Big data applications in food supply chains

International Conference On Engineering And Computer Science (ICECS) 2022: The use of innovative technology in accelerating problems sustainable development. 13 December 2022, Bandar Lampung City, IndonesiaFood supply chains are characterized by innovation, not only in products but also in processes. This paper aims to identify big data applications in the food and drink sector and present its findings as a state-of-the-art literature review. Academic databases were searched using ‘food’ or ‘drink’ and ‘big data’ keywords. Scholarly publications from 2015 onward are identified and presented in broad categories of demand prediction and retail operations optimization. The review recognized big data applications as a great opportunity for food supply chains. The applications aimed 1) to understand the customer base and inform marketing communications strategy, 2) to predict demand and organize retail operations to meet this demand, and 3) to optimize prices, assortment, and inventories based on demand patterns. Applications in this review focused more on descriptive and predictive analytics than prescriptive analytics, possibly due to the emergent nature of these applications. Descriptive analytics applications focused on capturing data, summarizing the status quo, and developing customer segments which can then be managed using varying marketing strategies. Predictive analytics applications focused on demand prediction with novel approaches proposed by the machine learning community. Prescriptive analytics applications aimed at promotion optimization and pricing for profit maximization. Cognitive analytics applications extracted customer reviews from online stores to inform which products should be marketed in what way. The review offers managerial insights on circumstances where big data analytics could prove beneficial. Managerial implications suggest that data integrators enable big data applications by ensuring the data collected are accurate, timely, and complete to inform descriptive, predictive, and prescriptive analytical models

Minimizing food waste in grocery store operations: literature review and research agenda

Research on grocery waste in food retailing has recently attracted particular interest. Investigations in this area are relevant to address the problems of wasted resources and ethical concerns, as well as economic aspects from the retailer’s perspective. Reasons for food waste in retail are already well-studied empirically, and based on this, proposals for reduction are discussed. However, comprehensive approaches for preventing food waste in store operations using analytics and modeling methods are scarce. No work has yet systematized related research in this domain. As a result, there is neither any up-to-date literature review nor any agenda for future research. We contribute with the first structured literature review of analytics and modeling methods dealing with food waste prevention in retail store operations. This work identifies cross-cutting store-related planning areas to mitigate food waste, namely (1) assortment and shelf space planning, (2) replenishment policies, and (3) dynamic pricing policies. We introduce a common classification scheme of literature with regard to the depth of food waste integration and the characteristics of these planning problems. This builds our foundation to review analytics and modeling approaches. Current literature considers food waste mainly as a side effect in costing and often ignores product age dependent demand by customers. Furthermore, approaches are not integrated across planning areas. Future lines of research point to the most promising open questions in this field

Drivers of Product Expiration in Consumer Packaged Goods Retailing

Product expiration is an important problem in the consumer packaged goods (CPG) industry costing 1%-2% of gross retail sales and eroding industry profits substantially. It can be caused by several factors related to store operations, supply chain practices, and product characteristics. Existing methods used in the industry are inadequate to identify the causes of expiration, leading to inadequate efforts to reduce expiration. Using retail data for 768 SKUs and 10,000 stores (745,638 store-SKU-level observations), as well as upstream supply chain data from a CPG manufacturer, we show the extent to which expiration of products in retail stores is driven by case size, inventory aging in the supply chain, minimum order rules, manufacturers' incentive programs for the sales force, replenishment workload, and many control variables. A counterfactual analysis based on the model shows that our subject manufacturer can reduce expiration by up to $38.82 million per year by implementing four selected initiatives involving case size, supply chain aging, minimum order rules, and sales incentives. Further, targeted initiatives can be designed using combinations of these variables for subsets of products with the highest occurrence of expiration