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Integrating reverse logistics into the strategic planningof a supply chainKanchan Das aa Technology Systems Department , East Carolina University , Greenville , NC , USAPublished online: 09 Sep 2011.
To cite this article: Kanchan Das (2012) Integrating reverse logistics into the strategic planning of a supply chain,International Journal of Production Research, 50:5, 1438-1456, DOI: 10.1080/00207543.2011.571944
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International Journal of Production ResearchVol. 50, No. 5, 1 March 2012, 1438–1456
Integrating reverse logistics into the strategic planning of a supply chain
Kanchan Das*
Technology Systems Department, East Carolina University, Greenville, NC, USA
(Final version received February 2011)
This paper proposes a mixed-integer programming (MIP) model for the strategic production and distributionplanning of a supply chain (SC) integrating reverse logistics system. Such reverse logistics planning addressesthe collection, recovery and marketing of recovered products, in addition to returned components andpacking/wrapping materials. The model includes an approach that uses retail outlets as a two-way channel formarketing new products, collecting used/returned products and remarketing recovered products as a way ofpromoting an effective product recovery system in SC operation and optimising costs. The recovery ofproducts/components is planned through a pool of recovery service providers (RSPs), so that maximumrecovery can be ensured through combining the expertise of RSPs within optimum costs. The model follows atwo-step process that addresses strategic decisions about product recovery in the first step, and the integrationof the recovery process into overall SC decisions in the final step. A numerical example illustrates theapplicability of the model. A sensitivity analysis has been conducted to show the effects that changes in therecovered product quantity have on the overall SC performance.
Keywords: strategic MIP SC model; reverse logistics; end-of-life products; product recovery; recovery serviceprovider, sensitivity analysis
1. Introduction
Reverse logistics (RL) have gained the attention of environmentalists as an effective way to address sustainabilityissues. Industries are also taking note with the intention of improving the cost base through product recovery. End-of-life (EOL) product recovery eliminates waste and makes companies more resource efficient, while establishing anenvironmentally responsible consumer image. In recent years, the study of RL has drawn significant attention fromresearchers (Pokharel and Mutha 2009, Ilgin and Gupta 2010). The main reasons for this growing interest, asdescribed in the literature, are economic, legal and social (Thiary et al. 1995, De Brito and Dekker 2004, Ravi et al.2005). While the legal reasons (enforced by governmental regulations) may play a vital role (Porter and van derLinde 1995) in developing innovative recovery plans and overall RL systems, the economic reasons (Dowlatshai2005, Rubio and Corominas 2008) provide the bulk of the motivation for SC managers. The positive financial gainsthat result from the recovery of returned EOL/used electric and electronic items, as reported by Xanthopoulos andIakovou (2009), reinforce the mounting interest in product recovery-related studies, as does their potential forimproving the profitability of original equipment manufacturer (OEM) items in automobile industries, as reportedby Hammond et al. (1998). While the benefits of RL in terms of economic and environmental indices are quiteapparent, successful strategic SC planning that integrates the collection, recovery, re-manufacturing andredistribution of recovered products remains a real challenge. The available literature on strategic decisionmaking in this field is quite limited (Subramoniam et al. 2009). A successful methodology has not yet beenestablished in the literature for the collection of returnables (EOL, used or broken/failed products or components),which could be defined as a generic planning procedure to be pursued by SC managers for addressing RL in theirbusiness. A similar argument is applicable to the recovery process and the remarketing of recovered products.
SCs should critically consider RL as a complex business process (Krumwiede and Sheu 2002) where recoveredproducts would occupy a part of the demand for new products (Horvath et al. 2005) to obtain RL-related businessadvantages or comply with regulatory/environmental requirements. As such, RL influences the overall productdemand, input requirements, manufacturing and marketing process of an entire SC through the collection ofreturnables, the recovery of components and the marketing of recovered products. This fact suggests that RL shouldbe considered as a strategic planning issue. A successful strategic planning approach that integrates RL in the
*Email: [email protected]
ISSN 0020–7543 print/ISSN 1366–588X online
� 2012 Taylor & Francis
http://dx.doi.org/10.1080/00207543.2011.571944
http://www.tandfonline.com
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overall SC process should address the following key factors: strategies for the collection of returnables and therecovery of components/products; the estimation of returnables and recovered quantities; the re-manufacturing andre-marketing of recovered items; supply management and the manufacturing of new products and the distribution ofnew and recovered product to the market.
Several studies have explored strategies for collecting returnables (Savaskan et al. 2004, Schultmann et al. 2006,Wojanowski et al. 2007, Cruz-Rivera and Ertel 2009). These strategies consider collection by the manufacturer,third-party (3-P) logistics and retail outlets. A 3-P collection strategy is suitable if it is driven by reasonable profit. Inorder to consider ecological factors and regulatory requirements, SC managers often need to integrate RL insituations where each and every returnable may not be profit driven (Schultmann et al. 2006). Savaskan et al. (2004)considered 3-P logistics, retailers and the original manufacturer for the collection of returnables, and concluded thatcollection through retailers was the best option. SC management also needs to address customer motivation toreturn the products by offering incentives when considering collections of returnables (Guide et al. 2003).
Another factor is the recovery process, which may be handled by the product’s original manufacturer or 3-PRSPs. It is very difficult to pre-determine the quantity and quality of returnables, so it may not be feasible for an SCto open a recovery facility. In addition, the recovery process for different product types (Thierry et al. 1995) wouldinvolve different kinds of expertise. Considering the economic factors and capability requirements, contractualarrangements with a network of RSPs seem to be the most suitable option for employing a successful recoveryprocess. Based on the factors discussed, SCs would greatly benefit by implementing an RL integrated strategicplanning model that involves retailers for collecting returnables – using appropriate incentives and managingrecovery through a network of RSPs – in addition to addressing the manufacturing, re-manufacturing anddistribution of products to the customers. This model can be effectively implemented by creating contractualarrangements with retailers and RSPs that have the potential to establish long-term partnering for mutual benefits.Such RL planning will aid SCs in addressing the complexities of becoming environmentally responsible by collectingentire sets of recyclable items; making the collection process customer friendly by using retailers (one place forbuying and selling) as well as economic because several manufacturers have the option to collaborate using the sameretailer for their products. Using retailers in the proposed two-way channel (collection and marketing) wouldimprove SC performance in terms of cost, environmental image and customer responsiveness. Using a network ofRSPs for recovery would complement this process through their joint capacity and capability. This researchproposes an MIP model for strategic-level SC planning that integrates RL by involving retailers in an agreement forthe collection of returnables using incentives to motivate consumers, pooling the capabilities of a network of RSPsfor the recovery process and marketing new products, as well as re-marketing recovered products – as a way ofimproving the SC’s overall performance in terms of profit and environmental requirements. The model resolves thestrategic-level planning in two steps to reduce computational complexity. In the first step, the model addresses thecollection of returnables and the recovery of products/components. In this step a scenario-based stochastic processestimates the returnables, relating them to the incentive plans pursued by the retailers. In devising the incentive plan,the SC managers would be able to resolve the economically prohibitive environmental issues (collection of recyclablenon-profit items), taking the option of trade-offs while making contractual arrangements with retailers. In the nextstep, the model integrates recovered components into overall SC planning to optimise profit.
