assignment
JOURNAL OF BUSINESS LOGISTICS
Volume 30, Number 1 Table of Contents 2009
1 “Marketing/Logistics Relationships: Influence on Capabilities and Performance”
Patricia J. Daugherty, Haozhe Chen, Daniel D. Mattioda, and Scott J. Grawe
Effective marketing/logistics relationships can help to create, develop, and maintain critical
capabilities to support long-term firm success. Recent research focuses on two distinct
capabilities—information capabilities and firm-wide integration. Empirical results are provided
confirming the proposed relationship between marketing logistics relationship effectiveness
and
the two capabilities. Further, the capabilities are shown to positively impact logistics performance.
Key Words: Firm-wide integration; Information capability; Logistics performance;
Marketing/Logistics relationships; Structural equation modeling
19 “Modeling Uncertain Forecast Accuracy in Supply Chains with Postponement”
Larry J. LeBlanc, James A. Hill, Jerry Harder, and Gregory W. Greenwell
We examine a situation where a manufacturer operates in a two-mode production environment.
The first mode could involve overseas vendors and manufacturing facilities. If additional units are
later required, the company must use its second mode—more expensive last-minute domestic
vendors and manufacturing sites. We develop a new methodology for analyzing the impact of
forecast accuracy on the decision to postpone production. We examine the interaction of forecast
accuracy, shortage vs. holding costs, transportation costs and the cost of postponing production in
the supply chain of a single product facing uncertain demand. Our model can be used to analyze
the cost of important changes, such as increasing forecast accuracy, reducing the cost of
backorders, lowering the cost of delaying production, or lowering transportation costs. Our model
allows a firm to understand its overall cost structure so that it can accurately evaluate the impact of
improved forecast accuracy and lowered costs in the context of postponement.
Key Words: Forecasting; Probabilistic models; Production planning; Supply chain management
33 “Product Returns Processing: An Examination of Practices of Manufacturers,
Wholesalers/Distributors, and Retailers”
James R. Stock and
Jay P. Mulki
Few research studies have published specific empirical data regarding the reverse logistics
practices of companies. This multi-stage study employed interviews, site visits, and a mail survey
to collect responses from 230 members of the Warehousing Education and Research Council
(WERC) regarding their reverse logistics practices. Results suggest that in spite of the growing
importance of reverse logistics, few executives have product return processing as their primary
responsibility and often undertake this activity along with other job responsibilities. Most firms
handle the product returns process themselves and typically within the same facilities that handle
forward logistics. Returning items directly to stock, repackaging and returning to stock, and
selling as scrap, were the three top disposition options employed by firms. Results indicate that,
contrary to general understanding, the majority of retailers and wholesalers reported a recovery
rate of over 75% of product cost. Several hypotheses developed from the published literature on
reverse logistics were tested. In many instances, these hypotheses were formulated on anecdotal
information or single case studies and had not been empirically tested prior to this research being
conducted.
Key Words: Manufacturers; Product returns; Retailers; Reverse logistics; Wholesalers/Distributors
vii
JOURNAL OF BUSINESS LOGISTICS, Vol. 30, No. 1, 2009 33
PRODUCT RETURNS PROCESSING: AN EXAMINATION OF PRACTICES OF
MANUFACTURERS, WHOLESALERS/DISTRIBUTORS, AND RETAILERS
by
James R. Stock
University of South Florida
and
Jay P. Mulki
Northeastern University
INTRODUCTION
Processing product returns has become a critical activity for organizations in the as the volume of goods
flowing back through the supply chain rapidly increases (Guide et al. 2006). It has been reported that the value of
products being returned exceeds an estimated $100 billion per year and averages about 6 percent of sales (Guide et
al. 2006; Stock 2001). It is estimated that product returns could range from 15% for mass merchandisers to 35% for
e-commerce retailers (Gentry 1999). Product returns are part of reverse logistics which includes a combination of
other activities such as recycling, refurbishing, and repair, as well as waste disposal (Stock 2001). It is believed that
while product returns are known to account for a large proportion of reverse logistics activities, manufactures are
able to recover only a portion of the value of the returned products because of processing delays (Guide et al. 2006).
For more than two decades, practitioners and researchers have been concerned with issues relating to “product
returns” and “reverse logistics.” They have repeatedly advocated the need for more specific data, that is, empirical
research on the topics (e.g., product remanufacturing and refurbishing, product returns, environmental aspects of
packaging, product disposal, recycling, reusable containers, source reduction, life cycle analysis, product
stewardship, green marketing, sustainability). Organizations have also realized that a better understanding of product
returns and efficient management of reverse logistics can provide them with a competitive advantage. Sound
practices in product returns and reverse logistics can be a “win-win” situation benefiting both customers and the firm
(Stock 2004). When effectively handled, product return processes can help firms recover value. Furthermore, they
can aid in the development of customer return policies that can increase customer loyalty (Rogers et al. 2002) and
improve product sales (Mukhopadhyay and Setoputro 2005). Better understanding of issues related to product
returns can also help identify areas in manufacturing or marketing where corrective actions might be necessary. In
addition, with growing environmental concerns and legal regulations associated with green marketing and
sustainability, activities related to product disposal in reverse logistics can provide insights into strategies for
sustainable development (Srivastava and Srivastava 2006).
However, it is possible that some organizations still do not realize the critical nature of product returns as it
relates to profitability and customer service, nor the benefits associated with efficient product returns. Organizations
are more likely to perceive the product returns function as an additional cost to be incurred in their normal business
practices (Stock 2004). In view of this, there is a need to understand the place of product returns and reverse
logistics in an organization’s marketing mix strategy, and the level of importance they would assign to reverse
logistics as compared to traditional forward logistics.
Product return policies and processes differ by type of business. For example, manufacturers may want to define
return policies in stricter and narrower terms and may be concerned about the more liberal return policies of retailers
34 STOCK AND MULKI
(Gentry 1999). Firms also vary in whether or not they perform product returns processing in-house or outsource it to
a third-party. Outsourcing or partnering with others can be an attractive option to exploit benefits of economies of
scale if the firm’s product return volumes are low (Stock 1998). Often, firms specializing in handling product returns
are able to achieve economies of scale by combining volumes from multiple companies. Small individual firms see
outsourcing to these firms as an attractive option to lower costs associated with processing product
returns.
An important component of the reverse logistics process is to accurately evaluate each product returned in order
to determine the most optimal disposition option. Typically, stations or physical locations are set up in the facility
handling the product returns where personnel evaluate each item being returned. Personnel are trained to make
determinations whether items should be discarded, repackaged, repaired, refurbished, remanufactured, or a myriad
of other possible options (Rogers and Tibben-Lembke 1999; Stock 2004).
Product disposition is another area where further studies can help the process. Retailers may decide to return the
product to the supplier due to defects, obsolescence or overstocks (Rogers and Tibben-Lembke 1999). Options for
disposal processes of product returns can vary with firms and can range from refurbishing, reselling, recycling, or
destroying the returned products (UK Department of Transport 2004). While some returned products can be
repackaged and sold as new, due to legal or other restrictions some products can not be resold as new once the
product has been returned by customers. For example, while an electronic part could be refurbished and sold, a
circuit breaker may have to be disposed of differently (Rogers and Tibben-Lembke 1999). If a firm is not able to
resell the items, they often end up in land fills, or perhaps recycled. Also, the profit margins could be lower for the
manufacturer because in addition to the refurbishing cost, the product often must be sold at a lower price. In view of
this, manufacturers’ desire to maximize profits often dictates the proportion of product that gets refurbished
(Vorasayan and Ryan 2006).
Stock, Speh, and Shear (2002) state that in the U.S. consumers return products valued at more than $100 billion
each year, which is more than the GDP of 66% of the countries in the world. As firms begin to grasp the cost
implications of product returns, “return avoidance” is being considered as a desired alternative. The Reverse
Logistics Executive Council (RLEC) states that return avoidance entails examining ways to minimize the number of
products entering the return stream. Return avoidance, which can be acomplished by ensuring higher quality
products, increasing user friendliness of product, and managing promotional programs aimed at unloading the
products to the retailers, could be a critical part of a reverse logistics program.
This article uses the empirical data collected from manufacturers, wholesalers/distributors and retailers on
product returns processing to achieve the following major objectives. First, results should facilitate a better
understanding of what takes place in these business sectors and provide some benchmarks regarding reverse
logistics practices. Second, it will examine several hypotheses that have been suggested in various published
articles, but never tested. It is expected that testing of these hypotheses will help provide answers to questions such
as the level of importance of product returns, whether recovered value is high enough to justify product recovery
efforts, and the resources expended in various stages of the product return process.
SELECTED LITERATURE REVIEW
During the last decade, much has been published in terms of case studies and anecdotal information regarding
product returns. However, there are relatively few research studies that have examined empirical data (Srivastava
and Srivastava 2006). Stock (1992) was one of the earliest writers to call for more research in the area, although his
White Paper on reverse logistics was primarily a literature review of the topic. Much of his research dealt with
environmental aspects of reverse logistics, specifically source reduction, recycling, substitution and waste disposal.
He developed five major findings: (1) logistics executives needed to anticipate future environmental regulatory
changes; (2) logistics executives needed to be aware of the “green marketing revolution,” (3) procurement should be
aware of the need to acquire secondary raw materials, (4) logistics executives should implement efficient and
effective reverse logistics systems, and (5) some management persons should be assigned reverse logistics and
environmental responsibilities (Stock 1992, p. vi).
Sarkis (1995) addressed the reverse logistics chain and its role in product development, the product life cycle,
and recycling. He provided examples for each item, and discussed and posited several issues that needed to be
JOURNAL OF BUSINESS LOGISTICS, Vol. 30, No. 1, 2009 35
addressed through further research. Later, Stock (1998) and Rogers and Tibben-Lembke (1999) expanded the view
of reverse logistics to include additional activities such as processing product returns, disposition of physical goods
to obtain maximum recovery value, and the remanufacturing/refurbishing of items. These researchers included some
empirical data about reverse logistics, but each employed different research designs in collecting their data.
Stock (1998) utilized qualitative methods to analyze published and proprietary company reports and other
materials relating to reverse logistics and product returns. He then conducted in-depth case studies of several
companies located in North America and Europe. His findings were based on a qualitative analysis of published
documents and personal interviews with multiple executives in a number of companies. While he developed a large
number of findings from his research, some of the most significant included the potential cost savings and customer
service improvements that can result from implementing good reverse logistics practices, identifying the importance
of process mapping of the reverse logistics process, highlighting the need for specific cost information regarding
reverse logistics activities, recognizing that reverse logistics strategies and tactics require the multi-functional
approach of many areas within and between firms, and good reverse logistics processes usually have positive
environmental impacts (Stock 1998, pp. 7-8).
Rogers and Tibben-Lembke (1999) utilized a combination approach, i.e., company interviews and a mail survey
of reverse logistics executives. Based on the findings, Rogers and Tibben-Lembke (1999) recommended that firms
could improve the economics of reverse logistics by focusing on improving gate keeping technology, making
disposition decisions earlier, decreasing cycle times by speeding up the pace of returns processing, and better data
management. Both publications, while examining different industries and companies, highlighted the need for more
empirical research on the topics of reverse logistics and product returns. Prior to these studies, most published
material was anecdotal, that is, overviews of what individual companies were doing to handle product returns, reuse
packaging, remanufacture or refurbish products, and other reverse logistics practices.
