ارزیابی پویای کیفیت محصول مبتنی بر مدل قیمت گذاری برای زنجیره تامین مواد غذایی فاسد شدنی

Waste stemmed from inappropriate quality control and excessive inventories is a major challenge for perishable food management in grocery retail chains. Improvement of visibility and traceability in food supply chains facilitated by tracking and tracing technologies has great potential to improve operations efficiency. This research aims to reduce food spoilage waste and maximise food retailer's profit through a pricing approach based on dynamically identified food shelf life. The proposed model is evaluated through different pricing policies to exploit the benefits from utilising accurate product shelf life information captured through innovated tracking and monitoring technologies. Numerical analysis is conducted in an illustrative case study.

Perishable foods are increasingly important to grocery retailers as they have become a main reason for many consumers to choose one supermarket over another. Despite their strategic importance, the management of perishable products is far from satisfactory, and perishable food loss at grocery retailers can be as high as 15% due to damage and spoilage [1]. The loss in a grocery supply chain mainly stems from inappropriate quality control or excessive inventories that have to be either marked down before the “sell-by-date” or thrown away after it. The financial consequence of waste for retailers and manufacturers is severe. In the European grocery sector, products that are not purchased before their sell-by date are estimated to cause costs running into billions of dollars each year [2]. Due to the nature of perishable food, its quality can be considered as a dynamic state that decreases continuously until the point when it is unfit for sale or consumption. There is a limited length of time during which the food is fit for sale and consumption. This is also known as product shelf life and printed on product labels. According to IFT [3], shelf life can be defined as the period between manufacture and retail purchase of a food product during which the product is of satisfactory quality. In practice, the shelf life information printed on food labels is coded when products are packed by producers. However, most perishable foods are temperature sensitive and their shelf life is therefore a function of product characteristics, conditions under which the product is maintained, and time [4]. One limitation of current practice is that the printed “sell-by-date” does not reflect the real temperature variations that occur through its life cycle. In fact, actual conditions frequently deviate from specified conditions through food storage and transportation processes, and food quality can be compromised due to unexpected development of different kinds of bacteria such as botulism, listeriosis and salmonella [5]. It may affect product quality and result in a difference between the actual remaining shelf life and that printed on the product label. The consequence of such a difference can be anything from increased waste to legal action. As a commitment of food supply chain management, perishable foods must be sold to consumers before foods spoil to ensure food safety and quality while maximising profit. This research deals with this challenge through managing retailing price dynamically against enriched food quality information. The research attempts to prove the proposition that pricing decisions based on dynamically identified food shelf life can improve the retailers' operations performance by minimising the consumer health risk and reducing food waste at grocery stores. The emergence of advanced product identification and sensory technologies such as ratio frequency identification technology (RFID) and time temperature indicator (TTI) provide great opportunities for effective management of perishable food. While these technologies are more widely adopted [6], [7], [8] and [9], it enables to automatically capture product information regarding product identity, properties and related data (e.g. temperature, humidity, and the time period during which products have been exposed to the temperature in the supply chain process, etc.) in real time. Such transparency generates the possibility that, as products pass through a supply chain, the shelf life can be dynamically predicted based on the environmental conditions during storage and transportation as well as the varied time required for these operations. In this paper, the proposed pricing approach focuses on utilising dynamically identified food shelf life information to support the pricing decision when retailers mark down the price of food products, which are approaching their expiration dates. The benefit of such an approach is evaluated by modelling the retailer's operational performance under different pricing policies. The technical details and implementation issues are not discussed in the research.

This paper attempts to explore the opportunities from using tracking and tracing technologies to improve the performance of perishable food supply chains. With the technological development and application of food traceability systems, supply chain visibility and accuracy of product shelf life information can be significantly improved. Consequently, consumer buying intentions can become more dependent on real-time product quality or shelf life features, rather than, as at present, on expiration dates printed on packaging. When consumers are able to perceive product shelf life variations over time through appropriately designed indicators, it becomes crucial to make a pricing decision based on the dynamically identified quality features, so that waste due to food spoilage is minimised and profit maximised. A dynamic quality evaluation based pricing model is developed and evaluated under different pricing policies. The analysis results demonstrate the critical importance of dynamically tracking and tracing food supply chain processes for sustaining business competitiveness. A major contribution of this research is the proposed innovation of using dynamically identified product quality to support the pricing decisions. In comparison with the existing dynamic pricing models [27] and [28], discrete methods as discussed in this research do offer some advantages over those dynamic pricing strategies, as price cannot generally change continuously over time for many practical reasons. When prices change too often, it costs retailers more to inform their customers of the changes. Retailers also risk upsetting consumers who expect prices to remain constant over most of the selling period. In addition, our analytical and simulation results have also demonstrated that product shelf life features, demand sensitivity, the length of the price markdown period and quality control throughout the supply chain processes affect model performance considerably. An appropriate price policy needs to be adopted according to shelf life features and accurately identified product deterioration rates in order to guarantee the maximisation of profit. Such a transformation would not only improve food safety management, but be a strategic innovation for marketing, food quality management, customer service and supply chain operations. One limitation of the research is, for simplification, that a linear deterministic demand is assumed. From a different perspective, there have been numerous empirical studies investigating demand functions for a wide variety of products. These demand functions can be an iso-elastic or exponential functions of price. It will be useful to investigate further research problems using stochastic models. In addition, we also recognise that the decision of when and how much to discount perishables as they approach their expiration dates requires a more in-depth analysis of the profit margins for each product, stock volumes, cross category effect, and the effect that discounting has on store traffic. Furthermore, globalisation has made the food supply chain more complex than ever before. One obstacle of implementing the proposed application is that it requires integration of traceability systems or/and adoption of advanced tracking and tracing technologies throughout the whole food supply chain. This does not only require large investment from retailers but also imposes extra costs on other parties in the food supply chain. This adds a substantial amount of uncertainty to model performance. This research only focuses on evaluating the benefits from the retailer's perspective. However, the success of such an innovation requires collaboration between all parties of the food supply chain. Therefore, it is essential to consider the costs incurred throughout the entire supply chain. With dynamic tracking information of the key factors that affect shelf-life, the discrepancy between the printed shelf-life and dynamically identified shelf-life changes reflecting what actually happened in the supply chain would be detected. Without awareness of this inconsistence, serious penalties may be incurred by retailers due to potential food safety incidents involving consumers. This impact is not analysed here. However, it is an important issue and one future direction of research would be an analysis of risk and cost of selling expired foods to consumers by retailers. This cost could be potentially substantial due to consumers' health incidents caused by the consumption of such foods. Such analysis would make a further contribution to the applicability and accuracy of the pricing model.