The novel contributions of this research are: (1) the integration of RL planning at the strategic level to optimiseoverall product costs; (2) the development of a plan that addresses marketing, the collection of returnables and theremarketing of recovered products using retailers as a two-way channel; (3) the planned recovery of products andcomponents using a pool of RSPs; and (4) the development of a total SC view that addresses suppliers,manufacturers, DCs, retailers and RSPs, in addition to appointing suppliers for specific supplies, allocatingproduction to manufacturers and assigning factories to DCs.
The remaining part of the paper is organised in the following way. Section 2 reviews the literature. Section 3builds the conceptual flow diagram, describes the problem statement and develops the mathematical models. Section4 illustrates a numerical example and includes a sensitivity analysis of the model output due to the change in therecovered product quantity. Section 5 provides concluding remarks.
2. Literature review
In recent years, the study of RL has attracted significant attention from researchers and SC managers due to theapparent long-term benefit potential, the enactment of local laws and the special importance to the environment
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shown by customers at large. Successfully designing an effective recovery plan that gets customers interested inreturning their EOL/used or unusable products remains a significant challenge. Xanthopoulos and Iakovou (2009)proposed a two-stage model for the recovery of EOL electric and electronic products. In the first phase, the studyused a goal programming approach to identify the components suitable for disassembly and recovery. The secondphase produced a tactical MIP model that decided optimal lead times and re-manufacturing operation parameters.The study also used a lean-based production management approach to address cost and resource issues in theremanufacturing decision process. Gou et al. (2008) focused on an open-loop, RL system and developed a stochasticinventory model to determine the economic batch size for a single, centralised return centre, as well as the economicinventory level of multiple local collection points for minimising the overall SC cost.
Subramonium et al. (2009) reviewed the literature on RL and remanufacturing (reman) for the automotiveindustry and covered several topics including reman cost benefit analysis, used product supply management, remancompetencies and skills, product life cycle strategies, reman and RL network design, environmental considerations,regulations and the impact of RL-based emerging economies. The study recommended that OEM should developtheir products with reman requirements in mind, as adopted by the World Commission on Environment andDevelopment (WECD). This would ensure that their products could be designed to refurbish the EOL product. Theauthors also recommended future research in areas such as: (1) the development of a strategic SC planning processthat addresses reman; (2) the integration of reman design with the upstream OE process as a way of making morereman parts available for service; and (3) the development and integration of a sustainable framework within themainstream business process.
Francas and Minner (2009) investigated the use of capacity planning and network configuration choices forproduct recovery using a two-stage stochastic programming model. Their model considered remanufacturedproducts, assuming such recovered products to be as good as new. Wadhwa et al. (2009) considered uncertainties inthe timing, quantity and quality of EOL products returned by customers, as well as the availability of severalreprocessing alternatives when they modelled their RL system, using fuzzy decision making to select the bestproduct recovery option.
Cruz-Rivera and Ertel’s (2009) comprehensive RL study addressed several issues related to locating collectioncentres, demand and the recovery framework for EOL automotive vehicles in Mexico. The study proposed astrategic network design model for collecting EOL vehicles following the fixed charge facility location model ofDaskin (2003). In a similar automotive RL study, Schultmann et al. (2006) modelled their RL problems on thespecific case of EOL vehicles in Germany in a closed-loop SC. Their study used 3-P collection centres and evaluatednetwork design concepts for separating and reprocessing plastic EOL vehicle components. Schultmann et al.recommended establishing a collaborative approach to recovery networks when recovery occurs free of charge tofulfill legal requirements. An RFID-based, reverse logistics system has been proposed by Lee and Chan (2009) tooptimise transportation costs and keep track of collection quantities at collection points. This study used aGA-based heuristic for selecting optimal locations for collecting returned products from customers. De Figueiredoand Mayerle (2008) proposed an analytical model for designing collection networks where manufacturers – definedas recyclers – were regulated to use a decided percentage of recovered products or components. The modelconsidered incentives paid to the consumer or collection agents for returned items, the number of collection centresand the location of collection centres when designing collection networks.
Lieckens and Vandaele (2007) proposed a mixed-integer nonlinear programming (MINP) model for addressingRL in an SC problem. To address inventory, production lead time and the uncertainty of RL-related parameters,the authors included a queuing model that extended the MINP formulation. The complex model thus formedseemed to be of theoretical interest only. Kara and Onut’s (2010) recent study proposed a revenue maximisationstochastic model for a paper company that wanted to consider recycling and recovering paper. The model aimed atdeciding the best locations for recycling centres by estimating the flow of product from customers to recyclingcentres and back to customers.
Yang et al. (2009) proposed a model for a closed-loop SC network that included the cost and business objectivesof raw material suppliers, manufacturers, retailers, consumers and recovery centres. The model used the approach ofvariational inequality in their stochastic-based model for achieving the optimality condition and maintaining theequilibrium of the network members. Chouinard et al. (2008) studied the quality level of returned products in astochastic modelling approach. The authors considered product quality levels in five states to decide recoveryprocessing alternatives such as: s¼ 0, unknown; s¼ 1, new; s¼ 2, good condition; s¼ 3, deteriorated; and s¼ 4,unusable. Thierry et al. (1995) defined five product recovery systems: re-manufacturing, repairing, refurbishing,cannibalisation and recycling. The study included a comprehensive discussion of the product design approach to
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recovery, including the importance of reducing disposal waste, the benefits of preparing customers for greenproducts and environmental legislation issues for recovery systems.
The literature shows that the existing research has not given due importance to most of the vital strategic RLfactors identified in Section 1 of this paper. This study is an attempt to effectively address the strategic-level RLplanning factors for the collection of returnables, including retailers using a suitable incentive plan, environmentalrequirement fulfillment, product recovery via an RSP network and overall SC performance improvement.
