Johnson is candid about the struggles that artists like him face, and said that VoyceMe streamlined his artistic process, from promotion and marketing assistance, to technological aids, to helping reduce the physical strain.

“Working on art is a physical labor, it’s not just a hobby,” said Johnson. “I know that’s probably going to blow someone’s mind…But that’s where the hardships come in, the repetitive tasks. I had a wrist injury last year and Dylan really had his ear out, and said ‘let me do something to help.’ In my experience, VoyceMe understands and is sympathetic to artists, and that speaks volumes.”

One of the things about manga and webcomics is that creators are readers, and readers are creators. If you know anyone who loves anime or manga, you know that love is often lifelong. 

“The past few years have taught us that consumer is hard, and that a truly fanatical following is required to build an enduring platform,” said Redpoint Ventures principal Meera Clark via email. “The sheer number of hours these weebs (a new word for me) spend consuming content and engaging in and around it is nutty—and presents the clearest pull we’ve seen for a vertical social network in a very long time.”

I maintain that webtoons on their own aren’t venture-backable, but VoyceMe’s ambitions are clearly bigger. And I think there’s still a lot we don’t know about the limits of the combined manga, anime, and webtoon market in the U.S. It may be the wrong question anyway. After all, as One-Punch Man hero Saitama asks: “Who decides limits? And based on what?”

See you tomorrow,

Allie Garfinkle Twitter: @agarfinks Email: [email protected] Submit a deal for the Term Sheet newsletter here .

Nina Ajemian curated the deals section of today’s newsletter.

VENTURE DEALS

- Setpoint , an Austin, N.Y.-based infrastructure provider for the credit industry, raised $31 million in Series B funding. 645 Ventures led the round and was joined by Citi , Wells Fargo , Andreessen Horowitz , and others.

- CodeRabbit , a Walnut Creek, Calif.-based code review AI platform, raised $16 million in Series A funding. CRV led the round and was joined by Flex Capital , Engineering Capital , and angel investors.

- ArborXR , a remote mobile device management software for VR and AR, raised $12 million in funding. Mercury Fund and Cortado Ventures led the round and was joined by Impact Venture Capital and Lewis & Clark Ventures .

- Spline , a Middletown, Del.-based collaborative 3D design platform, raised $10 million in Series A funding. Third Point Ventures led the round and was joined by Gradient Ventures , Y Combinator , Firestreak , and others.

- Collo , a Tampere, Finland-based liquid process performance solution, raised €5 million ($5.5 million) in funding. SEB Greentech Venture Capital and FORWARD.one led the round and were joined by existing investor Scale Capital .

- Holman acquired a minority stake in FM Capital , a Boulder, Colo.-based venture capital firm focused on the automotive and transportation industries. Financial terms were not disclosed.

PRIVATE EQUITY

- Advent International agreed to acquire a majority stake in ​​​​SYSPRO , a Tustin, Calif.-based ERP software provider for the manufacturing and distribution industries. Financial terms were not disclosed.

- Haveli Investments and Bregal Milestone agreed to a majority recapitalization of M-Files Corporation , an Austin-based knowledge work automation platform. Financial terms were not disclosed.

- An affiliate of H.I.G. Capital acquired Axis Europe , a London-based property maintenance services provider. Financial terms were not disclosed.

- Tide Rock acquired Glenn Wayne Wholesale Bakery , a Bohemia, N.Y.-based wholesale bakery and outlet. Financial terms were not disclosed.

- Tide Rock acquired Global Electronics Recycling , a Phoenix-based electronics recycling company. Financial terms were not disclosed.

- Tide Rock acquired Premier LogiTech , a Dallas-based supply chain solutions provider. Financial terms were not disclosed.

- ToxStrategies , a portfolio company of Renovus Capital Partners , acquired Suttons Creek , a Westlake Village, Calif.-based service provider for pharmaceutical companies. Financial terms were not disclosed.

- Vista Equity Partners acquired JAGGAER , a Durham, N.C.-based enterprise procurement and supplier collaboration software company, from Cinven . Financial terms were not disclosed.

- Carlyle agreed to acquire Baxter’s Kidney Care segment from Baxter International , a Deerfield, Ill.-based medical equipment manufacturer, for $3.8 billion.

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