Much of the research on product returns and reverse logistics has been specific to an industry or product
category. For example, Autry, Daugherty, and Richey (2001) reported on the predictors of reverse logistics
performance and satisfaction for firms selling electronic goods through catalogues. They found that performance
measured by indicators such as satisfaction and profitability was influenced by size of the firm, sales volume, and
whether the company had an internal or external arrangement for disposition.
Other researchers such as Meade and Sarkis (2002) focused on the critical determinants in firm’s selection of a
third-party logistics provider. Their model suggested that product position in its life cycle, organizational strategic
performance requirements, and the role played by reverse logistics in meeting firms environmental and customer
needs, were important.
De Koster, de Brito, and Van de Vendel (2001) outlined the factors that contributed to combining or separating
inbound and outbound flows during the handling of product returns for food stores, department stores, and mail
order companies. Their findings suggested that retailers were not as good in performing reverse logistics compared
to their ability in handling forward flows.
Richey, Genchev, and Daugherty (2005) examined automobile after-market firms and showed that reverse
logistics program efficiency and effectiveness could be increased by innovation and properly allocating resources.
Tan and Kumar (2006) compared the economics of refurbished parts versus part replacements for the computer
industry. The findings of their study stated that delay in transportation associated with processing returns negatively
impacts the economic viability of reverse logistics. Wu and Cheng (2006) looked at the supply chains in China,
Taiwan and Hong Kong to identify problems and developed a common model of reverse logistics for the industries
and companies examined. Their research led them to suggest that processing of product returns was not
economically viable due to lower values of recovered products since the cost of recovery exceeded the recovered
value.
Mukhopadhyay and Setoputro (2004) examined reverse logistics in an e-business context, specifically looking
at pricing and return policies of Internet businesses. This study linked the e-tailer return policy to customer
sensitivity to the rate of return parameter. Findings of this study suggested that sellers’ return policies were more
restrictive if customers were sensitive to the rate of return parameter and were more likely to abuse the seller’s
return policies. The same authors (Mukhopadhyay and Setoputro 2006) examined the role of 4-PL’s in the
36 STOCK AND MULKI
outsourcing of reverse logistics activities and identified situations where optimal “win-win” results could be
obtained by all parties.
While each of the research studies examined a variety of reverse logistics issues, they were all in agreement that
more empirical research needed to be done on the topic. As evidenced in the overview of logistics and supply chain
management doctoral dissertations (Stock and Broadus 2006), more researchers have begun to examine reverse
logistics/product returns. The authors identified 13 dissertations completed between 1999 and 2004 dealing with
some aspect of reverse logistics/product returns. While 13 dissertations is not a large number, the number of
dissertations increased twofold from the 6 dissertations published on the subject between 1992 and 1998. Only 12
were published in the twenty years between 1970 and 1991 in the U.S. As a result of the continuing calls for more
empirical research on reverse logistics and product returns, this present research study was initiated to examine what
reverse logistics activities were being undertaken within three major industry sectors—manufacturing,
wholesaler/distributor, and retailing—and to identify some benchmarks for evaluating company practices. The focus
of this research was only on product returns and not packaging materials or waste disposal.
HYPOTHESES
Until recently, the majority of published articles on reverse logistics and product returns provided anecdotal
evidence of the rising importance of these issues. While the assertions and recommendations were intuitively
appealing and straightforward, most research studies have not specifically developed and tested hypotheses related
to product returns processing. While this research was primarily descriptive in nature, some of the previously
published literature suggested that certain hypotheses could be developed and tested. In those instances, this research
study examined several previously untested hypotheses and they are discussed in the following paragraphs.
Academics and practitioners agree that there is a growing focus on reverse logistics and product returns as firms
are beginning to take a strategic perspective of the process (Wu and Cheng 2006). Managers state that a well
administered reverse logistics program can reduce costs, improve customer service, and project an environmentally
friendly image, thus providing the firm with a competitive edge in the current market (Rogers et al. 2002; Srivastava
and Srivastava 2006). Stockholders, on their part, place a lot of emphasis on effective reverse logistics and product
returns partly prompted by the need to comply with legislative and legal obligations (Alvarez-Gil et al. 2007). The
increased strategic importance and the realization of the competitive edge offered by effectively managed reverse
logistics processes should make product returns a critical function.
One issue that has not yet been resolved is that of whether organizations should establish separate supply chain
channels for forward and reverse logistics. Rogers and Tibben-Lembke (2001) stated: “for returns to be proceeded
effectively and efficiently, they should usually be separated from the forward channel” (p. 141). Chopra and Meindl
(2007) argued that because customer priorities and supply chain strategy for the distribution of products are different
than for product returns, different supply chains should be established. Speh (2007) also seemed to infer that
multiple supply chains are needed to handle forward versus reverse logistics when he stated: “reversing the flow of
product in the supply chain… is a valuable service because reverse processes are outside the normal supply chain
process and often require significant time and attention” (pp. 235-236). Finally, Wisner, Leong, and Tan (2005)
seemed to take the view that reverse logistics could be accomplished in the same supply chain as forward logistics
when they stated: “Extending integration can also include reverse logistics, or integrating the process of product
returns back up the supply chain” (p. 458). The authors then went on to suggest that a separate reverse logistics
channel could be established: “Competitive pressures, increased legislation, and the desire to better utilize resources
are forcing many firms to design an effective reverse flow system” (p. 458).
None of these authors cited any specific research to support their positions, so it is possible that reverse logistics
could be accomplished in the same or different supply chain channels, although most authors appear to support
separate channels being established. Irrespective of whether an organization utilizes the same or different supply
chains however, there is general agreement that some specific person, group or department should be directly
responsible for reverse logistics.
Relating to the notion that a specific person should be directly responsible for reverse logistics, Stock, Speh, and
Shear (2002) recommended that if the firm intends to make a profit on the product returns activity, then this
JOURNAL OF BUSINESS LOGISTICS, Vol. 30, No. 1, 2009 37
responsibility should be assigned to senior managers with good business acumen. Many of the firms surveyed in this
research believed that the effectiveness of reverse logistics could be improved by making it a separate function in
the organization instead of having it attached to the forward distribution network (Rogers and Tibben-Lembke
2001). Based on these previously published articles, the
following hypothesis is
presented:
H1: Product returns are primarily handled by a management-level person in manufacturing,
retailing or wholesale/distributor firms.
Utilizing articles authored by House (1971) and Autry (2005), elevating reverse logistics and product return as a
separate function at a management level would imply that reverse logistics and product returns processing should
enjoy a similar status as that of forward distribution. Having reverse logistics as a separate function would also
reduce or eliminate its subordinate status to forward distribution, thus minimizing chances of multiple reporting and
role conflicts.
In organizations, managers are tasked with providing structure and specific directions in allocating tasks,
establishing procedures, setting expectations and rewards, thus reducing ambiguity and conflicts regarding functions
and goals (House 1971). Organizational behavior theories suggest that growth in organizations results in greater
differentiation in structure (Blau 1970). When faced with handling (i.e., processing) multiple functions and different
specialties, managers realize that they end up spending more time supervising these functions compared to
managing a homogenous function (Blau and Schoenherr 1971). Research also indicates that formalization of rules,
processes and procedures to guide operations increase efficiency (Autry 2005). Sub-division of responsibilities and
creating a functional group is likely to improve performance (Blau 1970). Studies propose that functional
differentiation and professionalization are known to infuse commitment to move beyond the current status towards
greater acceptance of technological innovations and provide motivation to be recognized within the organization
(Damanpour 1987). Having executives higher in the organizational hierarchy provides weight or importance to the
function as these executives can act as champions for improvements and improve communication and coordination
throughout the firm (Sinha and Van de Ven 2005). This would suggest that as the importance of the product return
function increases, this function would require management by a senior executive in the firm. Based on this, the
following hypothesis is presented:
H2: A majority of manufacturing, retailing or wholesale/distributor firms are likely to have a single
person responsible for
product returns processing.
Meade and Sakris (2002) found that selection of third-party options are often guided by a firm’s strategic
performance requirements. Businesses recognize the need to focus on core competencies and view third-party
sources as a logical choice for handling reverse logistics activities in the absence of a separate function within the
organization for product returns. Researchers also believe that firms should give serious consideration to third-party
processing, if the current product return function is a part-time operation handled by more than one employee along
with other functions (Stock, Speh, and Shear 2006). This is because third-parties with reverse logistics as a core
competency have efficiencies of operation and are able to combine volumes from multiple companies for economies
of scale (UK Department of Transport 2004). In addition, third-parties specializing in product returns have unique
channels for product disposition in addition to providing a single central place for potential buyers of returned goods
due to the large volumes they process (Rogers and Tibben-Lembke 2001). Thus, outsourcing is a viable option for
firms without a dedicated returns process and for those that are unable to realize costs savings due to lower volumes
of product returns (Discount Store News 1999; Gorick 2005). Based on these articles, the following hypothesis is
presented:
H3: When reverse logistics or product returns is not a single person responsibility, the product
returns function is most often outsourced to third-parties.
Another reverse logistics matter that has been examined by a number of writers has been the education and
training of employees. Trade reports indicate that product returns may cost as much as three to four times the cost of
outbound shipments (Andel and Aichlmayr 2002). Return product handling costs can be as high as $35-$42 billion
per year or about 3-4 % of the $1.1 trillion 2005 logistics costs (Cooke 2006). This implies that there is an urgent
need to improve the product return process to make it more effective and thus enhance competitiveness in the
marketplace (Rogers and Tibben-Lembke 2001; Stuart et al. 2005). As the profile and visibility of reverse logistics
38 STOCK AND MULKI
rises in the organization, processes and strategies for reverse logistics are also attracting greater interest and scrutiny
(Rao, Stenger, and Wu 1994). In addition, since the returned product goes through various stages in the process,
potentials for errors increase. To counter this, experts have recommended better training of employees in the product
returns process as part of best practice (Stock 1996). Industry reports suggest that this training can be both formal
and informal and can range from overseeing how returns are processed to teaching how to repackage items
(Kuzeljevich 2004). Current training methods involve providing employees with operating procedures manuals,
mentoring of workers by other more experienced employees, or more informal methods (Stock, Speh, and Shear
2006). Thus most of the training could be on-the-job training and may consist of looking over employee’s shoulders.
Based on these published articles, the following hypothesis is presented:
H4: A minority (less than 50%) of firms use formal methods involving written materials, Internet,
etc. to train employees involved in product returns processing.
Firms are motivated to recover as much value as possible from returned products. However, there is not enough
published information about the recovered values of returned products in relation to the costs incurred in
processing
them. In fact, Wu and Cheng (2006) suggested that publishers are better off discarding the returns rather than
processing them. Further, it is also believed that if the returned products remain longer in reverse channels, they can
negatively impact profitability. This could be due to higher inventory levels, transportation and warehousing costs,
as well as deterioration and product obsolescence with the passage of time (Blackburn et al. 2004; Stock 2001). On
the other hand, if returned products are processed at points closer to the customer, the time lag is shorter as products
avoid traveling up the distribution channel to the manufacturer and then back down to the wholesaler and retailer.
Quicker processing and turn-around help recover greater value from the returned products (Rogers and Tibben-
Lembke 2001). In view of this, it is likely that retailers who are closest to the consumer in the distribution chain
should be able to get higher product recovery rates compared to wholesalers/distributors or
manufacturers.
Based on
these articles, the following hypothesis is presented:
H5: Recovery rates (as % of cost) are higher for retailers when compared to manufacturers or
wholesalers/distributors.
