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Economic and Environmental Assessment ofRemanufacturing Strategies for Product + Service

Firms

Anton OvchinnikovDarden School of Business, University of Virginia, Charlottesville, Virginia 39040, USA, [email protected]

Vered BlassFaculty of Management, Tel Aviv University, Tel Aviv, Israel, [email protected]

Gal RazDarden School of Business, University of Virginia, Charlottesville, Virginia 39040, USA, [email protected]

T his article provides a data-driven assessment of economic and environmental aspects of remanufacturing for prod-uct + service firms. A critical component of such an assessment is the issue of demand cannibalization. We therefore

present an analytical model and a behavioral study which together incorporate demand cannibalization from multiplecustomer segments across the firm’s product line. We then perform a series of numerical simulations with realistic prob-lem parameters obtained from both the literature and discussions with industry executives. Our findings show thatremanufacturing frequently aligns firms’ economic and environmental goals by increasing profits and decreasing the totalenvironmental impact. We show that in some cases, an introduction of a remanufactured product leads to no changes inthe new products’ prices (positioning within the product line), implying a positive demand cannibalization and a decreasein the environmental impact; this provides support for a heuristic approach commonly used in practice. Yet in other cases,the firm can increase profits by decreasing the new product’s prices and increasing sales—a negative effective canni-balization. With negative cannibalization the firm’s total environmental impact often increases due to the growth in newproduction. However, we illustrate that this growth is nearly always sustainable, as the relative environmental impactsper unit and per dollar rarely increase.

Key words: remanufacturing; demand cannibalization; product + service firms; environmental impactHistory: Received: June 2012; Accepted: February 2013 by Daniel Guide, after 2 revisions.

1. Introduction

For remanufacturing to be truly sustainable, it mustachieve two goals: increase the firm’s profits anddecrease its environmental impact. In this article, weanalyze economic and environmental impacts ofremanufacturing to understand when these two goalsalign. We present a general framework for such ananalysis in the context of a product line offered by aproduct + service firm and conduct a detailed data-driven analysis motivated by the following example.On June 24th, 2010, Apple, Inc. launched its much

anticipated iPhone 4. This was the fourth generationof the iPhone (following the original iPhone, iPhone 3,and iPhone 3GS), and, together with its carrier part-ner, AT&T, Apple took preorders for 600,000 iPhone 4handsets on the first day preorders were available,the highest one-day preorder volume it has ever taken

(Ogg 2010). For AT&T this meant that in the fall of2010, it was offering a product line that consisted(among other things) of the new iPhone 4—a “high-end” device—new “low-end” devices such as thePantech Breeze II, as well as various voice and dataplans that accompany these devices, reflecting theproduct + service nature of firms like AT&T. But inaddition to these new devices, AT&T also faced astream of used products: the previous generationiPhones from customers who wanted to upgrade theirdevices to iPhone 4 as well as the returns from someof the iPhone 4 customers who for various reasonswere not satisfied with their recent purchase. The for-mer clearly needed some refurbishing, since theywere in use for typically close to 2 years, but, accord-ing to industry norms (Ovchinnikov 2011), even thelatter nearly new devices could only be sold as refur-bished.1 In either case, the speed of technological

Vol. 0, No. 0, xxxx–xxxx 2013, pp. 1–18 DOI 10.1111/poms.12070ISSN 1059-1478|EISSN 1937-5956|13|00|0001 © 2013 Production and Operations Management Society

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innovation was so fast that those used items were notnear the end of their physical life (Kogan 2011) andcould be profitably resold. An important question is:How should AT&T integrate these refurbisheddevices in its product line of new products in a waythat balances profits with environmental impact?Interestingly, there is no obvious definition of what

constitutes a good balance between profits and envi-ronmental impact. In the ideal situation, the firm’sprofit increases but its total environmental impactdecreases; we refer to this case as the absolute positiveenvironmental effect. The problem, though, is that ifthe increase in profits comes from growth (e.g., manu-facturing and selling more units), then the firm’s totalenvironmental impact might actually increase, creat-ing an absolute negative environmental effect. Hence,we also consider two relative measures of the balancebetween profits and environmental impact: relativeper unit and relative per dollar. Such relative mea-sures provide additional insight into the balancebetween profits and environmental impact by evalu-ating if the firm’s growth is sustainable as they disen-tangle the growth in the volume of the firm’s salesfrom the effect of substituting products within theproduct line.Economic and environmental assessment of reman-

ufacturing has been the focus of both OperationsManagement and Industrial Ecology literatures; seesection 2 for review. Researchers in these disciplinesagree that the central issue in such an assessment isthat of demand cannibalization. Our article expands theanalysis of demand cannibalization along two impor-tant dimensions: multiple demand segments (demandside) and cannibalization along a product line (supplyside).On the demand side, we consider a multi-segment

random utility demand model, which allows us tocapture heterogeneity in consumers’ attitudes towardthe price, type of product (new vs. remanufactured),and type of service. The latter is particularly salientfor product + service firms like AT&T: such firms sellnot only products but also services that accompanythose products2; hence, introduction of remanufac-tured products could be an effective strategy fortargeting customer segments that are not willing to paya premium price for new high-end (NH) products,but are willing to pay for a high-end service. On thesupply side, we consider not only demand cannibal-ization of the “parent” new product—a setup ana-lyzed in most previous works—but also demandcannibalization along the product line that consists ofnew and remanufactured high-end products and anew low-end (NL) product offered with high- or low-end services, respectively.In our model, the firm optimizes the composition of

the product line (with or without the remanufactured

product), and the prices of the products in the prod-uct line. We investigate two pricing strategies: globaloptimization—a comprehensive approach where thefirm possibly adjusts the prices of the new productswhen the remanufactured product is introduced—and a heuristic, when the firm first optimizes theprices of the new products as if there is no remanufac-tured product, and then given those prices, it opti-mizes the price of the remanufactured product. Sucha heuristic, although potentially suboptimal, appearsto be a rather common approach in practice; seeOvchinnikov (2011).We estimate the demand model from consumer

choice data and, on the basis of the problem parame-ters drawn from both the literature and conversationswith industry executives (AT&T 2009, 2010), performa series of numerical simulations, documenting theresulting optimal prices, production/remanufactur-ing quantities, profits, and environmental impact.For the economic assessment, we find that remanu-

facturing was profitable in all instances we consid-ered. The heuristic was frequently optimal; that is, itwas profitable to introduce the remanufactured prod-uct into the firm’s product line, but it was not optimalto change the new products’ prices. In such cases,remanufactured product cannibalized some newproduct sales. When the heuristic was suboptimal,however, the firm was able to obtain higher profits byre-optimizing the product line and changing the newproducts’ prices. Interestingly, in most such instances,the quantity of the new products manufactured/soldincreasedwhen the remanufactured product was intro-duced—the effect we refer to as the negative effectivecannibalization and explain in section 5.2.For the environmental assessment, we find that in

the majority of instances remanufacturing resulted ina positive absolute environmental effect—a “win-win” situation when profit increased and environ-mental impact decreased, also known as the absolutedecoupling; see section 2. These instances include allthe cases when the heuristic was optimal and some ofthe cases with negative effective cannibalization. Yetin other instances, we observed a negative absoluteenvironmental effect caused by the growth in thefirm’s sales: an increase in the new production as aresult of the negative effective cannibalization. Whatis very important, however, is that in nearly allinstances the growth was sustainable: the energy useper unit of the product as well as the energy perdollar of profit decreased—positive relative environ-mental effects, also known as relative decoupling.We finally examine the sensitivity of the above-

mentioned effects to various model parameters andobserve that the absolute effects are most sensitive tothe recovery rates and technological progress, whilethe relative effects are most sensitive to the changes in

Ovchinnikov, Blass, and Raz: Economic and Environmental Assessment of Remanufacturing2 Production and Operations Management 0(0), pp. 1–18, © 2013 Production and Operations Management Society

energy consumption at various stages in the products’life cycle.Overall, our findings reveal that from an eco-effi-

ciency optimization perspective, remanufacturing isusually a highly beneficial activity: it increases thefirm’s profits and in most cases decreases its totalenvironmental impact. Our results also demystifytwo common assertions: we see no support for theexistence of green segment consumers (who preferrefurbished products based on their assumed envi-ronmental benefits), yet show that an introduction ofa remanufactured product could lead to an increasein the new products’ sales—a negative effective canni-balization. Our results also suggest that remanufac-turing may not always lead to a decrease in the firm’stotal environmental impact: because of the negativeeffective cannibalization, remanufacturing may leadto a significant growth; however, as we show, thisgrowth is nearly always sustainable as the environ-mental impacts per unit of the firm’s product or perdollar of profit decrease.We finally note that while the numerical estimates/

parameters in our article are specific to the cell phoneindustry, the overall approach and framework arequite general and can be applied to analyzing eco-nomic and environmental aspects of remanufacturingfor other industries.In the remainder of the article, section 2 reviews

the literature, section 3 presents the analytical model,and section 4 describes the behavioral study we usedfor demand estimation. Section 5 discusses theparameters and results of the numerical simulations.Section 6 presents a sensitivity analysis. Section 7summarizes our discussion and concludes the article.