3. Reverse logistics model
This section formulates an MIP-based, RL integrated, strategic-level SC model. The model includes theuncertainties related to EOL/used products returned by the customer as well as the recovery process for returnables,in addition to addressing ecological factors and SC performance in terms of profit. The strategic model progresses intwo steps. In the first step, the reverse SC part of the model decides the optimal quantity of recovered product thatwill be sold based on the product demand history (assuming normal distribution), estimated returnables (using ascenario-based stochastic approach), recovery costs from the RSP network and the expected price for recoveredproducts – in addition to several other inputs. The outcomes of this step are then integrated into the next stage,where the model addresses the entire strategic SC planning process based on the new/standard product demand,taking into account the part of the demand that has been fulfilled by the recovered product. This section begins witha brief problem statement and indices for the model, and ends with the formulation and description of the model. Aconceptual flow diagram is presented in Figure 1 to establish a clear idea of the problem statement and model.
3.1 Problem statement
Consider a supply chain (SC) network comprised of manufacturers m¼ 1, 2, . . . ,M that produce productsp¼ 1, 2, . . . ,P to address the customer requirements of a market, using a network of DCs k¼ 1, 2, . . . ,K anddistributing products to retailers r¼ 1, 2, 3, . . . ,R. The demand for the product from the retailers is considered to benormally distributed with mean and variance �pr and �2pr, respectively. The SC network organisation is closelyrelated to a network of RSPs s¼ 1, 2, . . . ,S and it pools the capacities and capabilities (the expertise) of the RSPsthrough contractual arrangements to address the recovery process of an entire set of customer returnables. Inaddition, it is assumed that the SC has a long-term partner relationship with new component suppliers v¼ 1, 2, . . . ,Vfor obtaining cost and quality advantage from their supplies. Retailers have contractual agreements withmanufacturers regarding the collection of returned products w¼ 1, 2, . . . ,W from customers. Retailers are paid afixed amount for setting their returned product processes once they are in agreement. In addition, they are also paid
Figure 1. Conceptual flow diagram.
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by the manufacturers for collecting returned used products w from customers using various agreed motivationaloffers, and for keeping the returnables w in their warehouses until they can be transported to the RSPs by themanufacturers. RSPs recover usable product u¼ 1, 2, . . . ,U from returned product w through several reclamationprocesses, and then supplies it to the manufacturer at a price RSswu per units of recovered product. This amountincludes the costs incurred and the profits gained by the RSPs during the recovery process. The recovered usableproduct u is finally converted into saleable recovered product l by the manufacturer, who assures the quality of therecovered product. Since the quantity of returned products is only a fraction of the standard product demand, it isassumed that the current recovered product demand can consume more than the quantity of recovered productsavailable in the entire market. The research objectives are to maximise the overall SC profit, paying due attention toenvironmental requirements, customer satisfaction and reasonable RSP/retailer profits.
3.2 Indices and parameters
Indices
k2 f1, 2, . . . ,Kg Distribution centre/warehouse, described as a ‘DC’l2 f1, 2, . . . ,Lg Recovered product that has been readied for sale and includes repaired, remanufactured,
refurbished, cannibalised or recycled products – described henceforth as ‘saleable’m2 f1, 2, . . . ,Mg Manufacturero2 f1, 2, . . . ,Og Incentive offer to the customer from the retailer for returning the used productp2 f1, 2, . . . ,Pg New product, described as ‘product’r2 f1, 2, . . . ,Rg Retailers2 f1, 2, . . . ,Sg Reclamation/recovery service provider, described as ‘RSP’
w2 f1, 2, . . . ,Wg Returned used product, which may include EOL full products, defective or broken products,product components, or wrapping/packing materials – to be described as ‘returned product’ or‘returnables’
u2 f1, 2, . . . ,Ug Usable components or products that have been converted or made from returnables w,described henceforth as ‘usable’ or ‘usable product’ – products for RSPs
(p-w) Returned used product w from product p(u-l ) Recovered product u to be used in l(w-u) Returned product w converted to usable by RSP u
Parameters
BM A large numberCSpiv Cost per unit of input i needed to produce p supplied by supplier/vendor vCMpm Cost of manufacturing product p by manufacturer mCMSml Capacity of manufacturer m for manufacturing saleable recovered product lCWrpw Cost to retailer r for collecting returned product w in the (p-w) combinationCTpmk Cost of transporting product p from manufacturer m to DC kDCpk Capacity of DC k for product p
DCClpk Capacity of DC k for product l in the (l-p) combinationDRCkrl Cost of distributing saleable recovered product l from DC K to retailer rFMm Fixed cost for opening manufacturing mFSs Fixed cost for ordering inputs from supplier s
LMms Fixed cost to be paid by manufacturer m for making service contract with RSP sMPCmul Cost of manufacturing m to convert usable recovered product u into saleable product l
NPpr Demand for product p at retailer r after recovered product has met demand adjustments�rp Demand for product p at retailer r
�2rp Variance of demand for product p at retailer rOZrp Overall demand from retailer r for product p following normal distribution and non-stock out
service factor SFPEmrp Fixed service charge or set-up cost for retailer r to collect, maintain and keep returned product
w in the (w-p) combination until RSPs collect themPFR Saleable as a decided percentage of total retailer demand
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Porpw Probability of returning used product w in the (p-w) combination to retailer r, based on offer o
QRorpw Estimated fraction of product p to be returned by the customer as used product w in the (p-w)
combination to retailer r, based on offer o and assuming zpkr is sold by the retailer to thecustomer
Qpr Price agreed by retailer r for new product pRCrl Price agreed by retailer r for recovered product l
ROrpw Cost to retailer r for keeping and maintaining returned product w in the (p-w) combinationRSswu Price for RSP s to supply usable product u after recovering it from returnables w
RSCswu RSP’s capacity for recovering returned product w to uSCpiv Capacity of supplier v for supplying inputs i for product p
SF Service factor for addressing demand variationsTRsrw Cost of transporting returnable w from retailer r to recovery service provider s
upi Specific usage of inputs i for product putul Specific use of usable u for producing recovered saleable product l
Variables
aivm 1, if supplier s supplies input i to manufacturer m; otherwise, 0Dk 1, if DC k is open; otherwise, 0
EQrlp Quantity of product l that may be considered equivalent to product p at retailer rERmr 1, if retailer r is in agreement with manufacturer m for collecting the returned product;
otherwise, 0EZmrpw Quantity of returned product w in the (p-w) combination collected by retailer r for
manufacturer mINmrpw Incentive to retailer r by manufacturer m for collecting returned product w in the (p-w)
combinationMOm 1, if manufacturing m is open; otherwise, 0Mms 1, if RSP s has a contractual agreement with manufacturer m to provide reclamation service;
otherwise, 0MPmswu Amount of transformed product u received by manufacturer m from RSP s made from w in the
(w-u) combinationMYmul Quantity of saleable product l made from usable u by manufacturer mMZkrl Recovered saleable product l distributed from DC k to retailer rRYmkl Quantity of recovered saleable product l sent by manufacturer m to DC kSpivm Inputs i required for product p to be supplied by supplier s to manufacturing mwkr 1, if k is allocated to supply retailer r; otherwise, 0
Xpmk Quantity of product p manufactured by m to transport to DC kZpkr Quantity of new product p distributed by DC k to retailer r
ZRrpw Fraction of used product w collected at retailer r in the (w-p) combination
3.3 Product recovery model
In this subsection, the model addresses the strategic decision process for collecting returnable products,recovering usable products/components from the returnable ones and transforming the usable into recoveredsaleable products.
