سیاست جدید تکمیل دوباره مبتنی بر سن برای بهینه سازی زنجیره تامین موجودی محصولات با قدرت فاسد شدنی بالا

A simulation optimization framework is proposed for supply chain inventory management of highly perishable products. A new replenishment policy based on old inventory ratio is developed, hence called OIR policy. It is an age-based policy using only partial age information to measure the freshness of the entire inventory. The efficiency of the new policy is evaluated in detail for a single-vendor-multi-buyer platelet (with a limited shelf life of 5 days) supply chain. The inventory objective is to minimize the expected system outdate rate under a predetermined maximal allowable shortage level. The new OIR policy is compared with two existing order-up-to policies: one is the order-up-to policy without age consideration; the other one is the “EWA” policy developed by Broekmeulen and van Donselaar (2009). The three policies are compared under both decentralized and centralized controls for different levels of the fill-rate constraint. The computational results show that adopting centralized control over the whole platelet supply chain greatly helps reducing the system expected outdate rate from 19.6% down to 1.04% on average while keeping sufficiently high fill rate at each entity. The two policies with age consideration are generally better than the policy without age consideration under both control strategies. This is particularly true for decentralized control. The new OIR policy is recommended because it is the best among all three, consistently yielding good results in all cases studied.

The strategic importance of perishable goods in food, chemical, pharmaceutical and healthcare industries cannot be emphasized more. The sale of perishable products makes up over 50% of the 550 billion US retail grocery industries (Ferguson and Ketzenberg, 2006). For supermarkets, perishables are the driving force behind the industry's profitability. Customer's choice is significantly influenced by the assortment and shelf availability of fresh food. The increasing food prices and at the same time billions of dollars' worth of food expiring every month (Grocery Manufacturer Association, 2008) raise public concerns. Perishables loss at grocery retailers can be as high as 15% due to damage and spoilage (Ferguson and Ketzenberg, 2006). On the other hand, supermarkets lose revenue when products are not available. Gruen et al. (2002) report that the worldwide average out-of-stock rate is 8.3%, in the US is 7.9%. The financial consequences for retailers and grocery producers/manufacturers are severe. For healthcare practice, the problem gets more serious since almost all blood products have limited shelf lives. While the supply is voluntary and costly, shortage of blood products may put life at risk. Blood banks and hospitals have strived to minimize shortages and outdates of all their blood products. In general, inventory control of perishables is very challenging. Major challenges come from uncertain demand, limited shelf life, and high customer service level requirements. A close match between supply and demand is the essence. This paper tackles the above-described problem by proposing a simulation optimization formulation of supply chain inventory system for highly perishable products. In addition, a new age-based replenishment policy that accounts for not only the stock levels but also the age distribution of the stock items is proposed. This newly developed policy is named the “old inventory ratio” policy (OIR) and it uses only partial age information of the stock and hence is easy to implement in practice. The effectiveness of the proposed policy is evaluated in detail for platelet supply chains. We chose platelets (PLTs) mainly due to its critical medical importance. Among those various blood products, PLTs is the one with the shortest shelf life, typically 5 days, and sometimes extendable to 7 days. PLTs are a very important component of today's therapies including those related to bone marrow transplants, chemotherapy, radiation treatment and organ transplants. The very short shelf life of PLTs makes their production and inventory management a really challenging task. In 2008, 12.7% of the platelets produced were outdated, slightly more than those outdated in 2006 (10.9%), according to the 2009 National Blood Collection and Utilization Survey Report (Washington, DC:Department of Health and Human Services, 2009). In the numerical example, we focus on the production and inventory management of PLTs for a supply chain consisting of a blood center supplying primarily one major medical center and a number of smaller peripheral hospitals. On the supply side, PLTs collections and/or productions are done by the blood center. The unit production cost of apheresis PLTs, which includes collection, testing, processing, and distribution costs, is high (approximately $500 according to Fontaine et al., 2009). For any blood center, PLTs production volumes must be set carefully to prevent large numbers of outdated units without risking a major shortage. At the hospitals’ side, they paid even more with an average of $538.56 in 2008 versus $525.05 in 2006, a significant increase of 2.6% (p<0.005), according to the 2009 National Blood Collection and Utilization Survey Report (Washington, DC:Department of Health and Human Services, 2009). The critical issue to decision makers at hospitals is how to manage PLTs inventory in a manner that minimizes outdates while satisfying demand. The inventory objective is set to minimize the system outdate rate under a pre-specified fillrate constraint. This is achieved by an efficient supply chain decision support system (DSS) that optimizes the production and inventory of platelets from blood centers to hospitals. One of the critical elements of the DSS is the embedded non-convex constrained stochastic optimization model. For comparison, we study both decentralized and centralized controls and three replenishment policies, one without age consideration and the other two with age consideration, under three different fillrate constraints. While our focus is on PLTs, the age-based policy of the proposed structure is also applicable to other perishable items with short shelf life as well. We are hopeful that this study would initiate further research in this direction. The main contributions of this paper include: • developing a quantitative formulation of the highly perishable supply chain inventory model with production/delivery lead time, • proposing a simulation optimization approach based on a hybrid metaheuristic algorithm to find near-optimal order-up-to policies for the entire supply chain (SC) system, • being the first to develop both centralized and decentralized models for the highly perishable supply chain and quantify the potential savings of centralized control, • developing a new replenishment policy (OIR) that bases reordering decisions not only on the stock levels but also on the age of the stock, which is shown to help improve the SC performance. The remainder of the paper is organized as follows. Section 2 reviews the existing literature. Section 3 describes the proposed SC system and the simulation optimization methodology in detail. Section 4 presents the testing results of various policies under different levels of the fillrate constraint for both decentralized and centralized controls. Finally, the paper is concluded.

This paper has presented a novel modeling and solution framework to optimize replenishment policies for highly perishable supply chains. The framework is based on a metaheuristic simulation optimization methodology that is set to minimize the expected system outdate rate under a predetermined fillrate constraint. Some interesting insights are gained from applying this methodology to platelets SCs, with one blood center and several hospitals. Three instances with one, three and six hospitals have been tested. Moreover, a new age-based replenishment policy called the old inventory ratio (OIR) policy is proposed and it outperforms two other order-up-to policies in the literature. The general conclusion is that policies accounting for age distribution of stocks are superior to policy without age consideration; and this result is particularly true for a decentralized supply chain. Adopting centralized control over the whole PLTs supply chain greatly helps in reducing the system expected outdate rate from 19.6% to 1.04% on average. Centralized control coordinates the SC very well so that the demand and the supply are matched closely. Employing a policy without age consideration in centralized control can achieve a much lower system outdate rate than those policies with age consideration in decentralized control. The new age-based OIR policy consistently provides good results in all cases studied. The advantages of our OIR policy can be summarized as: (1) achieving the same performance by using less information about inventory ages; (2) offering superior performance in both centralized or decentralized controls (it may not be easy to implement centralized control in some supply chains due to the conflicting interest); (3) ease of implementation in the real-world practice. Warehouse manager can easily count the number of “old” items in stock and calculate the corresponding ratio to avoid the need for sophisticated computation of estimated outdates as dictated in the EWA policy. Therefore, the OIR policy is worthwhile to be communicated to the interest readers in academics and industries alike. Possible topics for future studies include allowing transshipments among hospitals, considering different blood types and cross-matching, developing a true generic model to cover as many specific cases of highly perishable products as possible, etc.