2. Literature Review

Remanufacturing (or refurbishing) is a practice of col-lecting and reprocessing used products to the “likenew” condition and then selling such products in themarketplace (see Ferguson and Souza 2010 for a com-prehensive discussion of remanufacturing). Althoughinitially viewed as a cost-center addressing the needto deal with product returns, today many practition-ers and academics view remanufacturing as an inno-vative business strategy that combines elements ofmarketing (Atasu et al. 2008), product-line design(Krishnan and Lacourbe 2010), and environmentalstrategies (Orsato 2009). Studies of remanufacturingspan both Operations Management and IndustrialEcology bodies of literature, which with some overlapcover its economic and environmental performance.Because both bodies of literature are very large, weonly review works that are directly relevant to ours.For the economic assessment of remanufacturing,

“[t]he central question manufacturers seem to face is,

‘When do benefits from remanufacturing outweighlosses from cannibalization?’” (Atasu et al. 2008).Guide and Li (2010) echo this statement by writingthat the “managers at OEMs that offer remanufac-tured products revealed that they concede cannibal-ization may occur,” while also stating that “[w]hethercannibalization actually decreases the overall profit-ability of the firm is a subject of much debate.”Demand cannibalization is a broad issue that mani-

fests itself far beyond remanufacturing and wasexamined in depth in the marketing literature; forexample, see Van Heerde et al. (2010) and referencestherein. As they conclude, while managers aretypically aware of the possibility of cannibalization,they are “less clear on how to quantify the size of thecannibalization” because in practice both within-category and between-category demand cannibaliza-tion must be considered as well as brand switchingand the actual new demand. Van Heerde et al. pres-ent an econometric model that decomposes product’sdemand into these sources. Using an application oftheir model to the introduction of a new RX300Luxury SUV to Lexus’ existing product line, theyshow that while 26% of the new SUV demand canni-balized sales of other Lexus vehicles, the other 74% ofits buyers were new to Lexus, making the RX300 ahuge success. The authors also elaborate on whatcompetition could have done to reduce demand can-nibalization, with suggestions ranging from quantify-ing the necessary price decreases to increases incompetitors’ advertising. Our approach is quite in linewith this literature: by studying/quantifying canni-balization across a product line, we too allow forwithin- and between-category cannibalization andconclude that cannibalization from introducing aremanufactured product could be profitable for thefirm overall.Within the operations management literature on

remanufacturing, demand cannibalization has beenimplicitly incorporated by many authors using theextended Hotelling (1929) line model, in which con-sumers have valuation v 2 U [0, 1] for the new pro-duct and dv for the remanufactured product. Agrawalet al. (2011) estimate d using a behavioral experiment;they also estimate a change in the valuation of thenew product once the remanufactured product isintroduced. Abbey et al. (2011) discuss consumer per-ceptions for remanufactured products. Guide and Li(2010) estimate cannibalization using eBay auctions,while Ovchinnikov (2011) estimates it via a behavioralexperiment. The latter two papers find that at theoptimal prices cannibalization is rather small. Ourarticle extends the cannibalization analysis along twodimensions: first, we consider a multi-segmentrandom utility demand model, which is much morerealistic than the Hotelling line demand model used

Ovchinnikov, Blass, and Raz: Economic and Environmental Assessment of RemanufacturingProduction and Operations Management 0(0), pp. 1–18, © 2013 Production and Operations Management Society 3

by most researchers, and, second, we consider canni-balization along a product line.The estimation of cannibalization is also important

from an environmental perspective, since displace-ment of new production is the core assumption in cal-culating environmental gains of remanufacturing (forexample, EPA 2006, Kerr and Ryan 2001). Therefore,with little cannibalization, the environmental gainsmight be questioned. Most Operations Managementpapers take a somewhat simplified view withoutdetailed analysis of the environmental impact. Forexample, Thierry et al. (1995) write that “the potentialbenefits of remanufacturing are… a reduction in theoverall environmental impact” and Atasu et al. (2008)argue that “[b]ecause it [remanufacturing] reducesboth the natural resources needed and the waste pro-duced, remanufacturing helps reduce the environ-mental burden.” These authors take as given thatthe more that is remanufactured the better is theenvironmental performance. A more comprehensiveapproach, the one used in this article and some others,is to explicitly assess the environmental impact alongthe life cycle of the product: the Life-Cycle Assess-ment (LCA) approach.One such work is Quariguasi-Frota-Neto and

Bloemhof (2011), who compared the eco-efficiencies ofnew and remanufactured mobile phones and comput-ers. To do so, they estimated what they called “cumu-lative energy demand” based on the published LCAstudies from the early 2000s. Our measure, totalenergy consumption, is equivalent to theirs, but weuse more recent industry LCA data. They found thatremanufactured products reduce the amount ofenergy used compared over their life span when thesecond lifetime of the product is smaller than the first.From an eco-efficiency perspective combining eco-nomic and environmental views, they argue thatremanufactured products are not always more eco-efficient—a finding consistent with our negativeenvironmental effect, but derived from a different per-spective. Agrawal et al. (2011) also integrated a morecomprehensive LCA-based approach to environmen-tal assessment, but their work is focused on leasingmodeling and not in the context of remanufacturing.Environmental assessment of remanufacturing is

also addressed in the Industrial Ecology literature, forexample, Thomas (2003, 2011) and Geyer (2004). Thatliterature, however, for the most part makes a some-what simplistic assumption about demand cannibal-ization, assuming that a remanufactured productdisplaces the need to produce a new one; that is, theyassume 100% cannibalization. More recent research inthat area, for example, Gutowski et al. (2011), lookedat the environmental benefits of remanufacturingfrom the use phase perspective, suggesting thatremanufacturing does not always improve the energy

savings. This was found to be true especially whenimprovements in energy efficiency during the usephase of the new products exceed the energy savingfrom saved materials and the manufacturing processof remanufactured products. We make a very conser-vative assumption in this regard, assuming in thebase case that the efficiency during the use phase isthe same, and then present sensitivity analyses withrespect to energy estimates.The Industrial Ecology literature also emphasizes

the concept of decoupling economic growth fromresource use and environmental impact, so far mainlyat the economy/sector level. It measures decouplingin two ways: relative decoupling refers to “when thegrowth rate of the environmentally relevant variableis positive, but less than the growth rate of the eco-nomic variable” (OECD 2001, UNEP 2011) and abso-lute decoupling, the situation when the “resource usedeclines, irrespective of the growth rate of the eco-nomic driver” (UNEP 2011). In the Operations Man-agement literature, as far as we know, decoupling hasbeen used only in Raz et al. (2013). It is easy to seethat the positive environmental effects we discussand decoupling are conceptually identical, except thatwe (and Raz et al. 2013) consider environmentaleffects on the firm level and not on the level of theentire economy or sector. Hence, to avoid possibleconfusion, we will use the term environmental effectand not decoupling throughout the article.

