Recipient Organization
TEXAS A&M UNIVERSITY
750 AGRONOMY RD STE 2701
COLLEGE STATION,TX 77843-0001
Performing Department
Agri Economics
Non Technical Summary
A threat to the competitiveness of the perishable produce industry is how to maintain product availability without spoilage. Waste is growing and costs an average supermarket approximately $450,000 per year, with perishables accounting for 56% of total store shrink. Spoilage throughout the supply chain is estimated to be as much as 10% of all perishable goods. Most perishable items like greens, meats, or dairy, have non-deterministic, random lifetimes. These random lifetimes depend on the time it takes for products to flow through the supply chain, as well as the product's temperature history and other environmental factors. These factors are generally unknown and highly variable, leading to considerable uncertainty with regard to the timing of product expiration and setting an appropriate sell-by date. The condition of many perishables cannot be ascertained by simple visual inspection. Thus, there is the danger that the product has expired before the sell-by date, and an expired product may be sold to the customer. The results of selling an expired product may range from the cost of a product return and loss of customer goodwill, to health and safety costs, public loss of reputation, and even litigation costs in case of a large-scale health scare. These complications and risks associated with perishable grocery products make effective inventory management challenging. In essence, the inventory control policy needs to balance the cost associated with waste (from throwing away good product), and the cost associated with selling expired product. This research fills the need for a developed inventory control model to demonstrate economic value of information contained in RFID (radio frequency identification) chips, a novel technology increasingly being adopted in the food industry. The outcome will be a mathematical model identifying optimal re-order policy and costs or benefits of acquiring additional information on time and temperature history through RFID tags. The expected impacts include enhanced efficiency in operations of the food supply chain, through the in-depth understanding of the value proposition of information in the hands of primary producers, logistics managers, or food retailers.
Animal Health Component
90%
Research Effort Categories
Basic
(N/A)
Applied
90%
Developmental
10%
Goals / Objectives
Goals and Objectives: The long-term goal is to extend the fundamental knowledge on the value of information that stems from technological innovation in the food products supply chain. This goal will be accomplished by completing the following objectives: (1) to identify optimal stocking levels and sell-by dates for perishable products having random lifetimes; (2) to analytically and mathematically determine the economic value of having data from environmental condition sensors in RFID (radio-frequency identification) tags ; and (3) to examine the value to primary producers of information systems that integrate time and temperature history data with information about production processes in accordance with international standards for food safety. Expected outputs include: (1) an inventory control system to utilize the time and temperature history data; (2) insights and models that are immediately applicable by companies in the perishable food products industry; and (3) improvements in foundational economic knowledge relevant to the competitiveness of businesses in the farm-to-consumer value chain. These models and insights will also provide societal benefits by enabling companies and governmental authorities to reduce public exposure to health and safety risks that arise from perishable product spoilage in supply chains. While our main focus is on perishables, specifically dairy products, the findings are expected to generalize to a variety of perishable products in which quality is significantly affected by time, temperature, and other environmental conditions in the supply chain. Some agribusiness sectors may not be well-represented by the model. For example, the total cost of inventory in some product lines and business outlets may not justify technology adoption even if the RFID - time and temperature history management system is optimal from a cost-minimization point of view. Nevertheless, the relevance of our results for operations research and for managerial applications is compelling because the foundational knowledge in stochastic inventory control has widespread applications in the food and agribusiness sectors. This project will produce a firm-level model and as such, cannot reflect all the ways in which performance depends on exogenous conditions in the market. Demand is parameterized rather than linked with a working price-dependent demand relationship or a game-theoretic structure. This specification is appropriate for the project's cost orientation and will fulfill our intent to provide decision support for companies choosing to invest in RFID or other technologies to enable traceability goals. Timeline: The period of performance for this research is January 15, 2011, through August 15, 2012. The research team members will work collaboratively with regular bi-monthly meetings and concurrent effort during summers. The project findings will be presented at the INFORMS annual meeting (mid November 2011, in Charlotte, NC). The entire project is expected to be completed in 19 months (early 2011 - August 15, 2012).
Project Methods
The researchers will create a series of mathematical models of the perishable inventory control problem for the case when products have random lifetimes, a given sell-by date, and product expiration is not observable. The base model represents the case when there is no time and temperature history data available. The first task is to complete the base model with representative data for fresh produce and a manufactured dairy food. The base mathematical model represents a supplier that sells a single perishable product. The product lifetime is random, and possesses a maximum lifetime of M periods. The replenishment problem is an infinite-horizon stochastic dynamic program (MDP) formulation where the objective is to find the supplier's allowable shelf life (T) (sell-by date) and optimal reorder policy (q) so that its average per period expected cost is minimized. In this model, T is a decision variable, therefore it is possible for expired product to be sold and unexpired products to be discarded. If an expired unit is sold, a hazard cost k is incurred. Any units remaining in inventory at the end of T periods, expired or not, are discarded (outdated) at a cost per unit c. Next, we will incorporate time and temperature history data into the model. This step necessitates linking temperatures to spoilage rates for the product. In the scientific literature, there are several models, most predicated on the Arrhenius law. The parameters for this step will be evaluated and validated through industry contacts. In the third task, we will complete the analytical representation of time and temperature history information along with its value in terms of costs avoided. The project intersects several research streams that include value of information, perishable inventory, cold chain management, food safety, temperature monitoring, RFID, and shelf life prediction. These streams cut across a wide array of disciplines that include agribusiness, computing technology, industrial engineering, technology management, food science, microbiology, and horticulture. From this perspective, our study represents a multidisciplinary contribution to the research state of the art that uniquely ties together multiple fields of research. It is expected that the analytical dynamic programming model will be too large to practically implement. Therefore, emphasis will be placed on developing well-performing heuristic policies. Simulation studies will be conducted, based on real world products and supply chains. The results will provide an assessment of the value of information and the conditions in which time and temperature history information is most valuable.