Multiple authors have indicated that firms often utilize return authorizations (RA’s) for accepting returns. Much
of the published information on the use of RA’s has been anecdotal, that is, viewpoints of practitioners working in
the field of product returns, qualitative interviews of reverse logistics practitioners, and case studies of companies
involved in various aspects of product returns processing (Guide and Van Wassenhove 2002; Mukhopadhyay and
Setoputro 2004; Richey et al. 2005; Rogers and Tibben-Lembke 2001; Stock 1998, 2004). While the benefits of
RA’s seem apparent, there have not been any published studies that specifically demonstrate that a majority of firms
utilize these documents as a means of processing product returns. In this research study, this specific issue is
addressed, leading to the following hypothesis:
H6: A majority of firms (more than 50%) use return authorizations (RA’s) for accepting product
returns.
Product disposition refers to the different ways business organizations try to recover the costs of the products
that were returned. The following examples illustrate the multiple ways that returned items are processed. For items
with product dating that are nearing their expiration dates, they can be maintained in temporary storage and picked
and shipped to customers first. Thus, they are not “mixed” with other items with longer expiration dates (Stock
2004). If the products being returned are in damaged boxes, yet are in otherwise perfect condition, repackaging of
the items can take place immediately if packaging supplies are maintained at the returns processing facility. While
this is not a common occurrence, some computer and electronic components are processed in this way, resulting in
the items being returned to inventory much more quickly (Stock 2004).
Another example in the electronic components industry occurs when returned items may only have small
cosmetic imperfections that do not impact usability, or they may have a defective part that has been replaced and are
now in working order, but not in “as new” condition. These items can be used as warranty replacements and/or
resold with appropriate indication that they are not “brand new.”
As per industry sources, often the returned items are in fact not defective but have entered the return stream
because the customers changed their mind or did not understand how to operate the product (Rogers and Tibben-
Lembke 2001). A recent study reported that retailers most often send back the non-defective customer returns to the
JOURNAL OF BUSINESS LOGISTICS, Vol. 30, No. 1, 2009 39
manufacturers without even testing them. This results in manufacturers returning these non-defective items directly
back-to-stock or into inventory after a cursory examination (Rogers and Tibben-Lembke 2001). Studies indicate that
between 17-20% of the product returns went directly back-to-stock to be sold as new (Blackburn et al. 2004; Rogers
and Tibben-Lembke 2001). Based on these published articles, the following hypotheses are presented:
H7: Manufacturers will have more product returns placed directly back-in-stock or inventory than
retailers or wholesalers/distributors.
H8: Manufacturers will have more products repackaged and returned to stock than retailers or
wholesalers/distributors.
Often the manufacturers are concerned about selling the returned products to brokers. This is because, in
addition to lower prices, manufacturers are concerned about the loss of “brand equity.” Once the product is sold to
the broker, manufacturers do not control how these products are sold. Firms fear the impact on brand image if these
products end up in bargain outlets or sold in flea markets (Rogers and Tibben-Lembke 2001). Thus the final option
for manufacturers, when the product can not be sold as is or can not be refurbished, is selling it as scrap or
destroying it to recover primary materials. Thus, the following hypothesis is presented:
H9: Manufacturers will have more returned products sold as scrap or destroyed than retailers or
wholesalers/distributors.
The largest category of customer product returns is attributed to buyer’s remorse, usage problems or defects
(UK Department of Transport 2004). Retailers and wholesalers are at the front line and are closer to the customer
and generally faced with more returns. Rogers and Tibben-Lembke (2001) showed that, in spite of the overall desire
to tighten return policies, retailer return policies were still considered liberal. This liberal policy could have been
based on the retailers’ wish that manufacturers bear the cost of generous return policies (Tsay 2001). However, this
seems to be changing. There are indications that the retailers are beginning to focus on individual customer
profitability , minimization of unprofitable customer transactions, and getting rid of ‘bad’ customers (Triest 2005;
Zeithaml, Bitner, and Gremler 2006). This would suggest an increased tightening of product return policies to
discourage customers who indulge in too many returns. Several of the respondents in Rogers and Tibben-Lembke’s
(2001) study felt that the liberal return days are going to be a thing of the past – “it was mentioned in a number of
interviews that the days of ‘no questions asked’ returns are ending” (p. 136). Based on these published articles, the
following hypothesis is presented:
H10: More product returns are refused by retailers than by manufacturers or wholesalers/distributors.
To test these hypotheses, a research design involving a mail survey of practitioners involved in reverse
logistics/product returns was utilized.
METHODOLOGY
Practitioners involved in some aspect of product returns processing were the subjects of the data collection
effort. Personal interviews were conducted with executives who had reverse logistics responsibilities at more than
20 manufacturers, retailers, and wholesalers/distributors. The firms interviewed were approximately equally
distributed between the three groups. The interviews helped refine the questions being asked of survey respondents
and also helped to supplement the data obtained via a mail survey of the Warehousing Education and Research
Council (WERC) membership. The majority of survey questions were developed from the literature and/or the
personal experiences of the authors from previous reverse logistics research. The interviews provided insights into
the wording of the survey questions to maximize understandability and response rate. Respondents are more likely
to answer surveys that they perceive to be relevant to them and what they do. The actual site visits required 4-6
hours of time and involved tours of the product returns processing facility.
An interview guide was used for all site visits, although a few questions varied between firms because they
were partially dependent on the specific products, customers, and markets of each firm. In about 75 % of the firms
40 STOCK AND MULKI
who agreed to participate in the site visit phase of the research, confidentiality or non-disclosure agreements were
used. As a result of the site visits, a review of secondary source materials and the researchers’ experience in the
field, a 4-page mail survey was developed and pre-tested with more than two dozen practitioners directly involved in
reverse logistics activities (Note: a copy of the survey instrument can be found in the Appendix).
With the development of the finalized instrument, three mailings of the survey were sent to manufacturer,
retailer, and wholesaler/distributor members of WERC. Potential respondents to the survey were selected from a
review of the WERC membership list. First, the list was reviewed and only manufacturing, wholesale/distributor and
retailing firms were included. If only one person was shown for a particular company, they were selected if they
held some type of management position. If more than one individual was a WERC member from a specific
company, the titles of the persons were examined. If one of the members had specific reverse logistics or product
returns in their job title, they were selected. If not, and this was typically the case, the highest ranking person in the
company was selected. It was believed that the senior person would have the most knowledge about their firm’s
product returns processing.
The survey questionnaire indicated that this was a WERC-sponsored research project. Respondents were asked
to indicate the type of business organization at which they were employed from the choices provided
(manufacturing, retailing, wholesaler/distributor, government, or other). Survey questions also required them to
choose the industry category from the seventeen sectors (categories) provided. (e.g., pharmaceutical, appliances,
electronics). In addition, they were asked to indicate their job title from the list of job titles (corporate officer,
manager, director, supervisor, staff specialist or other). Finally respondents were asked to indicate their primary job
responsibility; the one responsibility that required most of their time (general management, logistics, marketing,
reverse logistics, warehouse operations or other).
Respondents could request a summary of the survey findings by sending their business card with their returned
survey, by indicating their name and address on the survey, or by requesting the survey results in a separate letter.
An e-mail pre-contact from WERC was sent to all potential respondents approximately 7-10 days prior to mailing of
the survey, encouraging their response to the survey they would be receiving. As a result of these efforts, the total
response rate for the survey prior to the removal of some responses that failed to provide necessary information for
analysis was 242 (22.1 % of 1095).
Tests for Non-response Bias
The four-page surveys were color coded for each mailing and as responses were returned, they were date
stamped so that early versus late respondents could be compared and thus test for non-response bias (Armstrong and
Overton 1977). Responses were received over an eight-week period. Differences between early and late respondents
were checked using the 145 responses received during the first two weeks versus 45 responses received during the
last three weeks. ANOVA models and t-tests did not show any statistically significant differences in responses
between the early responses versus late responses.
As an additional test for non-response bias, a single mailing of a one-page survey to non-respondents was done
two weeks after the third mailing of the four-page survey. A total of 103 one-page surveys were returned, which
were used as a second measure to test for non-response bias. Such tests for non-response bias are important in that
the results of the survey could not be generalized to the population-at-large if bias existed.
After analysis of the early versus late respondents and a comparison of the non-respondents to those completing
the full survey, it was determined that there were no statistically significant differences that existed and results
obtained could be generalized to the entire WERC member population of manufacturers, retailers, and
wholesalers/distributors.
Sample Summary Statistics
The survey responses represented a total of 16 industry sectors plus “other.” Of the 230 responses, 23 did not
indicate any industry category, and 55 responses stated their industry as “other.” Six industry sectors, namely,
Automotive (11), Chemicals & Plastics (10), Clothing & Textiles (12), Department Stores (14), Food & Beverage
JOURNAL OF BUSINESS LOGISTICS, Vol. 30, No. 1, 2009 41
(42) and Paper and related (17) sectors accounted for most of the (106 out of 230) responses. The remaining 58
responses were distributed among the 10 other industry categories named in the survey.
Respondents included Corporate Officers (N = 40), Directors (N = 60), Managers (N = 108), and Supervisors
(N = 7). Fifteen indicated “Other” and 12 respondents did not indicate their position. Of the business groups,
responses were provided by manufacturing firms (N = 92), retailing (N = 23) and wholesalers/distributors (N = 115).
There was only one (1) response from government, seven (7) indicated “other” and four (4) did not indicate any
business group. In sum, the majority of the respondent population consisted of approximately equal proportions of
manufacturers and wholesalers/distributors, with a small number of retailers included. In view of this, all the
analyses were conducted by using the 230 responses from three business groups: manufacturers, retailers, and
wholesalers/distributors.
FINDINGS
SPSS 14 for windows was used for conducting the analysis. Descriptive statistical analysis and ANOVA
comparisons were used to test the various hypotheses. Contingency table analysis utilizing the Chi-square statistic
was used for the nominal-scaled data. When the overall Chi-square was significant, the various pairs of attributes
were examined to determine which relationships were statistically significant. The findings of the study are
presented in two parts. In the first part we describe the details of the product return process based on the study data.
In the second part we provide results from testing the stated hypotheses.
Section I: The Product Returns Process
Steps in Product Returns Processing
In general, product return process activities can be grouped into four steps or stages: (1) Receiving—includes
unloading, distribution of product returns to processing centers; (2) Processing—consists of activities such as data
entry and issuing customer credits; (3) Sortation—inspection and routing of returns to disposition point; and (4)
Disposition—putting the product back into inventory or temporary storage, repackaging, repair, refurbishing or
remanufacturing. It could be argued that there is a step that precedes these four which might be labeled “pre-
receipt.” This would include activities such as shipping the product returns to the processing facility, getting
authorization and completing the return authorization forms, and preparing the returned item for processing. In this
research, we specifically examined the process once the items reached the product returns processing facility.
Survey results indicate that the last three (2, 3, and 4) steps consume a large percentage of the time spent in
product return process. Results indicate that on average, respondents in the three business groups spent about 31% of
the time on processing, about 26% on sortation, 26% on disposition and about 17% on receiving.