3. The Model

3.1. Model SchematicConsider a product + service firm with a product linethat consists of NH and NL products, offered withhigh-end (H) and low-end (L) services, respectively.For example, AT&T offers Apple iPhone (NH) withvoice & data (H), and Pantech Breeze (NL) with justvoice (L) plans. We assume (as is the case of AT&T),that the NL product cannot be offered with the H-service for technical reasons, while the NH is notoffered with L for economic reasons.Because of technological progress, newer (faster,

better, etc.) versions of NH and NL become availableperiodically. We refer to such new versions as genera-tions and denote them by i = 1, 2, …. We assume thatthe firm sells generation i products until generationi + 1 becomes available, at which point the sales ofnew generation i products discontinue and sales ofnew generation i + 1 products begin. In this article,we focus on the economic and environmental assess-ment along the life cycle of generation i of the firm’sproducts as a representative scenario for the firm’soverall business. The major decision that we study inthis article is whether in addition to NH and NL thefirm should also offer a remanufactured version of


NH, which we denote as RH, in its product linewith either H- or L-service and how it should priceit. In the context of our article such a decision rep-resents a strategic choice for the firm rather than atactical one: it either engages in remanufacturingacross multiple generations or not (as opposed to atactical decision that can be generation specific). Inpractice, to succeed in reverse logistics, firms needto establish multiple operational capabilities fromdesigning products that are easy to remanufactureto collecting used items all the way to marketingremanufactured products, etc., and our approachreflects such strategic choices.Levels of remanufacturing range from products

that were returned close to the purchase date andtherefore only need to be tested and repackaged allthe way to products that were in use for months oryears and were collected as part of a take-back pro-gram, during upgrade or disposal of the product. Ineither case, there is some lag between the sales of NHand RH. To offer a unit of RH, the firm must acquire aunit of previously sold NH, which we refer to as theremanufacturable core. To capture this lag, we assumethat the selling horizon of generation i new productsconsists of two sub-periods: i1 and i2. In period i2, thefirm collects and remanufactures some fraction of NHfrom generation i sold in period i1. These productslikely correspond to false returns (Ferguson et al.2006), remanufacturing of which is typically inexpen-sive. In the subsequent period, i + 11, the firm alsocollects some NH from generation i, (which nowcorrespond to the products that reach the end of theirinitial use) and remanufactures them (at a higher cost

than false returns) and sells in period i + 11. That is,the selling season of RH phones from generation i alsoconsists of two periods: i2, when the firm’s productline consists of NH, RH, and NL from generation i,and period i + 11, when the product line consists ofNH and NL products from generation i + 1 and RHfrom generation i. Correspondingly, in period i1 thefirm’s product line also consists of NH and NL prod-ucts from generation i and RH from generation i�1.Figure 1 illustrates the above discussion as well asprovides justification for our model setup based onthe snapshot of the product line of AT&T.Before we proceed with formal model definitions,

we make two simplifying assumptions which help toavoid cumbersome notation later in the article with-out impacting insights:

(A1) The new versions of NH and NL productsbecome available at the same time.

(A2) The product prices are constant throughoutgenerations i�1, i, and i + 1. (Note that thepricing policy of AT&T depicted in Fig-ure 1 is consistent with this assumption).

3.2. The No-Remanufacturing CaseLet pNH, pNL and cNH, cNL denote the prices and costsof the corresponding products, and let mH, mL denotethe net present values of the profit margins of the H-and L-services. This setup is consistent with the casewhen consumers sign a service contract with the firmand pay for service over time, but purchase the prod-uct upfront upon signing the contract. LetD

ijNHðpNH; pNLÞ, D

ijNLðpNH; pNLÞ and S

ijNHðpNH; pNLÞ,

Period i-12 Period i1 Period i2 Period i+11

Product generation i-1 Product generation i+1Product generation i

Figure 1 Model Schematic


SijNLðpNH; pNLÞ be the respective demands and sales in

period ij (j = 1, 2). We assume that the firm manufac-tures/procures products as needed, so that in the casewithout remanufacturing, demand ≡ sales. Then thefirm’s profit in period ij, j = 1, 2, is

pijðpNH; pNLÞ ¼ ðpNH � cNH þmHÞSijNHðpNH; pNLÞþ ðpNL � cNL þmLÞSijNLðpNH; pNLÞ:

ð1Þ

Finally, the total profit from generation i productswithout remanufacturing is

piðpNH; pNLÞ � pi1ðpNH; pNLÞ þ s� pi2ðpNH; pNLÞ; ð2Þ

where τ is the discount factor for the time value ofmoney.

3.3. The Remanufacturing CaseLet �pk, �D

ijk ð�pNH; �pNL; �pRHÞ, �S

ijk ð�pNH; �pNL; �pRHÞ, for

k = NH, RH, NL denote the prices, demands,3 andsales and let c

ijRH denote the cost of remanufacturing

(including collection and testing) for the correspond-ing products and periods, j = 1, 2. As before, let mH,mL denote the net present values (NPVs) of the profitmargins of the H- and L-services. Further, if RH is ini-tially offered with an L-service, let D be the NPV ofthe probability of an upgrade in the duration of theservice contract; the “net present value” indicates thatan upgrade can happen at an uncertain moment inthe duration of the service contract—a model featurethat was emphasized in our discussions with execu-tives at AT&T (2009, 2010).As discussed earlier, a fundamental feature of

remanufacturing is that the supply of remanufactur-able cores in period ij, and hence the sales of RH in ij(j = 1, 2), is constrained by some multiple of the NHsales in the previous periods. Specifically, from Fig-ure 1, for remanufactured products in period i1(which are of generation i�1), the supply is con-strained by the total sales of generation i�1 NH netthe quantity remanufactured in period i�12, while forremanufactured products in period i2 (which are ofgeneration i), the supply is constrained by the genera-tion i NH sales in period i1. Let bj be such multiples4

for period ij.Then the sales of RH in period ij (j = 1, 2) are equal

to the following:

�SijRHð�pNH;�pNL;�pRHÞ ¼min �D

ijRHð�pNH;�pNL;�pRHÞ;Kj

h i; ð3Þ

where, reflecting the above discussion,

K1 ¼ b1 �Si�11NH ð�pNH;�pNL;�pRHÞþ �Si�12

NH ð�pNH;�pNL;�pRHÞ�

��Si�12RH ð�pNH;�pNL;�pRHÞ

�;

ð4Þ

and

K2 ¼ b2�Si1NHð�pNH; �pNL; �pRHÞ: ð5Þ

Consider period i1. If �Si1RHð�pNH; �pNL; �pRHÞ ¼�Di1RHð�pNH; �pNL; �pRHÞ, then all remanufactured product

demand is satisfied. Otherwise, the first K1

�Di1RH

ð�Þ percent

of customers will face the choice among the threeproducts (as well as, obviously, the option to buynothing), while the rest—the overflow—will face thechoice between NH and NL only. The purchasingbehavior of the overflow customers, and this is critical,is described by the demand functions from the casewithout remanufacturing because they choosebetween two and not three products (since RH is soldout). Combining the two cases, the behavior of the

first�Si1RH

ð�Þ�Di1RH

ð�Þ percent of customers is described by the

remanufacturing case functions demand functions�Di1k ð�pNH; �pNL; �pRHÞ, k = NH, RH, NL, while the behav-

ior of the remaining 1� �Si1RH

ð�Þ�Di1RH

ð�Þ

� �, the overflow, is

described by the corresponding no-remanufacturing

case demand functions Di1k ð�pNH; �pNLÞ, for k = NH, NL.

Therefore, the expected sales for the NH and NLproducts in period ij are equal to the following:

�Sijk ð�pNH;�pNL;�pRHÞ ¼D

ijk ð�pNH;�pNL;�pRHÞ�

�SijRHð�Þ

�DijRHð�Þ

þDijk ð�pNH;�pNLÞ� 1�

�SijRHð�Þ

�DijRHð�Þ

! ð6Þ

for k = NL, NH, j = 1, 2, where �SijRHð�Þ is given in

Equation (3).The firm’s profit in period ij (j = 1, 2) is, therefore,

as follows:

�pijð�pNH; �pNL; �pRHÞ ¼ ð�pNH � cNH þmHÞ� �S

ijNHð�pNH; �pNL; �pRHÞ

þ ��pRH � cijRH þ DmH þ ð1� DÞmL

�� S

ijRHð�pNH; �pNL; �pRHÞ

þ ð�pNL � cNL þmLÞ� �S

ijNLð�pNH; �pNL; �pRHÞ: ð7Þ

So that the total profit in the case with remanu-facturing is

�pi ¼ �pi1ð�pNH; �pNL; �pRHÞ þ s� �pi2ð�pNH; �pNL; �pRHÞ; ð8Þwhere τ is the discount factor for the time value ofmoney.


