Assessing performance and uncertainty in developing carpet reverse logistics systems

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Abstract

The US carpet industry is striving to reach a 40% diversion rate from landfills by 2012, according to a memorandum of understanding signed by industry and government officials in 2002. As a result, they are interested in methods of setting up a reverse logistics (RL) system which will allow them to manage the highly variable return flows. In this paper, we simulate such a carpet RL supply chain and use a designed experiment to analyze the impact of the system design factors as well as environmental factors impacting the operational performance of the RL system. First, we identify the relative importance of various network design parameters. We then show that even with the design of an efficient RL system, the use of better recycling technologies, and optimistic growth in recycling rates, the return flows cannot meet demand for nearly a decade. We conclude by discussing possible management options for the carpet industry to address this problem, including legal responses to require return flows and the use of market incentives for recycling.

Introduction

The area of reverse logistics (RL) management is currently attracting more interest both commercially and academically. However, distinct differences exist in various regions of the world on that impact the design and sustainability of RL networks. In this paper, we assess the particular needs of US carpet manufacturers as they attempt to develop RL networks in an environment distinctly different from the majority of the industrialized world.

Europeans exhibit a great deal of environmental awareness, and people are amendable to green branding and the setup of new markets for returned goods [1]. Laws require the recycling of many types of goods, effectively increasing and stabilizing return flows of products. The benefits to companies that use RL, along with recycling or remanufacturing, can be manifold. Such firms can save 40–60% of the cost of manufacturing a completely new product [2] or cut down delivery lead times, e.g., if service parts or complex components are remanufactured rather than manufactured from scratch [3]. The Europeans’ environmental awareness and activities have also led to more research and applications in the area.

In US, the main environmental driver for [reducing the level of] non-toxic solid waste is the need to reduce the amount of material going into landfills [4], [5], [6]. Increasingly, local governments are trying to reduce landfill use and are putting pressure on manufacturers to take steps towards source reduction. Federal, state/provincial and municipal governments in North America have started implementing energy management programs [7], [8], [9], in which they promote the purchase of carpet with at least 25% recycled content. Partly as a result of this government pressure, US carpet manufacturers signed a memorandum of agreement in 2002, targeting a 40% diversion of carpet waste flows from landfills by 2012. Between 20% and 25% of all used carpet is to be recycled [10].

To accomplish this, the carpet industry needs to set up an RL system to handle the collection of used carpet, the separation of carpet components, and the re-distribution of recyclable materials to carpet manufacturers. Unlike forward logistics, RL operations are complex and prone to a high degree of uncertainty [11], affecting collection rates, the availability of recycled production inputs, and capacities in the reverse channel. Thus, US carpet manufacturers and other players in the RL chain need to know how best to structure their RL systems and what operational difficulties they will face.

It still remains to be seen, however, whether the US carpet industry as a whole will be able to meet their 2012 goals. In order to assess the situation we use current carpet industry data to design and simulate a RL system for US manufacturers of commercial broadloom carpet, taking into account several possible design variables, including the number of collection centers, the variability in collection volumes, the type and setup of forecasting and control systems, as well as market return rates. We use a designed experiment to establish the impact of these RL system design and environmental parameters on the RL performance. In doing so, we are able to identify the relative importance of these strategic variables for the design of the RL system. Note that we do not set out to optimize an existing system but seek to provide guidance regarding the importance of various design parameters for designing a new system. We also show that even in the best of scenarios, the carpet industry's 2012 goals will be hard to reach.

The paper is structured as follows: First, we assess the factors critical to the design of a well performing carpet RL system to identify those that should be tested in the experiment. While doing this we also review the current literature on RL as it pertains to the problems faced in the carpet industry. Then we explain the setup of our experiment and the methodology used. In Section 4, we present the results, which are then discussed in Section 5. In Section 6, we provide insights of direct importance to the US carpet industry.

Section snippets

Types of reverse logistics networks

RL is defined by REVLOG as “The process of planning, implementing and controlling flows of raw materials, in process inventory, and finished goods, from the point of use back to a point of recovery or point of proper disposal” [13]. Dowlatshahi [2] argues that “from design through manufacture to consumer, firms should explore and integrate RL as a viable business option in the product life cycle.” However, the design and development of a RL network differs from that of forward logistics in

Experimental methodology

In this section, we discuss the design of the simulation model used to represent a carpet RL network, including its environment and the assumptions made regarding the modeling approach and data. We then present in more detail the main experimental factors and performance measures. Finally, we describe the setup of the experiment used to analyze the carpet RL network model.

Results

In this section, we present the results regarding the performance variables described above. Since we tested for main and five levels of interaction effects, the ANOVAS were rather long. Hence, the ANOVA tables used in this section are abridged (please contact the authors for a full version). All effects with p-values of greater than 0.10 have been deleted. In our discussion, however, we consider only p-values of 0.05 or better to be significant. Also, for the sake of brevity, we will discuss

Critical factors for the design of a carpet RL supply chain

The results from the simulation clearly indicate several factors US carpet manufacturers can directly address in order to improve RL design and performance. These are:

  • 1.

    Structuring the reverse supply chain and invest in IT systems to provide greater visibility to the RL network.

  • 2.

    Carefully managing return flows to ensure availability of recyclables, including

    • (a)

      increasing the number of collection centers, and

    • (b)

      developing methods to reduce the uncertainty of return flows.

Our simulation results showed

Conclusions and future research

The carpet industry has agreed to divert a substantial proportion of spent carpet from landfills by 2012, amounting to 40% of return flows. If the industry is to be able to reach this goal, it must clearly expand its RL supply chain capabilities. This will likely entail greatly increasing the number of collection centers, providing more convenient opportunities for residents and contractors to turn in their carpet for recycling. It also means that manufacturers must invest in technology. Our

Acknowledgements

We thank the editor (Tamer Boyaci) and two anonymous reviewers for their helpful comments and suggestions.

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