Upload
shaan1284
View
217
Download
0
Embed Size (px)
Citation preview
7/30/2019 Re-Engineering a Reverse Supply Chain
1/36
Acknowledgements: This research was supported by a research grant from the FedEx Center for
Supply Chain Management, at the FedEx Institute of Technology, The University of Memphis
1
RE-ENGINEERING A REVERSE SUPPLY CHAIN FOR A DIRECT
RETAILERS PRODUCT RETURNS SERVICES
Carol C. Bienstock, Ph.D. *Assistant Professor
Department of Management and MarketingRadford University, USA
M. Mehdi Amini, Ph.D.Professor
Department of Marketing and Supply Chain ManagementAssociate Director of FedEx Center for Supply Chain Management
The University of Memphis, USA
Donna Retzlaff-Roberts, Ph.D.
ProfessorDepartment of Management
The University of South Alabama, USA
ABSTRACT
An important service management activity, particularly in a retail environment,
is return services. This article discusses the strategic issues surrounding the effective
management of product return services and the importance of the role of effective
reverse logistics operations to the design and execution of successful and profitable
reverse supply chains to support product return activities.
We present a case study to illustrate how a reverse supply chain and the logistics
activities that support it were reengineered to enhance the effectiveness and
profitability of the product returns process for a major direct retailer in the US.
Keywords: simulation; retail; product returns; supply chain management; reverse
logistics
*Corresponding AuthorCarol C. Bienstock, Ph.D.Radford, VA 24142, USATel: 540.831.5301Fax: [email protected]
7/30/2019 Re-Engineering a Reverse Supply Chain
2/36
2
RE-ENGINEERING A REVERSE SUPPLY CHAIN FOR A DIRECT
RETAILERS PRODUCT RETURNS SERVICES
INTRODUCTION
How do companies differentiate themselves when operating in industries where
most, if not all firms offer high quality products and customer service at the time of
sale? As James Stock put it, After a while, those features just become your admission
to the game (Lambert and Stock, 1993, p. 28). A potential solution to this dilemma
is offered by Dennis and Kambil (2003), using what they term service management,
which provides both competitive differentiation and an opportunity to increase
profits. Service management is the sum of all customer interactions that follow a
products sale . . . (Dennis and Kambil, 2003, p. 42). The benefits of service
management can also be related to the service profit chain framework, which
integrates investments in service operations with customer loyalty and firm
profitability (Heskett, Jones, Loveman and Sasser, 1994; Wagner, Mittal and Mazzon,
2002).
One of the most important of these service management activities, particularly
in a retail environment, is return services. In an effort to enhance service management
activities and, thus, engage in what Flack and Evans (2001, p. 19) term marketing
on customer terms to increasingly demanding customers, a growing number of
retailers are liberalizing return policies and becoming more reliant on consignment
inventory, activities which can result in a greater number of returned products.
Because catalogue and online retailers typically face higher rates of return than
7/30/2019 Re-Engineering a Reverse Supply Chain
3/36
3
traditional retailers, effective service management of their product returns is even
more important (Daugherty, Autry and Ellinger, 2001). Not surprisingly, the
existence, effectiveness, and efficiency of service management activities, such as
return services, depend heavily on effective reverse logistics operations.
Because reverse logistics operations and the supply chains they support are
significantly more complex than traditional manufacturing supply chains (Dennis and
Kambil, 2003) an organization that succeeds in meeting the challenges presents a
formidable advantage that is not easily duplicated by its competitors. Effective
reverse logistics operations benefit both the organization and its customers.
Successfully accomplished service management activities, such as product return
operations, positively impact customers satisfaction and, consequently, customer
loyalty and return sales (Cohen and Whang, 1997; Fitzsimmons and Fitzsimmons,
1998; Retzlaff-Roberts, 1998; Daugherty, Autry and Ellinger, 2001).
In the next section, we discuss the issues surrounding the value of product returns
as service management activities. We also discuss the importance of the role of
effective reverse logistics operations to the design and execution of successful and
profitable reverse supply chains to support product return activities.
In the last section we present a case study to illustrate how a reverse supply chain
and the logistics activities that support it can be reengineered so that the effectiveness
and profitability of a direct retailers product returns process are enhanced.
PRODUCT RETURNS SERVICES
Product returns have been and remain an essential part of the retail landscape.
