Annotated Bibliography
LeiMommy23
Reverselogisticspeerreviewarticle3.pdf
Reverse logistics disposition decision-making
Developing a decision framework via content analysis
Benjamin T. Hazen, Dianne J. Hall and Joe B. Hanna Department of Supply Chain and Information System Management, College of Business, Auburn University, Auburn, Alabama, USA
Abstract
Purpose – The purpose of this study is to identify the critical components of the reverse logistics (RL) disposition decision-making process and suggest a decision framework that may guide future investigation and practice.
Design/methodology/approach – The authors utilized a problem-driven content analysis methodology. RL literature from 2000 through 2010 was content analyzed to determine which components may impact a firm’s RL disposition decision.
Findings – The authors extrapolated seven RL disposition decision components from a compilation of 60 variables identified in the literature. Practical implications and suggestions for future research are offered, and a RL disposition decision-making framework is presented.
Research limitations/implications – Although methodological techniques were carefully followed, the nature of a content analysis may be subject to author bias. Future investigation and use of the framework presented will verify the findings presented here.
Practical implications – This study identifies seven components that should be considered when deciding which RL disposition alternative should be adopted and integrates these components into a decision-making framework. Supply chain professionals who refer to this framework during the decision process will benefit from a more comprehensive analysis of potential RL disposition alternatives.
Originality/value – Congruent with recent assertions suggesting that RL research is evolving from an operational-level focus to a holistic business process approach for maximizing value recovery, this study synthesizes operational-level research to develop a practical framework for RL disposition decision-making.
Keywords Reverse logistics, Returns, Disposition, Content analysis, Decision making
Paper type Research paper
Introduction Managing return product flow is becoming increasingly important to the success of supply chain firms, particularly as the volume of return flow substantially increases (Guide Jr et al., 2006). Because $100 billion worth of products are returned in the USA each year (Stock et al., 2002), the returns management process can be an integral part of a firm’s supply chain (Rogers et al., 2002). Accordingly, returned product disposition should not only happen quickly (Blackburn et al., 2004), but disposition decision-makers must consider a variety of decision parameters to ensure that the chosen disposition policy is the most advantageous for the organization. To date, no study has investigated what components comprise the disposition decision-making process. Thus, the purpose of this study is to identify relevant decision parameters
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0960-0035.htm
IJPDLM 42,3
244
Received 14 October 2010 Revised 9 March 2011, 16 August 2011, 22 October 2011 Accepted 24 October 2011
International Journal of Physical Distribution & Logistics Management Vol. 42 No. 3, 2012 pp. 244-274 q Emerald Group Publishing Limited 0960-0035 DOI 10.1108/09600031211225954
and create a framework that will help guide business decisions-makers and future research regarding which disposition option to choose.
Much of the extant reverse logistics (RL) disposition literature seeks to optimize operational processes. For example, much of the literature discusses various aspects of managing returns for remanufacturing (Atasu and Cetinkaya, 2006; Inderfurth, 2005; Lu and Bostel, 2007; Teunter et al., 2006; Webster and Mitra, 2007). However, Guide Jr and Van Wassenhove (2009) posit that research in closed-loop supply chains (CLSCs) is evolving from a technical focus on operational-level activities to a holistic business process approach for maximizing value recovery. Additional research has noted the importance of understanding the factors involved in carefully examining the impact of the disposition decision on the rest of the firm (Blumberg, 1999). However, the literature is sparse in the area of RL disposition decision-making and is therefore an area in need of further study (Stock and Mulki, 2009). The current study capitalizes on the abundance of operational-level research to lay the groundwork for future decision-making research.
Background Literature on RL and the nature of decisions regarding RL processes is becoming more abundant as the area matures from primarily a step-retracing supply chain process to one that stands on its own as a necessary process to which management should pay close attention. While some research begins to investigate considerations for pursuing returns management activities in general (Rogers et al., 2002), a comprehensive understanding of the elements inherent in the disposition decision has not been developed. Without such an understanding, practitioners struggle to develop best practices and researchers cannot provide support to them. Research is needed to adequately provide an understanding of this area. The current study provides the foundation for that understanding by identifying the key components of the RL disposition decision and providing a framework for practice and future research.
We define the RL disposition decision as leading to the establishment of an organizational policy regarding which recovery option to pursue for a specific product or line of products. Because this decision should be made in accordance with a firm’s current policies, market position and objectives, we propose that the RL disposition decision requires great consideration. RL is comprised of all functions that begin with acquiring a returned product and end when the owning firm has extracted all possible value from the item through proper disposition. This disposition process includes options ranging from simply reusing the product to properly disposing of the product.
Much literature has been devoted to identifying and describing the actual disposition alternatives, such as reuse, recycling, and remanufacturing (Blackburn et al., 2004; Carter and Ellram, 1998; Krikke et al., 2004; Thierry et al., 1995). Some of this research has also addressed a variety of design considerations for reverse channels, such as investigating the tradeoff between efficient and responsive reverse supply chains (Blackburn et al., 2004) and modularity (Krikke et al., 2004). However, research regarding which RL disposition alternative should be employed by a given firm for a given product line is markedly absent in the literature. When determining a policy regarding how to handle returns, it would behoove decision-makers to follow an established decision-making process that takes into account all relevant considerations. To date, no study has assimilated all of these considerations or attempted to create such a decision-making framework. This lack of understanding makes it difficult to fully
RL disposition decision-making
245
comprehend which issues are important when making these increasingly important RL decisions. To determine a foundation for such an understanding, this research uses content analysis of RL literature to identify RL disposition decision criteria.
The remainder of this article is structured as follows. First, we briefly review foundational RL literature, where we describe how numerous factors have been acknowledged in extant literature to affect the RL process. We then offer a brief background of the four disposition alternatives that are generally described in the literature. The methodology of the content analysis is then described, followed by a discussion of the findings. Next we present a validity check of our findings, where components derived from our analysis are compared with factors addressed in existing RL frameworks. We then offer a discussion of the practical implications of our results, which also describes ideas for future research in RL decision-making. Finally, we integrate our findings with extant decision-making literature to create a RL disposition decision-making framework.
RL defined A review of supply chain management (SCM) literature reveals that some terms often encompass numerous definitions. Notably, the terms “logistics” and “SCM” lack universal definitions as multiple conceptual perspectives exist (Larson et al., 2007; Stock and Boyer, 2009). Similarly, there is not a consensus in the literature regarding the terms used to describe the reverse processes within the supply chain (Lambert, 2008). Therefore, the term “RL” will be used in this research to encompass all returns processes and is synonymous with terms such as “closed-loop supply chain” or “returns management”kopic. For the purpose of this paper, we adopt Stock’s (1998, pp. 20-1) comprehensive definition of RL:
[. . .] from a business logistics perspective, the term refers to the role of logistics in product returns, source reduction, recycling, materials substitution, reuse of materials, waste disposal, and refurbishing, repair, and remanufacturing; from an engineering logistics perspective, it is referred to as reverse logistics management (RLM) and is a systematic business model that applies best logistics engineering and management methodologies across the enterprise in order to profitably close the loop on the supply chain.
Foundational literature A variety of internal and external forces affect a firm’s RL processes. Building upon previous marketing research (Achrol et al., 1983; Stern and Reve, 1980) and their review of the logistics literature that specifically addresses external marketing factors (Barry et al., 1993; Bronstad and Evans-Correia, 1992; Cairncross, 1992; Kopicki et al., 1993; Livingstone and Sparks, 1994; Murphy et al., 1995; Pohlen and Farris II, 1992; Stock, 1992), Carter and Ellram (1998) developed a framework that describes the forces that affect RL. Their framework posits that the task environment consists of four distinct organizational entities that affect the firm’s RL operations. They are:
(1) suppliers (input);
(2) buyers (output);
(3) government (regulatory); and
(4) competitors (competitive).
IJPDLM 42,3
246
The task environment is embedded within the overall market environment consisting of legal, economic, political, and social variables.
Carter and Ellram’s (1998) conceptual model is widely regarded as the first comprehensive RL framework. It takes into account factors that are beyond the normal scope of logisticians and illustrates the holistic nature of RL. Their work provided the foundation for further investigation, as demonstrated by Knemeyer et al. (2002) when they updated the model to account for factors recognized in more current research. Considering the theory-building work of Dowlatshahi (2000) and a review of the contemporary literature at the time, Knemeyer et al.’s (2002) model accounts for concerns at multiple levels of an organization. Similarly, Skinner et al.’s (2008) research suggests that cross-functional integration is critical to the continued success of the returns management process. Additionally, Jayaraman and Luo (2007) recognized the system-level effects of a firm’s RL policies. Their framework describes the interdisciplinary nature of the RL disposition decision and demonstrates how a firm may derive value from its returned products, thus promoting the idea that all of a firm’s activities should seek to increase profits.
Considerations regarding RL encompass issues beyond that of many other business processes. For example, De Brito and Dekker’s (2003) model emphasizes corporate citizenship, legislation, and economics as the driving forces behind RL practices. Furthermore, Rogers and Tibben-Lembke (2001) highlight the overlapping considerations between green logistics and RL, suggesting the impact that green principles may have on RL decision-making. Specifically, they describe the activities of recycling, remanufacturing, and use of reusable packing as overlapping between green logistics and RL. Conversely, Wolf and Seuring (2010) suggest that, although environmental concerns are often considered when organizations contract with third party logistics providers, those concerns are given cursory examination at best. Research also suggests that understanding customer needs regarding returns may enable organizations to develop better product placement strategies (Ofek et al., 2011). In that same vein, Jack et al. (2010) found that RL may be examined from both the viewpoint of front-end customer relationship strategies and back-end RL processes. Their research suggests that back-end processes have a positive impact on RL capabilities, which in turn increases cost savings.
The above research highlights the dynamic nature of RL and underscores the importance of identifying factors that impact the general RL process. Not specifically addressed, however, is the disposition decision. Whether or not to implement RL processes is not an issue; RL will endeavor to exist for organizations that produce or move materials through the forward supply chain. While there is some need to align the RL process with the rest of the organization’s supply chain objectives, only the disposition decision has the ability to add value to the organization when the decision is based on appropriate information (Tan and Kumar, 2006). The research reviewed thus far describes the nature of RL. Our focus now turns to describing the common RL disposition alternatives that a firm may employ.
RL disposition alternatives In this paper, the term disposition alternative is synonymous with what some authors have referred to as recovery option (Krikke et al., 2004). Deciding upon the most advantageous disposition alternative can bolster a firm’s success (Croxton et al., 2001).
RL disposition decision-making
247
However, not only do decision-makers need to understand the laws and regulations that govern proper material handling and disposal, they must also be able to recognize potential financial gains that may be realized by capitalizing on opportunities to reuse operational products, recondition damaged or used products, or recover valuable materials from products that are beyond their useful life.