Use of Warehouse Space
Almost all respondents in the three business types (>90%) indicated that they used their regular
warehouses/distribution centers to process product returns as opposed to having a dedicated returns processing
facility. Most manufacturers and wholesalers use less than 25 % of their existing warehouse space for processing
returns. Interestingly, about 19 % (4/23) of the retailers indicated that they used more than 75 % of their warehouse
space for returns processing, suggesting that they combined forward and reverse logistics in the same area. Of
course, this would apply to retailers that have dedicated product returns processing facilities. Facilities performing
both forward and reverse logistics activities would only utilize a small portion of their buildings for processing
product returns. Compared to this, 1.3 % (1/78) of manufacturers and 2.9 % (3/103) of wholesalers indicated use of
more than 75 % of warehouse space for product return processing. This finding is consistent with the researcher’s
experience that retailers utilize dedicated product returns facilities to a larger degree than do manufacturers and
wholesalers/distributors. Typically, retailers will see more returns than other supply chain members who are further
away from the final customer.
When return rates and/or volumes are low, a combined facility is usually optimal. A combined facility is
defined as a warehouse or DC where both forward and reverse logistics activities occur in the same location. When
42 STOCK AND MULKI
return volumes are low, they can typically be handled in a portion of the warehouse or DC where forward logistics
takes place. When return volumes are high, or when significant processing of the returns is necessary, such as
refurbishing or remanufacturing of the items, a dedicated facility makes more sense.
Warehouse Operations
Respondents indicated that a total of 1725 full time employees (FTE) were involved in warehouse operations.
Of these, 1217 (71 %) were classified as operations (those people that actually handle the returns), 238 (14 %)
administrative/clerical, 157 (9 %) supervisory, and 113 (7 %) managerial employees. On average, there were 6.6
FTE operations workers, 1.6 FTE administrative positions, 1.1 FTE supervisory positions, and 1.2 FTE managerial
positions in the warehouse processing product returns.
Product Disposition
Product disposition refers to the ways business organizations deployed to recover the costs of the products that
were returned. Products that went through the return process were generally dispositioned as follows:
1. returned directly to inventory
2. repackaged and returned to inventory
3. repaired or refurbished
4. destroyed or sold as scrap
5. turned over to a third-party/secondary market
6. donated to charity
Often, product disposition is handled in multiple ways as opposed to a single approach. As shown in Table 1,
responses suggest that 88.3 % of them send a portion of their products directly to inventory, 81.8 % destroy or sell
portions as scrap, 61.4 % repackage items and return some portion to inventory, 4.1 % refurbish, and 37 % indicate
they donate some product returns to charity. This indicates that the returning to stock either directly or by
repackaging and selling as scrap are the two major disposition methods. The survey did not specifically examine
what recovery percentage was obtained from each disposition option nor was data collected regarding the actual
amounts of returned products placed back into inventory, and these questions will have to be answered with
additional future research. For example, even though a large percentage of survey respondents (88.3 %) return items
directly to inventory, we do not know how many products that represents. It is also possible that a product category
or SKU is returned to inventory, but only a small percentage of all of the products or SKU’s received.
TABLE 1
PROCESSED PRODUCT DISPOSITION
(% responding that they utilize the method of disposition)
Method of Disposition Percent
Response
Recovery
Rate
Returned directly to inventory 88.3 % High
Repackaged and retuned to inventory 61.4 High
Repaired or refurbished 4.1 High
Destroyed or sold as scrap 81.8 Low
Third-party/secondary market 19.0 Medium
Donated to charity 37.2 Low-Medium
Other 19.9 Low
JOURNAL OF BUSINESS LOGISTICS, Vol. 30, No. 1, 2009 43
Existence of Published Standards
Respondents were asked about the existence of published standards for each step of the product returns process.
For the receiving activity, manufacturers and retailers indicated that they used standards about one-half of the time.
Wholesalers/distributors did not use standards for receiving as often (42 % of the time). In the processing activity,
results were similar. Manufacturers and retailers utilized standards about two-thirds of the time, while
wholesalers/distributors used them about one-half of the time. For sortation, only retailers used standards frequently
(64 % of the time), while manufacturers and wholesalers/distributors employed standards 46 % and 39 %
respectively. Finally, for disposition, all respondent categories utilized standards about one-half of the time.
However, these differences among the components of the product return process were not statistically significant.
With reverse logistics and product returns programs still not fully developed in some firms, it was not surprising
that a larger number of companies did not have standards in the product return process. On the other hand, a
reasonable number had standards, so there is progress taking place regarding firm’s awareness that reverse logistics
is an important aspect of the business. As more firms place additional emphasis on managing product returns more
effectively and efficiently, the use of standards will no doubt increase.
Type of Standards Used
Respondents were asked to indicate the type of standards used by them for the eight activities commonly used
by firms in evaluating product returns processing efficiency and effectiveness (see Table 2). Responses suggested
that a majority of the respondents in all three business groups did not use standards for these activities. When the
business groups did use standards, the extent of the use of standards varied. This reflects the general condition
relative to the use of standards and metrics within many companies and industries and has been identified as an area
of “need” in supply chain performance standards.
For example, the Council of Supply Chain Management Professionals has published a series of Supply Chain
Management Process Standards that includes the returns process. Supply Chain Visions, the author of the series,
identified five process areas where standards were required: (1) Receiving and warehousing; (2) Transport; (3)
Repair and Refurbishment; (4) Communicate; and (5) Manage Customer Expectations. They also identified typical
best-practice processes. To illustrate, as part of receiving and warehousing, the sub-process of “systems integration”
was identified. A suggested minimum process standard was the following: Order management and returns processes
are integrated using common systems to capture orders, shipments, and return authorizations/information. The
authors identified the following as a best practice: Returns are matched against original orders by item and quantity
(Supply Chain Visions 2004).
Table 2 shows the percent of respondents in each group indicating that they use standards for the above eight
activities. It appears that use of standards seems to be higher for activities 1, 3, 4 and 6 for retailers and 1-3, 6, and 7
for wholesalers. Part of the reason for this is that retailers and wholesalers/distributors are positioned closer to the
final customer and are expected to be more responsive to customer returns. Thus, they would be more likely to have
standards for product returns processing. The results in Table 2 indicate that a higher percent of retailers use
standards for pieces/returns handled by employee per hour (activity 1) compared to wholesalers and manufacturers.
A higher number of retailers also used standards for total pieces/returns processed per day and error rates for
items scanned (activity 3 & 4). A higher percent of wholesalers used standards for time from receipt to crediting
customer’s account (activity 2) as well as for assessing total returns processing time (activity 6). There were no
significant differences in the respondents regarding the use of standards for time to receipt to initial returns
processing (activity 7) step and for assessing the number of pieces returned to stock per day (activity 8). Almost
none of the manufacturers used standards for assessing error rates for items scanned (activity 4), perhaps assuming
that such errors would be minimal since all of the returns they receive should be theirs and not some other
manufacturers.
44 STOCK AND MULKI
In general, successful management of reverse logistics/product returns requires the use of productivity,
utilization and performance metrics. For each category, the following definitions are offered (A. T. Kearney 1991).
Productivity = Output produced ÷ Input consumed
Utilization = Capacity or resources used ÷ Capacity or resources
available
Performance = Actual output produced ÷ Standard output produced
TABLE 2
USE OF STANDARDS IN THE PRODUCT RETURN PROCESS
(% responding that they utilize standards)
Type of Business Organization
Activity Manufacturing Retailing Wholesaler
2
1. Pieces/returns handled by
employee per hour 10 % 52 % 23 % 0.00
2. Time from receipt to crediting of
customer account 16 14 30 0.04
3. Total pieces/returns processed
per day 19 48 26 0.02
4. Error rates for items scanned 1 33 11 0.00
5. Error rates for incorrect
disposition 1 20 7 0.00
6. Total returns processing time 13 23 28 0.03
7. Time from receipt to initial
returns processing 17 19 26 0.27
8. Number of pieces/items returned
to stock per day 11 23 20 0.26
Table 3 identifies some selected metrics used by companies to more efficiently and effectively manage the
reverse logistics/product returns process. The metrics should be useful for companies seeking to measure and
evaluate various aspects of their product returns/reverse logistics process. Not every metric will be useful for every
firm but they do provide a good starting point for companies.
Section II: Results of Hypotheses Testing
A total of ten (10) hypotheses were tested for this study using survey responses from 230 respondents. Results
showed support for all of the hypotheses ( <0.05) except for H2, H3 and H7. H1 was supported as the results show
that return processing was generally assigned to middle or senior management positions in the organization. As
shown in Table 4, out of the 230 total responses, 208 (90 %) held managerial or higher positions. On a percentage
basis, 47 % (108/230) of the respondents were managers followed by directors (60/230 = 26 %) and corporate
officers (40/230 = 17 %). Chi-square ( 2) was 11.02, degrees of freedom (df) = 3, and p = 0.00. An analysis of the
three combinations of business organizations (mfg.-retail; mfg.-wholesale; retail-wholesale) revealed that only the
manufacturers and retailers were significantly different statistically (p = .02). Retailers were much more likely than
manufacturers to have more senior management personnel responsible for reverse logistics.
JOURNAL OF BUSINESS LOGISTICS, Vol. 30, No. 1, 2009 45
TABLE 3
PRODUCTIVITY, UTILIZATION AND PERFORMANCE METRICS
Productivity Metrics
• Number of Employees (regular full time, regular part time, flex/temporary) per month (average of all days
in the month, total at end or beginning of the month, or measured on a specific day during each month)
• Units processed per hour, day, month and/or week (overall, receipt, initial sort, refurbishing, return to
vendor, charity/donation, destroy)
• Cost per unit returned for: labor (returns processing labor, contracted labor, other), supplies, packaging,
administrative
• Number of units/pieces processed per hour (overall and for each employee) for each stage of the product
returns process
• Labor cost per piece received
• Percentage of total units/pieces bar-coded
• Units/pieces received divided by units/pieces salvaged on a daily, weekly, or year-to-date (YTD) basis
• Number of pallets received versus number of pallets processed
• Total number of product scans at initial processing per hour, day, week or YTD
• Number of returned pieces/items still not processed after 48 hours (time will vary by company and
individual standards)
• Total units/pieces received versus RA units/pieces authorized
• Percentage of items authorized for return but not received
• Percentage of items received and authorized
• Package condition of returns that are received
Utilization Metrics
• Amount of temporary storage space utilized at end of day, week or month
• Employees (regular full time, regular part time, flexible) used in returns processing versus employees
available
• Number of totes/containers used versus number of totes/containers available
• Receiving and/or shipping doors used versus doors available
• Units/pieces received for each inbound transportation carrier (overall, daily, weekly, monthly, YTD)
Performance Metrics
• Sortation accuracy (total unit errors inventories that are inaccurately sorted as compared to total locations
checked)
• Over/short accuracy (total items inventoried as compared to total items shipped)
• Salvage percentage for each product class/category
• Hours required to complete each stage of the returns process and hours overall (broken down by employee,
product category/class, time period)
• Units/pieces processed per hour, day, week and YTD
• Salvage value per unit/piece
• Accuracy level for each employee in terms of number of items handled, number of errors, and percentage
correct decisions
• Damage amounts (in units, percent of the total) by type of damage
Source: Stock, J. R. (2004), Product Returns/Reverse Logistics in Warehousing: Strategies, Policies and Programs,
Oak Brook, IL: Warehousing Education & Research Council, pp. 53-55.
46 STOCK AND MULKI
TABLE 4
JOB TITLE
(Number of Responses)
Type of Business Organization
Manufacturers Retailers Wholesalers Total
2
Corporate Officer 3 5 32 40 0.00
Director 24 3 33 60
Manager 57 12 39 108
Other/Supervisor 8 3 11 22
Total Number of Responses 92 23 115 230
Note: Cramer’s V = 0.2506.