Customers return products for a variety of reasons, e.g., they change their minds, the
7/30/2019 Re-Engineering a Reverse Supply Chain
4/36
4
product shipped to them is defective; the product is damaged in transit; the wrong
quantity or the wrong product is shipped. Customers also return products that are
under warranty or products that are the subject of manufacturers recalls.
Particularly in the case of direct retailers, e.g., catalogue and online retailers, where
customers generally perceive more risk associated with product purchases
(Schoenbachler and Gordon, 2002), a solid record of product return services can
significantly enhance customer loyalty and increase the probability of repeat
purchases (Daugherty, Autry and Ellinger, 2001).
While the return of a particular item is generally not expected at the time of
sale, many organizations have some means of forecasting what percent of their sales
volume is typically returned. The magnitude of this percentage depends upon the
nature of the business and the organizations return policy, and can vary from as low
as 2% to as much as 50% (Lambert and Stock, 1993). Generous return policies have
made the structuring of the required reverse supply chains and the management of
the reverse logistics that support these unplanned returns particularly difficult
because organizations do not know what products will be arriving when (Meyer,
1999).
REVERSE LOGISTICS
Reverse logistics accounts for 5-6% of total logistics costs in both the
manufacturing and retail sectors. One of the more interesting and significant trends in
supply chain management is the recognition of the strategic importance of reverse
logistics operations (Retzlaff-Roberts and Frolick, 1997; Handfield and Nichols, 1999;
Daugherty, Autrey and Ellinger, 2001). These reverse logistics operations support a
7/30/2019 Re-Engineering a Reverse Supply Chain
5/36
5
variety of activities ranging from what is termed green logistics, i.e., efforts to
reduce the environmental impact of the supply chain (Rogers and Tibben-Lembke,
2001, p. 130), to activities that encompass product returns, repairs, and
refurbishment.
Estimates of the costs of reverse logistics operations range from $37 - $921
billion annually. Despite this, four in ten logistics managers consider reverse logistics
operations to be a very low priority for their companies. Obviously, the type and
extent of reverse logistics activities vary according to industry, but the extent of these
activities are already significant in many industries and they continue to grow
(Rogers and Tibben-Lembke, 2001).
Although recognition of the strategic importance of reverse logistics operations
is not by any means universal, but there is some evidence that this is changing.
According to Meyer (1999), the
. . . new frontier of management is reverse logistics . . . after companies havedownsized, reengineered, TQMed, racheted up customer service, and wrung out everyconceivable cost efficiency, it may well be one of the last business frontiers businesscan conquer (p. 27)
Most logistics systems are not well-equipped to manage product movement in
a reverse direction. In addition, the costs associated with reverse logistics may be nine
times higher than moving the same product in a forward channel. Another
complicating factor is that returned products that are handled by reverse logistics
operations often cannot be transported, stored and/or handled in the same manner as
when they are distributed in a traditional supply chain (Lambert and Stock, 1993).
Since the activities involved tend to be so varied, reverse logistics operations are quite
7/30/2019 Re-Engineering a Reverse Supply Chain
6/36
6
complex to manage, In addition, demand can be difficult to predict, making product
and information flows challenging to manage. Complicating the problem of managing
reverse logistics operations is the fact that very few, if any, standardized software
solutions designed for reverse logistics operations exist (Meyer, 1999; Rogers and
Tibben-Lembke, 2001).
Although reverse logistics operations in general can be quite difficult to
manage, there are some particular challenges to managing reverse logistics operations
for product returns. Not only does a retailer have to effectively manage the actual
product return, which is a challenge in itself, as discussed above, but, once returned,
the product must be disposed of in some way. The most common disposal method for
returned product is return to the manufacturer, but some returned products are
repackaged and resold, resold as is, destroyed, or sold at other retail outlets (e.g., off
price retailers or manufacturers outlets) (Daugherty, Autry and Ellinger, 2001).
Despite the fact that effectively managing the complex reverse logistics
operations required to support what Dennis and Kambil (2001, p. 42) term service to
profit supply chains, requires considerable skill and integration, Dennis and Kambil
stress the potential advantageous competitive positioning and market opportunities
for firms that handle these important activities effectively. Dennis and Kambil also
point out the value of using reverse logistics activities to develop service-centric
supply chains to adequately support customers in such activities as product returns.
