assessing performance and uncertainty in developing carpet ... · 2. critical parameters for a...

Computers & Operations Research 34 (2007) 443–463www.elsevier.com/locate/cor

Assessing performance and uncertainty in developing carpetreverse logistics systems

Markus Biehla,∗, Edmund Praterb, Matthew J. RealffcaSchulich School of Business, York University, Toronto, Ont., Canada M3J 1P3

bDepartment of IS and OM, University of Texas at Arlington, Arlington, TX 76019-0437, USAcSchool of Chemical Engineering, Georgia Tech, Atlanta, GA 30332-0100, USA

Available online 18 April 2005

Abstract

The US carpet industry is striving to reach a 40% diversion rate from landfills by 2012, according to a memorandumof understanding signed by industry and government officials in 2002. As a result, they are interested in methodsof setting up a reverse logistics (RL) system which will allow them to manage the highly variable return flows. Inthis paper, we simulate such a carpet RL supply chain and use a designed experiment to analyze the impact of thesystem 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 thedesign 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 optionsfor the carpet industry to address this problem, including legal responses to require return flows and the use ofmarket incentives for recycling.Crown Copyright © 2005 Published by Elsevier Ltd. All rights reserved.

Keywords: Reverse logistics; Recycling; Carpet industry; Simulation; Design of experiments; Performance analysis

1. Introduction

The area of reverse logistics (RL) management is currently attracting more interest both commerciallyand academically. However, distinct differences exist in various regions of the world on that impact the

∗ Corresponding author.E-mail addresses: [email protected] (M. Biehl), [email protected] (E. Prater), [email protected]

(M.J. Realff).

0305-0548/$ - see front matter Crown Copyright © 2005 Published by Elsevier Ltd. All rights reserved.doi:10.1016/j.cor.2005.03.008

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444 M. Biehl et al. / Computers & Operations Research 34 (2007) 443–463

design and sustainability of RL networks. In this paper, we assess the particular needs of US carpetmanufacturers as they attempt to develop RL networks in an environment distinctly different from themajority of the industrialized world.

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

In US, the main environmental driver for [reducing the level of] non-toxic solid waste is the need toreduce the amount of material going into landfills [4–6]. Increasingly, local governments are trying toreduce 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 energymanagement programs [7–9], in which they promote the purchase of carpet with at least 25% recycledcontent. Partly as a result of this government pressure, US carpet manufacturers signed a memorandumof agreement in 2002, targeting a 40% diversion of carpet waste flows from landfills by 2012. Between20% 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 ofused carpet, the separation of carpet components, and the re-distribution of recyclable materials tocarpet manufacturers. Unlike forward logistics, RL operations are complex and prone to a high de-gree of uncertainty [11], affecting collection rates, the availability of recycled production inputs, andcapacities in the reverse channel. Thus, US carpet manufacturers and other players in the RL chainneed to know how best to structure their RL systems and what operational difficulties theywill face.

It still remains to be seen, however, whether the US carpet industry as a whole will be able to meettheir 2012 goals. In order to assess the situation we use current carpet industry data to design and sim-ulate a RL system for US manufacturers of commercial broadloom carpet, taking into account severalpossible design variables, including the number of collection centers, the variability in collection vol-umes, the type and setup of forecasting and control systems, as well as market return rates. We use adesigned experiment to establish the impact of these RL system design and environmental parameterson the RL performance. In doing so, we are able to identify the relative importance of these strategicvariables for the design of the RL system. Note that we do not set out to optimize an existing systembut seek to provide guidance regarding the importance of various design parameters for designing a newsystem. We also show that even in the best of scenarios, the carpet industry’s 2012 goals will be hardto reach.

The paper is structured as follows: First, we assess the factors critical to the design of a well per-forming carpet RL system to identify those that should be tested in the experiment. While doing this wealso review the current literature on RL as it pertains to the problems faced in the carpet industry. Thenwe 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 UScarpet industry.

M. Biehl et al. / Computers & Operations Research 34 (2007) 443–463 445

2. Critical parameters for a carpet reverse logistics network

2.1. Types of reverse logistics networks

RL is defined by REVLOG as “The process of planning, implementing and controlling flows of rawmaterials, in process inventory, and finished goods, from the point of use back to a point of recoveryor point of proper disposal” [13]. Dowlatshahi [2] argues that “from design through manufacture toconsumer, 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 severalways. Differences include the supply chain composition and structure (new parties may be involved andnew roles assumed by existing parties, and the forward network may be different from the RL network);additional government constraints; rapid timing and uncertainty in the environment [1,14–16].

A number of RL network issues have been explored in the literature, mostly in papers by Europeanresearchers. For example, Spengler et al. [17] discussed recycling steel byproducts, while Barros et al.[18] researched sand recycling. Electronic equipment collection, remanufacturing and distribution wasassessed by Jayaraman et al. [19] and carpet recycling by Realff et al. [20]. Recently, Georgiadis andVlachos [21] developed a general model investigating the various effects of environmental variables onreturn flows. Moritz Fleischmann has done significant work in the area of RL. Fleischmann et al. [1]devised a framework of three typical RL network structures: RL networks for bulk recycling, remanu-facturing, and reuse (Table 1). In a second paper, Fleischmann et al. [22] proposed a generic RL networkmodel based on a mixed integer linear program and discussed applications and extensions to the model.In a third paper, Fleischmann [23] extended his previous work and presented a continuous optimizationmodel for RL network design.

Using Fleischmann et al.’s [1] framework, carpet recycling falls in to the bulk recycling category,which is characterized by substantial initial RL investment costs relative to the product value (low valuedensity) as well as a high vulnerability with respect to uncertainty in the supply volume. The networkstructure is often flat and centralized at the recycling stage due to the expensive recycling equipment.The system is open-loop, meaning that the recycling activities do not interfere with new product sales.Cooperation within the industry often takes place, probably to ensure input volumes. It is easy to see thatcarpet recycling closely matches this category, making the volume and variability of recyclable carpet

Table 1Overview of RL Network Types (based on Fleischmann et al. [1])

Bulk recycling Remanufacturing Reuse

Structure • Centralized • Decentralized • Decentralized• Flat • Multi-level • Flat• Open loop • Closed loop • Closed loop• Branch-wide cooperation • No branch cooperation • No branch cooperation

Generation New reverse networks Extension of forward networks Extension of forward networks

Ownership Third parties, material suppliers, OEMs Mostly OEMs OEMs, third parties


a problem of prime importance. In addition to affecting the networks’ economics, the uncertainty in thequantity and timing or product returns [5] often leads to increased difficulties in planning for RL ascompared to forward logistics.