Hypotheses H2 which stated that majority of manufacturing, retailing or wholesale/distributor firms are likely to
have a single person who is responsible for product returns processing was not supported. Results show that except
for retailer sector, product handling was a multiple person responsibility. As shown in Table 5, 65 % (58/89) of
manufacturers and 55 % (63/114) of wholesalers indicated that product handling was a multiple person
responsibility while 65 % (15/23) of the retailers indicated it was a single person responsibility. Chi-square ( 2) was
7.2, degrees of freedom (df) = 2, and p = 0.03. An analysis of the three combinations of business organizations
(mfg.-retail; mfg.-wholesale; retail-wholesale) revealed that only the manufacturers and retailers were significantly
different statistically (p = .02). Retailers typically had a single person responsible for product handling, while
manufacturers had several people responsible.
H3 was also not supported as results showed that majority of the firms have reverse logistics/product return
function done in-house. About 75 % (72/97) of the respondents who had indicated single person responsibility for
reverse logistics conduct product returns in-house. A majority of the respondents indicated in-house processing
irrespective of whether a single or multiple persons were responsible for reverse logistics/product return functions.
In fact, manufacturers’ utilized 3-PL’s only 25 % of the time, retailers only 18 % of the time, and
wholesalers/distributors only 12 % of the time. The differences were not statistically significant, but it does show
that most companies still perform product returns processing in-house.
TABLE 5
PRODUCT HANDLING RESPONSIBILITY
(Number of Responses)
Type of Business Organization
Manufacturers Retailers Wholesalers Total
2
Single Person 31 15 51 97 0.03
Several People (more than one) 58 8 63 129
Total Number of Responses 89 23 114 226
Note: Cramer’s V = 0.1785.
JOURNAL OF BUSINESS LOGISTICS, Vol. 30, No. 1, 2009 47
TABLE 6
PRODUCT HANDLING RESPONSIBILITY—IN-HOUSE VS. THIRD-PARTY
(Number of Responses)
Single Person Responsibility Type of Business Organization
Manufacturers Retailers Wholesalers Total
2
Firm 23 11 38 72 0.07
Third-party 3 3 7 13
Combination 5 1 6 12
Total Number of Responses 31 15 51 97
Further analysis showed that a small portion of the respondents indicated reverse logistics as their primary
responsibility. Table 7 shows the breakdown for primary job responsibility. It appears that in spite of the growing
importance of reverse product flows, few executives (6/227 = 2.6 %) in the industry have reverse logistics as their
primary job responsibility. On the other hand, about 50 % (116/227) of all the respondents indicated that their
primary job responsibility was Warehouse Operations and Management followed by General Management (50/227
= 22 %) and Logistics Planning (49/227 = 22 %). Thus, more often, the reverse logistics function is assigned as a
part of some other organizational function such as Warehouse Operations, General Management or Logistics
Planning. Chi-square ( 2) was 12.55, degrees of freedom (df) = 4, and p = 0.02.
An analysis of the three combinations of business organizations (mfg.-retail; mfg.-wholesale; retail-wholesale)
revealed that manufacturers and wholesalers and retailers were significantly different statistically (p = .02).
Wholesalers were significantly different than both manufacturers and retailers, in that they were much more likely to
have general management responsible for reverse logistics activities. Wholesalers often have to balance the
conflicting demands of manufacturers to overstock versus retailer’s concern about finite selling seasons and
uncertain demand (Tsay 2001). They also may have to consolidate returns from multiple retailers for economic
processing of returned products. This would require that this function be handled by employees with good
management skills.
TABLE 7
PRIMARY JOB RESPONSIBILITY
(Number of Responses)
Type of Business Organization
Manufacturers Retailers Wholesalers Total
2
General Management 13 2 35 50 0.02
Logistics Planning 23 8 18 49
Reverse Logistics 2 2 2 6
Operations/Management 47 10 59 116
Other 5 0 1 6
Total Number of Responses 90 22 115 227
Note: Cramer’s V = 0.1708.
48 STOCK AND MULKI
In view of the above, handling of product returns is likely to be a multiple person responsibility in most
organizations. These findings would support the conclusion that product returns processing is managed usually on a
part-time basis by more than one employee in combination with other forward logistics activities. In addition, the
study results show that over 80 % of the three business groups reported that they had less than five full time
employees under managerial, administrative and supervisory category. H4 stated that only a minority of firms use
formal methods of training. Results support the hypothesis. Only 89 out of 228 (39 %) respondents indicated they
had a formal method involving written training methods.
Recovery rate was defined as the monetary value recovered from the item being returned as a percentage of
original cost after processing the returned product item. Overall results indicate a high rate of recovery. More than
one-half of all the respondents (97/184) indicated that product recovery rate as a percentage of original cost was
above 75 %. For this analysis, recovery rates as percent of costs were divided into four quartiles: small, medium,
large and very large. Recovery rate was termed as “very large” for recoveries over 75 %, “large” for 51-75 %,
“medium” for 26-50 %, and “small” for less than 25 % recovery. Again however, as in other product return
activities, there was variability between the three business groups.
H5 stated that recovery rates (as % of cost) for returned products are higher for retailers compared to
manufacturers or wholesale/distributors. Study results supported this as 69 % (11 of 16) retailers reported recovery
rates in the top quartile, followed by 61 % (58 of 95) of wholesalers and 38 % (28 of 73) of manufacturers. As we
had hypothesized, closer proximity to the customers allows the retailers, and to some extent wholesalers, to put the
returned product back to the stock to be sold thus avoiding potential for devaluing the product due obsolescence.
Table 8 shows the quartile breakdown of responses for recovery rates by business group. Chi-square ( 2) was 12.74,
degrees of freedom (df) = 6, and p = 0.05. An analysis of the three combinations of business organizations (mfg.-
retail; mfg.-wholesale; retail-wholesale) revealed that only the manufacturers and wholesalers were significantly
different statistically (p = .02). Wholesalers tended to recover greater amounts of the original cost of returned
products than manufacturers. As stated before, wholesalers are much closer to customers in the logistics chain and
thus are able to turn around the returned product quicker and realize higher returns.
TABLE 8
RECOVERY RATE AS A PERCENTAGE OF ORIGINAL COST
(Number of Responses)
Type of Business Organization
Manufacturers Retailers Wholesalers Total
2
Quartile 1 –Small (0- 25%) 15 2 9 26 0.05
Quartile 2 -Medium (26- 50%) 12 2 15 29
Quartile 3 –Large (51- 75%) 18 1 13 32
Quartile 4-Very Large (76% and above) 28 11 58 97
Total Number of Responses 73 16 95 184
Note: Cramer’s V = 0.1861.
Being close to the point-of-sale, retailers have more disposition options as well as shorter processing time.
These factors seem to result in retailers reporting high recovery rates while manufacturers who have fewer
disposition options report lower percentage recovery rates. Clothing, textiles and general merchandise were in the
high recovery category for retailers while it was automotive parts, paper and related products, food and beverages
for wholesalers. For example, a large Internet and mail order catalogue retailer experienced 80-90 % recovery rates
for its returned products. If the return requires minimal cleaning or replacement of missing or damaged buttons or
clasps, and the garments only need pressing to remove wrinkles, in most instances, the item can be resold. Many of
the items returned to the company were still in their original, unopened packaging and thus could be placed directly
JOURNAL OF BUSINESS LOGISTICS, Vol. 30, No. 1, 2009 49
back into inventory. On occasion, customers are known to order more than one size, color or style of an item to
compare them with the intention of returning the ones they do not want.
Another clothing retailer, with both direct marketing and “brick and mortar” stores located in shopping malls,
experienced similar recovery rates for returned products due to their thorough and detailed processing procedures.
Employees tasked with processing returns are provided detailed instructions about steaming, cleaning, repairing and
refurbishing items. The detailed instructions are provided so that employees fully understand the process and can
accomplish their tasks in the shortest possible time period, with the results being lower costs and higher productivity
levels (Stock 2004).
Additional analyses indicated that about 26 % of the retailing, 7 % of manufacturing and 9 % of wholesalers
had more than 5 processing stations. Stations are physical locations where each product return is evaluated by a
person and usually includes scanners and computers that allow personnel to input information about the product
being returned. In essence, a higher percentage of multiple product return stations suggest that retailers handle
significantly more returns than manufacturers or wholesales/distributors. Second, it would mean that retailers want
returns handled more expeditiously compared to manufacturers and wholesalers. Third, retailers have the most
complex returns processing since there could be multiple reasons for product returns. Table 9 provides details of
product handling by the three business groups.
TABLE 9
NUMBER OF PRODUCT RETURN PROCESSING STATIONS (Percent Responses)
Type of Business Organization
Manufacturers Retailers Wholesalers
2
0 8 % 4 % 3 %
1 67 48 62
2 13 9 15
3-5 6 13 12
Number of Product
Stations
>5 7 26 9
.08
Results support the hypothesis H6 that majority of firms use “return authorizations” (85.4 %) for product returns
and require a pre-approval (71.8 %) of the return authorizations for accepting product returns. We had hypothesized
(H7) that manufacturers will place a greater portion of products directly in stock compared to retailers or
wholesalers. However, results of the analyses showed that the wholesale segment had the highest percentage of
recovered product returned directly to stock ( = 55.5, Std. Dev. = 32.3) followed by manufacturers ( = 37.8, Std.
Dev. = 30.8) and retailers ( = 32.7, Std. Dev. = 34.4). The differences between wholesalers and the two segments
were statistically significant ( = 0.05). There were no statistically significant differences in the mean percent of
products returned directly to stock between manufacturers and retailers.
Hypothesis H8 stated that manufacturers will have more product packaged and returned to stock compared to
wholesalers/distributors. Results support this hypothesis as manufacturers had the highest percent ( = 22.2, Std.
Dev. = 25.2) while wholesalers had the lowest percent of returns being repackaged ( = 12.3, Std. Dev. = 18.7)
before returning to stock. We believe that food industry product returns to stock were primarily at the wholesale
level and reflect the lower percentages of products being repackaged and returned to stock. Finally, in the product
disposition area, H9 was supported as wholesalers reported lower percent ( = 14.4, Std. Dev. = 18.7) of product
returns destroyed or sold as scrap compared to manufacturers who reported the highest percent ( = 23.7, Std. Dev.
= 26.7). The key factor of any product returns processing strategy is to identify the various options for the
disposition of items and to select the option(s) that maximize recovery rate(s).
Hypothesis H10 stated that more product returns are refused by retailers than wholesalers/distributors or
manufacturers. Results show that overall 51.3 % (118/230) of all the respondents refused to accept some of the
product returns. Refusal to accept product return was highest for retailers at 65 % (15/23), followed by wholesalers
50 STOCK AND MULKI
57 % (66/115) and lowest for manufacturers 40 % (37/92) thus supporting H10. Chi-square ( 2) was 8.01, degrees of
freedom (df) = 2, and p = 0.02. An analysis of the three combinations of business organizations (mfg.-retail; mfg.-
wholesale; retail-wholesale) revealed that only manufacturers and wholesalers were significantly different
statistically (p = .02). Wholesalers were significantly more likely to refuse product returns from customers (who
would be retailers) than manufacturers (whose customers would be some combination of wholesalers and/or
retailers).