3.4. The Firm’s Profit Maximization ProblemFor the case with no remanufacturing, and givenassumptions A1 and A2, the firm’s optimizationproblem is to select the prices p�NH; p

�NL which maxi-

mize pi(pNH, pNL). For the case with remanufacturing,the firm’s problem is significantly more involved.Generally speaking, because generation i RH over-laps with NH and NL products from generationi + 1, the effects of the introduction of a remanufac-tured product in one generation propagate through-out all future generations.5 At the same time, whenthe remanufacturing decision is made for generationi the firm likely does not have perfect foresight onwhat will happen in generation i + 1. Hence, it isplausible to assume that the technological progresswill impact consumer preferences for RH productfrom generation i when it will be offered togetherwith NH and NL products from generation i + 1 inthe same way as when RH from generation i�1 wasoffered with NH and NL from generation i. In ourmodel terms this logic translates into the followingassumption:

ðA3Þ �Dijk ðpNH; pNL; pRHÞ ¼ �D

i�1jk ðpNH; pNL; pRHÞ for

k ¼ NH;RH;NL:

With this assumption, the firm’s problem becomesseparable across generations: the demand for NH andNL from generation i + 1 that RH from generation icannibalizes in period i + 11, is the same as that can-nibalized in period i1 by RH from generation i�1.This fact combined with (A2) implies that if remanu-facturing is profitable for one generation, it is thenprofitable for all generations. That is, with assump-tions A1–A3, to assess economic and environmentalimpact of remanufacturing the firm only needs toconsider what happens in periods i1 and i2. Thus, wecan redefine K1 accordingly so that Equation (4)becomes

K1 ¼ b1��Si1NHð�pNH; �pNL; �pRHÞ þ ð�Si2NHð�pNH; �pNL; �pRHÞ

� ð�Si2RHð�pNH; �pNL; �pRHÞ�: ð9Þ

Thus, the profit in period i1 in Equation (7) can beexpressed only in terms of generation i products suchthat the sales of RH in period i + 11 and the associatedcannibalization of generation i + 1 NH, NL productsare both correctly accounted for in period i1. Theprofit function for period i2 in Equation (7) dependsonly on generation i products and is therefore notaffected by this assumption.Let p�K and �p�K for k = NH, NL, RH be the optimal

prices in the cases without and with remanufactur-ing, respectively. The economic assessment ofremanufacturing is then given by the following:

DEFINITION 1. The firm will remanufacture if

�pið�p�NH; �p�NL; �p

�RHÞ� piðp�NH; p

�NLÞ: ð10Þ

Note that our model considers the situation inwhich, when the firm decides to introduce the reman-ufactured product, it also may decide to re-optimizethe prices of the new products—that is, the firm stra-tegically optimizes its entire product line as a result ofthe introduction of remanufactured product. We referto this case as the globally optimal solution.There is some evidence, however, that some firms

may not view remanufacturing as a strategic initia-tive and not change the new products’ prices as aresult of the introduction of a remanufactured prod-uct. For example, the mini-case study reported inOvchinnikov (2011) states that “remanufacturingoperations had no effect on the [firm’s] pricing,procurement, or other decisions about the newproducts.” This means that the firm first optimizespi(pNH, pNL) over pNH and pNL and then uses these“optimal” new product prices, p�NH; p

�NL to maximize

�piðp�NH; p�NL; �pRHÞ over �pRH. Such an approach is clearly

suboptimal, and we refer to it as the heuristic, becauseit replaces firms’ global profit maximization with atwo-step sequential local optimization. We comparethese two approaches in section 5.2.

3.5. The Firm’s Environmental ImpactTo assess the environmental impact of remanufactur-ing, we utilize the total energy consumption/use/demand during the life cycle of a product. The energyconsumption metric is a common measure used in theliterature as a proxy for environmental impact, espe-cially in the context of remanufacturing; see DoctoriBlass et al. (2006), Geyer (2004), and Gutowski et al.(2011). In a recent paper, Quariguasi-Frota-Neto andBloemhof (2011) used the “cumulative energy demand(CED)” as their metric for the environmental impact;CED is identical to our total energy consumptionmetric. Total energy consumption is also a commonmetric used in the industry, for example, McLaren andPiukkula (2004), Apple (2011), Nokia (2011).Figure 2 demonstrates the life-cycle stages we con-

sidered in our analysis. For new products theseinclude raw materials, production and transportation,initial use, and disposal. For remanufactured prod-ucts these include collection and transportation,remanufacturing, secondary use, and disposal. It isimportant to note that the use phase of NH can beeither partial (reflecting product returns, b1), or full(b2).The total energy consumption per unit of product,

Ek(k = NL, NH, RH), is the sum of the correspondinglife-cycle stages described in Figure 2. We estimate


the energy consumption at each state in section 5.1.Then, the total energy, TE, is obtained as the sumproduct of the corresponding per unit energy Ek andthe total unit sales:Without remanufacturing:

TE� ¼ ENH � �Si1NHðp�NH; p�NLÞ þ Si2NHðp�NH; p

�NLÞ�

þ ENL ��Si1NLðp�NH; p

�NLÞ þ Si2NLðp�NH; p

�NLÞ�

With remanufacturing:

�TE� ¼ ENH��Si1NHð�p�NH;�p�NL;�p

�RHÞþ�Si2NHð�p�NH;�p

�NL;�p

�RHÞ�

þENL��Si1NLð�p�NH;�p

�NL;�p

�RHÞþ�Si2NLð�p�NH;�p

�NL;�p

�RHÞ�:

þERH��Si1RHð�p�NH;�p�NL;�p

�RHÞþ�Si2RHð�p�NH;�p

�NL;�p

�RHÞ�

With this we define three measures to assess theenvironmental impact of remanufacturing:

DEFINITION 2. If the firm remanufacturers (condi-tion (10) holds), we define the following environ-mental impact measures:

(a) The absolute measure, EnvA ¼ TE� � �TE�

(b) The relative (per unit) measure,

EnvR=u ¼ TE�SiTð�p�

NH;�p�

NLÞ �

�TE��SiTð�p�

NH;�p�

NL;�p�

RH;Þ

where SiTð�p�NH; �p�NL; �p

�RHÞ ¼

Pk¼NL;NH

�Si1k ð�p�NH;

�p�NLÞ þ Si2k ð�p�NH; �p�NLÞ�; and

�SiTð�p�NH; �p�NL; �p

�RHÞ ¼

Pk¼NL;NH;RH

��Si1k ð�p�NH;

�p�NL; �p�RHÞþ �Si2k ð�p�NH; �p

�NL; �p

�RHÞ�:

(c) The relative (per dollar) measure, EnvR=d ¼TE�

pið�p�NH

;�p�NL

Þ ��TE�

pið�p�NH

;�p�NL

;�p�RH

Þ ; where pi and �pi are given

by Equations (2) and (8).

By Definition 2, we evaluate remanufacturing alongthree dimensions: first, we examine how remanufac-turing affects the firm’s overall environmental impact.We also examine the firm’s environmental impact perunit of the product line (measured as the weightedaverage of energy across the product line mix) to sep-arate the impact of growth in total quantity sold fromthe change in the mix of products in the product line.Finally, we assess the firm’s environmental impactper dollar. This eco-efficiency measure evaluates if

the firm is growing in an eco-efficient way by increas-ing its profit at a higher rate than its environmentalfootprint. Or, in other words, does the firm use lessenergy for every dollar of profit?Next, we estimate the demand functions, the key

elements of the above model, using a behavioralstudy and perform a numerical analysis using thesebehavioral estimates of demand.

4. Demand Estimation

The above model has in total 10 demand functions:for each of the two periods, we have either twodemand functions (for the case of no remanufactur-ing) or three (if the firm decides to remanufacture).We estimate period i2 demand functions (when allproducts are of generation i) directly, and then adjustthem for the technology progress to obtain period i1demands.

