Elsevier

Omega

Volume 36, Issue 4, August 2008, Pages 535-548
Omega

Network design for reverse logistics

https://doi.org/10.1016/j.omega.2006.11.012Get rights and content

Abstract

Collection and recycling of product returns is gaining interest in business and research worldwide. Growing green concerns and advancement of green supply chain management (GrSCM) concepts and practices make it all the more relevant. Inputs from literature and informal interviews with 84 stakeholders are used to develop a conceptual model for simultaneous location–allocation of facilities for a cost effective and efficient reverse logistics (RL) network. We cover costs and operations across a wide domain and our proposed RL network consists of collection centers and two types of rework facilities set up by original equipment manufacturers (OEMs) or their consortia for a few categories of product returns under various strategic, operational and customer service constraints in the Indian context.

In this paper, we provide an integrated holistic conceptual framework that combines descriptive modeling with optimization techniques at the methodological level. We also provide detailed solutions for network configuration and design at the topological level, by carrying out experimentation with our conceptual model. Our findings provide useful insights to various stakeholders and suggest avenues for further research.

Introduction

Green supply chain management (GrSCM) is gaining increasing interest among researchers and practitioners of operations and supply chain management. Three drivers (economic, regulatory and consumer pressure) drive GrSCM worldwide. It integrates sound environmental management choices with the decision-making process for the conversion of resources into usable products. GrSCM has its roots in ‘environmental management orientation of supply chains’. Producing environmentally friendly products has become an important marketing element that has stimulated a number of companies to explore options for product take-back and value recovery [1].

Managers have been giving increasing importance to the environmental issues, their impact on operations and potential synergies [2], [3] since the early 1990s. Earlier literature is generally restricted to the plant or firm level focusing on green purchasing, industrial ecology, industrial ecosystems and corporate environment strategies [3]. Gradually, environmental management aroused increased interest in the field of supply chain management resulting in a growing literature on green supply chains [4], [5], [6].

For the purpose of this paper, we consider GrSCM as defined by Srivastava [4]. He defines GrSCM as “Integrating environmental thinking into supply chain management including product design, material sourcing and selection, manufacturing processes, delivery of the final product to the consumers as well as end-of-life management of the product after its useful life”. An interesting and significant trend in GrSCM has been the recognition of the strategic importance of reverse logistics (RL) as evident from classification and categorization of the existing GrSCM literature by Srivastava [4] shown in Fig. 1.

RL shall become vital as service management activities and take-back for products such as automobiles, refrigerators and other white goods, cellular handsets, lead-acid batteries, televisions, personal computers (PCs), etc. increase in future. A well-managed RL network cannot only provide important cost savings in procurement, recovery, disposal, inventory holding and transportation but also help in customer retention. Since RL operations and the supply chains they support are significantly more complex than traditional manufacturing supply chains, an organization that succeeds in meeting the challenges presents a formidable advantage not easily replicable by its competitors [7].

Today, India is the fourth largest country in terms of purchasing power parity (PPP) and constitutes one of the fastest growing markets in the world [5]. However, RL is yet to receive the desired attention and is generally carried out by the unorganized sector for some recyclable materials such as paper and aluminum. Some companies in consumer durables and automobile sectors have introduced exchange offers to tap customers who already own such products. The returned products are sold either as it is or after refurbishment by third parties.

Successful exchange offers have been marketing focused and no OEM (original equipment manufacturer) has come up with repair and refurbishing or remanufacturing facilities for the returned products and their sale. A summary of product–market characteristics for the wide category of products covered in our study is presented in Table 1. The cumulative annual growth rate (CAGR) shown is for the sales in the past decade and the expected demand in the next decade.