TABLE 10
PRODUCT RETURNS REFUSED
(Number of Responses)
Type of Business Organization
Manufacturers Retailers Wholesalers Total
2
Not refused 55 8 49 112 0.02
Refused 37 15 66 118
Total Number of Responses 92 23 115 230
Note: Cramer’s V = 0.1866.
SUMMARY AND CONCLUDING REMARKS
1
In this empirical examination of product returns processing in the manufacturing, wholesale/distributor and
retailing sectors, it was found that in spite of the growing importance of reverse logistics and product returns
processing in the business and academic literature, these activities have still not assumed a widespread high level of
importance within organizations. While senior executives are often given the responsibility of overseeing the
process, it is not their main function. It appears these executives generally handle this function along with other
responsibilities, so in essence, product returns processing is still a “part-time” activity in most organizations.
As previously discussed in the Introduction and Selected Literature Review, others have commented on the
potential benefits associated with having dedicated product returns personnel. There is no substitute for full-time
effort being devoted to a process such as product returns. Part-time effort does not allow sufficient time to fully
evaluate and investigate potential improvements in the process nor provide the day-to-day oversight needed to
ensure the process runs smoothly. Also, by having a full-time manager in charge of product returns, better
coordination of forward and reverse logistics can occur.
Regarding all of the hypotheses being examined, Table 11 provides a summary of the hypotheses that were
tested.
We found that business types typically use a single labor shift operation for the product returns process. This
was not unexpected inasmuch as the vast majority of organizations have a relatively small to moderate amount of
products being returned, thus requiring less time and fewer employees to handle returns. On average, organizations
employ 6.6 FTE (full time equivalent) production workers, 1.6 FTE administrative persons, 1.1 FTE supervisory
persons, and 1.2 FTE managers in the facility that processes product returns. Additionally, the majority of the
facilities operate with only a single product returns processing station.
As seen from the on-site visits, firms utilize a fairly consistent process for handling product returns; that is, the
steps or stages employed for processing product returns does not significantly vary from firm to firm. Once products
are received and the processing of the returns begins, the three most common methods of product disposal were
1
Material presented in this section are based exclusively on the mail survey, company interviews and on-site visits,
and/or previously published research that was cited earlier in the paper.
JOURNAL OF BUSINESS LOGISTICS, Vol. 30, No. 1, 2009 51
returning the product directly to stock, selling the items as scrap, and returning items to stock after repackaging
(although repackaging was less common in the food and beverage industry). In some instances, the percentage of
returns that go back into inventory for resale was much higher than has been previously reported in the literature.
Obviously, the recovery rates for items that go back into stock for resale are much higher than most other disposition
options, which accounts for the higher than expected recovery rates measured in this research study.
TABLE 11
HYPOTHESES TESTING
Hypothesis
H1
Product returns are primarily handled by a management-level person in
manufacturing, retailing or wholesale/distributor firms.
Supported
H2
A majority of manufacturing, retailing or wholesale/distributor firms are likely
to have a single person who is responsible for product returns processing.
Not supported
H3
When reverse logistics or product returns is not a single person responsibility,
the product returns function is most often outsourced to third-parties.
Not supported
H4
A minority (less than 50 %) of firms use formal methods involving written
materials, Internet, etc. to train employees involved in product returns
processing
Supported
H5
Recovery rates (as % of cost) are higher for retailers when compared to
manufacturers or
wholesalers/distributors.
Supported
H6
A majority of firms (more than 50 %) use return authorizations (RA’s) for
accepting product returns.
Supported
H7
Manufacturers will have more product returns placed directly back-to-stock or
inventory than retailers or wholesalers/distributors.
Not supported
H8
Manufacturers will have more products repackaged and returned to stock than
retailers or wholesalers/distributors.
Supported
H9
Manufacturers will have more returned products sold as scrap or destroyed than
retailers or wholesalers/distributors.
Supported
H10
More product returns are refused by retailers than by manufacturers or
wholesalers/distributors.
Supported
A surprising finding that has not been discussed widely in the literature previously was the recovery rates for
various return disposition options. In this study, product returns processing enabled many organizations to recover a
high percent of the original cost of the products. In some instances the recovery rates exceeded 80 %. Such levels of
recovery have not been widely reported previously. In fact, the typical level of 60-65 % recovery rate is higher than
expected given previously published data. This validates the importance of efficient and effective product returns
processing for improving profitability within organizations.
Studies have indicated the need to decrease the processing time and speed up the turn-around to maintain value
of the returned and reprocessed goods (Blackburn et al. 2004; Stock 2001). Results show that retailers are able to
recover a higher percentage of product value compared to wholesalers and manufacturers. This emphasizes the need
for the retailers and wholesalers, who are located closer to customers in the supply chain, to process the customer
returns instead of sending all or most product returns to suppliers. This will help not only to recover higher value for
the returned product but also helps to maintain the price levels for products in the distribution chain.
The use of outsourcing, or third-parties, for product returns processing has been widely discussed in the
business press. Many case studies have been presented about companies who successfully outsourced product
returns processing to various reverse logistics third-parties. While some organizations do outsource these activities,
results of this study suggest that the vast majority do not. Outsourcing of reverse logistics functions are partly driven
by the firm’s desire to redistribute the products quickly and thus recover value (Meade and Sarkis 2002).
52 STOCK AND MULKI
As determined in the site visits to companies, many of the large mass merchandisers such as Kmart, Sears, and
Target outsource at least a portion of their product returns to a third-party. In the manufacturing sector, electronics
and computer companies such as HP/Compaq outsource product returns, while firms such as CDW and Tech Data
handle returns internally. In the book publishing industry, most firms such as Harcourt and others perform product
returns processing internally. And so it goes; there is a great deal of variability in whether firms utilize third-parties
for processing returns, but in most cases, firms typically perform those activities themselves. As stated before, firms
base their outsourcing decisions on whether reverse logistics functions fit with the core competence of the firm and
based on the potential savings by eliminating expenses associated with activities such as evaluating returns and
repackaging them (Cottrill 2003; Gorick 2005). Lack of critical mass and economies of scale can also be a reason to
look for outside firms to handle product return functions (Discount Store News 1999; Gorick 2005). Thus, the
market potential for product returns outsourcing is likely greater than is presently thought, if, organizations can be
convinced that outsourcing is a viable alternative to doing it themselves. Most firms use existing facilities to handle
both forward and reverse logistics, so the market potential for outsourcing is significant.
This study found that retailers refuse a greater percent of returns compared to wholesalers and manufacturers.
Wholesalers reported refusing a higher amount from their customers (retailers) compared to manufacturer’s refusal
from wholesalers and retailers. As stated before, being close to the point of sale, retailers are often faced with more
customer returns and sales associates are reluctant to restrict returns because it might hurt sales. However, this
appears to be changing. Results show increased refusal from retailers pointing to a tightening of restrictions such as
time periods for return, receipt requirements, etc.
At the manufacturer level, product return transactions are primarily between them and wholesalers or retailers.
The transactions between manufacturers and retailers/wholesalers are generally more formalized with manufacturers
setting somewhat liberal policies of accepting all unsold products returned within prescribed periods of time.
Retailers have to consider manufacturer’s sentiments about costs and the margin impact of product returns (Rogers
and Tibben-Lembke 2001). There is an understanding on both sides about the need to reduce product return volumes
to maintain profitability. Manufacturers also realize that effectively designed vendor friendly return policies help
increase loyalty from some wholesalers or retailers (Rogers et al. 2002).
Very surprising was the fact that with so much academic and practitioner attention being given to benchmarks,
measurement and metrics relating to all aspects of supply chain management, so few organizations use published
standards for processing returns and evaluating elements or components of the process. The apparent lack of interest
in published standards needs to be explored to see whether productivity improvements could be possible with the
use of standards as might be expected intuitively. One would believe that with the higher level of manual operations
in product returns processing, significant improvements might be possible if organizations were measuring the cost
and service elements of the product returns process.
In sum, we posit that good product returns processing can result in improvements in profitability through cost
reductions and higher product recovery rates. It can also mean higher customer service levels as products being
returned are credited to customers sooner and more accurately (with fewer discrepancies). Organizations with
excellent product returns processing capabilities (defined as those having processes that are both efficient and
effective) can have a potential competitive advantage, which gets larger as the magnitude of product returns
increases. Through higher recovery rates of returned products and lower costs resulting from more efficient returns
processing, the “excellent” firms are able to maximize revenues and minimize costs, thus contributing more to the
firm’s bottom line.
As much of the literature on product returns has pointed out, many firms still do not place adequate emphasis on
the product returns process. They handle the product returns they receive, but they typically take longer to process.
Actual processing costs are higher and discrepancies and reconciliations are greater, and cause more customer
dissatisfaction. These firms are more likely to have part-time management personnel responsible for product returns
processing which is unlikely to provide the necessary oversight of the process to ensure optimal efficiency and
effectiveness.
Of course, the best way of optimizing the product returns process is to not have returns at all—referred to as
returns avoidance. Return avoidance policies aimed at minimizing product returns are becoming popular. These
strategies use customer education programs that focus on training the customer in the proper operation and use of
JOURNAL OF BUSINESS LOGISTICS, Vol. 30, No. 1, 2009 53
the product. This is critical since about 50 % of the product returns in consumer electronics are not due to product
defect, but due to customer difficulty in properly operating the product (Rogers et al. 2002). Retailer emphasis on
training customers in the proper use of their products can help in improving customer relations as well as decreasing
costs of product returns. Retailers can help a great deal by initial sorting and by making decisions on processing
versus returning to manufacturer. This could reduce the uncertainty in the timing and quality of returns that has been
blamed for the unpredictability of reconditioning and refurbishing returned products (Guide and Van Wassenhove
2002). The use of various return programs in retail stores that either encourage or discourage customers from
returning products are also important. The store policies on returns can have significant impact on the volume and
type of products being returned.
FUTURE RESEARCH
While the survey results revealed that firms are using metrics to measure and evaluate the product returns
process, much more needs to be done. Future research needs to be directed at establishing the specific criteria that
could be used to evaluate existing product returns metrics and to classify existing metrics from a process, rather than
functional, perspective (Caplice and Sheffi 1994). The metrics evaluated in this research study were identified in the
literature and by persons interviewed in the on-site visits, but that does not necessarily mean that they are the correct
metrics that should be used. Future research should evaluate these metrics using the eight evaluative criteria—
namely, validity, robustness, usefulness, integration, economy, compatibility, level of detail, behavioral soundness—
identified by Caplice and Sheffi (1994). This does not suggest that existing metrics being used are insufficient, but
the metrics were likely not developed with the eight criteria in mind and therefore may, or may not, be the right
measures.
Additional research on the standards being employed by companies processing product returns based on the
metrics selected could potentially reap significant rewards for companies. Historically, published standards have not
been researched by scholars, often because that information is proprietary. While proprietary issues are important,
data can be masked and the identification of key standards does not necessarily “give away” trade secrets or
competitive advantage. It is one thing to know what standards are being used by industry leaders; it is quite another
to have the right systems, policies and procedures in place and to implement these standards effectively and
efficiently. Such data will have to be obtained using qualitative research methods such as case studies. Companies
will typically not be willing to share such information in research that utilizes survey methods.
Additionally, more research utilizing hypotheses testing could be conducted. As indicated in the introduction to
this paper, many published studies, especially those in the trade or professional press, present anecdotal information.