Early work in regard to disposition decision models sought to identify and stratify disposition alternatives (Kopicki et al., 1993; Stock, 1992). Thierry et al.’s (1995) integrated supply chain model depicts a standard return process. Their model illustrates three separate disposition alternatives. First, a firm may employ direct reuse, which entails reusing or reselling the returned product in an as-is condition. Next, a firm may employ product recovery management, which entails processes such as repairing, refurbishing, remanufacturing, cannibalizing useable materials, and recycling materials of value. Finally, a firm may employ waste management, which entails incinerating waste or land filling.
Since the work of Thierry et al. (1995), others such as Carter and Ellram (1998), Krikke et al. (2004) and Rogers et al. (2002) have modified and stratified possible disposition alternatives. Although each study emphasized slightly differing alternatives and definitions, four common RL disposition categories seemingly emerge as comprising the core taxonomy in recent literature. In consideration of the work cited above, we propose that the following four disposition alternatives encompass the recovery options available for RL. In hierarchical order in regard to the potential residual value that can be recovered by a firm, the four alternatives are:
(1) reuse;
(2) product upgrade;
(3) material recovery; and
(4) waste management.
Reuse allows for the most value to be recovered while waste management allows for the least amount of value recovery. Although some decompose these four alternatives even further (Krikke et al., 2004), this general hierarchy is often utilized in the current literature (Blackburn et al., 2004; Prahinski and Kocabasoglu, 2006; Rogers et al., 2002; Staikos and Rahimifard, 2007). The following briefly defines each alternative and gives an example of research within each area.
Reuse. Direct reuse is an option that presents itself when a customer returns an unused product back to the place of purchase, thus inserting the product back into the supply chain for use. At the retailer level, once the product is no longer serviceable or requires some sort of upgrade (e.g. cleaning, replacing accessories, remanufacturing, repackaging, etc.) direct reuse is no longer an option. Generally, this option exists only if the location in which the product resides in the supply chain possesses the capability to return the product to retail condition. This process includes products that are completely unused and products that are returned after such light use that upgrade is not required in order to return the product to new status.
Assuming that the returned product is in new condition, a variety of options exist. The product can be again offered for sale by the retailer, shipped laterally to another retailer, shipped back to the distributor, or shipped to any other place within the forward or reverse supply chain where stock levels require such an item. Logisticians and retail
IJPDLM 42,3
248
managers are primarily concerned with accounting for the quantity and quality of these returns. These unknown quantities provide even more variability to a process that is already susceptible to forecasting error. This additional variability increases the bullwhip effect within the supply chain and can lead to increased inventory (Vlachos and Dekker, 2003). If returns can be adequately forecasted and properly managed, the returns that are available for direct reuse can reduce transportation, procurement, and storage costs while improving productivity as each item returned to the end of the supply chain offsets the need for another item to be pulled through the forward supply chain (Giuntini and Andel, 1995b; Mollenkopf and Closs, 2005).
Product upgrade. The product upgrade alternative is concerned with repairing, refurbishing, or remanufacturing an item in order to extend the life of and derive value from the original core unit (Krikke et al., 2004). Product upgrade becomes an option when the possibility of direct reuse is either no longer available (e.g. the product is in used condition) or not economical (e.g. there is no longer a market requirement for the product). If executed properly, product upgrade can create profitable business opportunities through recapturing value that would otherwise have been lost (Clendenin, 1997; Giuntini and Andel, 1995a). The term “upgrade” implies improving the product from its end-of-life condition to that of a condition acceptable for future use or sale. The condition and quality of upgraded products can vary greatly, depending on the upgrade technique chosen and the purpose of the upgrade.
The definitions of repair, refurbish, and remanufacture are debatable and the usage of such terms differs within the literature. However, Majumder and Groenevelt (2001) suggest that remanufacturing is the primary means of product upgrade and is the term usually assigned to any upgrade function. Remanufacturing is defined as:
[. . .] the process of disassembling used items, inspecting and repairing/reworking the components, and using these in a new product manufacture. A product is considered remanufactured if its primary components come from a used product (Majumder and Groenevelt, 2001, p. 125).
The current study adopts this definition. Material recovery. Material recovery involves recovering any portion of a returned
product that may contain value. Material recovery can entail cannibalizing entire pieces not requiring upgrade that can be reused (Krikke et al., 2004), recovering parts or pieces that may be reused (Blackburn et al., 2004), or extracting recyclable materials for reuse or to sell as a commodity. Early RL literature often focused on recycling (Guiltinan and Nwokoye, 1975; Pohlen and Farris II, 1992); thus, some scholars posit that RL has been most closely associated with recycling and environmental matters (Daugherty et al., 2002). Although the topic of sustainability is becoming popular in recent literature, determine how to extract value from returned products and ensure regulatory compliance are still prominent topics in this area (Roy et al., 2006).
Waste management. Once a firm has decided that it is no longer of value to reuse, upgrade, or recover materials from a specific product, the product then becomes waste. Lyons (2005, p. 71) defines waste as:
[. . .] something that is perceived to have either no inherent value to its owner, or the amount of effort required to access that value is greater than the expected return [. . .] waste is a residual that is discarded.
RL disposition decision-making
249
In regard to the disposition decision, relegating a product to waste entails deriving no more value from that product. Subsequently, this alternative is the least desirable disposition alternative as the business implications end at this juncture. However, waste management has become an important topic in recent years as economic and environmental forces demand environmentally-friendly and cost-effective handling of waste.
The preceding four disposition alternatives and their operational definitions assimilate the extant literature in this area for the purpose of bringing understanding to the options available to a firm when making the disposition decision. As noted above, the exact terminology and definitions of the alternatives differ within the literature. Readers interested in the disposition alternatives (or recovery options) are encouraged to Blackburn et al. (2004), Carter and Ellram (1998), Kopicki et al. (1993), Krikke et al. (2004), Prahinski and Kocabasoglu (2006), Rogers et al. (2002), Staikos and Rahimifard (2007), Stock (1992) and Thierry et al. (1995).
While much literature exists in the general area of RL, and a literature base is being built in disposition alternatives, no comprehensive analysis of extant literature for the purposes of extracting or deriving decision considerations or creating a disposition decision-making framework has been conducted. Because of the relative lack of literature specifically in the area of dispositions, our study analyzes literature in RL that addresses decision-making in general to determine those variables that appear throughout the literature. Then, we synthesize those findings into higher-level, decision-making components, which are applied to the disposition decision. The method used for identifying and assimilating these components is described next.
Methodology The purpose of this study is to identify the components of the RL disposition decision and suggest a decision-making framework. To serve this purpose, the authors required a method that would uncover the variables considered in extant RL decision-making literature. In short, we needed to derive meaningful content to address our specific purpose from a large amount of textual literature. Berelson (1952) suggests that revealing the focus of attention is one of the primary uses of content analysis. In addition, Neuman (2006) asserts that content analysis is useful for three primary types of research problems:
(1) problems involving a large amount of text;
(2) problems that must be studied from afar because of either necessity or to attain the proper scope; and
(3) problems where casual observation may not reveal the proper solution.
In the case of this research, we needed to assimilate a large amount of text to thoroughly investigate relevant variables. When viewed from afar and via conscious examination, this text may reveal a solution to our problem. Accordingly, a content analysis method was adopted for this study.
Content analysis is “a research technique for making replicable and valid inferences from texts (or other meaningful matter) to the contexts of their use” (Krippendorff, 2004, p. 18). Content analysis can be strictly quantitative when used to objectively and systematically count and record symbolic content from text. Content analysis can also
IJPDLM 42,3
250
be utilized for qualitative purposes to interpret meaning from text. Indeed, many content analysis procedures employ both qualitative and quantitative elements (Holsti, 1968).
As with many research methods, content analysis encompasses a wide variety of procedures and techniques that may be employed for use in a variety of settings to solve a multitude of problems. As such, there is no one systematic checklist to follow when conducting content analysis. This lack of standardization requires that great care be taken to develop a specific method that will yield appropriate results to answer the given research question at hand. Regardless of the specific procedure employed, the techniques must result in findings that are replicable in order to attain sufficient levels of reliability. In addition, measures must be taken to enhance validity whenever possible. Although discussion of the many ways in which to bolster reliability and validity are beyond the scope of this manuscript, we will discuss the specific techniques we employed in our research later in this article.
In this research, we adapted procedures for problem-driven content analysis suggested by Krippendorff (2004) to locate relevant materials for analysis, define the units of analysis, develop recording procedures, present the findings, infer results, and demonstrate empirical validity. These steps were carefully chosen and meticulously performed to efficaciously derive meaning from the textual content while enhancing reliability and validity to the fullest extent. The procedures and outcomes of each of these steps will be discussed throughout the remainder of this article in the order in which they were completed.
Relevant materials As discussed previously, the vast majority of extant RL literature addresses operational-level concerns of an organization. With a focus on optimizing specific operational efficiencies, much of this research is aimed toward clarifying various aspects of decisions that pertain to RL. As an example, Bhattacharya et al.’s (2006) research develops a mathematical model to determine optimal order quantities; Ferguson and Toktay’s (2006) research aims toward facilitating remanufacturing decisions. As such, this literature represents a rich body of content that is relevant to our problem, and thus useful for our analysis.
The scope of the literature search was limited to articles that designed, developed, tested, or otherwise utilized a decision support system (DSS) or simulation in regard to facilitating a decision within RL. Authors of these articles go to great lengths to identify and describe any possible variable or consideration that may be used in regard to their specific RL problem in order to enhance the relevance and validity of their research. To find articles that meet our criteria, the literature from the top eight journals in SCM, management information systems (MIS), and operations management (OM), as identified by Menachof et al. (2007, p. 151), Rainer and Miller (2005) and Gorman and Kanet (2005), respectively, was first considered. In alphabetical order by discipline, these journals are shown in Table I.
These journal listings yielded a total of 21 unique journals because of the interdisciplinary nature of both Management Science and Harvard Business Review. Logistics research spans a multitude of disciplines (Stock, 1997). However, the three selected disciplines encompass the vast majority of literature on the specific topic of RL decision-making and are therefore thought to appropriately limit the scope of the search. Although searching only the top journals in a field may not render exhaustive
RL disposition decision-making
251
results (Webster and Watson, 2002), a comprehensive interdisciplinary analysis of this nature requires a limited scope in the preliminary search of literature. Furthermore, this listing served more as a beginning reference than as a definitive boundary. Investigation into the literature revealed additional journal titles that pertained to this topic and were subsequently explored.