4.1. Behavioral StudyTo estimate period i2 demands, we used a web-basedchoice-based conjoint study of consumer behaviorwith respect to the choice between new and remanu-factured products. We implemented the study usingSSI Web system by Sawtooth software. The subjects inthe study were a panel of 102 randomly selectedemployees of a major North American university rep-resenting diverse age groups and income levels. Theobjects in the study are items in a product line of awireless communications firm, such as AT&T, wherethe NH (RH) product is a “new (refurbished) smart-phone,” NL product is a “new feature phone,” L-ser-vice is “voice only,” and H-service is a “voice anddata” plan. The subjects were given the followingdescriptions6 for the products:

A NEW SMARTPHONE with a Voice and Dataplan: A smartphone is a high-end mobile com-munication device, such as, for example, iPhone,BlackBerry, or Palm. It has multiple PC-like fea-tures, including a miniature keyboard, a touchscreen or a scroll-pad, a built-in camera, contactmanagement, an accelerometer, built-in naviga-tion hardware and software, the ability to readbusiness documents, media software for playing

Raw Materials

Production/Transport Initial Use Disposal

Collection

/TransportRe-

manufacturingSecondary

Use Disposal

β1 β2

New product

Remanufactured product

Figure 2 Life-Cycle Flow: New and Remanufactured Products


music, browsing photos and viewing videoclips, high-speed (3G) Internet browsers, full-featured e-mail capabilities, and a complete per-sonal organizer.A NEW FEATURE PHONE with a Voice onlyplan: This product is a new and fully function-ing camera phone, but with the smaller set offeatures than a smartphone. It has a smaller key-board, a smaller screen, and a slower processor.It is not suitable for high-speed data-intensivetasks; however, basic data features, such as textmessaging, are supported.

The descriptions for the plans were:

VOICE only: A substantial number of minutesfor a total monthly fee of $40. Other featuresinclude per second billing, no long-distance fees,and free nation-wide roaming.VOICE and DATA: The above voice plan, plusan unlimited high-speed (3G) data for a totalmonthly fee of $70.

In addition, the following statement was addedregarding plans:

With each plan you are signing a two-year con-tract that you cannot terminate, unless a signifi-cant penalty is paid.

Subjects first performed 12 choice-based tasks forthe no-remanufacturing case, that is, with three alter-native choices: NH, NL, and none with one attribute,

price, that ranged $0, $50, ..., $450 for NH and $0, $50,…, $250 for NL. Note that the product price is paidonce, while the service cost is paid monthly; this wasemphasized on each choice-task screen. Subjects thenperformed 12 choice tasks for the case with RH, thatis, with four alternatives: NH, RH, NL, and none, andtwo attributes: price and RH plan. Price ranges forNH and NL were the same; price for RH variedbetween $0 and up to the price for NH; RH plan tooktwo values: L and H descriptions were as wediscussed above. The following description7 for RHwas provided:

A REFURBISHED SMARTPHONE: This is afully functional device with the same set of fea-tures as in the Smartphone described above, butit is not new. It has been sold before, used byanother consumer and then returned for unspec-ified reason. It has been tested and refurbishedby an authorized service provider to meet origi-nal factory specifications. The product may haveobservable cosmetic blemishes; however, it isfully functional. Standard new smartphoneswarranty and return policy applies to this prod-uct.

We fit the choice data to a latent-class multi-nomiallogit model using built-in Sawthooth algorithms. Wefound that, without remanufacturing, the best fitoccurred with three latent classes, while with remanu-facturing, the model with four latent classes has thebest fit [minimizes Akaike 1974 information criterion].Table 1 presents the results of the estimation8 —the partworth utilities for different levels of each attri-bute for each segment.

Table 1 Partworth Utilities for the Case with and without the Remanufactured Product

Without Remanufacturing With Remanufacturing

Segment 1 Segment 2 Segment 3 Segment 1 Segment 2 Segment 3 Segment 4

Size (%) 40.5 22.5 37.0 30.6 15.8 25.2 28.4Product = NH 1.005 �0.793 3.089 0.688 �0.972 3.427 1.4Product = RH �0.027 0.255 1.031 1.061Product = NL �1.005 0.793 �3.089 �0.661 0.717 �4.458 �2.461Price = 0 7.557 9.635 3.076 8.244 4.214 6.33 1.431Price = 50 6.487 8.602 2.473 7.427 3.072 4.639 1.501Price = 100 4.481 8.567 2.213 6.147 4.309 3.787 1.098Price = 150 4.702 �0.078 1.931 �4.654 2.838 2.89 0.643Price = 200 4.070 �1.350 1.130 �5.879 0.82 1.328 0.216Price = 250 3.367 �2.200 0.135 �5.261 1.885 0.869 �0.063Price = 300 �14.503 �2.576 �1.539 �6.253 1.644 �0.515 �0.964Price = 350 �1.806 �3.094 �2.427 3.482 �0.364 �1.238 �0.957Price = 400 1.225 �4.176 �3.368 3.618 �9.095 �8.245 �1.245Price = 450 �15.580 �13.329 �3.623 �6.871 �9.323 �9.844 �1.66Plan = L 0.84 1.194 �0.241 0.203Plan = H �0.84 �1.194 0.241 �0.203None 6.484 �1.25 �0.413 8.093 �1.071 3.983 �1.931


These latent classes correspond to the followingcharacterizations of customers.Segment 1: “I do not really need a phone.” For seg-

ment 1, the utility of device or plan is much smaller(in the absolute value) than the utility of purchasingnothing. Only when the price is very low ($50 orbelow) does the utility of phone and price becomemarginally larger than purchasing nothing. Thus,segment 1 customers are effectively indifferentbetween getting a new smart phone device for $50 orless, a new feature phone for free, or not purchasinganything.Segment 2: “I want a feature phone (with no data).”

For segment 2, the utility of NL is positive and in com-parison to the utility of none is much larger than forsegment 1. Further, in the case with remanufacturing,the utility for the L plan is positive. Thus, segment 2customers want to purchase a phone and preferfeature phones with no data plan.Segment 3: “I want a new smartphone with data.”

For segment 3, observe that the utility of NH is posi-tive and large. The utility of H is also positive in thecase with remanufacturing. Such customers, there-fore, are a mirror image of segment 2—they want asmartphone with data.In the absence of remanufactured products, the seg-

ment sizes are approximately 40.5%, 22.5%, and 37%.When the remanufactured product is introduced, thesame three segments emerge in the estimation (withrespective sizes of 30.6%, 15.8%, and 25.2%), but inaddition a new segment emerges with the size of28.4%. Examining its utilities, this new segment corre-sponds to the following customer profile.Segment 4: “I want a smartphone and OK with

refurbished with or without data.” Segment 4 custom-ers have a positive utility for the smartphones, butnegative for the feature phone and buying nothing.They prefer the new smartphone to refurbished andalso the voice-only plan to the voice-and-data. How-ever, those are weak preferences: additional utilityfrom a new smartphone vs. refurbished is <0.4 com-pared to the additional utility from buying any smart-phone vs. buying nothing, which is >3. As such, theremanufactured product is most attractive to segment4 consumers; they would slightly prefer if it wasoffered with the voice-only plan, but even with thedata plan their utility from RH is high, much higherthan buying NL or nothing (even at relatively highprices).With these segments, consider the firm’s position-

ing of products. In the absence of the remanufacturedproduct, the firm should naturally target segment 3with NH and segment 2 with NL. With the remanufac-tured product, the firm’s goal is to keep the samepositioning for NH and NL and reach out to segment4 customers with RH. Most importantly, though, of

the 28.4% of consumers that comprise the refurbishedproduct’s “target” segment 4, only 11.8% come fromcannibalizing NH’s demand (size of segment 3decreases from 37% to 25.2%); the remainding 16.6%come from upgrading NL consumers (some 7% comefrom segment 2) and bringing in new consumers(some 10% come from segment 1). That is, while somecannibalization of the “parent” new product occurs,over 60% of the RH demand comes from othersources.Note that we do not see evidence for the existence

of the more environmentally conscious segment,that is, “green segment that values remanufactur-ing”: in all four segments, the utility for RH is smal-ler than the utility of the preferred new product(NH or NL).