While such information can be useful in aiding companies in pursuing better policies, procedures and programs,
they do not add a great deal to the “body of knowledge” relating to product returns specifically, and reverse logistics
generally. Specific research is needed on many aspects of product returns including such issues as cost recovery in
product returns disposition, optimal layouts of warehouses/DC’s when both forward and reverse logistics operations
are carried out in the same facility, acceptable return rates for various industries, companies and products, and
examination of the best methods of training and development of product returns employees.
While product returns processing is becoming more important, it is also vital that issues relating to eliminating
product returns be examined. For example, the return policies of retail stores impact whether or not customers return
items. Stock rotation and replenishment policies impact the number of items returned to vendors for credits as well
as product disposed of at the retail location. In the electronics industry, with some products being returned
fraudulently, research into the costs and benefits to retailers performing on-site inspections of returns could be
evaluated. Finally, the product returns process, which has been modeled descriptively by Stock (2004) and others,
could be more rigorously tested, with detailed flow charts of each stage of the process being developed. Stock
(2004) presented flow charts modeling each component of the five-stage process he identified, but these were only
examples used by companies in various industries. While they provide guidance to firms and researchers examining
product returns processing, generic process maps or flow charts need to be developed. This would provide a basis
for developing optimal product returns processing systems.
In sum, product returns will continue to be a part of business operations. In some fashion all members of the
supply chain are involved in the process. With increasing competition and higher customer demands, it is important
54 STOCK AND MULKI
that all facets of the supply chain operate at peak efficiency and effectiveness. As a part of the process, products
returns are no exception.
NOTES
Alvarez-Gil, M. Jose, Pascual Berrone, F. Javier Husillos, and Nora Lado (2007), “Reverse Logistics, Stakeholders’
Influence, Organizational Slack, and Managers’ Posture,” Journal of Business Research, Vol. 60, No. 5, pp. 463-
473.
Andel, Tom and Mary Aichlmayr (2002), “Turning Returns into Cash,” Material Handling Management, Vol. 57,
No. 8, pp. 51-56.
Armstrong, J. Scott and Terry S. Overton (1977), “Estimating Nonresponse Bias in Mail Surveys,” Journal of
Marketing Research, Vol. 14, No. 3, pp. 396-402.
A. T. Kearney (1991), Improving Quality and Productivity in the Logistics Process, Oak Brook, IL: Council of
Logistics Management.
Autry, Chad W. (2005), “Formalization of Reverse Logistics Programs: A Strategy for Managing Liberalized
Returns,” Industrial Marketing Management, Vol. 34, No. 7, pp. 749-757.
Autry, Chad W., Patricia J. Daugherty, and R. Glenn Richey (2001), “The Challenge of Reverse Logistics in Catalog
Retailing,” International Journal of Physical Distribution and Logistics Management, Vol. 31, No. 1, pp. 26-37.
Blackburn, Joseph D., V. Daniel R. Guide, Gilvan C. Souza, and Luk N. Van Wassenhove (2004), “Reverse Supply
Chains for Commercial Returns,” California Management Review, Vol. 46, No. 2, pp. 6-22.
Blau, Peter M. (1970), “A Formal Theory of Differentiation in Organizations,” American Sociological Review, Vol.
35, No. 2, pp. 201-218.
Blau, Peter M. and Richard A. Schoenherr (1971), The Structure of Organizations, New York: Basic Books, Inc.
Caplice, Chris and Yossi Sheffi (1994), “A Review and Evaluation of Logistics Metrics,” The International Journal
of Logistics Management, Vol. 5, No. 2, pp. 11-28.
Chopra, Sunil and Peter Meindl (2007), Supply Chain Management-Strategy, Planning and Operations, 3
rd
ed.,
Upper Saddle River, NJ: Prentice Hall.
Cooke, James A. (2006), “Costs Under Pressure,” Logistics Management, Vol. 45, No. 7, pp. 34-38.
Cottrill, Ken (2003), “Remedying Returns,” Air Cargo World, Vol. 93, No. 10, pp. L-19-19.10, pp. 124-125
Damanpour, Fariborz (1987), “The Adoption of Technological, Administrative, and Ancillary Innovations: Impact
of Organizational Factors,” Journal of Management, Vol. 13, No. 4, pp. 675-688.
DeKoster, Rene B. M., Marisa P. De Brito, and Majsa A. Van de Vandel (2001), “How to Organize Return
Handling: An Exploratory Study with Nine Retail Warehouses,” Economic Institute Report E1 2002-11.
Discount Store News (1999), “Outsourcing: Reverse Logistics Push into High Gear,” Discount Store News, Vol. 38,
No. 6, pp. 8-11.
Gentry, Connie Robbins (1999), “Reducing the Cost of Returns,” Chain Store Age, Vol. 75, No. 10, pp. 124-125.
Gorick, Jane (2005), “Reverse Logistics,” Soap, Perfumery & Cosmetics, Vol. 78, No. 6, p. 17.
JOURNAL OF BUSINESS LOGISTICS, Vol. 30, No. 1, 2009 55
Guide, Daniel R. V, Jr., Gilvan C. Souza, Luk N. Van Wassenhove, and Joseph D. Blackburn (2006), “Time Value
of Commercial Product Returns,” Management Science, Vol. 52, No. 8, pp. 1200-1214.
Guide, Daniel R. V., Jr. and Luk N. Van Wassenhove (2002), “The Reverse Supply Chain,” Harvard Business
Review, Vol. 80, No. 2, pp. 25-26.
House, Robert J. (1971), “A Path Goal Theory of Leader Effectiveness,” Administrative Science Quarterly, Vol. 16,
No. 3, pp. 321-339.
Kuzeljevich, Julia (2004), “Targeting Reverse Logistics,” Canadian Transportation and Logistics, Vol. 107, No. 9,
pp. 36-39.
Meade, Laura and Joseph Sarkis (2002), “A Conceptual Model for Selecting and Evaluating Third-party Reverse
Logistics Providers,” Supply Chain Management, Vol. 7, No. 5, pp. 283-295.
Mukhopadhyay, Samar K. and Robert Setoputro (2005), “Optimal Return Policy and Modular Design for Build-to-
Order Products,” Journal of Operations Management Vol. 23, No. 5, pp. 496-506.
Mukhopadhyay, Samar K. and Robert Setoputro (2004), “Reverse Logistics in E-business: Optimal Price and Return
Policy,” International Journal of Physical Distribution and Logistics Management, Vol. 34, No. 1, pp. 70-88.
Mukhopadhyay, Samar K. and Robert Setoputro (2006), “The Role of 4PL as the Reverse Logistics Integrator:
Optimal Pricing and Return Policies,” International Journal of Physical Distribution and Logistics Management,
Vol. 36, No. 9, pp. 716-729.
Rao, Kant, Alan J. Stenger, and Haw-Jan Wu (1994), “Training Future Logistics Managers: Logistics Strategies
within the Corporate Planning Framework,” Journal of Business Logistics, Vol. 15, No. 2, pp. 249-272.
Richey, R. Glenn, Haozhe Chen, Stefan E. Genchev, and Patricia J. Daugherty (2005), “Developing Effective
Reverse Logistics Programs,” Industrial Marketing Management, Vol. 34, No. 8, pp. 830-840.
Richey, R. Glenn, Stefan E. Genchev, and Patricia J. Daugherty (2005), “The Role of Resource Commitment and
Innovation in Reverse Logistics Performance,” International Journal of Physical Distribution and Logistics
Management, Vol. 35, No. 4, pp. 233-257.
Rogers, Dale S., Douglas M. Lambert, Keely L. Croxton, and Sebastián J. García-Dastugue (2002), “The Returns
Management Process,” The International Journal of Logistics Management, Vol. 13, No. 2, pp. 1-18.
Rogers, Dale S. and Ronald S. Tibben-Lembke (2001), “An Examination of Reverse Logistics Practices,” Journal of
Business Logistics, Vol. 22, No. 2, pp. 129-148.
Rogers, Dale S. and Ronald S. Tibben-Lembke (1999), Going Backwards: Reverse Logistics Trends and Practices,
Reno, NV: Reverse Logistics Executive Council.
Sarkis, Joseph (1995), “Reverse Logistics, Recycling and the Product Life Cycle,” in Proceedings of the GEMI ’95
Conference: Environment and Sustainable Development [www.uta.edu/infosys/CITM/Sarkis/abs3].
Sinha, Kingshuk K. and Andrew H. Van de Ven (2005), “Designing Work Within and Between Organizations,”
Organization Science, Vol. 16, No. 4, pp. 389-408.
Speh, Thomas W. (2007), “Warehouse Management,” in Handbook of Global Supply Chain Management, John T.
Mentzer, Matthew B. Myers, and Theodore P. Stank, eds., Thousand Oaks, CA: Sage Publications, Inc., pp. 223-
251.
Srivastava, Samir K. and Rajiv K. Srivastava (2006), “Managing Product Returns for Reverse Logistics,”
International Journal of Physical Distribution and Logistics Management, Vol. 36, No. 7, pp. 524-546.
56 STOCK AND MULKI
Stock, James R. (1998), Development and Implementation of Reverse Logistics Programs, Oak Brook, IL: Council
of Logistics Management.
Stock, James R. (2004), Product Returns/Reverse Logistics in Warehousing: Strategies, Policies and Programs, Oak
Brook, IL: Warehousing Education & Research Council.
Stock, James R. (1992), Reverse Logistics, Oak Brook, IL: Council of Logistics Management.
Stock, James R. (2001), “The 7 Deadly Sins of Reverse Logistics,” Material Handling Management, Vol. 56, No. 3,
pp. 5-11.
Stock, James R. (1996), “The Social Sciences and Logistics: Some Suggestions for Future Exploration,” Journal of
Marketing Theory and Practice, Vol. 4, No. 2, pp. 1-25.
Stock, James R. and C. Jared Broadus (2006), “Doctoral Research in Supply Chain Management and/or Logistics-
related Areas: 1999-2004,” Journal of Business Logistics, Vol. 27, No. 1, pp. 139-496.
Stock, James, Thomas Speh, and Herbert Shear (2006), “Managing Product Returns for Competitive Advantage,”
MIT Sloan Management Review, Vol. 48, No. 1, pp. 57-62.
Stock, James, Thomas Speh, and Herbert Shear (2002), “Many Happy (Product) Returns,” Harvard Business
Review, Vol. 80, No. 7, pp. 16-17.
Stuart, Julie Ann, Winston Bonawi-tan, Sarah Loehr, and Joyce Gates (2005), “Reducing Costs Through Improved
Returns Processing,” International Journal of Physical Distribution and Logistics Management, Vol. 35, No. 7, pp.
468-480.
Supply Chain Visions (2004), Supply Chain Management Process Standards—Return Processes, Oak Brook, IL:
Council of Supply Chain Management Professionals.
Tan, Albert Wee Kwan and Arun Kumar (2006), “A Decision-Making Model for Reverse Logistics in the Computer
Industry,” The International Journal of Logistics Management, Vol. 17, No. 3, pp. 331-354.
Triest, Sander van (2005), “Customer Size and Customer Profitability in Non-contractual Relationships,” Journal of
Business & Industrial Marketing, Vol. 20, No. 3, pp. 148-155.
Tsay, Andy A. (2001), “Managing Retail Channel Overstock: Markdown Money and Return Policies,” Journal of
Retailing, Vol. 77, No. 4, pp. 457-492.
UK Department of Transport (2004), “The Efficiency of Reverse Logistics,” [http://www.ciltuk.org.uk].