This review examined all applicable literature from 2000 through 2010. In their review of RL literature, Carter and Ellram (1998) propose that the first academic work in the field was not published until the early 1990s (Kopicki et al., 1993; Stock, 1992). Their review also notes that the majority of literature throughout the 1990s was exploratory in nature, offering little theoretical grounding. Accordingly, the vast majority of RL literature is published after the year 2000, thus presenting a logical limit to the scope of this review. The authors’ objective was to determine which variables are currently being utilized in the literature. This dictated that the review reach back far enough to provide an appropriate number of articles, but not so far as to lose contemporary relevance. The year 2010 was chosen as an upper limit so as to facilitate a comprehensive search of a selected period, thus limiting the possibility of inadvertently omitting newly published literature within the stated scope of the review.
All selected journals are searchable via electronic format and were thus accessed electronically. Broad keyword searches of each journal were used to generate a large number of search results that the authors were then able to evaluate more closely. The keywords used were: CLSC, end-of-life, return, disposition, RL, decision, model, and simulation. Titles and abstracts were then reviewed to find any literature that developed a DSS or simulation within RL. Specific articles not available in electronic format were requested and received through inter-library loan. The research process yielded 73 articles that the authors were able to read and analyze for adherence to the established criteria of utilizing a DSS or simulation within RL for the purpose of decision-making. Rudimentary citation analysis of the original articles directed the authors toward other journals which, although not encompassed within the original search, were deemed to be highly applicable to this study. These additional journals are discussed below.
Supply chain management Management information systems Operations management
Harvard Business Review Communications of the ACM IIE Transactions International Journal of Logistics Management
Decision Sciences Journal of Scheduling
International Journal of Physical Dist & Logistics Management
Decision Support Systems Manufacturing and Service Operations Management
Journal of Business Logistics Harvard Business Review Mathematics of Operations Research
Journal of Operations Management
Information Systems Research Management Science
Management Science Journal of Management Information Systems
Operations Research
Supply Chain Management Review
Management Science Production and Operations Management
Transportation Journal MIS Quarterly Transportation Science
Table I. Top journals by field
IJPDLM 42,3
252
Much of the literature in this area is in the OM field, which prompted further exploration into more of these top journals. Thus, we searched the additional journals found in Gorman and Kanet’s (2005) ranked listing of OM journals. The same search procedure was performed on the additional journals; this yielded a total of 25 additional articles. In total, 98 articles that potentially met our criteria were found and acquired via inter-library loan or downloaded in full from an electronic database. However, upon close examination of the 98 articles, only 62 met the specific criteria and were reviewed. A listing of the 62 articles used for content analysis can be found in the Appendix.
Unit of analysis and recording Krippendorff’s (2004, p. 83) first component of content analysis involves unitizing, which he defined as “the systematic distinguishing of segments of text – images, voices, and other observables – that are of interest to an analysis”. In this study, we are interested in the considerations used in RL literature. Accordingly, our unit of analysis was the individual variable or parameter (we will refer to these as “considerations”) utilized in the research.
In regard to content analysis, a primary purpose of recording is to transform original texts into analyzable representations (Krippendorff, 2004). In respect to maintaining consistency, this stage of the content analysis was conducted by only one of the authors. The author began the recording process by searching each article for the considerations used in the simulation. The author investigated model explanations, discussions of variables, parameters, assumptions, and other applicable areas of the selected literature to extract and tabulate all considerations addressed in the respective article. In sum, the author simply recorded the variables considered in each study, as stated explicitly in the journal article. Each consideration and the article of origin were then recorded on a spreadsheet. Articles had as few as five and as many as 18 considerations each. A master listing of findings was compiled on a spreadsheet document such that the authors could analyze the findings.
Findings The content analysis yielded a total of 60 considerations used by the authors of the selected literature. These considerations are shown in Table II, in order of most frequently utilized (top left) to least frequently utilized (bottom right). The number next to each consideration represents how many different times the consideration was identified during the analysis of the articles.
Inferred results Abductively inferring contextual phenomena from textual data bridges the gap between descriptive accounts of the text and what the data actually mean by pointing to unobserved phenomena of interest to the analysts (Krippendorff, 2004). At this point in the content analysis, we had compiled and counted a listing of considerations without assigning meaning to the data. Subsequently, we next reviewed this data to extrapolate autocorrelation functions. Upon reaching agreement, seven general factors were extrapolated from the listing of 60 variables. These factors are: supply chain capabilities, costs of RL, profit from RL, environmental impact of RL, regulation, market considerations, and customer behavior.
RL disposition decision-making
253
In the literature used for analysis, supply chain capabilities encompass a myriad of variables that address whether or not a firm is prepared to commence various RL operations from a logistical perspective. Examples of such variables include a firm’s remanufacturing and inventory capacity. Therefore, we define supply chain capabilities as a firm’s existing resource capacity available for RL activities.
Cost of RL was represented in the literature in a variety of ways, to include labor and administrative costs. As such, we define cost of RL as the costs incurred to facilitate effective RL activities. Similarly, profit from RL was represented with variables such as revenues from recycling and profit margins from RL activities. We therefore define profit from RL as any profit realized via the employment of RL activities.
We found several instances where the environmental impact of RL was considered in the literature (Aksen et al., 2009; Haas et al., 2003; Staikos and Rahimifard, 2007). We define environmental impact as any consequence (positive or negative) of the practice of RL activities to the natural environment. Although regulation is often developed to address environmental concerns, environmental issues are not the only drivers of regulation. As such, we consider regulation to be separate from environmental
Customer Demand (40) Manufacturing Capacity (13) Outsourcing (3PL/4PL) (6) Product Return Volume/Rate (38) Wholesale Price (12) Environmental
Considerations (6) Remanufacturing Costs (32) Number of OEMs (Monopoly,
etc.) (9) Length of Time Customer Holds Product (5)
Cost of Acquiring Returned Product (30)
Existing Logistics Infrastructure (9)
Cost of Capital (5)
Management Strategy/Policy (23) Salvage Value (12) Safety stock (4) Inventory Costs (23) Supply of Parts Required for
Remanufacture (12) Stocking points (4)
Disposal Considerations/Scrap Costs (23)
Market Size (11) Service Level (3)
Leadtime (22) Quality of Remanufactured Item (11)
Factory Location (3)
Retail Price (22) Disassembly (Cost/Time) (11) Number of Remanufacturers (3)
Transportation Costs (21) Remanufactured Item Inventory Level (11)
RL Administrative Costs (3)
Manufacturing Costs (20) New Item Inventory Level (9) Processing Times (2) Inspection Costs (20) Delays (9) Packaging (2) Remanufacturing Capacity (18) Recycling Costs (8) Discontinuation Price (2) Sales Lost/Backorder Costs (18) Demand for Remanufactured
Part (8) Customer Segment (2)
Remanufactured Item Sales Price (17)
Revenue from Recycling (8) Value of Time (2)
Profit Margin of Remanufacturing (15)
Lot/Batch Size (7) Penalty Costs of Uncollected Returns (2)
Product Lifecycle (15) Labor Cost (7) Sorting Policy (1) Fixed Costs (15) Quantity Recycled (7) Total Quantity of Items in
Supply Chain (1) Return Quality (14) Pattern of Recovery (Collection
Location) (7) Total Cost of RL (1)
Total Serviceable Item Levels (13) Legal Considerations (7) Forecast (1)
Table II. Variables used in quantitative RL literature
IJPDLM 42,3
254
impact and found that regulation is often addressed in the literature in regard to legal constraints and compliance with environmental law. We define regulation as any law or directive imposed by a governing body that influences RL.
Market conditions in the literature include the size of the market and number of competing firms. We narrow the definition of market conditions to entail the competitive forces realized via the existence and actions of industry competitors. In respect to Porter’s (1980) five forces, this includes the threat of new entrants, threat of substitute products, the bargaining power of suppliers, and rivalry among existing firms. Notably, this does not include the impact of customers. Our definition of market conditions differs in scope from existing conceptualizations in the supply chain and marketing literature and represents a fundamental difference between the forward and RL process. Extant supply chain literature suggests that market conditions are largely affected by customer demand (Fisher, 1997; Pagh and Cooper, 1998). In contrast, in a CLSC, we posit that customers encompass a role beyond that of simply placing a demand on the supply chain or wielding bargaining power. For example, in the CLSC literature, we found that the length of time a consumer holds a product and the condition in which the product is returned are important considerations (Geyer et al., 2007; Toktay et al., 2000; Vorasayan and Ryan, 2006). Thus, we separate customer behavior into its own category apart from market conditions in the RL context. We define customer behavior as any action taken by a customer that impacts a firm’s RL activities. For example, customer demand for reused, recycled, or remanufactured products and the willingness to return used or unused products to the supply chain constitute important customer behaviors.
These analytical constructs defined above (which we will refer to as components of the disposition decision) were extrapolated from the literature and used as the basis for categorization of variables based upon our knowledge and experience with the established theories of SCM. The extrapolation of these seven components suggests that these factors embody the considerations required for RL disposition decision-making. However, in order to qualify these results, empirical validity was tested.
Empirical validity In order to measure how much these components converge with and are discriminate from current RL literature, we evaluated current RL frameworks and compared the findings of the content analysis with the factors addressed in each framework. In this section, we discuss our literature search procedures for finding these RL frameworks, our evaluation of each framework, and the conclusions we drew regarding the validity of our findings.
Literature search The criterion for a usable article was simple and explicit: it must contain an RL framework. Although a rather broad parameter, an exhaustive search for such literature yielded very few results. Again using listings provided via the research of Menachof et al. (2007), Rainer and Miller (2005) and Gorman and Kanet (2005) as a starting point, the top SCM, MIS, and OM journals were searched. Keyword search terms were: closed-loop, end-of-life, return, disposition, RL, decision, model, and framework. The scope of this review in regard to range of dates was limited only by the start date, which was 1998. This date was chosen as this was the year of Carter and Ellram’s (1998) initial
RL disposition decision-making
255
framework publication. Although numerous articles were retrieved and reviewed for adherence to criteria, this process yielded just seven usable articles. In an attempt to find more literature, we conducted a broad search of the Business Source Premier and ABI/Inform databases. The same keyword searches generated a variety of additional articles. However, only four additional articles met the criteria of this search.
Evaluating frameworks The exhaustive search detailed above yielded 11 articles which met our criteria. In order to validate our results, the frameworks from these articles were evaluated to determine percentage of agreement with the considerations identified by our content analysis. Each of the three authors evaluated the frameworks and identified the areas addressed in each framework. Krippendorf’s a for reliability of this process was calculated to be 0.85, which is considered sufficient to draw meaningful conclusions (Krippendorff, 2004). The results of this comparison are shown in Table III, which indicates the considerations encompassed by each framework.
