O.R. Applications
The effect of environmental parameters on product recovery

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Abstract

Economical and environmental issues are the main driving forces for the development of closed-loop supply chains. This paper examines the impact of environmental issues on long-term behaviour of a single product supply chain with product recovery. The environmental issues examined are the firm's `green image' effect on customer demand, the take back obligation imposed by legislation, and the state campaigns for proper disposal of used products. The behaviour of the system is analyzed through a dynamic simulation model based on the principles of the system dynamics (SD) methodology. This model includes all major inventories of new, used and recovered products and the flows among them. Inventory levels and flow rates are linked through differential equations. The dynamic model provides an experimental simulation tool, which can be used to evaluate the effect of environmental issues on long-term decision making in collection and remanufacturing activities and on product demand. Numerical analysis illustrates the potential uses of the methodology.

Introduction

Product and material flows, in a world where re-use is considered environmental friendly, have changed throughout the past decades. The reverse supply chain has been continually developed not only as a result of the associated economic profit but also because of the ecological motivation. The ecological and economical benefits of the two-way material flows made several researchers to design and investigate such logistics networks in early 90s, resulting in many related publications nowadays. Fleischmann [3] provides a review of related academic literature divided in the conceptual/structuring area, the example/cases area and the quantitative models. Guide et al. [7] presents a detailed comparison between traditional and recoverable manufacturing environments and discusses the reverse flows impact on business functions. Fleischmann et al. [4] provides a review of the quantitative models for reverse logistics. They also report that most of the papers study the system performance on the operational level and they are confined to rather narrow views on single issues, while comprehensive approaches are rare. No standard methodology is yet in common use; neither a general framework has been suggested. Furthermore, long-term strategic management problems of reverse logistics systems have not been studied. The reason may be the variety of involved factors in a general approach and the complexity of their interdependencies. A notable exception is the work of Thierry et al. [12], which systematically describes the implementation steps of a copier recovery strategy.

The motivation behind this research is twofold: first, to examine the impact of environmental concerns on supply chains with product recovery and second, to develop a dynamic simulation model for the above system, which facilitates the evaluation of long-term environmental and remanufacturing capacity expansion policies. Specifically, the objective of this paper is to study long-term behaviour of reverse supply chains with product recovery under various `ecological awareness' influences. The design parameter is the capacity of remanufacturing facilities. The environmental issues examined are first, the `green image' effect on customer demand, and second, the effect of state environmental protection policies, such as the take-back obligation imposed from legislation and the state campaigns for proper disposal of used products, on reverse product flows.

The analysis tool used here is the system dynamics (SD) methodology. Forrester [5] introduced SD in the early 60s as a modelling and simulation methodology for analysis and long-term decision making in dynamic industrial management problems. Since then, SD has been applied to various business policy and strategy problems [2], [10]. There are already some publications using SD in supply chain modelling, but most of them refer to forward logistics. Forrester [5] included a model of supply chain as one of his early examples of SD methodology. Towill [13] uses SD in supply chain redesign to generate added insight into system dynamics behaviour and particularly into underlying casual relationships. The outputs of the proposed model are industrial dynamics models of supply chains. Minegishi and Thiel [8] use SD to improve the knowledge of the complex logistic behaviour of an integrated food industry. They present a generic model and some practical simulation results applied to the field of poultry production and processing. Sterman [10] presents two case studies where SD methodology is used to model reverse logistics problems. In the first one, Zamudio-Ramirez [14] analyses part recovery and material recycling in the US auto industry to assist the industry think about the future of enhanced auto recycling. In the second one, Taylor [11] concentrates on the market mechanisms of paper recycling, which usually lead to instability and inefficiency in flows, prices, etc.

In this paper, we set out to study the behaviour of a single product closed-loop supply chain with product recovery under “environmental” influences and capacity planning policies. Although such an analysis may differ from one product to another, we try to keep it as general as possible to facilitate the implementation of the proposed model to more practical cases. The next section presents the methodology we use to analyse and model the system in the form of a causal-loop diagram. The modelling details of the system are presented in Section 3. Numerical investigation, which examines the effect of alternative environmental and capacity planning policies, is presented in Section 4. The final section contains a summary and the main conclusions.

Section snippets

Conceptual model description

The structure of a system in SD methodology is described by causal-loop or influence diagrams. A causal-loop diagram represents the major feedback mechanisms. These mechanisms are either negative feedback (balancing) or positive feedback (reinforcing) loops. A negative feedback loop exhibits goal-seeking behaviour: after a disturbance, the system seeks to return to an equilibrium situation. In a positive feedback loop an initial disturbance leads to further change, suggesting the presence of an

Mathematical formulation

The quantitative phase of the SD methodology begins with the development of the dynamic simulation model using specialised software. Then, the simulation model is verified and validated. During this step it is probable to return and correct the conceptual modelling in order the model to accurately represent the system. Then, we run the model and log the dynamic behaviour of the variables. The final step is to analyse the sensitivity of the model by examining the results of what-if scenarios.

Model parameters

To simplify our analysis, we assume that there are no stockouts in the forward channel and that the collection and inspection capacities are also infinite. The initial demand is set to 100 items per time unit and the initial remanufacturing capacity is set to 10 items per time unit. Thus, we assume that in the beginning of our time horizon 10% of the products can be remanufactured.

The dependency between State Environmental Protection Policies and collection percentage p1 is depicted in Fig. 5.

Conclusions

The dynamic model for a reverse logistics supply chain for product recovery allows comprehensive description and analysis of the long-term system operation (flows and stocks) under alternative environmental protection policies concerning take-back obligation, proper collection campaigns, and green image effect. The increasing environmental consciousness will force states to introduce new laws, which will drive the industry decisions to a new competitive environment. In this case, our model may

Acknowledgements

The authors would like to thank professors G. Tagaras and S.D. Flapper and two anonymous referees for their helpful and detailed comments on previous drafts of this paper. The research in this paper was partially sponsored by the EU TMR project Reverse Logistics (ERB 4061 PL 97-5650). This TMR network comprises the Otto-von-Guericke University Magdeburg (D), the Erasmus University Rotterdam (NL), the Eindhoven University of Technology (NL), INSEAD (F), the University of Piraeus (GR) and the

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