Objective function: maximise Z1 ¼ FTO� INRET� RMFG, ð1Þ
FTO ¼Xr
Xl
RCrl
Xk
MZkrl , ð1aÞ
INRET ¼Xm
Xr
Xp
Xw
INmrpw, ð1bÞ
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RMFG ¼Xs
Xr
Xw
TRsrw
Xm
Xp
EZmrpw þXs
Xw
Xu
RSswu
Xm
MPmswu þXm
Xs
LMmsMms
þXm
MOmFMm þXm
Xu
Xl
MPCmulMYmul þXk
DkFDk þXk
Xr
Xl
DRCkrlMZkrl, ð1cÞ
s.t.
OZrp ¼ �rp þ SFffiffiffiffiffiffi�2rp
q� �, 8r, p, ð2Þ
ZRrpw ¼Xo
PorpwQRo
rpw, 8r, p,w, ð3Þ
EZmrpw ¼ ERmrZRrpwOzrp, 8m, r, p,w, ð4Þ
Xm
EZmrpw � ZRrpwOzrp, 8r, p,w, ð4aÞ
INmrpw ¼ ðCWrpw þ ROrpwÞEZmrpw þ PEmrpERmr, 8m, r, p,w, ð5Þ
Xu
Xs
MPmswu �Xp
Xr
EZmrpw, 8m,w, ð6Þ
MPmswu �MmsRSCswu, 8m, s,w, u, ð7Þ
MYmul � utulXs
Xw
MPmswu, 8m, u, l, ð8Þ
Xu
MYmul �MOmCMSml, 8m, l, ð9Þ
Mms �MOm, 8m, s, ð10Þ
ERmr �MOm, 8m, r, ð11Þ
Xk
RYmkl ¼Xu
MYmul, 8m, l, ð12Þ
Xm
RYmkl � DCCklDk, 8k, l, ð13Þ
Xm
RYmkl ¼Xr
MZkrl, 8k, l, ð14Þ
Xk
MZkrl ¼Xp
EQrlp, 8r, l, ð15Þ
Xl
EQrlp � PFR �OZrp, 8r, p, ð16Þ
Dk 2 f0, 1g, 8k, MOm 2 f0, 1g, 8m, Mms 2 f0, 1g, 8m, s, ERmr 2 f0, 1g, 8m, r: ð17Þ
For this model, the objective function in Equation (1) maximises profit Z1 for the SC, which is computed by
subtracting the retailer’s incentive (INRET ) and expenses for manufacturing and recovering returnables (RMFG)
from the final turnover (FTO) earned by the SC when it supplies saleable recovered products to the retailer. INRET
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in Equation (1b) is defined in detail in Equation (5). INRET takes the retailer’s incentives into account, and they arebased on a contractual agreement to collect returnables, keep them in store, communicate and maintain thereturnables until they are transported to RSPs for a fixed setup cost. RMFG in Equation (1c) computes themanufacturer’s (SC’s) expenses for transporting returnables from the retailer’s place to the RSPs, the payables tothe RSPs, the costs for manufacturing saleable recovered products, the fixed costs for opening DCs and thedistribution of saleables from DCs to retailers. Payables to RSPs include the per unit variable cost for supplyingrecovered usable products to the SC and fixed costs based on the contractual agreement with RSPs for recoveryservices. Costs for manufacturing include a fixed cost for setting up the plant and the variable cost forre-manufacturing recovered saleable products using usable recovered items. Costs for transporting the recoveredsaleable products from manufacturing to the DC are ignored because the transportation costs for recovered saleableproducts will be insignificant if the saleable products are transported along with new standard products.
The overall product demand from retailers is estimated in Equation (2), considering demand to be normallydistributed with mean �pr and variance �2pr. Based on the company’s mission to maintain a pre-decided customerservice level that is relevant to a non-stock out situation, a safety factor SF is applied. Equation (3) estimates acustomer’s possible product return as a fraction of the total product sold, considering the returned fraction at returninstances and the probability of said instances. Instances for the fraction of product returned may be obtained fromthe return history if the process of returning the product has been made popular for the customer by the retailer, andthe process has been in place for a reasonable period of time. The SCs may estimate the probability of returninstances based on consumer survey responses when there are several offers available to motivate consumers.Typical offers from the retailers may include the following.
. Providing a new replacement product for returning EOL products, if the customers agree to pay a pre-decided percentage (in a range from 60 to 80% for some products) of the product value. Product examplesfor a similar offer would be welding transformers, vacuum cleaners, lawn mowers, cars, forklifts, pallettrucks, hand grinders, powered hand saws, faucets, steel furniture and others (mostly hardware/engineeringitems).
. Payment to the customers of a decided cash amount or the presentation of a cash certificate for buyingother products in the store or for returning packing/packaging materials.
. Payment to the customers of a cash amount equivalent to 25–30% of the recovered product price forreturning products/components in prelisted conditions. This kind of offer is suitable for hand tools,engineering metallic components, electronics and electrical components.
. Any other incentive plan may be designed and decided by the joint collaboration between the SC andretailers.
It may be noted here that the SC managers have the option of considering trade-offs when devising incentiveplans that motivate customers to return EOL/used products.