2.2. Carpet returns

In the carpet industry, majority of returns are due to carpet replacements. Returns can be the result offire or water damage in residential or industrial properties, fashion changes, but rarely due to wear-out[24]. Unfortunately, there is no way to accurately forecast carpet returns. The lack of information is thebottleneck in the management of recycling systems [25]. The only information currently available ishistorical data on aggregate yearly returns, making it difficult to use more sophisticated RL forecastingmethods such as those developed by Toktay et al. [26].

The lack of data and the high variability in the availability of old carpet impacts the setup of the RLsystem. One design parameter that carpet recyclers must consider is the number of collection centers.Even if recyclables are processed centrally, an increase in the number of collection centers is likely todecrease the variability in the volume of recyclables. This is due to risk pooling, an effect widely describedin the supply chain literature [27]. Due to the importance of reliable old carpet supply to the system [1],the number of collection centers might be a major decision variable in the setup of the RL supply chain.

If the carpet industry wants to ensure higher rates of post-consumer recycled content, it must increasethe currently low return flow of spent carpet as well as reduce the variability of carpet returns. Thiscould be accomplished through mandatory return laws or providing marketing incentives (see [28]).These measures would increase the return flow over time and, possibly, lower the variation coefficient ofreturns.

2.3. Information technology for reverse logistics support

Given the complexity of RL supply chains and the uncertainty return flows, effective informationtechnology (IT) is necessary to support the management of return flows. In fact, it has been argued thatIT support is more essential in RL than in forward supply chains [5] because of the need to transferinformation between various echelons in the supply chain [30]. Unfortunately, there are two limitationsto effectively using IT in RL supply chains. First, data on product returns is often either nonexistent or ofpoor quality, making it rather than the technology the limiting factor for coordinating RL supply chains[29]. Moreover, commercial RL software is not yet available [31] and very few firms have successfullyautomated the information supporting the return process [11,32]. Adding to this, RL has not been an ITpriority in the past years due to a failure of management to pursue it, or because IT departments havebeen burdened with other high priority projects [31].

At the very least, once returns have entered the RL system, regular supply chain software such asMRP or ERP systems can be used to coordinate the processing and flow of cores [11]. Although thesesystems typically do not support the full data flows required for RL, basic information such as the quantity,location, and expected arrival times of cores can be relayed through the reverse supply chain.

As a result, two alternatives exist for the design of the RL system. Either IT could be incorporatedinto the reverse supply chain by building a RL IT infrastructure or adapting existing systems to includemechanisms that facilitate the entry of return flow data, including the types and quantities of carpets beingcollected at each center. This would provide a high degree of visibility of the network. Alternatively, as is


common practice, the carpet industry could use forecasting methods to estimate the volume of recycledmaterials available for manufacturing. The second alternative would likely be much less costly but, dueto the absence of supply chain visibility, [would] likely lead to inferior planning results.

2.4. Product design

The availability of recylables within the carpet industry can be increased not only through developingreturn flows but also through product re-design or new product design. Shaw Industries, for example,has increased the recyclability of carpets in their [12] EcoWorx product line. Note that if a manufacturerincreases the availability of recycled production inputs by increasing the products’recyclability rather thanincreasing return flows, this will have a positive effect not only on the availability of recycled productioninputs but also on the RL system’s efficiency (e.g., transportation, waste generation, etc.).

3. 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 thenpresent in more detail the main experimental factors and performance measures. Finally, we describe thesetup of the experiment used to analyze the carpet RL network model.

3.1. Simulation scenario

We devised a model to reflect the parameters discussed in the previous section. The carpet RL modelconsisted of three stages, as shown in Fig. 1. In accordance with our discussion in the previous section,the RL network model consisted of two or six collection centers, one central carpet recycling facility, andone central manufacturing facility. We now describe the physical setup and control logic of the model.

Reverse Logistics System

Collection• 2 or 6 collection centers• Lognormal, avg. 14 mn lbs,

SD = 50 or 90% of volume

Manufacturing. 1 central facility. Capacity: 184 mn lbs ± 15%. Inputs: virgin & recycled nylon. Output: carpet (Demand N(163,16)). Target service level: 90%. Target inventory level: 3%

Recycling• 1 central facility

Carpet Returns Carpet Demand

LT =

1 month

LT =

1 month

Virgin Materials

availability (if system visible)

ordering

Disposal

Nylon 6,6Backing

NylonCarpet

Nylon

Fig. 1. Carpet reverse logistics supply chain structure.


Collection process: Used carpet is accepted at the collection centers. The system collection rate at thebeginning of the simulation is 14 million lbs per month, which is considered by industry experts to bea reasonable amount for a carpet recycling system. We model two scenarios. In the first scenario, thecollection rate remains the same over the planning horizon. This would be equivalent to a constant salesand collection rate. In the second scenario the collection rate increases from 14 million lbs at the initialtime to 28 million lbs at the terminal time (30 years). This is equivalent to a yearly increase of about 2.4%in the collection rate.

To determine the collection rate per collection center the system collection rate is divided by the numberof collection centers in the system (two or six). It is known that in the case of carpets the variability ofreturns is very high [1], yet unknown. Hence, we use two standard deviations (SDs) of carpet returns inthe model: 50% and 90% of the return volume.

At each collection center the collection process follows a lognormal distribution. Although specialseasonal forecasting methods have recently been developed that take into account the product’s life cycledistribution and likeliness of return [26], the return of industrial carpets does not seem to follow thispattern. Kelle and Silver [33], in the context of container returns, argued that in practice there are “likelyto be several independent terms (e.g., duration of useful life, return distribution, random binomial variablethat models whether the customer returns the container, etc.), thus the normal distribution should serveas a good approximation” for the return flow (p. 351). Based on this, return rates at the collection centersare assumed to be independent of sales and identically distributed. In addition, returns are modeled asa normal process, although we use a lognormal function to ensure that we do not have negative returnflows.

Once received, the carpet is forwarded to the central recycling facility. The transfer from the collectioncenters to the recycling facility takes one month (lead time LT = 1 month). This transfer time includesthe time usually spent on accepting, storing, loading, and transporting the carpet.