Vorasayan, Jumpol and Sarah M. Ryan (2006), “Optimal Price and Quantity of Refurbished Products,” Production
and Operations Management, Vol. 15, No. 3, pp. 369-383.
Wisner, Joel D., G. Keong Leong, and Keah-Choon Tan (2005), Principles of Supply Chain Management: A
Balanced Approach, Mason, OH: South-Western.
Wu, Yen-Chun Jim and Wei-Ping Cheng (2006), “Reverse Logistics in the Publishing Industry: China, Hong Kong,
and Taiwan,” International Journal of Physical Distribution and Logistics Management, Vol. 36, No. 7, pp. 507-
523.
Zeithaml, Valarie A., Mary Jo Bitner, and Dwayne D. Gremler (2006), Services Marketing: Integrating Customer
Focus Across the Firm, 4th ed., New York: McGraw-Hill/Irwin.
JOURNAL OF BUSINESS LOGISTICS, Vol. 30, No. 1, 2009 57
APPENDIX
SURVEY INSTRUMENT
Survey on the Role of Warehousing in Product Returns
Instructions: We would like to get information on how your firm processes and dispositions product returns. If specific data are
not available to answer some items, please provide your best “guesstimate.” Please fill in each blank with the appropriate
information. For responses requiring a YES or NO response, just circle the appropriate answer.
Part I. The Product Returns Process
1. Is there one person in your company who has primary responsibility for reverse logistics/product returns?
NO YES If YES, what is their job title? ________________________________________
2. Of all product returns received by your firm, what percentage is handled by:
__________ % Your firm
__________ % Third party __________ % at a dedicated returns facility
__________ % Combination __________ % in our regular warehouse/DC
100 %
100 %
3. For all product returns combined, what is the mixture of those returns?
__________ % planned (e.g., repair, end-of-lease)
__________ % excess/not planned
100 %
4. What is the size of your warehouse/DC where product returns are processed? ________________ sq. ft.
5. What portion of the warehouse/DC is devoted specifically to product returns operations?
_______________ %
6. How many FTE employees do you have in your warehouse/DC that are involved in product returns?
__________ Production
__________ Administrative/clerical
__________ Supervisory
__________ Managerial
7. The wages (including benefits) of your full time non-management personnel that process product returns in the warehouse/DC
are:
( only one)
_______ Higher than other warehouse/DC personnel
_______ Same as other warehouse/DC personnel
_______ Lower than other warehouse/DC personnel
8. How many labor shifts are used to process returns? ( only one) _______ one _______ two _______ three
9. What type of training does your firm provide for product returns employees? ( only one)
_______ Formal methods involving written training manuals
_______ Informal methods such as mentoring programs, but not including written training manuals
_______ No formal or informal training of product returns employees occurs at our facility
10. What percentage of your products are included in vendor/supplier “zero returns” programs? _______________ %
58 STOCK AND MULKI
APPENDIX (cont.)
RECEIVING:
11. How are product returns received by the warehouse/DC?
__________ % Gaylord’s containing multiple items
__________ % Individual items (loose packages, boxes, totes &/or cartons)
__________ % Pallets of the same or mixed items
100 %
12. Do you use “return authorizations” (RA’s) for product returns? YES NO
13. Do you require pre-approval of “return authorizations” prior to accepting product returns? YES NO
14. Are customers issued return authorization numbers before returning items? YES NO
15. How many product returns receiving stations do you have in your warehouse/DC? ____________________
MATERIAL HANDLING:
16. What equipment do you use in your warehouse/DC to handle product returns? Indicate how many of each item you use. If you
do not use an item, place a zero (0) in the blank next to that item.
Number Equipment Type Number Equipment Type
_______ Forklifts
_______ Pallet jacks (electric & manual)
_______ Workstations
_______ Hand scanners
_______ Table scanners
_______ Belt conveyors
_______ Gravity conveyors
_______ Product containers (totes)
_______ Label printers
_______ Carts
_______ Hand held tape machines
_______ Automatic product sorters
_______ Box building machines
_______ Other: ________________
PROCESSING:
17. Are return authorizations computerized (e.g., available from the Internet or in other electronic form) or completed manually?
__________ % Computerized
__________ % Manual
100 %
18. For processing most returns, are receiving and customer crediting combined into one operation? YES NO
19. Do you utilize RETURN LABELS in the product returns process? YES NO
19a. If so, please identify the information contained on the returns labels:
YES NO SKU number
YES NO Customer name
YES NO Item description
YES NO Reason for return (reason code)
YES NO Date
YES NO Stocking location
YES NO Return authorization number
YES NO User ID of employee that processed the item
YES NO Other (please specify) ______________________________
JOURNAL OF BUSINESS LOGISTICS, Vol. 30, No. 1, 2009 59
APPENDIX (cont.)
SORTATION:
20. How are final sorted items assembled or accumulated (circle all that apply)?
UPC/SKU vendor customer disposition option product type Other: __________________
DISPOSITION:
21. Overall, what recovery rate do you get from returned products (respond for your most typical product return):
______________ % (as % of original cost) _______________ % (as % of units returned)
22. Does your firm do any product refurbishing, reconditioning and/or remanufacturing at your warehouse/DC where returns are
processed, or are they performed at another location?
__________ % On-site
__________ % At another location
100 %
23. Please indicate for all of your products that go through the returns process, how they are dispositioned:
__________ % Returned directly to stock
__________ % Repackaged and returned to stock
__________ % Repaired or refurbished
__________ % Destroyed or sold for scrap/salvage
__________ % Third party/secondary market
__________ % Donated to charity
__________ % Other: ________________________________________
100 %
Part II. Product Returns Metrics
24. Are there published standards for each component of the product returns process?
YES NO Receiving (including unloading, distribution to processing stations)
YES NO Processing (including data entry, customer credit)
YES NO Sortation (including inspection, routing to disposition point)
YES NO Disposition (including put-away, repackaging, refurbishing)
25. For each of the following activities, please indicate the standards (they may or may not be engineered standards) that you
utilize:
We use this metric? If used, what is the standard?
Pieces/returns handled by employee per hour YES NO ______________________________
Time from receipt to initial returns processing YES NO ______________________________
Number of pieces/items returned to stock per day YES NO ______________________________
Time from receipt to crediting of customer’s account YES NO ______________________________
Total pieces/unit/returns processed per day YES NO ______________________________
Error rates for items scanned (# or % of total) YES NO ______________________________
Error rates for incorrect disposition (# or % of total) YES NO ______________________________
Total returns processing time
(from receipt to final disposition) YES NO ______________________________
60 STOCK AND MULKI
APPENDIX (cont.)
26. How much time (in hours) are spent on each of the following product returns activities?
Hours Task/Activity
__________ Receiving (including unloading, distribution to processing stations)
__________ Processing (including data entry, customer credit)
__________ Sortation (including inspection, routing to disposition point)
__________ Disposition (including put-away, repackaging, refurbishing)
__________ Total of all product returns activities
27. What is the warehouse/DC throughput time of returned products (total warehouse hours divided by # of units processed)?
__________ hours per unit processed
28. On average, what is the total labor cost per unit to process a typical product return? $ __________ labor cost per piece
received
29. On average, what is the cost per unit/piece salvaged? $ ____________ cost per piece salvaged
30. In a typical month, what is the discrepancy rate of returned products received versus returned products that you were expecting
(e.g., items returned did not match return authorization data, fewer or more items than indicated were returned)? ___________ %
31. What percentage of returned products received are returned to the sender because they did not meet the return criteria for the
product?
_______________ %
Part III. Demographic Data
This information is required in order to identify major market segments and to provide more meaningful analysis of the previous
sections. Please use approximate figures in the event that exact data are not readily available.
32. Although your firm may be a multi-product company, can you provide an overall estimate of the value of product returns
(in dollars or # of units) as a percentage of the total products your firm ships to customers?
__________ % based on dollars __________ % based on # of units
33. On average, at our returns processing warehouse/DC, we receive _________________ (estimated number) returned
products/items/pieces each _________________ (per day, week, or month).
34. Type of Business or Organization ( the one most like your organization):
_______ Manufacturing firm
_______ Retailing firm
_______ Government/military
_______ Wholesaler/distributor
_______ Other: _________________________
35. Industry Category ( one category only if you indicated manufacturing or retailing firm in the previous question):
_______ Appliances
_______ Automotive & transport equipment
(including parts and aftermarket)
_______ Building materials/lumber products
_______ Chemicals & plastics
_______ Clothing & textiles
_______ Computer hardware/peripheral
equipment
_______ Department store/general
merchandise
_______ Electronics & related instruments
_______ Food & beverage
_______ Furniture
_______ Hardware
_______ Metal products (fabricated)
_______ Office equipment & supplies
(excluding paper)
_______ Paper & related products
_______ Petroleum & petrochemicals
_______ Pharmaceuticals, drug & toilet
preparations
_______ Tobacco products
_______ Other: _________________________
JOURNAL OF BUSINESS LOGISTICS, Vol. 30, No. 1, 2009 61
APPENDIX (cont.)
36. What is your job title?
_______ Corporate officer
_______ Director
_______ Manager
_______ Supervisor
_______ Staff specialist
_______ Other: __________________________
37. What is your primary job responsibility (the one responsibility that requires most your time)? ( only one)
_______ General management
_______ Logistics planning/management
_______ Marketing/sales
_______ Reverse logistics/product returns
_______ Warehouse operations/management
_______ Other: _________________________
62 STOCK AND MULKI
ABOUT THE AUTHORS
James R. Stock (Ph.D. The Ohio State University) is Frank Harvey Endowed Chair in Marketing at the
University of South Florida, Tampa. He holds B.S. and MBA degrees from the University of Miami (FL). He
previously held academic faculty appointments at the University of Notre Dame, University of Oklahoma, Air Force
Institute of Technology and Michigan State University. Dr. Stock has published more than 120 publications
including books, monographs, articles and proceedings papers. He formerly served as Editor of the International
Journal of Physical Distribution and Logistics Management and the Logistics Spectrum (published by SOLE). Dr.
Stock has received both the Armitage Medal and Eccles Medal from SOLE—The International Society of Logistics.
He is a frequent speaker at international meetings and other events held in Africa, Asia, Europe and South America.
Professionally, he works extensively with the Council of Supply Chain Management Professionals (CSCMP) and
the Warehousing Education & Research Council (WERC). His major areas of research interest include reverse
logistics and product returns, the marketing/logistics interface, and supply chain management.
Jay Prakash Mulki (Ph.D. University of South Florida) is an Assistant Professor of Marketing at Northeastern
University, Boston, MA. He holds B.S. degree in Chemical Engineering from the University of Mysore (India), and
a MBA from University of Hawaii at Manoa. Professor Mulki brings both academic research and business practice
to his classroom. He has spent nearly 20 years in business, holding senior positions in Fortune 500 companies
before leaving business to pursue his academic interest. In February 2008, Professor Mulki was honored with the
Renfro Fellowship, an award given by the University to recognize consistent achievement in research and for
someone “held in high regard for the quality of his/her teaching.” His research has been published in Journal of
Business Research, Psychology & Marketing, Journal of Personal Selling and Sales Management, International
Journal of Service Industry Management, Journal of Marketing Theory & Practice, and Journal of Business Ethics.
Contact author: James R. Stock, E-mail: jstock@coba.usf.edu
Order an Essay Now & Get These Features For Free:
Turnitin Report
Formatting
Title Page
Citation
Outline