Such supply chains are vital tools as companies seek to differentiate themselves from
their competitors, increase customer loyalty, and boost profit margins. For this
reason, firms that can effectively implement and manage the necessary reverse
7/30/2019 Re-Engineering a Reverse Supply Chain
7/36
7
logistics operations to meet these needs will significantly enhance their competitive
position.
With this background discussion of the strategic role of managing product
returns service management activities and the role of reverse logistics operations in
supporting these service-to-profit supply chain operations, we present a case study of
a project that reengineered reverse logistics operations for a major direct retailer in
the US. The project was designed to assist the company as it considered an
innovative approach to create a more convenient product return process and reduce
the cycle time for customers to receive refunds and exchanges when items are
returned.
The primary objective of the project was to enhance customer service quality
by reengineering the retailers reverse logistics processes in order to reduce the cycle
time of providing refunds and exchanges to customers. A secondary objective was to
enhance the internal efficiency of processing returned products by exploring
opportunities for lowering product returns and related operational and capital costs.
In order to accomplish the reengineering effort, computer simulation models
were developed and examined to compare the current process with a proposed new
reverse logistics process under different operational scenarios.
CASE STUDY OF A DIRECT RETAILERS PRODUCT RETURNS PROCESS
The case study presented here involves a direct retailer located in the US. This
retailer markets apparel and household goods with sales in excess of one billion US
dollars annually. The forward supply chain and the attendant logistics processes are
efficient and effective with most customer orders being shipped within 24 hours.
7/30/2019 Re-Engineering a Reverse Supply Chain
8/36
8
During the holiday season well over 100,000 packages may be shipped daily.
Customer satisfaction is a high priority.
In the spirit of continuous improvement, the retailers management was eager
to explore new opportunities to enhance customer satisfaction with the product
returns process. In general, increasing customer satisfaction with the product returns
process means (1) reducing the cycle time of customer receipt of the refund or
exchange, and (2) increasing the convenience of sending a return.
For customers of direct retailers, one of the disadvantages of transactions is the
inconvenience and time involved in returning an item. Many direct retailers try to
mitigate this inconvenience by providing a return form and a preaddressed shipping
label with each order. Nevertheless, the typical return process for customers of direct
retailers is typically something like this:
1. Fill out the return form or write a letter to the retailer to indicate the reason forthe return and the requested action, e.g., exchange for another item, issue a refund
check, or credit a card credit account.
2. Repackage the item and enclose the paperwork,3. Go to the post office and stand in line to have the package weighed and postage
assessed.
4. Wait for the package to be received and processed, and the requested action to becompleted by the retailer.
All of this is time consuming and inconvenient for the customer. The
inconvenience of the return process is often cited by customers of direct retailers as a
major deterrent to initiating retail transactions (Cho, Im and Hiltz, 2003). Finding
7/30/2019 Re-Engineering a Reverse Supply Chain
9/36
9
ways to reduce the inconvenience and cycle time of a customer return can increase
customer satisfaction, thus enhancing customer retention and sales.
The Direct Retailers Current Product Returns Process
The direct retailers current product returns process begins when a customer
decides to return one or more products. The majority of the time these are new
products which the customer has recently ordered and received. Reasons for product
return are numerous, e.g., the customer may have changed his/her mind, the customer
may decide that they want a different color or size, In addition to return of new
products customers also return used products that they feel did not live up to their
expectations. Regardless of whether the product is old or new the customer will
request either an exchange or a refund.
Figure 1 depicts a simplified version of the current reverse logistics process
map. This process includes a number of main processes and a large number of sub-
processes to effectively manage arrival of a large volume of packages containing items
from a variety of product lines. These packages need to be sorted out and routed to
the correct locations. This can be a difficult task, since the only indication of what the
package contains is the size and shape of the box. Each package is processed by
opening it, reading the contained documentation, and assessing the package contents.
This is the point at which the customer transaction is separated from the merchandise
and the two processes proceed independently and in parallel.
The returns documentation is transferred to the financial transaction process,
where depending on the initial means of transaction, e.g., credit card, personal check,
gift certificate, customers are reimbursed for the returned merchandise. If an
7/30/2019 Re-Engineering a Reverse Supply Chain
10/36
10
exchange has been requested, the appropriate information proceeds to the
distribution center, from which the exchange item is shipped. This completes the
customer transaction process.