Separation process: At the uncapacitated separation center, returned carpets are divided into its com-ponents, including nylon, nylon 6-6, and backing (essentially calcium carbonate and latex polymer). Partof the carpet is recycled, and the remainder is disposed off. Most manufacturers would like to recyclethe nylon face fiber, either nylon 6 or nylon 6-6, which accounts for about 20–50% of the carpet’s mass,depending on the type of carpet. Hence, we model two recycling rates: 30% and an optimistic 60%,which might be possible to be achieved in a few years. The nylon is then delivered to the new carpetmanufacturing facility. The lead time is again one month.

The manufacturing facility, in which new carpet is produced, consists of three entities: the nyloninventory, the capacitated carpet manufacturing process, and the carpet inventory. All incoming nylon isfirst admitted to the nylon inventory. This includes recycled nylon received from the separation facilityand virgin nylon delivered by a supplier. The order lead time for virgin nylon is one month. Note that,since the lead time for recycled and virgin nylon are equal, the inventory system is easy to control ascompared to the case in which the two which lead differ [34]. To facilitate the description of the processesthat take place in the manufacturing facility, we first describe demand, then how production targets areset and, finally, the algorithms used for ordering virgin nylon. We use two methods for determining howmuch virgin nylon to order—the so-called visible system, in which the manufacturer knows how muchrecycled nylon will be received in the next month, and the traditional system, in which the separationfacility is not visible to the manufacturing facility.

The production volume during period t, Pt , is based on the production forecast, set to satisfy demandwith a 90% probability, while maintaining a safety stock of 10 million lbs of new carpet (see Eq. (1)).


Demand for carpet follows a normal distribution with a mean of 163 million lbs per month, whichresembles the demand for commercial broadloom carpet in the US [35,20], and an estimated standarddeviation of 10%. Hence, at a service level of 90%, the production forecast, Pf , equals 184 million lbs.Note that, if the carpet inventory is less than 10 million lbs, the production forecast is increased by thedifference between the carpet safety stock and the inventory level. After this determination, a series ofchecks for upper and lower bounds takes place. First, the production level cannot be higher than the upperproduction limit (i.e., capacity), PU. Then, production cannot be less than the lower production limit, PL.A restriction on volume flexibility is usual in process industries that have to run their equipment at highutilization rates to recoup their capital investment. Lastly, the lower production limit may be undercut ifnot enough production inputs (i.e., nylon) are available:

Pt = min[max(min[pf − (ICt − ICS), PU], PL), IVt + IRt ], (1)

where Pt is the production volume during period t, Pf the production forecast at 90% service level = 184million lbs of carpet, ICt the carpet inventory level at beginning of period t, ICS the carpet safety stock= 10 million lbs, PU the upper production limit = production target (163 million lbs) + 15% = 187million lbs, PL the lower production limit = production target (163 million lbs) −15% = 139 millionlbs, IVt the amount of virgin nylon on-hand at the beginning of period t and IRt the amount of recyclednylon on-hand at the beginning of period t.

In the visible system the amount of virgin nylon to be ordered is determined by the formula shown inEq. (2). The order for virgin nylon at time t, DVt , is based on the production volume at t, Pt , which isdiminished by the available nylon inventory, IRt + IVt , (minus a safety stock, INS) and the amount ofrecycled nylon that will be delivered during the next month, IRt+1:

DVt = max[(Pt ∗ NC) − (max(IRt + IVt − INS, 0) + IRt+1), 0], (2)

where DVt is the amount of virgin nylon to be ordered at the beginning of period t, to be delivered at thebeginning of period t + 1, NC the proportion of nylon in the new carpet = 30% and INS the nylon safetystock = 55.2 million lbs.

In the traditional system, the firm has no IT connection to the separation stage and, hence, does notknow how much recycled nylon inventory to expect at the beginning of the next month. Hence, the orderfor virgin nylon has to be determined independently of activities in the upstream supply chain. Tibben-Lembke and Amato [36] point out that manufacturers in the US most commonly use weighted-meanaverage, straight-line projection and exponential smoothing forecasts, with the latter being the methodmost likely offered in a forecasting software package. Due to the widespread use of exponential smoothingin practice, we use it as the method of choice in our traditional model for determining order sizes. Eqs.(3) and (4) show the algorithm used to determine how much virgin nylon is to be ordered for the nextperiod. Eq. (3) shows the two usual parts of the exponential smoothing function: last period’s forecast,weighed by alpha, and the forecast from two periods ago, weighed by (1 − �). The disadvantage ofthe standard forecasting function is, however, that it does not take into account changes in inventorylevels, demand, and other exogenous factors. Hence, we have replaced the term that usually depictsthe last period’s forecast with a term that computes how much the firm should ideally have ordered inretrospect (see Eq. (4)). This is determined by taking the difference between the last period’s demand fornylon by production and the sum of the last period’s nylon inventory (less safety stock) and incoming


recycled nylon:

DVt = �(D′Vt−1) + (1 − �)(DVt−2) with (3)D′Vt−1 = max[(Pt−1 ∗ NC) − (max(IRt−1 + INt−1 − INS, 0) + IRt ), 0], (4)

where D′Vt is the amount of virgin nylon that should have been ordered during period t.When needed for manufacturing, the appropriate amount of nylon is withdrawn and used in the new

carpet manufacturing process. All other materials used in the production of carpet are assumed to beon-hand. Recall that the carpet manufacturing industry has set itself a post-consumer recycling target of25%. To test the impact of this target, we have modeled two ways of retrieving nylon from inventory forproduction: a try system and a force system. In the try system the manufacturing operation attempts toreach a minimum recycled content (based on the use of recycled nylon) of 25% but uses virgin nylonwhenever not enough recycled nylon is on hand. In the force system the minimum 25% post-consumerrecycled content is guaranteed. As a result, if not enough recycled nylon is available, the production ratefalls to however much can be produced with 25% recycled content. Note that the proportion of nylonin the carpet’s mass (25–50%) determines the upper bound for recycled content. For the purpose of thispaper, we set the proportion of nylon to a conservative 30%. Both the traditional and the first visiblesystem attempt to reach this bound, whereas the second visible system is set up to guarantee a recycledcontent of at least 25%. Hence, we are testing a total of 3 systems.

Note that the first two models assume that carpet manufacturers will be able to sell their productsregardless of their recycled content. In contrast, the force system models the effect of purchasing guidelinessuch as introduced by federal, provincial and municipal governments across North America. In otherwords, the force system shows the impact of market restrictions imposed on products that do not meet the25% recycled content guideline. The market restrictions could affect manufacturers in two ways: first,not enough carpet could be produced if not sufficient amounts of nylon were recovered (e.g., the ratio ofrecycled carpet to new carpet was too low) and, second, enough carpet could be produced but not sold.The second scenario is very unlikely to occur. Thus, we restrict ourselves to simulating only the firstscenario.