Correlative validity analysis Convergent validity is “the extent to which results correlate with variables known to measure the same phenomena and considered valid” (Krippendorff, 2004, p. 319). In this study, we were concerned with the validity of the components that we extrapolated from the content analysis. Subsequently, we tested the convergent validity of our findings by comparing each framework against the seven components identified by our analysis. If each component was represented in at least one other framework, then it would follow that it was relevant to RL. Percent agreement was calculated by dividing the number of frameworks that utilized each component by the total number of frameworks evaluated and is shown in the bottom row of Table III. As the results demonstrate, all components have been utilized in previous frameworks. This suggests adequate convergence, which indicates that our findings are relevant to the context of RL.
Discriminant validity is “the extent to which correlations are absent between results and variables known to be valid but measuring phenomena that are distinctly different” (Krippendorff, 2004, p. 319). If an existing RL framework encompassed all the components that we found in our analysis, then it would indicate that the disposition decision problem may not differ much from other RL problems. However, we found that this was not the case. We compared each framework against the seven components identified by our analysis to determine levels of divergence. Percent agreement was calculated by dividing the number of components included in the respective framework by the total number of components found in our analysis and is shown in the far right column of Table III. As demonstrated by the level of agreement, no one framework accounts for all components, suggesting adequate divergence from existing frameworks.
Key components, practical implications, and future research Due to the potential impact RL can have on customer relations and the considerable assets/value now consumed by RL related activities, RL has become a managerial priority for many firms (Daugherty et al., 2005). Given the heightened awareness of RL by management, our findings are presented as key components of this relatively recent managerial priority. Our findings suggest seven components which may affect a firm’s decision as to which RL disposition activity to employ. Each of these seven components
IJPDLM 42,3
256
A u
th or
(s )
C u
st om
er b
eh av
io r
E n
v ir
on m
en ta
l im
p ac
t M
ar k
et co
n d
it io
n s
S u
p p
ly ch
ai n
ca p
ab il
it ie
s R
eg u
la ti
on P
ro fi
t fr
om R
L C
os t
of R
L C
om p
on en
ts ad
d re
ss ed
in fr
am ew
or k
(% )
C ar
te r
an d
E ll
ra m
(1 99
8) U
U U
U U
71 .4
D ow
la ts
h ah
i (2
00 5)
U U
U U
U 71
.4 G
u id
e an
d P
en ti
co (2
00 3)
U U
U U
57 .1
Ja y
ar am
an an
d L
u o
(2 00
7) U
U U
U U
U 85
.7 K
n em
ey er
et a l.
(2 00
2) U
U U
U 57
.1 M
ol le
n k
op f et
a l.
(2 00
7) U
U U
U 57
.1 R
og er
s et
a l.
(2 00
2) U
U U
U U
71 .4
S k
in n
er et
a l.
(2 00
8) U
U U
42 .9
S ri
v as
ta v
a an
d S
ri v
as ta
v a
(2 00
6) U
U U
U U
42 .9
S ta
ik os
an d
R ah
im if
ar d
(2 00
7) U
U U
U U
U 85
.7 T
an an
d K
u m
ar (2
00 6)
U U
U 42
.9 F
ra m
ew or
k ad
d re
ss in
g co
n si
d er
at io
n (%
) 72
.7 36
.4 72
.7 81
.8 54
.5 63
.6 72
.7
Table III. Evaluation of
RL frameworks
RL disposition decision-making
257
has meaningful implications for practitioners confronted with the RL decision-making process. As a result, each component will now be presented and discussed.
Cost is the first of the seven key components identified in our research results and includes any costs incurred to facilitate effective RL activities. Our analysis found that nearly every article of the DSS and simulation literature used for content analysis considered costs in some regard. In addition, costs associated with the RL process are included in eight of the 11 frameworks evaluated. For example, Tan et al.’s (2003) study of a US-based computer company’s Asia-Pacific operations noted many inefficiencies and high costs in their RL programs. Consequently, Tan and Kumar (2006) developed a decision model to aid practitioners in controlling costs and maximizing profits in their potential RL activities. Some of the costs discussed by Tan and Kumar (2006, p. 335) include transportation, customs duty, acquisition, handling, repair, reuse, scrap, storage, and freight costs. Furthermore, Guide Jr and Pentico’s (2003) framework addresses the expected costs of remanufacturing, logistics costs, and machine and labor costs. Some of these costs include remanufacturing costs, costs of acquiring returned products, value of time (e.g. opportunity costs), costs of lost sales, and inspection costs.
These are just a few of the examples of key costs that must be evaluated when determining which RL disposition to pursue. Indeed, a wide variety of costs associated with RL must be considered when deciding which RL disposition option to adopt. These costs associated with disposition may deter some firms from choosing certain disposition alternatives. As such, future research could serve to identify and investigate additional costs associated with each disposition alternative and determine whether or not lower cost alternatives are necessarily preferred by firms making the disposition decision:
Practical implication 1. Costs will endure to be a primary consideration in business decision-making. As noted in our content analysis, a wide variety of different costs are associated with each RL alternative and with RL in general. Thus, those in practice should carefully consider the second- and third-order ramifications of the selected alternative, realizing that unanticipated costs may surface.
Another key component identified by evaluating RL frameworks is profit, which encompasses any profits realized from the employment of RL activities. The profit component was included in seven of the 11 frameworks evaluated. In fact, several of the frameworks evaluated explicitly address the ability of RL activities to generate profits (Guide Jr and Pentico, 2003; Tan and Kumar, 2006). Accordingly, potential profitability is a component that must be weighed whenever deciding which RL disposition option to employ.
Although some potential profits may be relatively obvious and easy for a firm to account for (e.g. profit margins from remanufacturing a certain item), future research should explore further the profit potential of each RL disposition alternative in hopes of uncovering additional means in which to generate profit. For example, the content analysis suggests that additional revenues realized from recycling efforts may bolster profits associated with some RL activities. While profit typically has a clear relationship with RL costs, each RL disposition alternative may also present a unique opportunity to realize more obscure sources of profit, such as tax breaks for environmentally-friendly activities or the ability to charge premium prices for products made via “green” techniques.
IJPDLM 42,3
258
If done correctly, oftentimes a consequence of aggressively pursuing and implementing RL processes is an enhanced reputation with potential customers, especially those with an orientation towards “green” organizations. The indirect result of this enhanced reputation can be improved sales performance, potentially generating additional profits for the organization. Furthermore, acting in a manner that is interpreted by potential customers as being socially responsible may lead to increased profits.
Friedman (1970) asserts that the social responsibility of business is to increase profits. Upon a thorough review of the profit potential of each disposition alternative, all else being equal, firms will most likely choose the alternative that will generate the most profit. Although this seems obvious, empirical research has not yet thoroughly investigated this assertion or the many avenues in which RL may drive or enhance profitability. Thus, we suggest further research in this area to determine the extent to which perceived profitability drives the disposition decision:
Practical implication 2. Business organizations exist for the benefit of shareholders and stakeholders. When making the disposition decision, one must always consider the bottom line. Firms should take the time to thoroughly consider each alternative’s ability to generate profits.
A third key component identified by our RL framework review is market conditions. Market conditions are considered in eight of the 11 frameworks evaluated. In the RL context, we posit that market conditions are generally concerned with the competitive forces in the marketplace. As described in our definition of market conditions earlier in this manuscript, this includes the threat of new entrants, threat of substitute products, the bargaining power of suppliers, and rivalry among existing firms (Porter, 1980). Our content analysis reveals that variables such as market size and number of competing firms are considerations often used in the literature.
Market conditions represent an important component because commencing with an RL disposition practice may often entail entrance into a new market. For example, the decision to pursue remanufacturing means that the firm will now be in the remanufactured products business. Depending on the industry, this market may be substantially different from the new products market for the same item, creating new and unique challenges for the organization.
Another example can be found when a firm decides to recover recyclable materials as part of their RL program. Assuming that the firm is an original equipment manufacturer, the firm will then enter a completely different market when trying to sell its recyclable materials. Accordingly, SWOT analysis, gap analysis, and other market measures must be considered when making the RL disposition decision (Porter, 1980). If these evaluations are not done prior to setting RL disposition policy, the organization may subsequently find itself in unfamiliar territory and faced with building a new business model to handle the consequences of an RL disposition decision involving the recovery of recyclable materials.
Carter and Ellram (1998) and Knemeyer et al. (2002) emphasize the importance of market forces in their conceptual framework and research into RL. Similarly, our research suggests that considering components such as overall market size, market position, and number of competing remanufacturers may be important when making
RL disposition decision-making
259
the RL disposition decision. For example, let us assume that a firm is choosing between product upgrade or material recovery for disposition of a certain line of mechanical gearboxes. If the market is saturated from competing firms that offer the same or substitutable remanufactured gearboxes, then this fierce competition may dissuade the firm from choosing the product upgrade alternative. Using the same example, let us assume that the gearboxes are made with a certain trace metal that is not being supplied by other competitors in the marketplace. It may then be in the firm’s best interest to choose the material recovery activity for disposition and sell the raw materials in the marketplace. Competitive forces in the marketplace may significantly hinder a firm’s ability to implement certain RL disposition alternatives. Alternatively, a lack of competition may facilitate a smooth entry into a new marketplace, thereby encouraging the selection of a given disposition alternative. Therefore, a thorough evaluation of the market-based ramifications of the RL decision must be part of the RL disposition analysis:
Practical implication 3. Firms should understand that pursuit of a new RL disposition alternative will likely involve entrance into a new market environment. Even if the market is similar (e.g. selling the remanufactured version of an item that the firm already manufactures new), the competitive landscape may be vastly different. Firms are advised to use the same market analysis approaches that it would for entrance into any new business.
A fourth key component of the RL disposition decision is customer behavior. Customer behavior is considered in nine of the 11 frameworks evaluated above and is defined as any action taken by a customer that impacts a firm’s RL activities. For example, customer demand for reused, recycled, or remanufactured products and the willingness to return used or unused products to the supply chain constitute important customer behaviors that affect a firm’s RL functions. As emphasis toward initiatives such as lean manufacturing and just-in-time continue to diffuse within supply chain organizations, the focus on customers and their role in the supply chain is receiving greater attention (Shah and Ward, 2007). To this end, our study found that customer demand was considered more often than any other factor in the DSS and simulation articles analyzed, suggesting that the role of customers is critical to determining which RL disposition option to employ.
