Contingency planning is the first stage in developing a formal set of production planning and control activities for the reuse of products obtained via return flows in a closed‐loop supply chain. The paper takes a contingency approach to explore the factors that impact production planning and control for closed‐loop supply chains that incorporate product recovery. A series of three cases are presented, and a framework developed that shows the common activities required for all remanufacturing operations. To build on the similarities and illustrate and integrate the differences in closed‐loop supply chains, Hayes and Wheelwright’s product–process matrix is used as a foundation to examine the three cases representing Remanufacture‐to‐Stock (RMTS), Reassemble‐to‐Order (RATO), and Remanufacture‐to‐Order (RMTO). These three cases offer end‐points and an intermediate point for closed‐loop supply operations. Since they represent different positions on the matrix, characteristics such as returns volume, timing, quality, product complexity, test and evaluation complexity, and remanufacturing complexity are explored. With a contingency theory for closed‐loop supply chains that incorporate product recovery in place, past cases can now be reexamined and the potential for generalizability of the approach to similar types of other problems and applications can be assessed and determined.
Recoverable product environments are becoming an increasingly important segment of the overall push in industry towards environmentally conscious manufacturing. Integral to the recoverable product environment is the recoverable manufacturing system that focuses on recovering the product and extending its life through remanufacture or repair. Remanufacturing provides the customer with an opportunity to acquire a product that meets the original product standards at a lower price than a new product. The¯ow of materials and products in this environment occurs both from the customer to the remanufacturer (reverse¯ow), and from the remanufacturer to the customer (forward¯ow). Since most of the products and materials may be conserved, essentially this forms a closed-loop logistics system. We present a 0±1 mixed integer programming model that simultaneously solves for the location of remanufacturingadistribution facilities, the transshipment, production, and stocking of the optimal quantities of remanufactured products and cores. We also discuss the managerial uses of the model for logistics decision-making.
It has been noted in the past that economically sound, environmentally preferable life extension modes such as remanufacturing and recycling have special characteristics that complicate the management of their production, logistics and associated operations. This paper considers these complicating characteristics from the perspective of the nine different modes of product life extension. In doing so, a framework and guide is provided to what issues, resource requirements and management capabilities are required for specific life extension modes. This framework provides guidance to practitioners and academics on commonalities between different product life extension modes, thereby assisting practitioners in leveraging current internal skills and capabilities and researchers in determining the generalizability of research.
In this note, we consider a variation of the economic order quantity (EOQ) model where cumulative holding cost is a nonlinear function of time. This problem has been studied by Weiss (1982), and we here show how it is an approximation of the optimal order quantity for perishable goods, such as milk, and produce, sold in small to medium size grocery stores where there are delivery surcharges due to infrequent ordering, and managers frequently utilize markdowns to stabilize demand as the product's expiration date nears. We show how the holding cost curve parameters can be estimated via a regression approach from the product's usual holding cost (storage plus capital costs), lifetime, and markdown policy. We show in a numerical study that the model provides significant improvement in cost vis-à-vis the classic EOQ model, with a median improvement of 40%. This improvement is more significant for higher daily demand rate, lower holding cost, shorter lifetime, and a markdown policy with steeper discounts.
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