PHD, Kent State University, 2016, College of Business and Entrepreneurship, Ambassador Crawford / Department of Management and Information Systems

As competition in the global market continues to grow and customers are becoming more environmentally as well as cost conscious, recent trends in retail practices attest to the attention and resources devoted to the returns in supply chains. Topping the list is the economic incentive to reap as much value as possible from returns. With the growth of an internet consumer base, vigorous competition and the advance of online sales regardless of product type, size and locations, many firms provide generous return policies. This has caused a significant increase in the volume of reverse flows and therefore great potential for value recovery from returns. According to the National Retail Federation, the value of merchandise returned amounted to $260.5 billion in 2015. Hence, ways of improving the performance of a supply chain through effectively and efficiently closing the loop have received considerable attention both from academic researchers and industry practitioners over the past two decades. One way to recoup returns value as quickly as possible is to decentralize reverse logistics functions to third party reverse logistics providers (3PRLP). Outsourcing to a 3PRLP allows a firm to gain a state-of-the-art reverse logistics program immediately thereby avoiding the capital investment and start up delay required to implement an in-house RL program. This dissertation proposes two models of a Closed-Loop Supply Chain (CLSC) with independent 3PRLP for returns processing. The first model presents a CLSC where demand is generated by a stochastic process. A fraction of the units that are initially sold are returned by the consumers for a full refund in every period. We model the forward flow interaction between the supplier, the retailer and 3PRLP by a widely accepted control policy that is lot size-reorder point inventory policy, which is detailed by the Markov process. We further propose a queuing network to capture reverse flow activities of the 3PRLP, which consists o (open full item for complete abstract)

Committee: Alfred Guiffrida (Committee Chair); Butje Eddy Patuwo (Committee Co-Chair); Emmanuel Dechenaux (Committee Member) Subjects: Business Administration

PHD, Kent State University, 2016, College of Business and Entrepreneurship, Ambassador Crawford / Department of Management and Information Systems

As competition in the global market continues to grow and customers are becoming more environmentally as well as cost conscious, recent trends in retail practices attest to the attention and resources devoted to the returns in supply chains. Topping the list is the economic incentive to reap as much value as possible from returns. With the growth of an internet consumer base, vigorous competition and the advance of online sales regardless of product type, size and locations, many firms provide generous return policies. This has caused a significant increase in the volume of reverse flows and therefore great potential for value recovery from returns. According to the National Retail Federation, the value of merchandise returned amounted to $260.5 billion in 2015. Hence, ways of improving the performance of a supply chain through effectively and efficiently closing the loop have received considerable attention both from academic researchers and industry practitioners over the past two decades. One way to recoup returns value as quickly as possible is to decentralize reverse logistics functions to third party reverse logistics providers (3PRLP). Outsourcing to a 3PRLP allows a firm to gain a state-of-the-art reverse logistics program immediately thereby avoiding the capital investment and start up delay required to implement an in-house RL program. This dissertation proposes two models of a Closed-Loop Supply Chain (CLSC) with independent 3PRLP for returns processing. The first model presents a CLSC where demand is generated by a stochastic process. A fraction of the units that are initially sold are returned by the consumers for a full refund in every period. We model the forward flow interaction between the supplier, the retailer and 3PRLP by a widely accepted control policy that is lot size-reorder point inventory policy, which is detailed by the Markov process. We further propose a queuing network to capture reverse flow activities of the 3PRLP, which consists o (open full item for complete abstract)

Committee: Alfred Guiffrida (Committee Chair); Butje Patuwo (Committee Co-Chair); Emmanuel Dechenaux (Committee Member) Subjects: Business Administration