Additionally, our study found other customer-related behaviors that have received much less attention, such as the length of time a customer is in possession of a product before return and the customer’s concern for the environment. Research focused toward identifying additional customer behaviors and enhancing understanding of known customer behaviors such as these may bolster practitioners’ understanding of how the RL disposition decision affects customers, and vice versa. Firms should understand who their customers are, what they value, and what they are willing to pay for in the marketplace. Continued research in this area will help to foster this understanding. In sum, although customer demand will always be a key interest in the supply chain, other customer behaviors also affect the RL disposition decision. More research is required to uncover additional pertinent behaviors and determine how important each is to consider when making the disposition decision:
Practical implication 4. A firm must understand its customer base. Not only do firms need to anticipate and forecast demand, but they should understand how their customers feel about
IJPDLM 42,3
260
the firm and its products. This knowledge will help the firm to make decisions that will enhance the firm’s ability to attract, serve, and retain customers.
A fifth key component is supply chain capabilities, which are considered in nine of the 11 frameworks evaluated. This component is defined as a firm’s existing resource capacity available for RL activities. Skinner et al. (2008) asserts that if adequate resource support in the supply chain is not present, the only feasible option is to dispose of or destroy the returned product because all other alternatives require a great amount of resources. This limited access to necessary resources reduces the number of alternatives that a firm may consider for disposition. Accordingly, a firm must take into account transportation, warehousing, information technology, and other resource-intensive considerations when determining if the logistical infrastructure exists to pursue the desired RL activity. If not, the cost of additional capacity must be weighed against potential profits to determine the feasibility of the given RL activity. For example, if a firm already possesses the resources required to remanufacture a given product, then it may be more advantageous to choose remanufacturing over other potential alternatives. Conversely, if a firm does not readily possess the resources necessary to initiate remanufacturing, then it will likely choose a different method of disposition for which is does possess the capability.
Our findings suggest that future research should investigate how outsourcing (i.e. 3PL and 4PL) may offset existing capacity and capability shortfalls; thus assisting firms in better understanding their options when considering their existing capabilities in reference to employing a new RL disposition alternative. Additional methods for adding capacity may also expand the number of disposition alternatives that a firm may consider. Regardless of whether the necessary supply chain capabilities to pursue a given disposition alternative are intrinsic to the firm, easily procurable, or otherwise affordably attainable, a firm is more likely to seriously consider a given disposition alternative when it has access to the proper resources to implement that alternative than if the resources are not as readily available. Thus, understanding exactly what resources will be required of each disposition alternative is important when making the disposition decision:
Practical implication 5. The disposition decision-maker must be aware of his or her firm’s existing supply chain capabilities as well as the resources required to properly conduct a given disposition alternative. If gaps exist between existing and required capabilities, then the decision-maker must understand what is required to fill those gaps and whether or not the firm is prepared for and/or willing to commit the additional required resources. Otherwise, other disposition alternatives may provide a better option for the firm.
A sixth key component of the RL disposition decision is regulation. Regulation is considered in six of the 11 frameworks evaluated above and is defined as any law or directive imposed by a governing body that influences RL. The seventh key component of the RL disposition decision, environmental impact, is considered in four of the 11 frameworks. Environmental impact is defined as any consequence (positive or negative) of the practice of RL activities to the natural environment.
Although separate components, both the academic literature and practitioners often regard the environment and regulation as one and the same because environmental
RL disposition decision-making
261
concerns often drive regulation. Our research found that both concepts are considered often and are not necessarily dependent upon one another; therefore, we treat them as two separate concepts. Regulatory requirements can be driven by environmental concerns; however, there are also many instances where the two concepts do not appear to be highly related.
Often times, firms have little choice in implementing an RL activity if recovery of their product (e.g. automobile tires or paint) is required by law (Roy et al., 2006). However, research suggests that firms may still choose to be reactive, proactive, or value-seeking in implementing its environmental practices (Kopicki et al., 1993; Srivastava, 2007; Van Hoek, 1999). A reactive approach entails simply abiding by regulations as they are implemented whereas a proactive approach entails anticipating and staying ahead of regulation. A value-seeking approach entails initiating environmentally-friendly activities (such as recycling) as calculated initiatives.
Determining which of the above approaches to follow can affect a firm’s decision as to when, how, and why it will implement a given RL disposition activity. As such, the effect of regulation and environmental impact on the disposition decision may be vastly different for each individual firm. Although a topic of recent interest, literature is still relatively limited in the area of green RL and more research is encouraged to determine how both the environment and regulation affect the RL disposition decision. Although we uncover that regulation and environmental impact surely affect the disposition decision, we do not have sufficient evidence at this time to characterize the specific nature of these relationships:
Practical implication 6. Firms must continuously be aware of existing and pending regulation that may affect their businesses. This situational awareness should also transcend into the disposition decision-making process so that the implications of a desired alternative are fully understood.
Practical implication 7. Those in the firm who are charged with making disposition decisions must be aware of the firm’s policy toward environmental practices to ensure that their decisions are congruent with existing policies and programs. Furthermore, the environmental impact of each disposition alternative must be understood and considered when making the disposition decision.
The seven key components of RL disposition and their practical implications on the RL decision-making process should be used to guide future research efforts. These efforts should be focused in part on additional exploration of the RL disposition decision and how these seven key components combine to impact the RL decision-making process. Each of these key components can be used as the basis of future research designed to delve deeper into each of the individual components identified by the current research. However, it should be noted that the results of our research effort also suggests interactions or relationships between each of the seven components. While many of the practical implications above seem to suggest simple if/then scenarios for RL disposition decision-making, clearly the overall RL disposition decision is not that simple. While a thorough examination of each key component is warranted, there are relationships between the various components that must also be considered. Each of these components may work with and/or against other components.
IJPDLM 42,3
262
This research effort uncovered what are proposed to be the relevant components in making the disposition decision. Future research should also seek to prioritize the importance of these components and investigate the relationships between components in a variety of RL scenarios. For example, should costs be given greater consideration than environmental impact? If market competition is fierce yet profit potential is high for a given alternative, should that alternative still be pursued? Does the potential for high profits offset the lack of existing supply chain capabilities? By investigating the relationships proposed in this research and establishing the relative importance of each, future research could help to provide answers to such questions.
Although each individual firm may weigh the importance of each component differently when making the RL disposition decision, future research in this area may be able to provide a standard point of departure. For example, in some organizations, it may be of upmost importance to control costs. In this case, costs would be the most heavily weighted component in the disposition decision. Alternatively, organizations may be looking to expand their operations or delve into new businesses. In these cases, lack of existing supply chain capabilities may not be a highly important consideration. Instead, adopting a resource-intensive disposition option such as remanufacturing may be a desirable alternative in spite of the lack of existing resources because it is viewed as an opportunity to expand. It is apparent that these seven components are not independent of one another. As a result, we offer one additional practical implication:
Practical implication 8. One must consider each of the considerations identified in this research when making the disposition decision. Although we are not certain as to the relative importance of each consideration to every firm, those charged with making the disposition decision may be able to rely on corporate goals, values, and objectives to construct a weighted decision matrix to assist in making disposition decisions within their own organization. Clearly, the prioritization of the relative importance of each of the considerations related to the disposition decision will differ depending on the specific RL application being evaluated, the overall organization, and the overall RL objectives of the organization.
It is within this last practical implication that the impetus for the decision-making framework that we present below is derived. Decision-makers in each organization must not only consider, but also weigh the seven components when deciding upon which disposition alternative to pursue. The next section presents an RL disposition decision-making framework, which serves assimilate the fundamental ideas discussed thus far in this article.
Decision-making for RL disposition The components discussed above provide the foundation for examining an organization’s decision as to which disposition alternative to choose. However, identification and explanation of these components alone is not enough to develop a comprehensive framework for RL disposition decision-making; all of the pieces must now be put together. Accordingly, the next step involves integrating these components into a decision-making framework. Thus, we examined the literature for existing decision-making models that may be used as a foundation for explaining the RL disposition decision-making process. Given our review of the foundational RL literature and our study results,
RL disposition decision-making
263
the decision regarding which disposition alternative that an organization should adopt for a given product line appears to encompass similar components as identified in extant strategic decision-making frameworks.
Although the RL disposition decision may not necessarily be “strategic” for a given firm, one interesting finding of our research is that the decision components of the RL decision-making process appear to be very similar to those addressed when making strategic decisions. As such, extant strategic decision models may provide a sound foundation in which to integrate our findings and develop an RL disposition decision-making framework. A strategic decision is one that involves market positioning, is highly complex, involves multiple functions, affects firm performance, and represents a substantial commitment to resources (Arendt et al., 2005; Eisenhardt, 1989). Thus, by nature, a strategic decision is often rare, consequential, and sets the precedent for lesser decisions and future actions throughout the organization (Hickson et al., 1986; Hunger and Wheelen, 2007). As highlighted in our review of the literature, these characteristics may also, to some degree, describe the RL disposition decision.
Extant literature offers a variety of strategic decision process models (Schwenk, 1995). One such model is presented by Hunger and Wheelen (2007), which is based on their synthesis of competing and complimentary strategic decision literature (Eisenhardt and Sull, 2001; Hickson et al., 1986; Mintzberg, 1973; Mintzberg et al., 1976; Quinn, 1980). Hunger and Wheelen’s (2007, p. 13) model consists of an eight step strategic decision-making process. These steps are:
(1) evaluate current performance results;
(2) review corporate governance;
(3) scan the external environment;
(4) scan the internal corporate environment;
(5) analyze relevant factors;
(6) generate, evaluate and select the best alternative;
(7) implement selected alternative; and
(8) evaluate implemented alternative.
We integrate our RL disposition decision-making components with Hunger and Wheelen’s (2007) decision-making process, which results in the proposed RL disposition decision-making process (Figure 1).
As shown in Figure 1, the components of the RL disposition decision are merged within an existing decision-making process to create a framework for making the RL disposition decision. The first step of this process entails evaluating current organizational performance. This requires a review of the current health and posture
Figure 1. RL disposition decision-making process
Evaluate current performance (mission, objectives, and policies)
Review applicable corporate governance
Scan external environment: - Customer behavior - Market conditions - Environmental impact - Regulation
Scan internal environment: - Supply chain capabilities - Profit from RL - Cost of RL
Select and implement alternative
Review disposition alternatives: - Reuse - Product upgrade - Material recovery - Waste management
Evaluate implemented
alternative
IJPDLM 42,3
264
of the organization, to include a review of the organization’s mission statement, business objectives, and any current policies regarding logistics and/or product returns. This purpose of this step is to give decision-makers an overview of the current status of the organization. The next step involves reviewing corporate governance that may affect the decision at hand. This may entail gaining the support of the board of directors for investigating new RL options or reviewing the corporate charter to determine if adopting a new RL function is tenable.