Equation (4) estimates the effective returnable quantity collected by a retailer from the customers, based on theoverall product demand in Equation (2) and the fraction of product return determined in Equation (3), combinedwith the contractual arrangement made with the manufacturer. Equation (4a) is a technical constraint. It ensuresthat the effective returnable quantity collected by the retailers for the manufacturers is less than the maximumpossible returnable quantity that could be collected by the retailers. This maximum quantity is a business decisionmade by strategic-level SC managers based on their marketing strategy for recovered and new product mix. Fixingthis maximum quantity would also provide trade-off options for controlling the quantity of returnables collected bySCs. Constraint (6) ensures that the usable recovered product made by the RSPs does not exceed the returnablequantity from which it was made. Constraint (7) ensures a supply of usable product to a manufacturer through anRSP, according to its capacity and contractual agreement with the manufacturer. According to constraint (8),saleable recovered products produced by the manufacturer from usables are based on the amount of recoveredusable products supplied by the RSP to the manufacturer, as well as the specific usage of the item when producingper unit saleables. Equation (9) limits the production of saleable recovered products from usables within thecapacity of the manufacturer if it is set up or opened for producing saleables. Constraint (10) ensures that amanufacturer is set up to produce saleable recovered products before making contracts with RSPs. Similarly,constraint (11) ensures that a manufacturer makes a contract with the retailer for collecting returnables only when itis set up to produce recovered saleables. Equation (12) balances the recovered saleable products sent to the DC withthe quantity made by the manufacturer from the usable products and components. Constraint (13) ensures a supplyof saleables to a DC, according to its capacity when open. Equation (14) balances a DC’s distribution quantity with
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the supply quantity from the manufacturer. Constraints (15) and (16) are included to integrate the outcomes of theProduct Recovery model with that of the New Product model. Constraint (17) imposes integrality.
3.4 New product model
The new/standard product model presented in this subsection integrates the outcomes of the recovered productmodel with the strategic planning process for the new product.
Objective function: maximise profit ¼ REV� ðINPCþMFGCþ TRANSþDIST Þ, ð18Þ
REV ¼Xp
Xr
Qpr
Xk
Zpkr, ð18aÞ
INPC ¼Xp
Xi
Xv
CSpiv
Xm
Spivm þXv
FSv
Xi
Xm
aivm, ð18bÞ
MFGC ¼Xp
Xm
CMpm
Xk
Xpmk þXm
FMmVm, ð18cÞ
TRANS ¼Xp
Xm
Xk
CTpmkXpmk, ð18dÞ
DIST ¼Xp
Xk
Xr
CDRpkrZpkr þ dkFDCk, ð18eÞ
s.t.
OZrp �Xl
EQrlp ¼ NPpr, 8p, r, ð19Þ
Zpkr � NPpr, 8p, k, r, ð20Þ
Zpkr � BM � wkr, 8p, k, r, ð21Þ
Xk
Xpmk � PCpmvm, 8p,m, ð22Þ
Xm
Xpmk ¼Xr
Zpkr, ð23Þ
Xm
Xpmk �WCpkDk, 8p, k, ð24Þ
wkr � dk, 8k, r, ð25Þ
Spivm � SCpivaivm, 8p, i, v,m, ð26Þ
upiXk
Xpmk �Xs
Spivm, 8p, i,m, ð27Þ
wkr 2 f0, 1g, 8k, r, dk 2 f0, 1g, aivm 2 f0, 1g, 8i, v,m: ð28Þ
The objective function in Equation (18) maximises profit by maximising revenue (REV) and minimising the inputcost for components (INPC), manufacturing (MFGC), transportation (TRANS) and distribution (DIST). REV in
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Equation (18a) is earned by supplying product to the retailer/customer at a customer-agreed price. INPC inEquation (18b) computes the cost paid to the supplier in terms of the variable cost for the amount of inputspurchased, and the fixed cost for making a contract with the supplier. Manufacturing cost MFGC in Equation (18c)is computed considering the variable cost of manufacturing the products, and the fixed cost for setting the factoryup for operation. TRANS in Equation (18d) defines the cost of transporting products from the manufacturer to theDCs. The last component for the objective function is the distribution cost DIST in Equation (18e), which accountsfor the cost of distributing products from the DCs to the retailers, and the cost of opening the DCs for theassignment.
Equation (19) determines the new product requirements when a part of the demand is already fulfilled by therecovered product through the integration process established by the recovered product model. Equation (20)ensures the distribution of product from a DC to a retailer to satisfy customer demand. Constraint (21) allocatesDCs to supply the retailers. Constraint (22) limits production by a manufacturer based on its capacity when it isopen to produce. Constraint (23) balances the distribution from the DC with the transported quantity from themanufacturer to the DC. Constraint (24) limits the quantity supplied to a DC by the manufacturer, based on itscapacity, if the DC is open. Constraint (25) ensures that a DC is open before it is allocated to supply a retailer.According to constraint (26), the quantity of inputs supplied by a supplier is limited by its capacity if the supplier isapproved by the manufactuer. Constraint (27) ensures that the inputs supplied by the supplier should at least fulfillthe requirements of the manufacturer. Constraint (28) imposes integrality.
4. Numerical example
To demonstrate the applicability of the proposed approach and the models, we consider an SC that involves sevenproducts produced in five manufacturing plants (MFGP), distributed through six DCs to five retail outlets. Each ofthe standard products is made up of two to four components. The SC includes RL and has contractualarrangements with retailers for collecting returnables from the customer, as well as re-marketing the recoveredproducts. This section illustrates in detail the collection of returnables and the recovery process for the components/products. The seven standard products produced and marketed by the SC have 22 types of returnables that consistof EOL or used products, their components and packing materials. The SC also involves seven RSPs undercontractual arrangements, who are supplied returnables collected through retailers. The RSPs convert the 22 typesof returnables into 26 types of usable recovered products/components, and supply them to the MFGPs. The numberof usables is more than the number of returnables because some returnables are full EOL or used products, whichare then divided into components when their condition does not justify recovering them as full products. Therecovered usable products are processed by the MFGPs, who transform them into eight types of saleable recoveredproducts. Out of these eight types of saleables, seven resemble the equivalent of the original seven products and thelast is a paper and pulp-based product made from packing materials. Based on the contractual agreement with theSC, the retailers in this example provide the following three offers to motivate customers to return products,components and packing materials.
Offer 1: Providing a replacement new product for returning a good EOL product when customers agree to pay60–75% (based on product type) of the new product price.
Offer 2: Payment of cash equivalent (25–30% of the recovered product price) for returning EOL products/components in good condition.
Offer 3: Cash certificate worth 20% of the equivalent price of the recovered product to buy other items in the store ifthe components/packaging materials/products are returned.
Table 1 provides the possible returnable items (complete products, components and wrapping/packing materials)for the product manufactured and marketed by the company. For example, there are four returnables for product 1:10 being the complete EOL product, 11 and 12 being the possible returnable components and 800 being thewrapping or packing materials (Table 1). A similar analysis is applicable for the other product–returnablecombinations.
Usable products/components that may be recovered from returnables are presented in Table 2. For example,returnable EOL total product 10 may be converted into usable total product 100, or into components 111 and 121through the required recovery process (Table 2). According to Table 2, returnable components 11 and 12 can be
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transformed into corresponding usable recovered components 111 and 121 through a suitable recovery process.Packing material 800 is transformed into usable product 8000 (pulp or shredded pieces) (Table 2).