Meanwhile the returned products have been removed from the package, are
sorted into the various product groups, and are conveyed to the merchandise
preparation area. Here the quality each item is assessed and the item is prepared as
needed for its destination. First-quality items are repackaged for return to the
distribution center. Lower quality items go to a variety of destinations depending on
their condition. For example, some items are donated to charity, while the lowest
level of quality is discarded. Items are consolidated and shipped to the appropriate
destination.
Notice that the customers financial transaction waits to commence until the
package has been received, opened, and its contents assessed. Only at this point can
the information needed for the customer transaction be separated from the
merchandise. This is the usual procedure in virtually all return processes; the
merchandise must be in hand before any further transaction takes place. The
majority of the cycle time for the product returns process is due to shipping time
through the reverse supply chain.
The Direct Retailers Proposed Product Returns Process
The proposed reengineering of the product returns supply chain for this direct
retailer hinges on the fact that the shipping time is removed from the customer
transaction by having customers call first and use a scanable postage-paid label. As
shown in Figure 2, the reengineered process proposes that customers telephone the
7/30/2019 Re-Engineering a Reverse Supply Chain
11/36
11
direct retailer to indicate what they are returning and specify the details of the desired
exchange or refund. A postage paid return label is provided with the initial order,
thus providing a significant convenience to the customer. The customer is charged a
nominal fee for this postage paid return label. When the carrier (e.g., FedEx, UPS,
USPS) receives the package containing the returned product, the label is scanned and
the information transmitted to the direct retailer. This allows the documentation
containing information about the product return to be separated from the
merchandise at a much earlier point in time, so that the customers return transaction
can be completed without the delay of waiting for the product return package to
arrive.
When the package arrives at the returns center all that remains is to reconcile
the transaction and complete the merchandise preparation. Since the scanable return
label included in the initial order allows information on the returned product to be
transmitted prior to the retailer actually receiving the package containing the
returned product, the returns center can be restructured to combine package
processing and merchandise preparation operations based on product lines. In the
current product returns process the contents are unknown until the package is
opened, making it impossible to process returned products by product line. This is the
reason for having package processing and merchandise preparation as separate
operations in the current process. Being able to route packages to the right location
based on product lines offers the potential for increasing the efficiency of operating
the returns center.
7/30/2019 Re-Engineering a Reverse Supply Chain
12/36
12
However, this streamlined process can be followed only if the customer calls
anduses the scanable label. If the customer does not call or does not use the label,
then the customer transaction cannot be completed prior to package arrival and the
package must follow traditional product returns processing, with its separate package
processing and merchandise preparation operations.
Comparison of Current and Proposed Product Returns Processes
In order to evaluate the proposed product returns process described above it must
be compared to the current process. The cycle times and other characteristics of the
current process are known, but the proposed process is very much a what-if?
scenario. Answers are needed to the following questions regarding the proposed
process:
1) How long would it take for a customer returning product to receive their desiredexchange product or credit for the returned merchandise (what is the customer
cycle time)?
2) How long would it take for returned products to be prepared for resale or disposal(what is the product cycle time)?
3) How many FTE (full time equivalent) employees would be needed for theproposed process?
The customer cycle time (CCT) is defined as the time from when a customer
ships a package until receipt of the refund or exchange. The product cycle time (PCT)
measures the time from when a customer returns an item until it is shipped out from
the returns center for resale or disposal. Under the proposed product returns process,
the CCT time would clearly be decreased, which was the major motivation for the
7/30/2019 Re-Engineering a Reverse Supply Chain
13/36
13
proposed change. The PCT was expected to remain approximately the same
(reducing this time was not an objective) and was measured simply as a characteristic
of the process. The required number of FTEs for staffing the proposed process was
unknown because many of the tasks are restructured by the reengineering the returns
center in the proposed process (i.e., combining the package processing and
merchandise preparation processes). Fewer FTEs should be needed to staff the
returns center because the use of scanable labels and customer calls should allow
packages to be sorted and processed very efficiently. However, the proposed product
returns process created a new job that did not previously exist personnel to answer
the phone calls for returning merchandise.
Computer Simulation Modeling
Due to the complexity of the reverse logistics activities for product returns, the
answers to the questions above were not easily determined. There appear to be two
possible methods of assessing which process the retailer should adopt. One method
was to adopt the proposed process, collect data, and evaluate in hindsight whether it
was great idea or a mistake. The second method was to create a computer simulation
model of the current and proposed processes to allow the organization to perform
analyses that would enable it to compare the current and proposed processes, as well
as to fine tune the proposed process and make an informed decision on adoption.