We cover the literature on GrSCM, primarily focusing on ‘RL’. We do not consider literature and practices related to green logistics as the issues are more of operational rather than strategic nature and may not be significant in the RL network design per se. We also do not focus in detail on literature on corporate environmental behavior, green purchasing, industrial ecology and industrial ecosystems as it is generally either regulatory-driven or firm-specific. We rather focus more on RL from resource-based viewpoint as establishment of efficient and effective RL and value recovery networks is a pre-requisite for efficient and profitable recycling and remanufacturing. This has received less attention in the GrSCM literature so far.

This paper is further organized as follows. In Section 2, we describe briefly our methodology in light of our objective. This is followed by contextual literature review in Section 3. To address some of the research issues and gaps related to designing RL networks for product returns, we develop a conceptual model in Section 4. The development of the corresponding mathematical model formulation for optimizing the decision-making is described in Section 5. Data collection in the Indian context is described in Section 6. Experimentation results for a few scenarios for decision-making using our model are discussed in Section 7. In Section 8, we conclude by describing the contributions as well as the limitations of our work and also suggest directions for further research.

Section snippets

Methodology

Our methodology consists of a theoretical part (literature review and conceptual model development) and an applied part (maximizing profits for various scenarios in practical settings using a hierarchical optimization model and drawing useful managerial insights and implications). A focused literature review seems to be a valid approach, as it is a necessary step in structuring a research field and forms an integral part of any research conducted. We focus mainly on RL literature deriving from

Literature review

The resource-based-view of the firm draws primarily from Hart [8] who proposes a theory of competitive advantage based upon the firm's relationship to the natural environment. He provides a conceptual framework comprising three interconnected strategies: pollution prevention, product stewardship and sustainable development along with their corresponding driving forces, key resource requirements and their contributions to sustained competitive advantage. Bloemhof-Ruwaard et al. [2] elaborate on

Conceptual model

To address some of the issues related to designing RL networks for product returns, we conceptualize a three-echelon (consumersreturnscollectioncentersreworksites) multi-period RL and value recovery network model as shown in Fig. 3. We try to address a number of strategic and operational questions related to disposition, location, capacity and customer convenience using this conceptual model. The definitions used in this conceptual model are described in Appendix A.

In our conceptual model,

Mathematical model formulation

We formulate a multi-product, multi-echelon, profit maximizing RL and value recovery network model covering activities from collection to first stage of remanufacturing. We ensure that it is a good representation of the real-life situation and is at the same time tractable. The objective function and various parameters and constraints have been clearly defined. The problem has been treated similar to a multi-stage resource allocation problem. Various decisions such as the disposition decisions,

Data collection

For application of the proposed model, its input data may be classified into two groups: (1) returns data which include the types of returned products, and the time-varying amount associated with each type of product, and (2) operations and cost related parameters such as costs of facilities, capacity block sizes, processing times, fraction recovery rates, average number of recoverable modules, storage costs, processing costs, distances, transportation costs, procurement costs, sale prices and

Results and discussion

In this section, we discuss the results of experimentation and analysis across the select category of products under various scenarios to gain insights into both the modeling and solution aspects of the RL and value recovery network design. Simultaneously, we present a few generalizations of results and their derived managerial implications.

First, we experiment with the collection center model to find out the impacts of various factors such as maximum distance limits for customer convenience,

Conclusions

This paper highlights and re-inforces the importance of RL—an important area for practitioners which has been under-explored by academics. We carry out RL and value recovery network optimization and explore the implications of setting up of remanufacturing and repair and refurbishing centers by OEMs or their consortia for certain categories of products in Indian context. The major contribution of this research lies in developing a formal framework for analyzing the network model and providing

Acknowledgments

This work would not have been possible without the co-operation of 84 respondents who were informally interviewed for this study and shared their knowledge, experience and expertise We also express our thanks to Srivastava and Srivastava [32] for permitting us to use their data. Finally, we thank the three anonymous referees for providing many pertinent and useful comments that helped in making the manuscript more focused, precise and useful.

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    This paper was processed by Guest Editors Angappa Gunasekaran and T.C. Edwin Cheng.

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