As previously discussed, usable products/components recovered by the pool of RSPs are sold to manufacturers.Although there are instances of total product recovery as usable products, such as {10 to 100} or {20 to 200},producing a total recovered product using only recovered usable components is often not possible for the RSPs.Based on our experience, additional refurbishing, testing and adjustments are required before sending them to themarket as recovered products. These final steps for the usable product, transforming them into saleable recoveredproducts, should be performed by the manufacturer of the original product. The model’s example in our researchfollows the same analogy. The transformation of usables into final, saleable recovered products is shown in Table 3.For example, saleable recovered product 1000 is obtained by some minor manufacturing processes on theusable total product, while usable components 111 and 112 are assembled to manufacture final saleable recoveredproduct 1001. It should be noted that 1001 and 1000 are the same products, transformed through different processesusing different inputs. Different codes are used for taking care of the higher cost implications for assembled products.
We assumed that retailer demand for new products follows a normal distribution. Table 4 shows the typicalmean and variance of product demand for products 1 and 2 from the five different retailers. The mean and variancedata for product demand were generated randomly following a normal distribution.
Because the quantity of returnable EOL complete products and components is very low, we assumed that thereare sufficient market demands for the entire quantity of recovered product.
A scenario-based analysis was considered for estimating the quantity of EOL or old products returned by thecustomer. The scenarios and probability of scenarios are developed randomly following a discrete distribution based
Table 2. Returnable–recovered usable combinations.
ReturnableRecoveredusable Returnable
Recoveredusable Returnable
Recoveredusable
10 100 30 300 50 500111 311 511112 321 521
11 111 331 53112 121 31 311 51 51120 200 32 321 52 521
211 33 331 53 531221 40 400 60 600231 411 611
21 211 421 62122 221 431 70 70023 231 41 411 711
42 421 72143 431 800 8000
Table 1. Product–returnable combinations.
Product Returnable Product Returnable Product Returnable
1 10 3 30 5 5011 31 5112 32 52800 33 53
800 8002 20
21 4 40 6 6022 41 80023 42 900800 43 7 70
800 800
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on the three offers described above. Table 5 shows an example of typical scenario-based data for the returnable 10,which is the EOL complete product 1 based on the three offers from the three retailers. Returnable 10 relevant toproduct 1, for example, has a 50% probability of returning 20% of the total product sold by retailer 1, based onoffer 1, and based on offer 2 the returnable fraction is 15% of the total product with a probability of 30%. Thereturnable fraction for offer 3 is only 10%, with 20% probability. The probability figures for an incentive offer maybe estimated based on a telephonic survey among retail outlet customers. Fraction scenarios (described as ‘instances’in the model description) are to be estimated based on the past history of the offers’ success at obtaining returnables.It may be noted that, based on the author’s experience, there may be large differences between the survey figures andthe actual fractions returned for several reasons, which include the following.
. A limited percentage of customers are covered in the survey.
. While responding to the telephonic survey may be easy for customers, returning used products ofteninvolves more time and planning.
. Customer mobility.
. The attraction of returning used products can decrease with time based on change of earning level.
As previously discussed, retailers are paid an extra service charge in addition to costs for collecting returnablesand maintaining them until they can be sent by the manufacturers to the selected RSPs. The typical assumed chargespaid by the manufacturers to the retailer for collecting and storing each type of product are shown in Table 6a.Retailer 1, for example, is paid $9.50/unit and $0.05/unit, respectively, to collect and store returnable 11 – acomponent from product 1. These cost figures are higher when the considered returnable is an EOL/used fullproduct like product 10. Retailers are paid fixed service charges for making arrangements to collect returnables fromproduct types. Examples of typical fixed service charges are shown in Table 6b. Table 7 displays the typical recoveryservice charges paid to the RSPs for transforming returnables into usables and supplying them to manufacturers.Table 8 shows the assumed typical costs incurred by manufacturer 1 for transforming the recovered products fromusables to saleables.
Table 5. Typical data for estimating returnable quantities.
Product Returnable Retailer Offers
Scenarios ofreturnablefraction Probability
1 10 1 1 0.2 0.51 10 1 2 0.15 0.31 10 1 3 0.1 0.21 10 2 1 0.2 0.51 10 2 2 0.15 0.31 10 2 3 0.1 0.21 10 3 1 0.2 0.451 10 3 2 0.15 0.351 10 3 3 0.1 0.15
Table 6a. Typical cost of returnables for collecting, storingand service charges.
Product Returnable RetailerCollectingcost ($)
Storingcost ($)
1 10 1 22.00 0.281 10 2 22.50 0.271 10 3 23.00 0.271 10 4 23.00 0.251 10 5 23.00 0.251 11 1 9.50 0.051 11 2 9.50 0.051 11 3 10.00 0.041 11 4 11.00 0.041 11 5 10.00 0.04
Table 3. Usable–final saleable recovered product relationships.
Newproduct
Recoveredsaleable
Newproduct
Recoveredsaleable
100 1000 300 300111 1001 311 3001112 321200 2000 331211 2001 600 6000221 611 6001231 621
Table 4. Typical demand and variance data for productdemand from retailers.
Product RetailersMeandemand Variance
1 1 568 281 2 251 121 3 944 261 4 731 181 5 444 18
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In addition to the inputs described in Tables 1–8, we also assumed data for an RSP’s capacity within therecovery process, the capacity of DCs for storing saleables, fixed costs for setting up factories, fixed costs to be paidby manufacturers to RSPs for opening DCs, costs for transporting returnables from the retailer to the RSPs and thecost of distributing saleables from DCs to retailers. These data were generated randomly, assuming suitable uniformdistribution parameters.
4.1 Model output
We first solve the Product Recovery Model (Section 3.3), which initially computes the overall product demand fromthe retailers as presented in Table 9, based on the assumed normally distributed mean demand, variance and safetyfactors (model Equation (2)). The model determined product 1’s demand from retailer 1 to be 575 items, for example(Table 9). Table 10 presents the model outputs on the typical quantity of returnables collected by the retailers(computed using Equation (3)), based on their contracts with the manufacturers. Based on Table 10, the modeldecided to set up or open MFGPs 2, 3 and 4 to produce recovered saleable products, then accordingly initiatedcontracts between MFGP 2 and retailers 1 and 4, MFGP3 and retailers 2 and 3 and MFGP 4 and retailers 4 and 5for the collection of returnables. To illustrate further, retailers 1 and 4, for example, collected 66 and 36 items ofreturnable 10, respectively, relevant to product 1 – based on the contractual agreement with MFGP2 (Table 10).