Computer simulation modeling is known as an effective approach for process
reengineering, particularly when the level of complexity is high. It allows for accurate
and effective study of alternative operational scenarios without costly and time-
consuming interruption of the real physical process. Also, simulation models are
7/30/2019 Re-Engineering a Reverse Supply Chain
14/36
14
capable of capturing the probabilistic nature of the processes under study, where
simpler analytical methods fail.
When using simulation to compare a proposed process with an existing one, it
is advisable to first model the current process to allow validation against reality.
After working out any bugs the current model can be modified for any number of
what-if scenarios to evaluate proposed changes (Law and Kelton, 1999; Rabinovich
and Evers, 2003). Therefore, the reengineering of this direct retailers product return
operations involved the simulation of both the current product returns operation, as
well as the proposed product returns operation. The complexity of the reverse logistics
activities for this direct retailers product return process is driven by the probabilistic
nature of the activities, events, and man-machine interactions within the different
sub-processes.
Figure 3 shows major steps involved in a computer simulation modeling and
analysis (Law and Kelton, 1999). Using this framework, the discussion below details
the computer simulation modeling and analysis process involved in the re-engineering
efforts for the direct retailer in this case study.
Step 1 begins with a clear objective and identification of what questions are to
be answered. The objective of the current study was to reduce cycle times (CCT and
PCT) and operational costs (in the form of FTE employees in the returns processing
center). In order to accomplish this objective, it was necessary to evaluate the
proposed process by comparing it to the current one, since measuring and
benchmarking the related cycle times (CCT and PCT) and required FTE requirements
under different operational scenarios was required to enable the direct retailer to
7/30/2019 Re-Engineering a Reverse Supply Chain
15/36
15
determine which of the two product return processes accomplished the objectives of
the reengineering.
In Step 2, process maps were prepared for the current and proposed processes.
These maps were designed to provide a clear view of the processes, facilitate
communication between the research team and the practitioners, and clarify the types
of data that would be required to construct the simulation model. This step was one
of the most time-consuming phases of the project, taking 70% to 80% of the project
duration. However, these process maps were vital, since they formed the basis for the
computer simulation model.
Operations within the product Returns Processing Center (RPC) of this direct
retailer include 15 major processes. Each of these 15 major processes themselves
consists of a network of sub-processes. During the reengineering effort, development
of a detailed process map of the RPC consumed approximately 60% of the total time
dedicated to the project. In addition to the fact that the authors
signed a legally
binding confidentiality agreement with the direct retailer, the sheer size of the current
and alternative process maps and prohibit complete representations of readable
versions on standard sized paper. For example, the smallest readable versions of the
process maps require 25 by 17 sized paper. The actual process maps, which guided
the simulation models for the current and alternative processes, included tracking of
returned packages and items from each process to the next. In addition, the related
financial papers were tracked until a returned item was either disposed of, or prepared
for resale and reshelved.
7/30/2019 Re-Engineering a Reverse Supply Chain
16/36
16
During the time the required data was being collected, for Step 3, Arena 3.01
(1997) software was utilized to develop the simulation model for the current product
return process (Step 4).
Step 3 involved the collection and analysis of the data for the simulation
model. Corresponding to the large size of the processes to be modeled, the data
requirements were quite large. Table 1 shows the number of data elements involved.
Since returning product and its associated paperwork are separated shortly after
entering the system, and passed though different processes, the values for product and
paperwork are shown separately in Table 1. A total of 85 separate processing time
distributions were needed. Although the delay time distributions in Table 1 do not
refer to entities waiting in a processor queue, distributions for delay times were needed
because returned items were batched at many points in the system after being
processed, causing a delay before being sent on to the next processing step. Thus data
was needed on these delay time distributions, of which there were 120. The term
splits in Table 1 refers to decision points in the return process where some items
went one way and some items another way. Proportions were needed for each of
these, with the total being 113.