4.2. Construction of Demand FunctionsWe construct period i2 demands directly from the util-ities in Table 1 using the latent-class multi-nomial lo-git (MNL) model. For a given segment, we calculatethe utility of a product k = NH, NL, RH given its priceand service plan, and then obtain the purchase proba-bility for a given product as a ratio of the exponent ofits utility to the sum9 of exponents of utilities of otheralternatives in the choice set. Weighing such probabil-ities by the size of the segment gives the product’sdemand.To construct period i1 demands in the case without

remanufacturing, we assume that Di1k ¼ Di2

k . Thisimplies that the periods are of equal length and thatconsumer preferences are not changing throughoutthe selling horizon of generation i products. Thisassumption is made to simplify exposition and can beeasily relaxed by introducing a coefficient capturingthe length of the period and change in preferences;doing so will not change the insights of the article.To construct period i1 demands in the case with

remanufacturing, we introduce a parameter, c, thatadjusts the utility of generation i RH presented inTable 1 downwards, because in period i1 RH is ofgeneration i�1 and thus naturally is less desirable toconsumers. In the MNL model, such an adjustmentwill have two effects: demand for generation i�1 RH(in period i1) will be smaller than for generation i RH(in period i2), while the demands for NH and NL willbe larger in period i1 than in i2.Figure 3 illustrates the demand functions. Panel a

depicts demand for NH: the dotted line is the demandfunction in the case without remanufacturing, Di

NH,the solid line is the demand function in period i1,�Di1NH, with c = 1, and the dashed line is the demand

function in period i2, �Di2NH, both for the case with

remanufacturing. Two observations are evident: first,the introduction of RH decreases the demand for NH;second, the NH demand is increasing in c (note that


c = 0 in period i2). Panel b depicts demand for RH: itis evident that demand for RH is decreasing in c. Thelatter two observations are natural: NH and RH are(imperfect) substitutes; thus, the more technologicallysuperior the generation i NH is, the lesser demandwill generation i�1 RH attract; hence a larger fractionwill demand NH.

5. Analysis and Results

In this section, we perform numerical simulations forthe economic and environmental assessment ofremanufacturing using the behavioral data obtainedabove. We begin by discussing the parameters usedin the simulation, which we obtained from both theliterature and from conversations with industry exec-utives (AT&T 2009, 2010). We note that although theresults we present are based on parameters anddemand estimates for the wireless communicationsproducts, the framework we present is generaland can be directly applied to other industries andproducts.

5.1. The SimulationWe sampled 1000 parameter combinations, with eachparameter value drawn at random from the rangesbelow, and for each parameter combination opti-mized the prices with and without remanufacturingusing the demand functions estimated in section 4.We used the following parameter values in the simu-lation:

• CNH 2 [$500; $700]. Goldman (2010) states that“[s]martphones generally cost carriers around$500 per unit,” while arguing that “the hotterthe phone, the more a carrier will pay to buyit.” Similarly, The Economist (2011) provides anestimate of $560 for the cost of an iPhone. Notethat c(�) parameters refer not to the costs ofmanufacturing a device but rather to thewholesale price that a product + service firmpays the manufacturer of the product.

• CNL 2 [$150; $300]; Ci1RH 2 [$10; $20], Ci2

RH 2[$10; $100]. These ranges were provided to usby executives at AT&T (2010). They noted,however, that the variance in costs of the fea-ture phones is larger than for the smartphones.Similarly, there is a significant variance in thecost of remanufacturing due to variable condi-tions of the remanufacturable cores; for exam-ple, see Galbreth and Blackburn (2006, 2010),Zikopoulos and Tagaras (2007), and Ovchinni-kov (2011) for further discussion.

• b1 2 [5%; 15%], b2 2 [15%; 35%]. These rangeswere used based on industry averages of reuserates in the secondary cell phone market (Doc-tori Blass et al. 2006, Geyer and Doctori Blass2010). As explained above, to be more accurateand cover different types of refurbished prod-ucts, we distinguish between products thatwere returned within a short period from theinitial purchase (b1) and products that wereused for a longer time (e.g., a year or two)before the initial user decided to upgrade to anewer model (b2).

• D 2 [50%; 100%]: This range was estimatedfrom the discussion with AT&T executives(AT&T 2009). They discourage consumersfrom purchasing smartphones without thedata plans to “save consumers from them-selves”: consumers without a data plan mayby accident use cellular data and incur sig-nificant costs. Further, an upgrade can hap-pen at an uncertain point in the duration ofthe service contract; D is therefore less than100%.

• mv 2 [$1; $10]; mV&D 2 [$10; $25] monthly coston a 24-month contract, which are standard inthe industry. These ranges are estimated bythe authors based on the prices of the voiceand data plans. We used a 1% discount rateper month to compute the net present valuesof the margins.

(a) (b)

Figure 3 Example of the Demand Curves for NH (a) and RH (b)


• Energy consumption parameters Ek for newproducts, that is, k = NH, NL are estimatedbased on the ranges of values from the producteco-profiles by Nokia (2011). We identified themodels that correspond to the high-end NHproducts, e.g., E7-00, and the models that cor-respond to the low-end NL products, e.g., 100/101/700. The eco-profiles for these productspresent the total LCA estimates for energy use,for example, 278 MJ for the Nokia E7-00model, as well as the percentage breakdown ofthe energy use for different life-cycle stages10

as per Figure 2. Energy estimates for theremanufactured product, ERH, are based onDoctori Blass et al. (2006) and on the assump-tion that the energy consumption during thesecondary use is the same11 as in the initialuse. Table 2 presents the resulting estimates.

We note that our energy estimates are somewhatdifferent from those used in Quariguasi-Frota-Neto and Bloemhof (2011). The differences aredue to the timing of the estimates; their estimatesare based on studies from 2003 to 2006, suggest-ing that the actual data collection happened per-haps in 2000–2003. Our data are from 2011 andthus are different in two ways. First, smartphones(“high-end” products in our study) as a categorydid not exist in 2000; hence, their “mobilephones” are more like our NL products. Second,

the manufacturing technology improved and thephysical weight of NL products decreased, thusdecreasing the energy consumption. In fact,adjusting for the weight (70 g for our typical NLvs. 90–135 g in their data), our estimates arerather similar to theirs.

● The technology choice parameter is initially set toc = 1 to reflect that there is some change betweenthe generations of the product, but yet to keepremanufactured product a viable alternative(observe that the utilities for RH in Table 1 are onthe order of one).

5.2. Economic ResultsWe start by discussing the economic results of thesimulation. As discussed in section 3, we considertwo approaches: global optimization, where the pricesof new and remanufactured products are optimizedjointly, and heuristic optimization, when the firm firstoptimizes the prices of new products as if there is noremanufacturing and then, given those new productprices, optimizes the price of the remanufacturedproduct. Our first observation is on how the tworelate:Observation 1: Remanufacturing was profitable in

all 1000 cases: In 63.7% of the instances, the heuristicand the globally optimal solutions were identical. Inthe remaining 36.3%, the firm was able to obtainhigher profits by adjusting the new product priceswhen the remanufactured product was introduced.By observation 1, in a majority of the cases, follow-

ing the heuristic was in fact optimal. In the remainderof the cases, the average profit shortfall as comparedwith the globally optimal solution was 4.4%; however,the maximal profit loss that we observed exceeded16%. Thus, firms should be aware of the potentialopportunity losses when following such a heuristic.Observation 2: In 35% of the cases (96% of those

when the heuristic was suboptimal), we observednegative effective cannibalization; that is, the quantity of

Table 2 Parameter Estimates for Energy Consumption

Life-Cycle Stage NH NL RH

Raw materials + manufacturing+ transport

222 MJ 105 MJ

Partial initial use b1, assuming1 month of initial use

2 MJ

Full initial use 53 MJ 49 MJCollection 5 MJRefurbishing 1.5 MJSecondary use 53 MJ

(a) Demand for NH with and w/o remanufacturing (b) Price for NH with and w/o remanufacturing

0

50

100

150

200

250

300

350

400

450

-15%

-10% -5

% 0% 5% 10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

85%

Num

ber

of c

ases

(out

of 1

,000

)

Percent change in demand for NH

0.2%

0.9%

19.0%

40.6%

12.2%

12.4% 14.7%

$0

$100

$200

$300

$400

$500

$600

$0 $100 $200 $300 $400 $500 $600

NH

Pri

ce -

w/ r

eman

ufac

turi

ng

NH Price - w/o remanufacturing

Figure 4 Negative Effective Cannibalization


NH increased as a result of introducing the remanufac-tured product.Figure 4 illustrates the negative effective cannibal-

ization (overall, over both periods, the result in eachperiod is effectively identical to the overall). As can beseen in Figure 4a, while when positive cannibaliza-tion occurs, the demand for NH is decreased by atmost 10%, when the negative cannibalization occurs,the increase in the demand for NH with remanufac-turing can be as high as 80%. As we will see in thenext subsection, when the negative cannibalization isrelatively high, this will correspond to cases in whichthe energy use overall was higher.Negative effective cannibalization may seem puz-

zling: indeed, from the discussion in section 4.2and Figure 3, demand for NH decreases when theremanufactured product is introduced. But that dis-cussion and figure correspond to the situationwhen the price of NH does not change, that is, aheuristic optimization approach.12 Under the globaloptimization, the firm is free to decrease the priceof NH, causing an increase in demand due to aprice drop that is larger than the decrease due toRH; for example, from Figure 4b, in 14.7% of thecases the optimal price of NH dropped from $350to $250, causing an increase in demand depicted inFigure 4a. But how and why can this be profitable?The reason is based on the connection between thenew product sales and the availability of remanu-facturable cores. If the firm can make money onremanufactured product and associated service,then it wants to do more of it. But supply of re-manufacturable cores is limited by the multiple biof new product sales. To increase the supply ofcores and the number of remanufactured products,the firm overall finds it profitable to lower theprice of NH, increase the sales quantity of NH, andconsequently increase the supply of cores and thequantity of RH.