Returnables collected by retailers are sent to the RSPs for conversion into usable products/components througha suitable recovery process and according to their contracts with the MFGPs/SC. Table 11 describes the quantity ofusables recovered from returnables by RSPs 8, 7 and 4, which are selected by the model based on the cost, capacityand capability parameters of the RSPs. A total of 102 items of usable product 100, for example, were supplied toMFGP 2 by RSP 8, according to the contract, after recovering them from returnable 10 (Table 11).
Table 12 presents the quantity of saleable products made by MFGPs, using usable recovered products andcomponents as obtained by solving the recovery model. For example, MFGP2 made 50 and 60 items of equivalentsaleable recovered product 1001, using usables 111 and 121, respectively. Based on Table 11, RSPs supplied 100items of usable 111, and 121 items of usable 121 – from which MFGPs made 50 and 61 items of 1001
Table 8. Typical manufacturing costs for transformingusables into saleable products.
Manufacturer UsableRecoveredsaleable
Manufacturing(transforming
cost) ($)
1 100 1000 4.001 111 1001 2.101 121 1001 2.101 200 2000 3.861 211 2001 1.341 221 2001 1.491 231 2001 1.49
Table 9. Overall product demand from retailers.
Retailer
Product 1 2 3 4 5
1 575 673 513 1699 4662 255 1479 660 929 13513 950 485 742 229 16164 736 1635 1463 1817 6025 449 475 878 1626 18806 324 1735 1043 1867 13617 700 216 1613 1711 252
Table 6b. Typical fixed service charges paid to the retailer forcollecting returnables.
Manufacturer Retailer Product Service charge ($)
1 1 1 147.751 1 2 103.751 1 3 134.251 1 4 90.51 1 5 116.751 1 6 149.51 1 7 165.25
Table 7. Typical recovery service charges paid to RSPs bymanufacturers.
RSP Returnable UsableRecoverycost ($)
1 10 100 4.441 10 111 1.871 10 121 1.871 11 111 1.871 12 121 1.87
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saleables, respectively. It may be noted here that to build saleable 1001, we need one component from each ofusables 111 and 121. In Table 12, we use equivalence in number of total products made by components to addressselling and cost implications in our strategic decision making.
The Recovery Model outputs, in terms of the quantity of recovered saleable products, are then integrated intothe New Product Model (Section 3.4) to obtain overall strategic-level SC planning that covers the entire market ofnew and recovered products. Table 13 describes the saleable recovered product marketed through the retailers, and
Table 11. Typical returnable-to-usable conversion by RSPs for manufacturers.
MFGP 2 MFGP 3 MFGP 4Returnable Usable RSP8 RSP7 RSP4
10 100 102 65 8610 111 0 0 010 121 0 0 011 111 100 74 9012 121 121 65 72
800 8000 0 0 020 200 54 56 020 211 0 0 020 221 0 0 020 231 0 0 021 211 30 57 022 221 30 46 023 231 26 116 0
800 8001 0 0 0
Table 10. Typical collection of returnables by retailers for manufacturers.
Product Returnable
MFGP 2 MFGP 3 MFGP 4
TotalRetailer 1 Retailer 4 Retailer 2 Retailer 3 Retailer 4 Retailer 5
1 10 66 36 17 48 12 73 25311 69 31 17 57 17 72 26212 72 49 17 48 0 72 258800 0 34 10 22 0 0 66
2 20 31 24 39 17 0 0 11021 7 24 42 16 0 0 8822 6 24 36 10 0 0 7623 4 22 103 13 0 0 142800 0 0 41 9 14 0 64
Table 12. Typical production of saleables from usables.
Usable Saleable MFGP 2 MFGP 3 MFGP4 Total
100 1000 102 65 86 253111 1001 50 37 45 131121 1001 61 32 36 1298000 8001 0 0 0 0200 2000 54 56 0 110211 2001 10 19 0 29221 2001 10 15 0 25231 2001 9 39 0 478000 8001 0 0 0 0
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the equivalence of said saleables with the standard product. For example, 260 items of saleable recovered product1001, equivalent to product 1, have been sent from DC 2 to retailer 2. The model only used DC 2 for distributingsaleables, which may be logical considering capacity requirements and cost implications. As expected, the quantityof saleable recovered products distributed to the retailers in Table 13 is the same as the quantity built by the MFGPsin Table 12. For example, total saleables 1001 built (shown in Table 12, last column)¼ 129þ 131¼ 260.
The RL integrated strategic SC production distribution model addresses the demand for standard new productsafter considering demand that has already been fulfilled through the equivalent recovered saleables. The adjustedfinal demand figures are shown in Table 14. These figures may be traced based on Tables 9 and 13. Column 2 ofTable 15 displays the model output on the selection (opening) of MFGPs (3, 4 and 5) to fulfill customer demandbased on cost data, capacity and MFGP capability. Table 15 also shows the quantity of products transported fromMFGPs to DCs. The model has not used DC 5. Based on Table 15, MFGP 5 distributed 1103 items of product 1 toDC 2. It may also be noted that DC 1 was not used for product 1 in Table 15.
Table 16 presents the input supply information from the selected suppliers to the opened MFGPs. For example,suppliers 4 and 5 supply 3100 and 312 items of input/component 1, respectively, to MFGP5 for product 1, whilesupplier 4 supplies 3412 items of input/component 2 for the product (Table 16). These combined supplies solvecomponent requirements for product 1. Table 17 describes the model output for the distribution of products fromthe DCs to the retailers. A total of 659, 253 and 191 items of product 1, for example, were distributed from DC 2 toretailers 2, 3 and 4, respectively (Table 17).
Based on the analysis of model outputs, it is clear that the model systematically and effectively addresses the RLin terms of returnable collection using planned incentives implemented by retailers in an agreement/contract, themanagement of the recovery process using RSPs in agreement, the remanufacturing and/or converting of recovered
Table 13. Saleable product (in standard product equivalence) sold by retailers.
DC Product Saleable
Retailer
1 2 3 4 5
2 1 1000 253 0 0 0 01 1001 0 260 0 0 02 2000 0 0 0 110 02 2001 0 0 0 101 03 3000 85 0 0 0 03 3001 0 0 0 0 924 4000 0 0 0 0 3094 4001 397 0 0 0 05 5000 0 0 343 0 05 5001 0 0 0 0 3506 6000 0 0 371 0 07 7000 0 0 0 328 00 8001 0 251 354 0 0
Table 15. Transportation of products from plants to DCs.