After gaining insight regarding current corporate performance and applicable governance, the decision-maker(s) should then gain an understanding of the RL disposition alternatives of reuse, product upgrade, material recovery, and waste management. This involves understanding what each alternative entails and how each alternative may align with the first two steps of the RL disposition decision-making process. This step takes place earlier in the RL disposition decision-making process than it does in Hunger and Wheelen’s (2007) generic process because, unlike some other decisions, the RL alternatives are already known in advance to decision-makers.
The decision process then branches into two related steps, which involve reviewing both the external and internal environment. This is where the components identified in our study are integrated within the decision-making process. Regarding external environment considerations, the decision-maker(s) should examine customer behavior, market conditions, existing regulation, and the environmental impact presented by each alternative. Regarding internal environment considerations, the decision-maker(s) should examine the organization’s existing supply chain capabilities as well as the potential costs and profits associated with each disposition alternative.
The next step involves making the actual decision as to which RL disposition alternative to adopt. This may be accomplished via use of a weighted decision matrix. The matrix could be populated with the four RL alternatives, the considerations identified by our content analysis, and a weighting scheme that would be particular to each individual organization and motivated by the considerations for current performance and corporate governance examined in steps 1 and 2. The final step of the RL disposition decision-making process involves a periodic review of the implemented RL practice to ensure desired results.
The resulting RL disposition decision-making process offers both a framework for business leaders posed with making such decisions and a foundation for future decision-making research in RL. Decision-makers may use this framework to create a decision matrix to help facilitate their decision as to which RL disposition alternative would be most advantageous to employ. Future research may further examine the weighting scheme adopted by firms to determine if some of these considerations are generally more important to most firms than other considerations.
Content analysis: future ideas and limitations The content analysis approach used in this research may be valuable in studying additional SCM topics. As the amount of published research in the expanding field of SCM continues to increase, so does the amount of content. Content analysis can aid in comparing content across many sources and help to reveal aspects of the content that may be difficult to see when viewing phenomena in the context and scope of other research methods. For example, content analysis has been used in the SCM field to discover various research trends (Frankel et al., 2005; Stock, 2001). However, recent literature in the MIS
RL disposition decision-making
265
field has employed elements of content analysis to help uncover the latest technology trends within industry so as to better align research with practice and report timely research results to practitioners regarding the use of such technology (Bakersville and Myers, 2009; Wang, 2010). We encourage similar use of content analysis in the SCM field.
The limitations of our study are inherent to most research endeavors employing a content analysis methodology. First, relevant data in the literature may have been inadvertently overlooked by the recorder. However, the sufficient number of articles analyzed and the large number of variables uncovered suggest that this limitation may not greatly affect the findings of this study. Second, although validated via comparison with current RL frameworks, the findings may be subject to author bias. While we minimized the potential for bias by repeatedly involving multiple authors in the evaluation process, this remains a risk of the method used. Therefore, future research designed to test the concepts presented here can serve to further qualify these results and expand overall understanding of the implications of the RL disposition decision. For example, Delphi methods or survey-based research can be employed to determine the relative impact of each of these components. Regardless of the methodology employed in further studies, our study lays a foundation for further investigation in this area.
Conclusion A content analysis of 62 technical articles published in top OM, SCM, and MIS journals between 2000 and 2010 uncovered the use of 60 RL decision considerations used in the literature. Seven individual components were extrapolated from this listing and compared with existing RL considerations. These components are: supply chain capabilities, costs of RL, profit from RL, environmental impact of RL, regulation, market considerations, and customer behavior. In addition to identifying and discussing these seven key components of the RL disposition decision, we also offer practical implications. These implications and supporting discussion should enhance understanding of each of the seven key components and aid in the identification of key issues worthy of further investigation into the integration of the returns management process with existing business processes. Our resulting decision-making framework is the first of its kind for RL disposition and lays the groundwork for future research while also providing a practical guide for business decision-makers.
References
Achrol, R.S., Reve, T. and Stern, L.W. (1983), “The environment of marketing channel dyads: a framework for comparative analysis”, Journal of Marketing, Vol. 47 No. 4, pp. 55-67.
Aksen, D., Aras, N. and Karaarslan, A.G. (2009), “Design and analysis of government subsidized collection systems for incentive-dependent returns”, International Journal of Production Economics, Vol. 119 No. 2, pp. 308-27.
Arendt, L.A., Priem, R.L. and Ndofor, H.A. (2005), “A CEO-advisor model of strategic decision making”, Journal of Management, Vol. 31 No. 5, pp. 680-99.
Atasu, A. and Cetinkaya, S. (2006), “Lot sizing for optimal collection and use of remanufacturable returns over a finite life-cycle”, Production and Operations Management, Vol. 15 No. 4, pp. 473-87.
Bakersville, R.L. and Myers, M.D. (2009), “Fashion waves in information systems research and practice”, MIS Quarterly, Vol. 33 No. 4, pp. 647-62.
IJPDLM 42,3
266
Barry, J., Girard, G. and Perras, C. (1993), “Logistics planning shifts into reverse”, The Journal of European Business, Vol. 5 No. 1, pp. 34-7.
Berelson, B. (1952), Content Analysis in Communications Research, The Free Press, New York, NY.
Bhattacharya, S., Guide, D.R. Jr and Van Wassenhove, L.N. (2006), “Optimal order quantities with remanufacturing across new product generations”, Production and Operations Management, Vol. 15 No. 3, pp. 421-31.
Blackburn, J.D., Guide, D.R. Jr, Souza, G.C. and Van Wassenhove, L.N. (2004), “Reverse supply chains for commercial returns”, California Management Review, Vol. 42 No. 2, pp. 6-22.
Blumberg, D.F. (1999), “Strategic examination of reverse logistics and repair service requirements, needs, market size, and opportunities”, Journal of Business Logistics, Vol. 20 No. 2, pp. 141-59.
Bronstad, G.H. and Evans-Correia, K. (1992), “Green purchasing: the purchasing agent’s role in corporate recycling”, paper presented at the Conference of the National Association of Purchasing Management.
Cairncross, F. (1992), “How Europe’s companies reposition to recycle”, Harvard Business Review, Vol. 70 No. 2, pp. 34-43.
Carter, C.R. and Ellram, L.M. (1998), “Reverse logistics: a review of the literature and framework for future investigation”, Journal of Business Logistics, Vol. 19 No. 1, pp. 85-102.
Clendenin, J.A. (1997), “Closing the supply chain loop: reengineering the returns channel process”, International Journal of Logistics Management, Vol. 8 No. 1, pp. 85-102.
Croxton, K.L., Garcia-Dastugue, S.J., Lambert, D.M. and Rogers, D.S. (2001), “The supply chain management processes”, International Journal of Logistics Management, Vol. 12 No. 2, pp. 13-36.
Daugherty, P.J., Myers, M.B. and Richey, R.G. (2002), “Information support for reverse logistics: the influence of relationship commitment”, Journal of Business Logistics, Vol. 23 No. 1, pp. 85-106.
Daugherty, P.J., Richey, R.G., Genchev, S.E. and Chen, H. (2005), “Reverse logistics: superior performance through focused resource commitments to information technology”, Transportation Research: Part E, Vol. 41 No. 2, pp. 77-92.
De Brito, M.P. and Dekker, R. (2003), A Framework for Reverse Logistics, Erasmus Research Institute of Management, Rotterdam, April.
Dowlatshahi, S. (2000), “Developing a theory of reverse logistics”, Interfaces, Vol. 30 No. 3, pp. 143-55.
Eisenhardt, K.M. (1989), “Making fast strategic decisions in high-velocity environments”, Academy of Management Journal, Vol. 32 No. 3, pp. 543-76.
Eisenhardt, K.M. and Sull, D.N. (2001), “Strategy as simple rules”, Harvard Business Review, Vol. 79 No. 1, pp. 107-16.
Ferguson, M.E. and Toktay, L.B. (2006), “The effect of competition on recovery strategies”, Production and Operations Management, Vol. 15 No. 3, pp. 351-68.
Fisher, M.L. (1997), “What is the right supply chain for your product?”, Harvard Business Review, Vol. 75 No. 2, pp. 105-16.
Frankel, R., Naslund, D. and Bolumole, Y. (2005), “The ‘white space’ of logistics research: a look at the role of methods usage”, Journal of Business Logistics, Vol. 26 No. 2, pp. 185-208.
Friedman, M. (1970), “The social responsibility of business is to increase profits”,NewYork Times Magazine, 13 September.
RL disposition decision-making
267
Geyer, R., Van Wassenhove, L.N. and Atasu, A. (2007), “The economics of remanufacturing under limited component durability and finite product life cycles”, Management Science, Vol. 53 No. 1, pp. 88-100.
Giuntini, R. and Andel, T.J. (1995a), “Advance with reverse logistics: part 1”, Transportation & Distribution, Vol. 36 No. 2, pp. 73-5.
Giuntini, R. and Andel, T.J. (1995b), “Reverse logistics role models: part 3”, Transportation & Distribution, Vol. 36 No. 4, pp. 97-9.
Gorman, M.F. and Kanet, J.J. (2005), “Evaluating operations management-related journals via the author affiliation index”, Manufacturing & Service Operations Management, Vol. 7 No. 1, pp. 3-19.
Guide, D.R. Jr and Pentico, D.W. (2003), “A hierarchical decision model for re-manufacturing and re-use”, International Journal of Logistics: Research & Applications, Vol. 6 Nos 1/2, pp. 29-35.
Guide, D.R. Jr and Van Wassenhove, L.N. (2009), “The evolution of closed-loop supply chain research”, Operations Research, Vol. 57 No. 1, pp. 10-18.
Guide, D.R. Jr, Souza, G.C., Van Wassenhove, L.N. and Blackburn, J.D. (2006), “Time value of commercial product returns”, Management Science, Vol. 52 No. 8, pp. 1200-14.
Guiltinan, J.P. and Nwokoye, N.G. (1975), “Developing distribution channels and systems in the emerging recycling industries”, International Journal of Physical Distribution, Vol. 6 No. 1, pp. 28-39.
Haas, D.A., Murphy, F.H. and Lancioni, R.A. (2003), “Managing reverse logistics channels with data envelopment analysis”, Transportation Journal, Vol. 42 No. 3, pp. 59-69.
Hickson, D.J., Butler, R.J., Cray, D., Mallory, G.R. and Wilson, D.C. (1986), Top Decisions: Strategic Decision Making in Organizations, Jossey-Bass, San Francisco, CA.
Holsti, O.R. (1968), Content Analysis for the Social Sciences and Humanities, Addison-Wesley, Reading, MA.
Hunger, J.D. and Wheelen, T.L. (2007), Essentials of Strategic Management, 4th ed., Pearson Prentice-Hall, Upper Saddle River, NJ.