Finished new carpet is placed into the new carpet inventory, from where it is immediately availableto customers. A safety inventory of 10 million lbs of new carpet is kept go help guard against stockouts.Given that the system is capable of maintaining this safety inventory, this is equivalent to raising theservice level from 90% to almost 97%.

3.2. Experimental design and simulation of the carpet reverse logistics model

The carpet reverse supply chain was modeled using a simulation package. It was coded using EX-TEND Vs. 5.0 [37] run on a 1.6 GHz Pentium 4 computer. Each component of the simulation model wasthoroughly tested for accuracy as it was coded. All inventories in the model were loaded with averageamounts of products or materials to speed the convergence of the model into steady-state. The model wasthen run for 12 months before data was collected for the duration of the planning horizon (360 months).The warm-up time was sufficient to run the system through four complete cumulative lead times.

Random numbers for the simulation were generated using the random number generator in EXTENDand were transformed into the appropriate distribution type. Common random number seeds were not


Table 2Design of experiment structure

Factors Levels Values

System type 3 0 (forecasting w/ alpha = 0.7)2 (Visible try)3 (Visible force)

No. of centers 2 2, 6Return standard deviation 2 50%, 90%Collection rate 2 0 (level)

1 (doubling over 30 years)Core recyclability 2 30%, 60%

Responses Average new carpet inventory levelPercentage of months with backlogsAverage production cushionAverage post-consumer recycled content

used since the simulation durations were sufficiently long (360 months). Three replications were used foreach scenario.

Recall that three experimental factors were used in the models: the number of collection centers, thestandard deviation of the collection rate, a change in the collection rate over time, the core’s recyclability,and the control system. Table 2 shows the values used for the experimental factors.

Clearly, a system can be reasonably designed only when the impact of the design choices on thesystem’s performance is measured. While there is a multitude of approaches to measuring operationalperformance, it certainly must respond to the demands of the customer, the goals of the manufacturer andthe goals set out in the carpet industry’s memorandum of understanding.

We measure the system’s performance using the percentage of periods in which backlogs occur (ameasure of customer service) and the average new carpet inventory (a cost to the manufacturer). Notethat in our models the firm must satisfy backlogs in future months. In other words, the firm is not allowedto reset its inventory balance to zero at the end of the month and treat unsatisfied demand as lost sales.Hence, new carpet inventory levels are truncated at a minimum of zero, after which backlogs are recorded.Equivalently, backlogs can never be negative, which would indicate a positive inventory level. To accountfor this break in an otherwise continuous function, we also monitor the average production cushion, whichcan be either positive or non-positive. The average production cushion equals the difference between theaverage production rate and the average demand.

To assess the system’s impact on the feasibility to reach the industry’s goals, we also measure theaverage post-consumer content in new carpet (a measure of environmental performance).

A full factorial experimental design was used to analyze the system [38]. Given the above factors,3 × 2 × 2 × 2 × 2 = 48 experimental scenarios were tested. Since each scenario was replicated threetimes, a total of 144 data sets were collected. For each variable we collected the average performancebut, for a deeper look at the system performance, we visually inspected each system’s performance overtime using graphical displays.

The results were transferred into Minitab for Windows, Vs. 12 [39]. For each scenario and responsevariable we determined the estimated relative precision of the results [40]. All precision estimates were


4.0% or better, a level considered appropriate by most researchers. The average estimated precision was0.4%. This suggests that the simulation results are very reliable due to a combination of the long runtimes and the number of replications run.

For each response variable an analysis of variance (ANOVA) was performed using the Design ofExperiments routine in Minitab. The analysis took into account both main and two-way interactioneffects. To ensure that the assumptions of normality were fulfilled, we visually checked the residual andnormal plots. No significant violations were found.

4. Results

In this section, we present the results regarding the performance variables described above. Since wetested for main and five levels of interaction effects, the ANOVAS were rather long. Hence, the ANOVAtables used in this section are abridged (please contact the authors for a full version). All effects withp-values of greater than 0.10 have been deleted. In our discussion, however, we consider only p-valuesof 0.05 or better to be significant. Also, for the sake of brevity, we will discuss in this section only thesignificant main and two-way effects, unless a higher level effect is outstanding.

4.1. Average new carpet inventory

In Table 3, we see that the only significant main effect was the system type. Significant two-wayinteraction effects included all of those related to the system variable. In addition, the combination ofcollection rate and recyclability rate was statistically significant. While these interaction effects weresignificant, however, an inspection of the effects’ adjusted mean squares revealed that they were quite

Table 3Abridged ANOVA for average new carpet inventory

Source DF Seq SS Adj SS Adj MS F P

RetSD 1 7601114 7601114 7601114 4.10 0.046Sys 2 3.5282E+10 3.5282E+10 1.7641E+10 9507.31 0.000NoCtr*ClnRte 1 6542006 6542006 6542006 3.53 0.063NoCtr*RecRte 1 4990555 4990555 4990555 2.69 0.104NoCtr*Sys 2 94031747 94031747 47015873 25.34 0.000RetSD*Sys 2 167740962 167740962 83870481 45.20 0.000ClnRte*RecRte 1 41388867 41388867 41388867 22.31 0.000ClnRte*Sys 2 102862224 102862224 51431112 27.72 0.000RecRte*Sys 2 43025718 43025718 21512859 11.59 0.000RetSD*RecRte*Sys 2 14158836 14158836 7079418 3.82 0.025ClnRte*RecRte*Sys 2 362585233 362585233 181292616 97.70 0.000NoCtr*RetSD*ClnRte*RecRte 1 8879955 8879955 8879955 4.79 0.031NoCtr*ClnRte*RecRte*Sys 2 18736712 18736712 9368356 5.05 0.008Error 96 178129367 178129367 1855514Total 143 3.6382E+10


Table 4Abridged ANOVA for % backlogs


NoCtr 1 0.01868 0.01868 0.01868 32.10 0.000RetSD 1 0.02392 0.02392 0.02392 41.12 0.000Sys 2 25.08265 25.08265 12.54132 2.2E+04 0.000NoCtr*Sys 2 0.00837 0.00837 0.00419 7.19 0.001RetSD*Sys 2 0.00745 0.00745 0.00373 6.41 0.002ClnRte*RecRte 1 0.01430 0.01430 0.01430 24.58 0.000ClnRte*Sys 2 0.01257 0.01257 0.00628 10.80 0.000RecRte*Sys 2 0.02556 0.02556 0.01278 21.97 0.000RetSD*ClnRte*Sys 2 0.00459 0.00459 0.00230 3.95 0.023Error 96 0.05585 0.05585 0.00058Total 143 25.27199

small in size as compared to the effect of the system. By far the largest interaction effect was attributableto the three-way interaction between the collection rate increase, the carpets’ recyclability and the systemtype, followed by the two-way interaction between the return standard deviation and the system.

