Closed-Loop Supply Chain Network Design with Recovery of Glass Containers

Reverse logistics (RL) and closed-loop supply chains (CLSC) have recently received enormous attention due to growing environmental concerns and legislations coupled with the lucrative business potential. The main purpose of this paper is to develop a closed-loop supply chain network design model capable of recovering glass containers. A multi-period multi-product mixed-integer linear programming model is proposed to maximize profit. The strategic design of the supply chain is dealt simultaneously with the tactical planning of its operation, which covers procurement, production, storage, distribution, take-back, reprocessing, reuse, and recycling. To illustrate the efficiency and practicability of the model, it is applied to a real-world case of beverage supply chain where the glass containers are either re-used or recycled into their original form, as raw materials. Finally, sensitivity analyses, from a financial perspective, have been conducted to reveal the determinants of profitable product recovery and grasp their managerial implications. The analyses showed that return rate and return acquisition cost have determinant impact on the economic viability of product recovery practice. DOI: 10.4018/jsds.2012100101 2 International Journal of Strategic Decision Sciences, 3(4), 1-26, October-December 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. increased waste generation. While many companies have focused supply chain management to maintain competitive advantage, the concern about environmental protection and consequent strict legislations (e.g., disposal bans on specific products) coupled with the lucrative business opportunity of product recovery has extended the scope of traditional supply chain management to reverse logistics (RL) networks and closed-loop supply chains (CLSC) (Meade & Sarkis, 2007; Petec & Glavic, 1996). Product recovery has enormous economic potential, but, only a small percentage of the value is being recovered (Atasu et al., 2008). Product Recovery is the process of reclaiming economic and ecological value from product returns including commercial returns, end-of-use returns, end-of life returns and repair / warranty returns. Product recovery involves return acquisition, reverse logistics, product disposition (sort, test, and grade), remanufacturing/repair, and remarketing (Guide & Van Wassenhove, 2002). Reverse logistics is defined as the process of planning, implementing and controlling the inbound flow and storage of secondary goods and related information opposite to the traditionalsupply chain directions for the purpose of recovering value and proper disposal (Fleischmann et al., 1997). Closed-loop supply chain management is today defined as “the design, control, and operation of a system to maximize value creation over the entire life cycle of a product with dynamic recovery of value from different types and volumes of returns over time” (Guide & Van Wassenhove, 2006). CLSCs were proposed to maximize the profit of supply chains after studies showed that integration of forward and reverse supply chains is the key to achieve maximum profit. This paper presents a dynamic mixed integer linear programming model for the design and optimization of closed-loop supply chain network capable of recovering glass containers. In addition to the particular features of beverage supply chain, this work contains novel features which can be applied to generic CLSC network design problems. The rest of the paper is organized as follows: In Section 2, first the evolution of reverse and closed-loop supply chain design problem and the relevant literature are reviewed; then R/CLSCs for glass containers are discussed. In Section 3, the function of each entity of the network is described which is followed by the detailed exposition of the model formulation. Section 4 presents a case study of a Malt beverage supply chain optimization using the proposed model. Next, in Section 5, the sensitivity of the solution to the case study is analysed regarding a number of important CLSC parameters. The conclusions and managerial implications are provided in Section 6. 2. PROBLEM CONTEXT AND RELATED STUDIES 2.1. Reverse and Closed-Loop Supply Chain Network Design Facility location problems are very complex and involve diverse and disparate qualitative and quantitative factors. These problems are usually addressed through an initial qualitative multiple criteria decision making method (Pirdashti et al., 2008) which leaves us with a set of fairly suitable options. The quantitative approaches, such as mathematical optimization, are then utilized to finalize the decision and determine the optimal facility location. Taking the supply chain context into account, the facility location (FL) problem has evolved into supply chain network design (SCND). The first SCND problems where then extended to further match supply chain characteristics and needs. Among these are production, inventory, purchasing, supplier selection, buyersupplier relationship, marketing, and financial preferences and limitations. Incorporation of problems previously introduced for non-SCM settings into SCND problems has also been popular. For instance, the Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP) are two of the most popular problems in the field of combinatorial optimization which have influenced many other problems, including 24 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/closed-loop-supply-chainnetwork/74353?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Business, Administration, and Management. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2