Product MFGPs DC1 DC2 DC3 DC4 DC5 DC6 Total
1 5 0 1103 787 14 0 1508 34122 3 185 1293 0 1446 0 836 3760
5 0 0 701 0 0 0 7013 4 1101 1686 0 1058 0 38454 3 1066 1838 1009 401 0 1234 55485 5 943 1543 1803 0 325 46146 4 995 1361 1867 0 1735 59587 3 0 1207 179 1778 0 999 4163
Table 14. Demand for new product after adjusting fulfilleddemand by recovered saleables.
Product
Demand from retailers
1 2 3 4 5 Total
1 322 673 253 1699 466 34132 255 1479 660 717 1351 44613 865 485 742 229 1524 38464 340 1635 1463 1817 294 55505 449 475 535 1626 1530 46146 324 1735 672 1867 1361 59597 700 216 1613 1383 252 4164
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items into saleable products and the development of successful strategic planning by SCs considering new andrecovered products.
4.2 Sensitivity analysis
It is evident that the quantity of recovered product to be marketed is directly proportional to the EOL/usedreturnables received from the customer at the collection centre. As such, the contribution of recovered products tooverall SC performance is influenced by the quantity of returnables. Since the model integrates the recoveredproduct quantity to address overall product demand, the quantity of returnables also influences the quantity of newproduct to be produced. A sensitivity analysis has been conducted to investigate the effects that changes in thequantity of returnables have on profitability and overall strategic model decisions. We also investigate thepercentage of total profit earned by the recovered products and new products, in relation to the percentage ofoverall costs for each of them. The model has been solved for an assumed increment rate of 5% in the returnablequantity to investigate the impact of such changes on SC performance in terms of cost and profitability. Since theassumed current level of returnables has been a very small percentage (1.5–15% of the total product demand), theeffect of decrement from the current level has not been studied. Returnables at the current level are considered tohave a base level of 100% for the sensitivity analysis.
Table 18 demonstrates the improvement of overall profitability and SC costs prompted by an increase inreturnable quantity, which is also represented in Figure 2. The overall profitability (profit/investment cost) of the SCincreased from 32.37 to 33.02%, while the overall investment cost dropped from 2.67 million to 2.58 million, with atotal increase in returnables of 35% over the current level (Table 18, Figure 2). It is noted that a 35% increase over
Table 18. Model output of SC performance for change in the returnable quantity.
%Returnable
Overallprofit (OP)
Recovered productOverall
cost (OC) Profitability
Recovery
Profit Profit as % OP Cost Cost as % OCa ($) b ($) c¼ (100*b)/a d ($) e¼ a/d F ($) g¼ (100*f )/d
100 867,320 82,248 9.48 2,678,907 0.3238 139,581 5.21105 851,179 86,510 9.97 2,630,057 0.3236 146,219 5.56110 852,662 91,088 10.68 2,624,285 0.3249 151,421 5.77115 853,342 95,111 11.15 2,619,845 0.3257 156,127 5.96120 852,333 99,689 11.7 2,614,116 0.3261 165,904 6.35125 853,934 104,578 12.25 2,607,989 0.3274 171,182 6.56130 854,713 108,879 12.74 2,603,238 0.3283 175,917 6.76135 853,021 113,392 13.29 2,583,344 0.3302 185,511 7.18
Table 16. Typical input supply by selected suppliers toopened MFGPs.
Products Inputs Suppliers
MFGPs
3 4 5
1 1 4 31005 312
2 4 34122 1 1 3760 701
2 2 37605 701
3 7 3760 701
Table 17. Typical model output for distribution productsfrom DCs to retailers.
Retailer
Product DC 1 2 3 4 5
1 2 659 253 1913 321 4664 146 1508
2 1 1852 633 6603 170 5324 95 13516 160 676
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the current assumed level of returnables (1.5–15% of the total product demand) may be considered a very small
change. With this level of increase in returnables, the overall profitability increases by 2.00% while the overall cost
decreases by 3.37%. It may also be seen from Table 18 that a product recovery business in the reverse SC for this
example needed only 5.21–7.21% (column 8) of investment costs to earn 9.48–13.29% (column 4) of overall profit –
inspiring for any business.
5. Conclusion
The strategic RL-integrated SC planning models proposed in this paper will facilitate simultaneous decision making
for the entire business process, including the management of RL using a two-step process. The proposed two-step
modelling approach will reduce the complexity of the problem and facilitate SC managers as they pursue a
systematic planning procedure for collecting returnables, managing recovery processes and integrating the recovered
products into their overall SC decision making. The research incorporated reasonably simple stochastic procedures
for addressing uncertainty in EOL/used product returns and overall product demand.This procedure will help SC managers estimate the quantity of EOL/used product returns based on the overall
product demand, which will create a foundation for long-term planning. The research presented in this studyintroduces a modelling approach that utilises retailers as a two-way channel through a service contract for collecting
returnables and marketing the recovered products, allowing the SCs to optimise their costs using retailers as their
partners. The model also considered pooling the capacities of RSPs to address the recovery process for the entire set
of returnables as a way of optimising costs. This recovery cost base introduces improvement potential through the
creation of long-term partnerships with RSPs and providing them technological training through the SC’s
manufacturing units. This paper also incorporates a discussion of the model results, and a sensitivity analysis based
on a numerical example to illustrate the applicability of the model. The sensitivity analysis will facilitate SCs in their
pursuit of a trade-off analysis for deciding a recovered and new product mix that fits their market.The proposed scenario-based returnable estimation procedure will also aid SC mangers in devising incentive
plans that motivate customers through the retailer pursuing a trade-off between the ecological and economicalobjectives. Further, our research has created an option for SC managers who wish to reduce the collection cost of
returnables by creating networks with other SCs for marketing their product using the same retailers taking a
collaborative approach. To the best of our knowledge, this is the first research that proposes a comprehensive
methodology for the following: collection of returnables by retailers using suitable incentive plans; and recovery of
items by pooling the capabilities of an RSP network and the creation of trade-off options to control the returnable
quantity – providing SC performance improvement potential in terms of cost and environmental responsiveness.
2580000
2600000
2620000
2640000
2660000
2680000
2700000
0.9
Tot
al c
ost (
TC
)
(PIR, TC)
1
(PIR, PB)
PIR: 1 means 100%, 1.1 means 110% etc.
1.1 1.2
% Returnable (PIR)
1.30.323
0.324
0.325
0.326
0.327
0.328
0.329
0.33
0.331
1.4
Prof
itabi
lity
(PB
)
Figure 2. Influence that the percentage of returnables has on profitability and overall SC cost.
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Although the research considers several input parameters for the decision-making process, based on the author’s
experience, most of these parameters are obtainable in real-world industrial applications. This research has thelimitation of using random data to illustrate the models’ applicability, which supports the value of future research tovalidate the model using real-world industry data.
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