Looking at the system effect in more detail, the forecasting system performed slightly better than thetry system (30.4 and 36.5 million lbs, respectively). This was due to the fact that the try system based itsorders for virgin nylon on its knowledge of new arrivals of recyclable nylon in the next month, combinedwith this term’s production rate. Even though the visible system had the advantage of advance knowledgeof recyclable nylon deliveries, dealing with a discrete time-type system distorts the ordering algorithm’sperformance. In contrast, the average inventory level of new carpet for the force system was almost zero(0.7 million lbs), indicating that this system had backlogs during most of the planning horizon (see nextsection).

An inspection of the interaction effects shows that the try system benefited from a decreased uncer-tainty in return flows, as facilitated by an increased number of collection centers and a reduced returnstandard deviation. The try system also slightly benefited from an increase in the collection rate overtime. In contrast, the forecasting system showed increased inventory levels when the return variabilitywas decreased. This may have been an effect of having backlogs more frequently, rather than a betterinventory management. One 3-level interaction effect is also notable. In particular, with a constant col-lection amount over time and a recyclability level of 30% the try system performed significantly betterthan average, with an average inventory level of more than 2 million lbs lower than average. (Note thatthe average inventory level is heavily influenced by the force system, see next section.) In contrast, underthe same circumstances that forecasting model had an average inventory level that was about 1 millionlbs higher than average.

4.2. Backlogs

Table 4 displays the abridgedANOVA for the average percentage of backlogs. One could have expectedthat the results for backlogs would be similar to those of new carpet inventory. Indeed, the system typewas again the most significant factor influencing performance, accounting for the majority of the total


SystemReturn SDNoCtrs1.0

0.8

0.6

0.4

0.2

Bac

klog

s [%

]

Average = 39.6%

2 6 0.5 0.9 0 2 3

Fig. 2. Significant main effects for backlog frequency.

variation in the data. The force system started out with a much lower production rate than necessary tofill demand, due to the lack of sufficient quantities of old carpets (i.e., recyclable nylon) and, for themost part, never made up the backlog (in 30 years!). This resulted in an average backlog of 98.5% (seeFig. 2). The forced system fared better only with an increase in the recyclability of cores and a decrease inthe standard deviation of returns, decreasing the frequency of backorders by almost 4 percentage points(see Fig. 2). Of this amount, the increase in the collection rate accounted for about 3 percentage points.Surprisingly, an increase in the cores’ recyclability had a negative impact on backlogs, increasing theirfrequency by 1.3 percentage points. This, however, went hand in hand with a decrease in the average newcarpet inventory. Note that in Fig. 2, the average backlog frequency was again strongly influenced by theresults of produced by system 3. For the try and traditional system alone, the average backlog equaled10.2% and was in line with the targeted customer service level.

Also significant is that the forecasting system fared slightly better than the try system, with 6.5%backlogs as compared to 13.8%. This was surprising particularly since this result did not correlate withthe average new carpet inventory levels. Specifically, the forecasting system ended up not only with thelower backorder rate, but also with the lower average inventory, resulting in an overall better systemperformance. These findings will be discussed in more detail in the next section.

Two more main effects had an impact on system performance: the number of collection centers andthe standard deviation of returns. As could be expected, a higher number of centers or a lower standarddeviation improved customer service level, resulting in a difference of about 2% points each. Note that,while these performance differences are dwarfed by those of the system type in Fig. 2, a 2% differenceis substantial in a commodity market such as carpets.

Another counter-intuitive result was the interaction between the recyclability rate and the collectionrate (see Fig. 3). One might have expected that as either increases, the frequency of backlogs woulddecrease. The least backlogs were observed, however, only when the levels of both factors were eitherhigh or low. If only one factor level increased, the backlogs increased by at least one percentage point.

4.3. Average production cushion

The results of analyzing the average production cushion should be capable of helping interpret thepartially counter-intuitive results presented above. Table 5 shows the abridged ANOVA for the production


01

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39

40

41

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60

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100

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ogs

[%]

System

Fig. 3. Important interaction effects on the frequency of backlogs.

Table 5Abridged ANOVA for average production cushion


ClnRte 1 4929954789 4929954789 4929954789 8262.04 0.000RecRte 1 1.8885E+10 1.8885E+10 1.8885E+10 3.2E+04 0.000Sys 2 1.0049E+11 1.0049E+11 5.0243E+10 8.4E+04 0.000ClnRte*RecRte 1 346600433 346600433 346600433 580.86 0.000ClnRte*Sys 2 9926534509 9926534509 4963267254 8317.87 0.000RecRte*Sys 2 3.8302E+10 3.8302E+10 1.9151E+10 3.2E+04 0.000ClnRte*RecRte*Sys 2 703885338 703885338 351942669 589.82 0.000NoCtr*RetSD*ClnRte*RecRte 1 2105683 2105683 2105683 3.53 0.063NoCtr*RetSD*ClnRte*RecRt*Sys 2 4066125 4066125 2033063 3.41 0.037Error 96 57283120 57283120 596699Total 143 1.7365E+11

cushion. Clearly, the largest effects relate again to the system type, the cores’ recyclability, and theinteraction between the collection rate and the system. These effects are discussed in the followingparagraphs.

Fig. 4 shows the significant main effects for the production cushion. Most numbers are negative, mean-ing that demand was, on average, larger than production. As could be expected, the systems performedbetter with an increasing collection rate or recyclability level. The traditional and try systems outper-formed the force system (i.e., they were closer to zero, where production and demand are balanced)which, because of the initially low collection rate, is understandable. These results show, however, thatthe effect of IT visibility is of limited impact compared to the other factors to be dealt with.

These main effects are strongly mediated by higher level interactions. Fig. 5, showing the significanttwo-way interactions, illustrates that an increase in either the collection rate or the recyclability havea large positive effect on the force system since, either way, more recyclable nylon becomes available.The picture is even more differentiated, however. Even the force system is capable of showing a positiveproduction cushion if both the collection rate and the recyclability increase simultaneously. This combineddifference increases the production cushion from −103 to 0.85 million lbs. In contrast, a change in thesefactors has almost no effect on either the forecasting or try system.