5.3. Environmental ResultsObservation 3: In 72.9% of the cases, we observed apositive absolute environmental effect (EnvA > 0); that is,by deciding to remanufacture the firm improved itsprofits and decreased its environmental footprint.This observation is certainly encouraging—it sug-

gests that remanufacturing is efficient in achievingthe “win–win” situation in which the firm’s economicand environmental goals are congruent. These 729cases include all the cases when the heuristic wasoptimal (in which the positive environmental effectshould be expected because the new products’ pro-duction is non-increasing and remanufacturing usesless energy) as well as some of the cases with negativeeffective cannibalization.But this observation also suggests that in 27.1% of

the cases we observe the negative absolute environ-mental effect: profit increases but so does the totalenergy use of the firm. These cases all correspond tothe instances with negative effective cannibalization,and the negative absolute effect is a result of the firm’ssales growth13 : because of the negative effective can-nibalization the new production increases, thus usingmore energy. But as the next observation shows, thisgrowth is in almost all cases still sustainable.Observation 4: In 99% of the cases, we observed a

positive relative (per unit) environmental effect�EnvR=u [ 0

�, and in all cases we observed the positive

relative (per dollar) environmental effect (EnvR/d > 0).Figure 5 illustrates the absolute environmental

effects; the shaded bars correspond to the cases withincrease in the energy use (negative effects), and thenon-shaded bars represent the cases with a decreasein energy use (positive effects).The negative environmental effects may also

appear puzzling; indeed, remanufacturing a unitrequires much less energy as opposed to that neededto produce a new one. So why does the negative effectoccur? The answer is related to the intricacies of

Absolute Effect Relative Effects

0

50

100

150

200

250

300

350

-25%

-20%

-15%

-10% -5

% 0% 5% 10%

15%

20%

25%

30%

35%

40%

Num

ber

of c

ases

(out

of 1

,000

)

Percent change in absolute energy use

Energy DecreaseEnergy Increase

0

100

200

300

400

500

600

700

-25%

-20%

-15%

-10% -5

% 0% 5% 10%

15%

Num

ber

of c

ases

(out

of 1

,000

)

Percent change in relative energy use

Energy DecreaseEnergy Increase

(a) (b)

Figure 5 Absolute and Relative Environmental Effects of Remanufacturing


demand cannibalization across the product line.Under the “usual” logic, demand cannibalizationimplies that the quantity of the new product manufac-tured/sold decreases when a remanufactured prod-uct is introduced; that is, a positive quantity iscannibalized. As seen in observation 2, in 350 casesthe quantity of NH manufactured/sold increasedafter a remanufactured product was introduced: thequantity “cannibalized” is therefore negative. Byobservation 3, the increased quantity of NH led to anincrease in the firm’s overall energy consumption andthe negative absolute environmental effect in 271of 350 such cases. By observation 4, even when wemeasure the relative effect per unit (mitigating theimpact of the quantity increase), we still obtain (veryfew) cases where the firm’s environmental impact perunit increases. The reason for this is the change in theproduct mix. When the firm remanufactures, theproduct mix changes to include the remanufacturedproducts (with a lower per unit energy consumption),fewer NL products but more NH. Since the energyconsumption of NH is higher than that of NL, thischange can cause the relative per unit impact toincrease as happens in 1% of the cases with very largenegative cannibalization.Overall, our results show that in most cases reman-

ufacturing provides both economic and environmen-

tal benefits. Our results validate the heuristicapproach used by many firms, as it is frequently opti-mal and leads to environmental improvements in allcases. We also show, however, that in the cases whenthe heuristic is suboptimal, a firm has a potential togrow because of the negative effective cannibaliza-tion, which may result in an increased total environ-mental impact for this specific firm. However, thisgrowth is nearly always sustainable: the environmen-tal impact per unit of product or per dollar of profit isalmost always lower when a firm remanufacturesthan when it does not.

6. Sensitivity Analyses of theEnvironmental Effects

In this section, we test the sensitivity of the environ-mental effects to various parameters of the model. Todo so, we take one parameter at a time and extend therange of its values beyond what was used in the mainsimulation, while keeping the other parameters’ranges unchanged. We measure the frequency ofobserving the positive environmental effects.Table 3 summarizes the insights from the sensitiv-

ity analyses (for brevity we do not present thedetailed sensitivity analysis in the article, but they areavailable upon request).

Table 3 Results of Sensitivity Analyses

Frequency of Cases with Positive Environmental Effect

Model Parameter Testing Range Absolute Relative per Unit Relative per Dollar

Sensitivity to costscRH, cNL [0, cNH] Moderate decrease

(from � 70% to 50%)Negligible change

cNH [200, 800] Bimodal: almost 100%when cost is very low orvery high, lowest at � 70%when cNH = 650

Sensitivity to service margins and upgrade probabilitymv, mV&D, Δ Inverse of the sensitivity to costs Negligible changeSensitivity to recovery ratesb1 [0, 100%] Bimodal (lowest at � 40%

when b1 = 60%)Negligible change Negligible change

b2 [0, 500%] Increases to 100% whenb2 > 250%

Minor decreasewhen b2 < 50%

Sensitivity to technological progressc [0, 2] Increases from �50%

when c = 0 to �80%when c = 2

Negligible change

Sensitivity to energy parametersENH increases 2–4 times Negligible change Minor decrease (to 95%) Negligible changeENL decreases 2–4 times Moderate decrease (to 80%) Negligible changeENH increases and ENL decreases 2–4 times each Moderate decrease (to 80%) Minor decrease (to 95%)Use phase energy increases over(re)manufacturing for all products

2–4 times Negligible change Negligible change No change

Use phase energy increases overmanufacturing and RH use energyincreases over NH use

2–4 times Minor decrease (to 95%) Negligible change


Table 3 reveals several interesting observationsregarding the impact of the economic and environ-mental parameters on the environmental effects:Observation 5: The impact of the economic param-

eters is as follows:

● The absolute environmental effect and the nega-tive effective cannibalization that underlines it aredriven by the ability of the firm to decrease thenew product’s price. When CNH is low, the opti-mal price is already low, and, consequently, thefirm has a very limited ability to decrease it fur-ther; likewise when CNH is high, price can bedecreased but it is not profitable: hence thedependency is bimodal—both low and high costsresult in nearly 100% positive effects.

● The absolute environmental effect and the nega-tive effective cannibalization are also criticallydriven by the constrained supply of remanufac-turable cores. As explained in section 3, ourmodel is flexible to accommodate the cases whenthe firm is collecting the remanufacturable coreswhich correspond to the new units sold by otherfirms in the market (e.g., if the firm with a 50%market share collects 60% of its own NH and 60%of NH sold by other firms in the market, thenb2 = 1.2). Our results indicate that when b2becomes large enough, the positive absoluteeffects occur in 100% of the cases because thefirm’s own sales of NH are no longer a constrainton its remanufacturing operations; the firm canprocure the cores from others.

● The positive relative effects (per unit and per dol-lar) are rather insensitive to the economic-relatedmodel parameters.

Observation 6: The impact of the environmentalparameters is as follows:

● The absolute environmental effect and the nega-tive effective cannibalization are insensitive to theenergy parameters.