Given the large size of the model, an explanation of how each distribution was
fitted would be impractical. The process used to collect the required data and fit
distributions for the model was two-fold. First, a data set was utilized which consisted
of 445 returned items that had been time stamped at various points in the process was
obtained from the direct retailer. Using these data, distributions were fitted using
Arenas distribution fitting capability. The fitted distributions obtained from this
7/30/2019 Re-Engineering a Reverse Supply Chain
17/36
17
pricess included the beta distribution, normal distribution, and exponential
distribution. A second process was used to fit distributions for the remaining
processes in Table 1, which would have required data that had time stamps at both
the beginning and the end of each process. Since this level of data collection was not
possible, in these cases, personnel who worked in these process areas (both managers
and operators) were interviewed to obtain their answers to typical, minimum, and
maximum times for these processes and the triangular distribution was used.
As the data for these various returns processes became available, we used
Arenas Input Analyzer capability and Fit All option to identify the best
distribution fitted to the collected input data. The best was defined as the
distribution with minimum square error, as determined by the p-value of the Chi-
square statistic for goodness of fit using a Kolmogorov-Smirnov analysis.
Steps 4 and 5 involved the development, verification, and validation of the
Arena simulation model. We created and verified (with management of the direct
retailer) a detailed process map of the current product returns process (Figure 1).
Using this map, we developed an Arena simulation model to depict the current return
process. For the purpose of ease of communication with management, as well as
verification and validation of the simulation model, the format of the Arena model
mimicked the layout of the process map.
As each major process and its related sub-processes were populated with the
identified best distributions, the logic involved in simulating each process was
validated. In addition, validation runs were conducted for each major process in
conjunction with the other major processes with which each process was interlinked.
7/30/2019 Re-Engineering a Reverse Supply Chain
18/36
18
During this exercise, we applied Arenas animation capabilities to the extent allowed
by hardware memory limitations. The validation process was completed when the
network of all fifteen major processes along with their related sub-processes were
simulated simultaneously. For verification and validation purposes, simulation results
for all major processes and their related sub-processes, were communicated and
discussed with the project team, including representatives from direct retailers
management. . In addition, results from the model of the current process were
successfully validated against existing operational data.
As discussed earlier with respect to the process maps, confidentiality concerns,
as well as the sheer scope of the product returns process prohibit a detailed
representation of the entire Arena simulation model for either the present or the
proposed product returns processes. For example, because of the scope of the
simulation model, a readable representation of the Arena simulation of the direct
retailers present product returns process requires 66 x 60 paper. However, in order
to provide the reader with an idea of the simulation model developed for the present
product returns process, we present, in Figure 4, a small section of the Arena
simulation, depicting the sub-processes within the current apparel and footwear
returns processes. As Figure 4 indicates, when a return package in a previous return
process has been identified to include apparel or footwear return items, the package
enters the apparel and footwear returns process. If any of the returned items require
repair, these are added to a batch. When this repair batch attains a certain size, it is
conveyed to the repair process. If the apparel and footwear items do not require
7/30/2019 Re-Engineering a Reverse Supply Chain
19/36
19
repair, they are processed, batched, and conveyed to the next sub-process within the
major apparel and footwear returns process.
After data collection was completed (step 3) and the simulation model of the
present product returns process was developed, verified and validated (steps 4 and 5),
full model experimentation and scenario analyses were conducted on the model of the
current product returns process (steps 6 and 7). Cycle times were measured under
alternative operational scenarios that characterized the direct retailers product
return operations under different product return levels.
The return process center operates five days, two shifts per day. Hence, we
decided a steady-state simulation approach should work very well. To determine the
simulation run, we ran a five-replication simulation of the entire returns process, with
a normal volume of packages, for a period of one, two, three, four, and five months.
Analyses of the collected cycle time statistics and graphs generated from these
statistics, which depicted changes in the cycle times for the five replications, indicated
no significance differences between two, three, four or five month simulation periods.
Hence, we decided to use a two-month simulation period.
As a result of the steady-state analyses described in the previous paragraph,
we realized that system steady-state is achieved within the first five days of
simulation. Thus, for the comparative study of the current and the proposed product
returns processes, all simulation runs used a two-month simulation period with a five-
day warm-up time. In addition, each simulation run assumed that system and related
resources are idle.
7/30/2019 Re-Engineering a Reverse Supply Chain
20/36
20
To develop a basic understanding about the performance of the current
product returns process, management of the direct retailer was interested in an
exercise that included three simulation scenarios. The only difference between the
three scenarios was the daily volume of packages arriving at the return facility. These
three package volumes were, according to management, the three typical volumes
historically processed at the center. These volumes represented a spectrum of a low
volume, the typically expected volume, and a high volume of package returns.