ClnRte RecRte Sys

-48000

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0 1 0.3 0.6 0 2 3

Fig. 4. Significant main effects for production cushion.

-80000

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Fig. 5. Significant interaction effects for production cushion.

4.4. Average recycled content

Table 6 and Fig. 6 show the interactions for recycled content. Again, the data behaved largely as ex-pected, with the major factors having an effect that was dwarfed by the overall system effects. Surprisingly,the return variability had an effect on the recycled content, if very small.

Again, the main effects were mediated by higher level effects, mainly involving the collection rate,recycling rate, and system type. At the high end of the recycled content scale we found the force systemwith increasing collection and recyclability rates as well as a low return variability (i.e., number of centersand return standard deviation). This system had an average recycled content rate of over 25%. On the lowend were the forecasting and try systems, with steady collection rates and a low recyclability of cores(about 9%).


Table 6Abridged ANOVA for average recycled content


NoCtr 1 0.000056 0.000056 0.000056 7.60 0.007RetSD 1 0.000052 0.000052 0.000052 7.02 0.009ClnRte 1 0.037123 0.037123 0.037123 5035.53 0.000RecRte 1 0.157672 0.157672 0.157672 2.1E+04 0.000Sys 2 0.257634 0.257634 0.128817 1.7E+04 0.000NoCtr*Sys 2 0.000081 0.000081 0.000040 5.49 0.006ClnRte*RecRte 1 0.000631 0.000631 0.000631 85.53 0.000ClnRte*Sys 2 0.015678 0.015678 0.007839 1063.34 0.000RecRte*Sys 2 0.070250 0.070250 0.035125 4764.54 0.000NoCtr*RecRte*Sys 2 0.000064 0.000064 0.000032 4.32 0.016RetSD*ClnRte*Sys 2 0.000068 0.000068 0.000034 4.62 0.012RetSD*RecRte*Sys 2 0.000047 0.000047 0.000024 3.21 0.045ClnRte*RecRte*Sys 2 0.001405 0.001405 0.000703 95.30 0.000NoCtr*RetSD*ClnRte*Sys 2 0.000055 0.000055 0.000027 3.71 0.028NoCtr*RetSD*RecRte*Sys 2 0.000039 0.000039 0.000020 2.67 0.074NoCtr*RetSD*ClnRte*RecRt*Sys 2 0.000052 0.000052 0.000026 3.50 0.034Error 96 0.000708 0.000708 0.000007Total 143 0.541745

SysRecRteClnRteRetSDNoCtr25.0

22.5

20.0

17.5

15.0Avg

. Rec

ycle

d C

onte

nt [%

]

Average = 19%

2 6 0.5 0.9 0 1 0.3 0.6 0 2 3

Fig. 6. Significant main effects for average recycled content.

We also found that the recycling rate increased as either the number of collection centers increasedor the return standard deviation decreased. In both cases, the essential issue is to keep the return flowssteady since they are easier to predict and schedule at a constant rate. As expected, when the collection orrecycling rates increased, the average recycling content increased more dramatically. These findings makeclear that firms have to be concerned with the management of their return flows to take some uncertaintyout of them and develop them over time as discussed by Fleischmann et al. [1].


5. Discussion of results

5.1. Critical factors for the design of a carpet RL supply chain

The results from the simulation clearly indicate several factors US carpet manufacturers can directlyaddress 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 RLnetwork.

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 limited differences between the traditional system (forecasting usingexponential smoothing) and the visible try system. This shows that the impact of supply chain visibilitythrough the use of IT is of limited impact in this situation. While visibility allowed the model firms tomanage their recyclables better and as a result offer a higher recyclable content in their new carpets, thiswas traded off with a slightly worse system performance (higher inventory and backlogs) as comparedto the forecasting system. Recall that commercial RL software is not available [31] and very few firmshave been successful at developing their own solutions [32]. Since the advantages of IT visibility wereclearly dwarfed by the other factors in the simulation, carpet manufacturers should look to other optionsfirst to improve RL performance.

We have seen in the simulation that Points (1a and b) and interconnected. The increase in the numberof collection centers has a risk pooling effect that can reduce the variation in return flows. This makesthe return flow steadier, easier to predict and better to schedule, as witnessed in a higher recycled contentin the visible over the forecasting models. In addition, as the collection or recycling rates increased, theaverage recycling content increased more dramatically, for obvious reasons. These findings make it clearthat firms have to be concerned with the management of their return flows to minimize uncertainty as wellas develop return flows over time as suggested by Fleischmann et al. [1]. With respect to management ofvariability, special seasonal forecasting methods may be used to take into account the product’s life cycledistribution as well as expected return rates during the return cycle [26]. To do this successfully, however,product-level data will have to be collected, including the composition and distributions of life cycles forcarpets. For example, consider the EcoWorx carpets that were introduced in 1999. With an anticipatedlifetime ranging from 10 to 15 years, Shaw can expect significant reverse flows starting in 2006 or2007 [41].

To develop return flows over time, carpet manufacturers may also want to think about regulations,contractual or marketing incentives for the user (e.g., acquisition pricing or information, see [42]). Man-ufacturers could lobby for regulation requiring customers to return spent carpet, much like the lawscurrently in effect in Europe. In the current political climate in the US, however, the likelihood of thispossibility seems remote. Second, recall that the majority of carpet returns are related to insurance claims.For example, after water damage in an office building, an insurance company pays to have all the carpetreplaced. If the insurance company either contractually required their vendors to recycle the carpet orprovided a financial incentive to do so, this would greatly help to increase return flows. Increasing the


-15,800,000

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Fig. 7. Force system dynamics with flat return rates.

number of collection centers to make it more convenient for users to drop of their carpet could alsobe beneficial. The third option is to provide market incentives [28]. Typical incentives include deposits,credits towards the purchase of new product, and cash incentives. Another incentive, used by Xerox forcopiers and Interface for carpet tiles, is to lease products in order to control quality and return flows moreprecisely. While leasing is an economical option for Xerox, however, it is not for Interface, due to the lowresidual value of spent carpet tiles [43].