Inderfurth, K. (2005), “Impact of uncertainties on recovery behavior in a remanufacturing environment: a numerical analysis”, International Journal of Physical Distribution & Logistics Management, Vol. 35 No. 5, pp. 318-36.
Jack, E.P., Powers, T.L. and Skinner, L. (2010), “Reverse logistics capabilities: antecedents and cost savings”, International Journal of Physical Distribution & Logistics Management, Vol. 40 No. 3, pp. 228-46.
Jayaraman, V. and Luo, Y. (2007), “Creating competitive advantages through new value creation: a reverse logistics perspective”, Academy of Management Perspectives, Vol. 21 No. 2, pp. 56-73.
Knemeyer, A.M., Ponzurick, T.G. and Logar, C.M. (2002), “A qualitative examination of factors affecting reverse logistics systems for end-of-life computers”, International Journal of Physical Distribution & Logistics Management, Vol. 32 No. 6, pp. 455-79.
Kopicki, R.J., Berg, M.J., Legg, L.L., Dasappa, V. and Maggioni, C. (1993), Reuse and Recycling – Reverse Logistics Opportunities, Council of Logistics Management, Oak Brook, IL.
Krikke, H., le Blanc, L. and van de Velde, S. (2004), “Product modularity and the design of closed-loop supply chains”, California Management Review, Vol. 46 No. 2, pp. 23-39.
Krippendorff, K. (2004), Content Analysis: An Introduction to Its Methodology, 2nd ed., Sage, Thousand Oaks, CA.
IJPDLM 42,3
268
Lambert, D.M. (2008), Supply Chain Management: Processes, Partnerships, Performance, 3rd ed., Supply Chain Management Institute, Sarasota, FL.
Larson, P.D., Poist, R.F. and Halldorsson, A. (2007), “Perspectives on logistics vs SCM: a survey of SCM professionals”, Journal of Business Logistics, Vol. 28 No. 1, pp. 1-24.
Livingstone, S. and Sparks, L. (1994), “The new German packaging laws: effects on firms exporting to Germany”, International Journal of Physical Distribution & Logistics Management, Vol. 24 No. 7, pp. 15-25.
Lu, Z. and Bostel, N. (2007), “A facility location model for logistics systems including reverse flows: the case of remanufacturing activities”, Computers and Operations Research, Vol. 34 No. 2, pp. 299-323.
Lyons, D. (2005), “Integrating waste, manufacturing and industrial symbiosis: an analysis of recycling, remanufacturing and waste treatment firms in Texas”, Local Environment, Vol. 10 No. 1, pp. 71-86.
Majumder, P. and Groenevelt, H. (2001), “Competition in remanufacturing”, Production and Operations Management, Vol. 10 No. 2, pp. 125-41.
Menachof, D., Gibson, B., Hanna, J. and Whiteing, A. (2007), “An analysis of the value of supply chain management periodicals”, International Journal of Physical Distribution & Logistics Management, Vol. 39 No. 2, pp. 145-66.
Mintzberg, H. (1973), “Strategy-making in three modes”, California Management Review, Vol. 16 No. 2, pp. 44-53.
Mintzberg, H., Raisinghani, D. and Theoret, A. (1976), “The structure of ‘unstructured’ decision processes”, Administrative Science Quarterly, Vol. 21 No. 2, pp. 246-75.
Mollenkopf, D. and Closs, D. (2005), “The hidden value in reverse logistics”, Supply Chain Management Review, Vol. 9 No. 5, pp. 34-42.
Murphy, P.R., Poist, R.F. and Braunschweig, C.D. (1995), “Role and relevance of logistics to corporate environmentalism – an empirical assessment”, International Journal of Physical Distribution & Logistics Management, Vol. 25 No. 2, pp. 5-19.
Neuman, L.W. (2006), Social Research Methods: Qualitative and Quantitative Approaches, 6th ed., Pearson, Boston, MA.
Ofek, E., Katona, Z. and Sarvary, M. (2011), “‘Bricks and clicks’: the impact of product returns on the strategies of multichannel retailers”, Marketing Science, Vol. 30 No. 1, pp. 42-60.
Pagh, J.D. and Cooper, M.C. (1998), “Supply chain postponement and speculation strategies: how to choose the right strategy”, Journal of Business Logistics, Vol. 19 No. 2, pp. 13-33.
Pohlen, T.L. and Farris, M.T. II (1992), “Reverse logistics in plastics recycling”, International Journal of Physical Distribution & Logistics Management, Vol. 22 No. 7, pp. 35-48.
Porter, M.E. (1980), Competitive Strategy: Techniques for Analyzing Industries and Competitors, The Free Press, New York, NY.
Prahinski, C. and Kocabasoglu, C. (2006), “Empirical research opportunities in reverse supply chains”, Omega, Vol. 34 No. 6, pp. 519-32.
Quinn, J.B. (1980), Strategies for Change: Logical Incrementalism, Irwin, Homewood, IL.
Rainer, R.K. and Miller, M.D. (2005), “Examining differences across journal rankings”, Communications of the ACM, Vol. 48 No. 2, pp. 91-4.
Rogers, D.S. and Tibben-Lembke, R. (2001), “An examination of reverse logistics practices”, Journal of Business Logistics, Vol. 22 No. 2, pp. 129-48.
RL disposition decision-making
269
Rogers, D.S., Lambert, D., Croxton, K. and Garcia-Dastugue, S.J. (2002), “The returns management process”, International Journal of Logistics Management, Vol. 13 No. 2, pp. 1-18.
Roy, J., Nollet, J. and Beaulieu, M. (2006), “Reverse logistics networks and governance structures”, Supply Chain Forum, Vol. 7 No. 2, pp. 58-67.
Schwenk, C.R. (1995), “Strategic decision making”, Journal of Management, Vol. 21 No. 3, pp. 471-93.
Shah, R. and Ward, P.T. (2007), “Defining and developing measures of lean production”, Journal of Operations Management, Vol. 25 No. 4, pp. 785-805.
Skinner, L.R., Bryant, P.T. and Richey, R.G. (2008), “Examining the impact of reverse logistics disposition strategies”, International Journal of Physical Distribution & Logistics Management, Vol. 38 No. 7, pp. 518-39.
Srivastava, S.K. (2007), “Green supply-chain management: a state-of-the-art literature review”, International Journal of Management Reviews, Vol. 9 No. 1, pp. 53-80.
Staikos, T. and Rahimifard, S. (2007), “A decision-making model for waste management in the footwear industry”, International Journal of Production Research, Vol. 45 Nos 18/19, pp. 4403-22.
Stern, L.W. and Reve, T. (1980), “Distribution channels as political economies: a framework for comparative analysis”, Journal of Marketing, Vol. 44 No. 3, pp. 52-65.
Stock, J.R. (1992), Reverse Logistics, Council of Logistics Management, Oak Brook, IL.
Stock, J.R. (1997), “Applying theories from other disciplines to logistics”, International Journal of Physical Distribution & Logistics Management, Vol. 27 Nos 9/10, pp. 515-33.
Stock, J.R. (1998), Development and Implementation of Reverse Logistics Programs, Council of Logistics Management, Oak Brook, IL.
Stock, J.R. (2001), “Doctoral research in logistics and logistics-related areas: 1992-1998”, Journal of Business Logistics, Vol. 22 No. 1, pp. 125-256.
Stock, J.R. and Boyer, S.L. (2009), “Developing a consensus definition of supply chain management: a qualitative study”, International Journal of Physical Distribution & Logistics Management, Vol. 39 No. 8, pp. 690-711.
Stock, J.R. and Mulki, J.P. (2009), “Product returns processing: an examination of practices of manufacturers, wholesalers/distributors, and retailers”, Journal of Business Logistics, Vol. 30 No. 1, pp. 33-62.
Stock, J.R., Speh, T. and Shear, H. (2002), “Many happy (product) returns”, Harvard Business Review, Vol. 80 No. 7, pp. 16-17.
Tan, A.W.K. and Kumar, A. (2006), “A decision-making model for reverse logistics in the computer industry”, International Journal of Logistics Management, Vol. 17 No. 3, pp. 331-54.
Tan, A.W.K., Yu, W.S. and Kumar, A. (2003), “Improving the performance of a computer company in supporting its reverse logistics operations in the Asia-Pacific region”, International Journal of Physical Distribution & Logistics Management, Vol. 33 No. 1, pp. 59-74.
Teunter, R.H., Bayindir, Z.P. and Den Heuvel, W.V. (2006), “Dynamic lot sizing with product returns and remanufacturing”, International Journal of Production Research, Vol. 44 No. 20, pp. 4377-400.
Thierry, M., Salomon, M., Van Nunen, J. and Van Wassenhove, L. (1995), “Strategic issues in product recovery management”, California Management Review, Vol. 37 No. 2, pp. 114-35.
IJPDLM 42,3
270
Toktay, L.B., Wein, L.M. and Zenios, S.A. (2000), “Inventory management of remanufacturable products”, Management Science, Vol. 46 No. 11, pp. 1412-26.
Van Hoek, R.I. (1999), “From reversed logistics to green supply chains”, Supply Chain Management, Vol. 4 No. 3, pp. 129-35.
Vlachos, D. and Dekker, R. (2003), “Return handling options and order quantities for single period products”, European Journal of Operational Research, Vol. 151 No. 1, pp. 38-52.
Vorasayan, J. and Ryan, S.M. (2006), “Optimal price and quantity of refurbished products”, Production and Operations Management, Vol. 15 No. 3, pp. 369-84.
Wang, P. (2010), “Chasing the hottest IT: effects of information technology fashion on organizations”, MIS Quarterly, Vol. 34 No. 1, pp. 63-85.
Webster, J. and Watson, R.T. (2002), “Analyzing the past to prepare for the future: writing a literature review”, MIS Quarterly, Vol. 26 No. 2, pp. 13-23.
Webster, S. and Mitra, S. (2007), “Competitive strategy in remanufacturing and the impact of take-back laws”, Journal of Operations Management, Vol. 25 No. 6, pp. 1123-40.
Wolf, C. and Seuring, S. (2010), “Environmental impacts as buying criteria for third party logistical services”, International Journal of Physical Distribution & Logistics Management, Vol. 40 Nos 1/2, pp. 84-102.
Appendix Articles used in content analysis Ahiska, S.S. and King, R.E. (2010), “Inventory optimization in a one product recoverable
manufacturing system”, International Journal of Production Economics, Vol. 124 No. 1, pp. 11-19.
Ahiska, S.S. and King, R.E. (2010), “Life cycle inventory policy characterizations for a single-product recoverable system”, International Journal of Production Economics, Vol. 124 No. 1, pp. 51-61.
Akcali, E. and Cetinkaya, S. (2010), “Quantitative models for inventory and production planning in closed-loop supply chains”, International Journal of Production Research, Vol. 49 No. 8, pp. 2373-407.
