The Value of RFID Technology Enabled Information to Manage Perishables

We address the value of RFID technology enabled information to manage perishables in the context of a supplier that sells a random lifetime product subject to stochastic demand and lost sales. The product's lifetime is largely determined by the time and temperature history in the supply chain. We compare two information cases to a Base case in which the product's time and temperature history is unknown and therefore its shelf life is uncertain. In the first information case, the time and temperature history is known and therefore the remaining shelf life is also known at the time of receipt. The second information case builds on the first case such that the supplier now has visibility up the supply chain to know the remaining shelf life of inventory available for replenishment. We formulate these three different cases as Markov decision processes, introduce well performing heuristics of more practical relevance, and evaluate the value of information through an extensive simulation using representative, real world supply chain parameters

Appendix to “Managing Slow Moving Perishables in the Grocery Industry”

Here, we characterize the distribution ψ ( β) � introduced in §3.1.2. Without information D sharing, the supplier only knows the batch size Q and the history of the number of periods since the retailer’s last order β. We follow the procedure outlined in Bai et al. (2007) to show how this information is used to determine the retailer’s order distribution. Let Xi be a random variable representing the usage of the product (sales and outdating) at the retailer on day i for i = 1, …, M. The Xi s are independent with the same mean and variance, but they may come from different distributions. Assuming the retailer uses a reorder point inventory control policy (a reasonable assumption in this industry), once the retailer’s approximate inventory position Ii is below the reorder point r, then an order quantity of size Q will be ordered at time ti. Thus, during the time interval [ti-1, ti) with length i D � = ti- ti-1, the relationship between accumulated usage and the retailer’s inventory position can be expressed a

Information Sharing to Improve Retail Product Freshness of Perishables (ed.3)

We explore the value of information (VOI) in the context of a retailer that provides a perishable product to consumers and receives replenishment from a single supplier. We assume a periodic review model with stochastic demand, lost sales, and order quantity restrictions. The product lifetime is fixed and deterministic once received by the retailer, although the age of replenished items provided by the supplier varies stochastically over time. Since the product is perishable, any unsold inventory remaining after the lifetime elapses must be discarded (outdated). Without the supplier explicitly informing the retailer of the product age, the age remains unknown until receipt. With information sharing, the retailer is informed of the product age prior to placing an order and hence can utilize this information in its decision–making. We formulate the retailer’s replenishment policies, with and without knowing the age of the product upon receipt, and measure the VOI as the marginal improvement in profit that the retailer achieves with information sharing, relative to the case when no information is shared. We establish the importance of information sharing and identify the conditions under which substantial benefits can be realized

Managing Slow Moving Perishables in the Grocery Industry

We address the value of information (VOI) and value of centralized control (VCC) in the context of a two–echelon, serial supply chain with one retailer and one supplier that provides a single perishable product to consumers. Our analysis is relevant for managing slow moving perishable products with fixed lot sizes and expiration dates of a week or less. We evaluate two supply chain structures. In the first structure, referred to as Decentralized Information Sharing, the retailer shares its demand, inventory, and ordering policy with the supplier, yet both facilities make their own profit-maximizing replenishment decisions. In the second structure, referred to as Centralized Control, incentives are aligned and the replenishment decisions are coordinated. The latter supply chain structure corresponds to the industry practices of company owned stores or vendor–managed inventory. We measure the VOI and VCC as the marginal improvement in expected profits that a supply chain achieves relative to the case when no information is shared and decision making is decentralized. Key assumptions of our model include stochastic demand, lost sales, and fixed order quantities. We establish the importance of information sharing and centralized control in the supply chain and identify conditions under which benefits are realized. As opposed to previous work on the VOI, the major benefit in our setting is driven by the supplier’s ability to provide the retailer with fresher product. By isolating the benefit by firm, we show that sharing information is not always Pareto improving for both supply chain partners in the decentralized setting

Sharing Information to Manage Perishables (ed.1)

We address the value of information (VOI) sharing in the context of a two-echelon,serial supply chain with one retailer and one supplier that provides a single perishable product to consumers. We evaluate information sharing under two supply chain structures where both supply chain members share their inventory levels and replenishment policies with the other. In the first structure, referred to as Decentralized Information Sharing, the retailer and the supplier make their own profit-maximizing replenishment decisions. In the second structure, referred to as Centralized Information Sharing, the replenishment decisions are coordinated. The latter supply chain structure corresponds to the industry practice of vendor-managed inventory. We measure the VOI as the marginal improvement in expected profits that a supply chain achieves relative to the case when no information is shared. Key assumptions of our model include stochastic demand, lost sales, and order quantity restrictions. We establish the importance of information sharing in the supply chain and identify conditions under which relatively substantial benefits are realized. As opposed to previous work on the VOI, the major benefit of information sharing in our setting is driven by the supplier's ability to provide the retailer with fresher product. By isolating the benefit by firm, we show that sharing information is not always Pareto improving for both supply chain partners

Appendix to "Managing Slow Moving Perishables in the Grocery Industry" (ed.2)

An on-line appendix to "Managing Slow Moving Perishables in the Grocery Industry" by Michael E. Ketzenberg and Mark E. Ferguso

Purchasing Speculative Inventory for Price Sensitive Demand

The problem studied is one of buying and selling products cost eficiently over a number of periods in a finite horizon setting. Unit purchase costs vary across periods acording to some known distribution and demand is deterministic but dependent on the price charged for the product. Thus, the problem becomes one of exploiting opportunities to “forward buy” and sell profitably in the face of costs for carrying product

The value of time and temperature history information for the distribution of perishables

We model a supply chain that transports a perishable product from product origin to a destination market via a waypoint. The operational decision of interest is the transportation mode choice from the waypoint to the destination market, dependent on available information, including time and temperature history via RFID and sensors. We use analytical modeling to derive optimal transportation policies and generate generalizable, managerial insights. We then apply the analytical model in a numerical case study investigating the transportation of vine-ripened tomatoes from the Netherlands to the United States. Our analytical and numerical studies result in a number of interesting findings. First, the quality of sensor measurements may or may not impact the optimal policy and the decision maker can be guided accordingly. Second, better information may enable more profitable transport decisions, but doing so can have a negative impact on product quality at the destination. Third, we show that more stringent quality requirements by retailers may drive salvaging produce at the waypoint and thereby negatively impact service levels, despite penalties. Fourth, we identify the factors that drive the value of information under multiple information scenarios and establish both the direction and magnitude of their effects. Finally, both the analytical and numerical findings indicate that the information value is robust under measurement error. Thus, even if measurements are not perfect, RFID and sensor technology enabled information can be used to dynamically adjust forwarding decisions for perishable products, which can yield significant improvements to operational performance.</p