Collection and distribution routing also have an effect on RL performance. Routing is influenced by thetype of reverse logistics network (in this case, a bulk recycling network, see [1]) which, due to the typicallycapital intensive recycling equipment is centralized and most efficient if large loads can be shipped (lowvalue density of the product). Fishbein [43] points out that, when an industry is held responsible for takingback cores, it typically forms a producer responsibility organization. This organization takes back andrecycles cores on behalf of its members. Having an industry organization manage return flows, ratherthan each producer by itself, allows for the setup of efficient reverse logistics and recycling systems.This is particularly important since carpet falls into the bulk recycling category. Apart from these options,carpet manufacturers can re-design their products to increase their recyclability. As seen in the simulation,doubling the core recycling rate from 30% to 60% nearly doubles the rate of recycled content. This showsthat if US carpet manufacturers are to meet their goals by 2012, they must invest in product design andthe best recycling technology available.

It must be noted that the efforts for taking back carpet may be motivated by incentives other thanreclaiming production inputs. By behaving responsibly to the environment, carpet manufacturers mayalso achieve a cost reduction due to the lower cost of recycled inputs, meet an increasing market demandfor carpet with recycled content, gear up for meeting demand for recycled nylon by other industries(e.g., car manufacturing), or preempt legislation targeted towards making manufacturers legally liable forcores [43].

Yet the final issue still remains to be answered: given all the options open to carpet manufacturers, is itstill possible to meet their 2012 goals? Our results show that it is unlikely that this goal will be achieved.Fig. 7 shows the demand, new carpet inventory and backorder positions for the force system, whichguarantees 25% post-consumer recycled content, assuming a steady collection rate (30% of new carpet


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Carpet Inventory

Demand

Fig. 8. Force system dynamics with return rates doubling over 30 years.

production) and 60% recyclability of cores. This is a very optimistic scenario due to the high core recyclingrate. Yet, the carpet manufacturer is never able to satisfy demand, and the backlog keeps mounting. Notethat this is a worst-case scenario, in which carpets could not be sold unless they contained at least 25%recycled content. Whereas many governments do adhere to this purchasing policy, most non-governmententerprises do not and, thus, facilitate the sale of carpet without little or no recycled content.

Now let us assume that carpet manufacturers will be able to develop return flows and collection rateswill increase (at steady demand for new carpet) over the time at a rate of about 2.4% per year. Fig. 8shows the performance of the force system under that scenario. Backorders pile up initially due to a lackof sufficient amounts of recyclables. After about 6 years the firm starts catching up, and after a decade theproduction has caught up with the backlog, at which point the recycled content rate increases from 25%to 30% (i.e., the amount of nylon used in the carpet). At that time no new nylon is purchased anymoreand the manufacturer has to start looking around for other firms to sell its recyclable nylon since therecyclable nylon inventory keeps increasing. Note that, if the content of nylon in the carpet is increasedto 50% or more, the point of time at which recycled content has to be dispersed is delayed.

In comparison, Fig. 9 shows the traditional system with an increasing collection rate and 60% recycling.Recall that in this system the manufacturer may use any quantity of recycled or virgin nylon. Hence, thissystem starts out with a recycled content rate of about 15% and slowly increases it over time as morerecyclable nylon becomes available. A recycled content of 20% is reached after about 10 years, a rateof 25% after about 25 years. This is because, in contrast to the force system, customer demand can besatisfied throughout the planning horizon, but at the expense of the recycled content rate. The results ofthe try system are similar to this, with a slightly higher recycled content rate.

These results reaffirm that, in order to meet its 2012 goals, the US carpet industry must start preparingfor the goal instantly. Manufacturers need to develop the rate of return flows immediately while increasingthe recyclability of their products.

6. Conclusions and future research

The carpet industry has agreed to divert a substantial proportion of spent carpet from landfills by2012, amounting to 40% of return flows. If the industry is to be able to reach this goal, it must clearly


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Fig. 9. Traditional system dynamics with return rates doubling over 30 years.

expand its RL supply chain capabilities. This will likely entail greatly increasing the number of collectioncenters, providing more convenient opportunities for residents and contractors to turn in their carpet forrecycling. It also means that manufacturers must invest in technology. Our simulation results indicatedthat investment is better placed in recycling technology [or product R&D that] increase recycling ratesthan in IT [that] make[s] the RL network visible. While IT-based supply chain visibility does have somebenefits, the visibility does not necessarily have to reach to the collection centers. If lead times are shortand deliveries frequent enough, a data link between the manufacturing and recycling facilities would besufficient. With longer lead times and less frequent deliveries, however, it might be necessary to link thecollection centers as well so data is available well in advance.

Our analysis also showed that even in the best scenario it is unlikely that the carpet industry can meetits goals without a large increase in the rate of return flows and recyclability of its products. Recycling,product (re-)design and return flow development activities must start immediately. The key question is,however, how higher return flows can be accomplished. One option is to implement legal requirements forreturn flows such as the Europeans have. The second is to provide financial incentives to groups such asthe insurance industry who have control over large amounts of carpet returns. Collection centers shouldbe easily accessible, as previously shown by Ammons et al. [35]. Only if the North American carpetindustry becomes immediately serious about its commitment to the municipalities and the environmentwill it be able to accomplish the goals set forth in its 2002 memorandum of understanding.

Acknowledgements

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

References

[1] Fleischmann M, Krikke HR, Dekker R, Flapper SDP. A characterisation of logistics networks for product recovery. Omega2000;28:653–66.

[2] Dowlatshahi S. Developing a theory of reverse logistics. Interfaces 2000;30(3):143–55.


[3] Minner S. Multiple-supplier inventory models in supply chain management: a review. International Journal of ProductionEconomics 2003;81/82:265–79.

[4] Ferguson N, Browne J. Issues in end-of-life product recovery and reverse logistics. Production Planning & Control2001;12(5):534–47.

[5] Hillegersberg JV, Zuidwijk R, Nunen JV. Supporting return flows in the supply chain. Communications of the ACM2001;44(6):74–9.

[6] EPA. Municipal waste in the United States: 2000 facts and figures. US Environmental Protection Agency; 2000.[7] US Department of Energy. Greening federal facilities, 2nd ed., vol. 2004. Washington, DC; 2001.[8] Friedman E, Deegler M. Purchasing power: the Massachusetts environmental procurement program. Government Finance

Review 2002;18(1):14.[9] Hughes M. Green procurement: becoming a way of life. Summit 2003;6(4):4–5.

[10] US Carpet Industry. Memorandum of understanding for carpet stewardship. 8 January 2002.[11] Kokkinaki A, Zuidwijk R, Van Nunen J, Dekker R. Information and communication technology enabling reverse logistics.