Assuming that A depicts the base, or low volume scenario, the volumes for
scenarios B and C relative to A increase by 43% and 114%, respectively. At
managements request, the focus of the simulation of the three package return
volumes was on the differences among the average cycle times associated with
customer reimbursement for four different customer purchase methods (1, 2, 3, 4) and
the cycle times associated with reshelving of six general categories of products (1, 2, 3,
4, 5, 6) returned to the returns facility.1
A summary the simulation results for the exercise described above is shown in
Table 2. The table shows Relative Average Percentage (RAP) changes in the customer
reimbursement cycle times when product return volumes for scenarios B and C are
compared to the base scenario A. When the RAP of customer reimbursement cycle
time associated with the four customer purchase methods under scenario B was
compared to scenario A, the RAP increased by a fraction of a percentage for all four
purchase methods. A comparison of the RAP for scenario C versus scenario A
1The confidentiality agreement with the direct retailer prohibits us from providing specific details of
the four customer purchase methods or the six product categories.
7/30/2019 Re-Engineering a Reverse Supply Chain
21/36
21
indicated an increase of between 4.81% to 10.45%, with customer purchase method 2
experiencing the largest increase, and customer purchase method 4 experiencing the
smallest increase in the RAP.
In addition, Table 2 shows the cycle times related to reshelving for each of the
six categories of products returned to the returns processing facility. The top three
product categories represent approximately 90% of the returned products. In
comparing scenario B versus scenario A, the reshelving cycle times show an increase
from 1.64% to 5.34%, where the minimum and maximum increases occur for product
categories 1 and 4, respectively. The reshelving cycle times for scenario C versus
scenario A, reveal a 93.49% increase between these two scenarios for product category
5; a 72.42% increase for product category 1; and only a fraction of a percent increase
for product category 6. As Table 2 demonstrates, the associated variances among
product categories between scenarios C and A is much larger than for scenario B
versus A.
These results discussed above helped management to objectively understand
how the typical returned package volumes within the current return process
interacted with (a) the customer purchase method to influence the customer
reimbursement cycle times and (b) the category of returned products to influence the
returned product reshelving cycle times. Also, this simulation exercise allowed
management to develop a deeper understanding of the nonuniform nature of the
impact of these factors on the cycle times. As a result of these simulations,
management realized that (a) they could use the information gleaned from the
simulations to effectively manage customer expectations with regard to when to
7/30/2019 Re-Engineering a Reverse Supply Chain
22/36
22
expect reimbursements from product returns; and (b) they should take into
consideration the information on product reshelving cycle times provided by the
simulations to help manage demand for products that are in short supply.
Using the thorough understanding of the current return process provided by
the model experimentation and scenario analyses discussed above, a simulation model
of the proposed new returns process was created. As discussed earlier, the key
difference between the current and proposed new returns processes is the percentage
of customers expected to call the returns processing center prior to returning their
products and provide detailed information about the products they anticipate
returning.
Analysis of the model of the proposed product returns process required a
number of iterative scenarios in which bottle necks were identified and resolved. The
throughput capacity of the product returns sub-processes were determined by the
probability distribution that describes the time needed for task completion and the
number of these tasks that could be performed in parallel. Many of the product
returns tasks were performed by people since returns processing is labor intensive.
Determining the number of FTEs needed for these various labor intensive tasks was
essential for eliminating bottlenecks and identifying FTE staffing needs.
In simulating the proposed new product returns process, the management of
the direct retailer desired to base the model experimentation and scenario analyses on
two different estimates of the percentage of customers who would call the returns
center prior to their product returns. The first estimate was a conservative one and
was believed to represent the percentage of customers who would call in advance of
7/30/2019 Re-Engineering a Reverse Supply Chain
23/36
23
returning products when the new return policy was initially being introduced. The
second estimate, 10% higher than the first one, was believed to be the long-term
percentage of customers who, once they became advised and further educated about
the new return process, would call in advance of returning products.
Using the two estimates of the percentage of customers who would call prior to
returning products, simulation scenarios D and E were designed. In simulating both
of these scenarios, we assumed the volumes of product return would be the same as in
scenario B (described above in the analysis of the current product returns process), a
two-month simulation period, and a five-day steady state period. The same level of
resources and number of processors/process centers, were considered for both scenarios
D and E. In addition, for these two scenarios, management wished to focus only the
top three product categories, since, as discussed above, constitute approximately 90%
of returned products.