Aras, N., Verter, V. and Boyaci, T. (2006), “Coordination and priority decisions in hybrid manufacturing/remanufacturing systems”, Production and Operations Management, Vol. 15 No. 4, pp. 528-43.
Bakal, I.S. and Akcali, E. (2006), “Effects of random yield in remanufacturing with price-sensitive supply and demand”, Production and Operations Management, Vol. 15 No. 3, pp. 407-21.
Brander, P. and Forsberg, R. (2005), “Cyclic lot scheduling with sequence-dependent set-ups: a heuristic for disassembly processes”, International Journal of Production Research, Vol. 43 No. 2, pp. 295-310.
Damodaran, P. and Wilhelm, W.E. (2004), “Branch-and-price methods for prescribing profitable upgrades of high-technology products with stochastic demands”, Decision Sciences, Vol. 35 No. 1, pp. 55-82.
DeCroix, G.A. (2006), “Optimal policy for a multiechelon inventory system with remanufacturing”, Operations Research, Vol. 54 No. 3, pp. 532-43.
DeCroix, G., Song, J.-S. and Zipkin, P. (2005), “A series system with returns: stationary analysis”, Operations Research, Vol. 53 No. 2, pp. 350-62.
DeCroix, G., Song, J.-S. and Zipkin, P. (2009), “Managing an assemble-to-order system with returns”, Manufacturing & Service Operations Management, Vol. 11 No. 1, pp. 144-59.
DeCroix, G.A. and Zipkin, P.H. (2005), “Inventory management for an assembly system with product or component returns”, Management Science, Vol. 51 No. 8, pp. 1250-65.
RL disposition decision-making
271
Easwaran, G. and Uster, H. (2009), “Tabu search and benders decomposition approaches for a capacitated closed-loop supply chain network design problem”, Transportation Science, Vol. 43 No. 3, pp. 301-20.
Farrell, R.R. and Maness, T.C. (2005), “A relational database approach to a linear programming-based decision support system for production planning in secondary wood product manufacturing”, Decision Support Systems, Vol. 40 No. 2, pp. 183-96.
Ferguson, M.E. (2009), “The value of quality grading in remanufacturing”, Production and Operations Management, Vol. 18 No. 3, pp. 300-14.
Ferrer, G. and Ketzenberg, M.E. (2004), “Value of information in remanufacturing complex products”, IIE Transactions, Vol. 36 No. 3, pp. 265-77.
Ferrer, G. and Swaminathan, J.M. (2006), “Managing new and remanufactured products”, Management Science, Vol. 52 No. 1, pp. 15-26.
Ferrer, G. and Whybark, D.C. (2001), “Material planning for a remanufacturing facility”, Production and Operations Management, Vol. 10 No. 2, pp. 112-24.
Fleischmann, M., Beullens, P., Bloemhof-Ruwaard, J.M. and Van Wassenhove, L.N. (2001), “The impact of product recovery on logistics network design”, Production and Operations Management, Vol. 10 No. 2, pp. 156-73.
Galbreth, M.R. and Blackburn, J.D. (2006), “Optimal acquisition and sorting policies for remanufacturing”, Production and Operations Management, Vol. 15 No. 3, pp. 384-92.
Galbreth, M.R. and Blackburn, J.D. (2010), “Optimal acquisition quantities in remanufacturing with condition uncertainty”, Production and Operations Management, Vol. 19 No. 1, pp. 61-9.
Guide, D.R. Jr, Souza, G.C., Van Wassenhove, L.N. and Blackburn, J.D. (2006), “Time value of commercial product returns”, Management Science, Vol. 52 No. 8, pp. 1200-14.
Jaber, M.Y. and El Saadany, A.M.A. (2009), “The production, remanufacture and waste disposal model with lost sales”, International Journal of Production Economics, Vol. 120 No. 1, pp. 115-24.
Ketzenberg, M.E. (2009), “Optimal pricing, ordering, and return policies for consumer goods”, Production and Operations Management, Vol. 18 No. 3, pp. 344-60.
Ketzenberg, M.E., Souza, G.C. and Guide, D.R. Jr (2003), “Mixed assembly and disassembly operations for remanufacturing”, Production and Operations Management, Vol. 12 No. 3, pp. 320-35.
Kilpi, J., Toyli, J. and Vepsalainen, A. (2009), “Cooperative strategies for the availability service of repairable aircraft components”, International Journal of Production Economics, Vol. 117 No. 2, pp. 360-70.
Kim, H.-J. and Xirouchakis, P. (2010), “Capacitated disassembly scheduling with random demand”, International Journal of Production Research, Vol. 48 No. 23, pp. 7177-94.
Lee, D.-H., Dong, M. and Bian, W. (2010), “The design of sustainable logistics network under uncertainty”, International Journal of Production Economics, Vol. 128 No. 1, pp. 159-66.
Minner, S. (2001), “Strategic safety stocks in reverse logistics supply chains”, International Journal of Production Economics, Vol. 71 Nos. 1-3, pp. 417-28.
Mukhopadhyay, S.K. and Setoputro, R. (2004), “Reverse logistics in e-business: optimal price and return policy”, International Journal of Physical Distribution & Logistics Management, Vol. 34 No. 1, pp. 70-88.
Nenes, G., Panagiotidou, S. and Dekker, R. (2010), “Inventory control policies for inspection and remanufacturing of returns: a case study”, International Journal of Production Economics, Vol. 125 No. 2, pp. 300-12.
Pineyro, P. and Viera, O. (2010), “The economic lot-sizing problem with remanufacturing and one-way substitution”, International Journal of Production Economics, Vol. 124 No. 2, pp. 482-8.
IJPDLM 42,3
272
Ravi, V., Shankar, R. and Tiwari, M.K. (2008), “Selection of a reverse logistics project for end-of-life computers: ANP and goal programming approach”, International Journal of Production Research, Vol. 46 No. 17, pp. 4849-70.
Ray, S., Boyaci, T. and Aras, N. (2005), “Optimal prices and trade-in rebates for durable, remanufacturable products”, Manufacturing & Service Operations Management, Vol. 7 No. 3, pp. 208-28.
Savaskan, R.C. and Van Wassenhove, L.N. (2006), “Reverse channel design: the case of competing retailers”, Management Science, Vol. 52 No. 1, pp. 1-14.
Savaskan, R.C., Bhattacharya, S. and Van Wassenhove, L.N. (2004), “Closed-loop supply chain models with product remanufacturing”, Management Science, Vol. 50 No. 2, pp. 239-52.
Serrato, M.A., Ryan, S.M. and Gaytan, J. (2007), “A Markov decision model to evaluate outsourcing in reverse logistics”, International Journal of Production Research, Vol. 45 Nos. 18/19, pp. 4289-315.
Shulman, J.D., Coughlan, A.T. and Savaskan, R.C. (2009), “Optimal restocking fees and information provision in an integrated demand-supply model of product returns”, Manufacturing & Service Operations Management, Vol. 11 No. 4, pp. 577-94.
Souza, G.C., Ketzenberg, M.E. and Guide, D.R. Jr (2002), “Capacitated remanufacturing with service level constraints”, Production and Operations Management, Vol. 11 No. 2, pp. 231-48.
Srivastava, S.K. (2008), “Value recovery network design for product returns”, International Journal of Physical Distribution & Logistics Management, Vol. 38 No. 4, pp. 311-31.
Su, X. (2009), “Consumer returns policies and supply chain performance”, Manufacturing & Service Operations Management, Vol. 11 No. 4, pp. 595-612.
Taylor, T.A. (2001), “Channel coordination under price protection, midlife returns, and end-of-life returns in dynamic markets”, Management Science, Vol. 47 No. 9, pp. 1220-34.
Teunter, R.H. (2001a), “A reverse logistics valuation method for inventory control”, International Journal of Production Research, Vol. 39 No. 9, pp. 2023-35.
Teunter, R.H. (2001b), “Economic ordering quantities for recoverable item inventory systems”, Naval Research Logistics, Vol. 48 No. 6, pp. 484-95.
Teunter, R.H., Bayindir, Z.P. and Den Heuvel, W.V. (2006), “Dynamic lot sizing with product returns and remanufacturing”, International Journal of Production Research, Vol. 44 No. 20, pp. 4377-400.
Teunter, R.H., Tang, O. and Kaparis, K. (2009), “Heuristics for the economic lot scheduling problem with returns”, International Journal of Production Economics, Vol. 118 No. 1, pp. 323-30.
Üster, H., Easwaran, G., Akçali, E. and Çetinkaya, S. (2007), “Benders decomposition with alternative multiple cuts for a multi-product closed-loop supply chain network design model”, Naval Research Logistics, Vol. 54 No. 8, pp. 890-907.
Van Wassenhove, L.N. and Zikopoulos, C. (2010), “On the effect of quality overestimation in remanufacturing”, International Journal of Production Research, Vol. 48 No. 18, pp. 5263-80.
Walther, G. and Spengler, T. (2005), “Impact of WEEE-directive on reverse logistics in Germany”, International Journal of Physical Distribution & Logistics Management, Vol. 35 No. 5, pp. 337-61.
Wu, S.J. and Closs, D. (2009), “The impact of integrating return components planning with purchasing decisions on purchasing performance: a contingency perspective”, International Journal of Physical Distribution & Logistics Management, Vol. 20 No. 1, pp. 57-78.
Xiao, T., Shi, K. and Yang, D. (2010), “Coordination of supply chain with consumer return under demand uncertainty”, International Journal of Production Economics, Vol. 124 No. 1, pp. 171-80.
RL disposition decision-making
273
Yuan, K.F. and Gao, Y. (2010), “Inventory decision-making models for a closed-loop supply chain system”, International Journal of Production Research, Vol. 48 No. 20, pp. 6155-87.
Reverse logistics frameworks Dowlatshahi, S. (2005), “A strategic framework for the design and implementation of
remanufacturing operations in reverse logistics”, International Journal of Production Research, Vol. 43 No.16, pp. 3455-80.
Mollenkopf, D., Russo, I. and Frankel, R. (2007), “The returns management process in supply chain strategy”, International Journal of Physical Distribution & Logistics Management, Vol. 37 No. 7, pp. 568-92.
Srivastava, S.K. and Srivastava, R.K. (2006), “Managing product returns for reverse logistics”, International Journal of Physical Distribution & Logistics Management, Vol. 36 No. 7, pp. 524-46.
Corresponding author Benjamin T. Hazen can be contacted at: [email protected]
To purchase reprints of this article please e-mail: [email protected] Or visit our web site for further details: www.emeraldinsight.com/reprints
IJPDLM 42,3
274
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