● The frequency of the positive relative effectsdeclines as the difference in energy consumptionbetween the high- and low-end products grows,which happens because of the substitution of thelow-end products with a mix of new and remanu-factured high-end ones in the product line.

● Interestingly, neither the absolute nor the relativeeffects are particularly sensitive to the balancebetween the energy used in the production vs.use phases of the product’s life-cycle changes aswell as to changes in the new generation prod-ucts’ energy efficiency: increasing the differencesin production/use and new/old by a factor offour led to at most a 20% decrease in the frequen-

cies of the relative effects and a negligible changein the absolute effects. In this sense, our resultsprovide a counter-example to those of Quarigu-asi-Frota-Neto and Bloemhof (2011) and Gutowskiet al. (2011), who considered products for whichuse phase was much more energy consuming rel-ative to manufacturing/remanufacturing andfound that the negative effects can be quite com-mon for such products.

7. Discussion and Conclusions

This article provides a data-driven assessment of theeconomic and environmental aspects of remanufac-turing for product + service firms—a business modelthat appears to be relatively common in the industriesin which remanufacturing is practiced (e.g., 11 of 24studies in Guide and Li 2010 consider product +service firms.) A critical component of such an assess-ment is the issue of demand cannibalization. Advanc-ing the previous research, on the supply side weconsider a case where the remanufactured product isnot only cannibalizing the sales of its new “parent”product but also the other products in the firm’s prod-uct line (multi-product supply). On the demand side,we consider a random utility consumer choice modelwith multiple consumer segments. We estimate thesizes of these segments and their purchasing behaviorusing a choice-based conjoint study and fit the data tothe multi-nomial logit model with three or four latentclasses (multi-segment demand).Integrating the multi-product supply with an

assessment of multi-segment demand, we performedextensive numerical simulations for one specificindustry/product category: mobile phones. It is criti-cal to note that while the numerical estimates/param-eters in our article are specific to that industry/category, the overall approach and framework aregeneral, and our approach could be easily replicatedfor other industries. And while the frequencies of theeffects we describe would change (because they aredriven by the industry-specific demand, cost, andenergy estimates), the fundamental relationshipswould not.To understand the economic and environmental

impacts of remanufacturing, we compared the result-ing optimal profits (which is our measure of the eco-nomic performance) and energy consumption (whichis our measure of the environmental impact).We found overwhelming support for the eco-effi-

ciency of remanufacturing: it was profitable in allcases we considered, and, in a majority of cases(73%), it also resulted in a decrease in the totalenergy use—a positive absolute environmental effect(absolute decoupling). Further, while in the remain-


ing cases remanufacturing resulted in an increase inthe energy use, that happened because of the firm’sgrowth. As we showed, in some 35% of all cases, anintroduction of a remanufactured product led (atoptimality) to an increase in the quantity of the newproduct sold—the negative effective cannibalization.Because of this effect, remanufacturing leads to morenew production and hence an expected increase inthe total energy: the firm’s sales simply grow. Butwhat is important, we show that this growth is sus-tainable: the firm’s relative energy consumption perunit of product decreased in 99% of the cases, andenergy consumption per dollar of profit decreased inall of the cases we considered—positive relativeenvironmental effects per unit and per dollar (rela-tive decoupling).Testing sensitivity, we found that the relative

effects are rather insensitive to the economic para-meters, while the absolute effects are not sensitiveto the energy parameters. The absolute positiveenvironmental effect becomes less pronouncedwhen the firm has no ability to procure cores soldby other firms, the new product’s cost is moderate,or when because of the technological progress thereis a significant difference in demand between thecurrent generation new product and previous gener-ation refurbished. The relative positive effectsbecome less pronounced when the difference inenergy between the low- and high-end productsincreases, but even in those cases the relative posi-tive effects still occur in over 80% of the instanceswe considered.We conclude the article by mentioning three inter-

esting extensions that are subjects of our future work:competition, recovery improvements, and productdesign. First, competition will impact both economicand environmental performance. In particular, if theadditional demand that the firm serves with remanu-factured product might come from reducing the newproduct’s demand of a competing firm, then the posi-tive environmental effects could occur with increasedfrequency. Second, recovery rate is assumed exo-genous in this article, but in practice the firm coulddecide to invest in increasing recovery if it is profit-able or if there are regulations in place that provideincentives for improving recovery. A major challengein analyzing such a case is obtaining reliable cost esti-mates for improving recovery; as an industry execu-tive pointed out to us “we are constantly looking forways to collect more, but have just not found a way todo it profitably.” One such way could be to designproducts that (once collected) can be easily remanu-factured and upgraded. All these extensions can shedadditional light on the balance between firms’ eco-nomic and environmental goals and are of interest forfuture research.

Notes

1As is common in the literature we use “refurbished” and“remanufactured” as synonyms.2For example, AT&T sells phones and voice and dataplans, HP sells printers and cartridges, BOSCH sellspower tools and consumables for those tools; in fact 11 of24 studies of remanufacturing mentioned in Guide and Li(2010) explicitly refer to product + service firms.3We emphasize that in general pNH 6¼ �pNH , and pNL 6¼ �pNL.More importantly, D

ijk ðpNH; pNLÞ and �D

ijk ð�pNH; �pNL; �pRHÞ are

different functions. We use the single bar notation, suchas �p, to refer to the case with remanufactured product todifferentiate it from the case without remanufacturing.4Note that our model is oblivious to whether the firm isselling a differentiated product sold by no one else or itsells a commodity that other firms sell as well. In theformer case bj, j = 1, 2 should be interpreted as recoveryfraction implying that bj ∊ [0, 1]. But in the latter case,bj could be above 1; for example, if a firm with a 50%market share collects 60% of its own and 60% of itscompetitor’s product sales, then bj = 1.2. Note also thatbj can be decision variables and the firm can decide toinvest in order to acquire more remanufacturable cores.In this article, we assume bjs are exogenous and testsensitivity to their values. The case when recovery ratesare determined endogenously is of interest for futureresearch; see section 7.5Sales of RH in period i + 11 cannibalize the NH salesfrom generation so fewer remanufacturable cores areavailable in period i + 12; hence RH from generation i + 1,if offered in period i + 12 cannibalizes fewer sales of NHfrom generation i + 1, which means that more cores areavailable in period i + 21, and so on.6These descriptions are a blend of descriptions of severaldevices and plans from AT&T’s website as well as the cor-responding Wikipedia pages.7This description also is a blend of the language used byAT&T and the description used in Ovchinnikov (2011).8The overall quality of fit, as measured by the so-called“percent certainty”—improvement in log-likelihood due tothe model predictions equals 56.41% and 48.51% for thecases without and with remanufacturing.9For example, for a segment 4 consumer, if the firm offersNH at $450, RH at $350 (with H-service), and NL at $200,then the utility of NH = 1.4�1.66�0.203 = �0.463, the util-ity of RH = 1.061�0.957�0.203 = �0.098, the utility ofNL = �2.461 + 0.216 + 0.203 = �2.042, and the utility ofNone = �1.931. The corresponding exponents are 0.629,0.906, 0.129, and 0.145, with the total of 1.810. Thus, thedemand share of NH = 0.629/1.810 = 34.7%, the demandshare of RH = 0.906/1.810 = 50.05%, and similarly forNL = 7.17% and for None = 8.01%.10We omitted the disposal and recycling stages since theenergy consumption in those stages is small in magnitude(2–3 MJ), and it is not clear how many products end upbeing recycled vs. landfilled and when.11There is no clear evidence for how the initial and sec-ondary use phase energy consumptions compare; on onehand, the older generation product can be more energyintensive, but on the other the secondary use phase may


be shorter (Quariguasi-Frota-Neto and Bloemhof 2011).We effectively assume that these two effects cancel eachother.12Non-coincidentally, there are no negative effective canni-balization cases among the 637 cases in which heuristicsolution was optimal. The heuristic prevents the firm fromdecreasing the price of NH, always resulting in the (posi-tive) demand cannibalization.13DISCLAIMER. As mentioned earlier, the unit of analysisin this article is a firm. Hence, our negative absolute effectonly suggests that the environmental impact of the specificfirm under consideration increased. This article providesno insight into whether the overall impact on the environ-ment increased; in particular, if the new demand that thefirm attracted with its remanufactured product came fromdecreasing new production at a competitor, then thecumulative impact (of these two firms) on the environ-ment could well decrease; see section 7.

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