In: Dekker R, Fleischmann M, Inderfurth K, Van Wassenhove LN, editors. Reverse logistics: quantitative models forclosed-loop supply chains. Berlin: Springer; 2004. p. 381–405.

[12] Segars JW, Bradfield SL, Wright JJ, Realff MJ. EcoWorx, Green engineering principles in practice. Environmental Science& Technology 2003;37(23):5269–77.

[13] REVLOG; http://www.fbk.eur.nl/OZ/REVLOG/PROJECTS/TERMINOLOGY/def_reverselogistics.html, 2004.[14] Daugherty PJ, Autry CW, Ellinger AE. Information support for reverse logistics. Journal of Business Logistics

2002;23(1):67–79.[15] Daugherty PJ, Autry CW, Ellinger AE. Reverse logistics: The relationship between resource commitment and Program

Performance. Journal of Business Logistics 2001;22(1):107–23.[16] Kokkinaki AI, Dekker R, Koster MB, Pappis C, Verbeke W. E-business models for reverse logistics: Contributions and

Challenges. Paper presented at the proceedings of the international conference on information technology: coding andcomputing; 2002.

[17] Spengler T, Puechert H, Penkuhn T, Rentz O. Environmental integrated production and recycling management. EuropeanJournal of Operational Research 1997;97:308–26.

[18] Barros AI, Dekker R, Scholten V. A two-level network for recycling sand: a case study. European Journal of OperationalResearch 1998;110(2):199–214.

[19] Jayaraman V, Guide VDR, Srivastava R. A closed-loop logistics model for remanufacturing. The Journal of the OperationalResearch Society 1999;50(5):497–508.

[20] Realff MJ, Ammons JC, Newton D. Carpet recycling: determining the reverse production system design. The Journal ofPolymer–Plastics Technology and Engineering 1999;38(3):547–67.

[21] Georgiadis P, Vlachos D. The effect of environmental parameters on product recovery. European Journal of OperationalResearch 2004;157:449–64.

[22] Fleischmann M, Beullens P, Bloemhof-Ruwaard JM, Van Wassenhove LN. The impact of product recovery on logisticsnetwork design. Production and Operations Management 2001;10(2):156–73.

[23] Fleischmann M. Reverse logistics network structures and design. In: Guide VDR, Van Wassenhove LN, editors. Businessaspects of closed-loop supply chains. Pittsburgh: Carnegie Mellon University Press; 2003. p. 117–48.

[24] Greene K. Firm finds destiny in recycling rugs. Wall Street Journal, 1997; S3.[25] Nagal C, Meyer P. Caught between ecology and economy: end-of-life aspects of environmentally conscious manufacturing.

Computers & Industrial Engineering 1999;36(4):781–92.[26] Toktay LB, van der Laan E, de Brito MP. Managing product returns: the role of forecasting. In: Dekker R, Fleischmann

M, Inderfurth K, Van Wassenhove LN, editors. Reverse logistics: Quantitative Models for Closed-Loop Supply Chains.Berlin: Springer; 2004. p. 45–64.

[27] Simchi-Levi D, Kaminsky P, Simchi-Levi E. Designing and managing the supply chain. 2nd ed., New York: McGraw-Hill/Irwin; 2002.

[28] GuideVDR,Van Wassenhove LN. Managing product returns for remanufacturing. Production and Operations Management2001;10(2):142–55.

[29] de Brito MP, Flapper SDP, Dekker R. Reverse logistics: a review of case studies. Working paper, Erasmus UniversityRotterdam, Rotterdam; 2002.

http://www.fbk.eur.nl/OZ/REVLOG/PROJECTS/TERMINOLOGY/defprotect LY1extunderscore reverselogistics.html


[30] Edwards P, Peters M, Sharman G. The effectiveness of information systems in supporting the extended supply chain.Journal of Business Logistics 2001;22(1):1–26.

[31] Caldwell B. Reverse logistics. Informationweek; 1999.[32] Rogers DS, Tibben-Lembke RS. Going backwards: reverse logistics trends and practices. Pittsburg, PA: Reverse Logistics

Executive Council; 1999.[33] Kelle P, Silver EA. Purchasing policy of new containers considering the random returns of previously issued containers.

IIE Transactions 1989;21(4):349–54.[34] Inderfurth K, van der Laan E. Leadtime effects and policy improvement for stochastic inventory control with

remanufacturing. International Journal of Production Economics 2001;71(1-3):381–90.[35] Ammons JC, Realff MJ, Newton D. Reverse production system design and operation for carpet recycling. Working paper,

Georgia Institute of Technology, Atlanta, GA; 1997.[36] Tibben-Lembke RS, Amato HN. Replacement parts management: the value of information. Journal of Business Logistics

2001;22(2):149–64.[37] ImagineThat Inc. EXTEND; Vs. 5, 2000; .[38] Box GEP, Hunter WG, Hunter JS. Statistics for experimenters.An introduction to design, data analysis, and model building.

New York: Wiley; 1978.[39] Minitab Inc. Minitab; Vs. 12.21 1998; .[40] Bienstock C. Sample size determination in logistics simulations. International Journal of Physical Distribution & Logistics

Management 1996;26(2):43–50.[41] EPA; 2003 Designing Safer Chemicals Award; www.epa.gov/greenchemistry/dsca03.html.[42] Kiesmüller G, Minner S, Kleber R. Managing dynamic product recovery. In: Dekker R, Fleischmann M, Inderfurth K,

Van Wassenhove LN, editors. Reverse logistics: quantitative models for closed-loop supply chains. Berlin: Springer; 2004.p. 221–48.

[43] Fishbein BK. Carpet take-back: EPR American style. Environmental Quality Management 2000;10(1):25–36.

http://www.imaginethatinc.comhttp://www.minitab.comhttp:www.epa.gov/greenchemistry/dsca03.html

Assessing performance and uncertainty in developing carpet reverse logistics systemsIntroductionCritical parameters for a carpet reverse logistics networkTypes of reverse logistics networksCarpet returnsInformation technology for reverse logistics supportProduct design

Experimental methodologySimulation scenarioExperimental design and simulation of the carpet reverse logistics model

ResultsAverage new carpet inventoryBacklogsAverage production cushionAverage recycled content

Discussion of resultsCritical factors for the design of a carpet RL supply chain

Conclusions and future researchAcknowledgementsReferences

assessing performance and uncertainty in developing carpet ... · 2. critical parameters for a...

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