Comparison of Current and Proposed Product Return Operations
Comparison of the current and proposed product returns processes involved an
analysis of scenario D for the proposed product returns process with scenario B of the
current product returns process. Table 3 depicts the relative average percentage
(RAP) cycle times associated with customer reimbursement for four different
customer purchase methods and the relative average percentage (RAP) cycle times for
reshelving of the top three product categories. In comparing scenario D versus
scenario B, we can see that improvements in RAP customer reimbursement cycle
times for the four customer purchase methods range from 19.91% to 35.39%. The
customer reimbursement RAP cycle times for customer purchasing methods 1
7/30/2019 Re-Engineering a Reverse Supply Chain
24/36
24
through 4, when scenario E is compared to scenario B, vary from 24.75% to 44.52%.
This is more impressive than the relative improvement realized by scenario D versus
scenario B.
Similar comparisons between scenarios D and B and scenarios E and B
regarding the RAP in reshelving cycle times for the three top product categories
demonstrated mixed results. There is an increase in RAP for reshelving cycle times of
from 3.31% to 10.05% for product categories 1 and 2 (in other words, the reshelving
cycle times worsened), but an improvement of 4.87% to 5.31% for product category 3
(i.e., the reshelving cycle times decreased).
The simulation exercise with the new product returns process enabled
management to understand two important issues. First, the new product returns
process, regardless of the percentage of customers who called prior to returning
products, significantly improved the cycle times associated with customer
reimbursement. Secondly, the impact of the new product returns process on product
reshelf cycle time for two product categories is negative (product categories 1 and 2)
and for the third product category (product category 3) is positive. However, the
difference in product reshelf cycle time between scenario D versus B and between
scenario E versus B is only a fraction of a percentage, indicating that variation in how
many customers call in advance of returning products has a minimal impact on the
product reshelf cycle time.
A follow up simulation exercise showed that adding one additional processor to
a bottleneck sub-process would improve the RAP product reshelf cycle time 10%
when compared to scenario B. Based on this follow up simulation, management was
7/30/2019 Re-Engineering a Reverse Supply Chain
25/36
25
convinced that the increase in cost for the additional processor resource was well
justified when the significant benefit of reduced product reshelf cycle time was
considered.
Results showed that reengineering of the returns center according to the
proposed product returns process would indeed improve efficiency and productivity
and would require approximately 65% of the current FTE staffing level for returns
processing of packages and merchandise in the returns center. However, since the
proposed product returns process creates the additional task of answering phone calls
for returns, the net staff FTE levels would be approximately 85% to 90% of current
levels. Staff that handles the financial transactions associated with product returns
remains essentially unchanged under the proposed process.
Note that the degree of reduction in staff FTEs in the returns processing center
under the proposed product returns process depends on the volume of customers who
fully utilize the new process by calling and using the scanable label. The reduction to
a net of 85% to 90% of current FTE staffing levels in the returns processing center is
based on the assumption that 35% of customers returning products will use the new
process. If the percentage of customers using the new process increases, the net FTE
staffing requirements in the returns processing center would decrease; conversely, if
fewer than 35% of customers returning products use the new process, the net FTE
staffing requirements in the returns processing center would increase.
Under the proposed product returns process, customer cycle times (CCT) were
substantially reduced, since the initiation of customers return processing begins prior
to shipping the product back to the returns processing center. Customers who use a
7/30/2019 Re-Engineering a Reverse Supply Chain
26/36
26
credit card can receive credit in only a few days. For other customers, who request a
refund via check or product exchange, the CCT would involve an additional three to
four days to complete these transactions. These reduced customer cycle times along
with the convenience provided by the postage paid return label represent a significant
increase in customer service levels.
SUMMARY AND CONCLUSIONS
One of the most important service management activities in a retail
environment is product return services. These services are important from a strategic
point of view because of their ability to positively impact customers satisfaction,
engender customer loyalty, and consequently, increase products sales. Successful
product return services depend on the design of competent reverse supply chains and
support of those supply chains by effective reverse logistics operations. Organizations
that are able to achieve competence in these service management activities have the
potential to enjoy significant advantages over their competitors, since the design and
operation of these activities are not easily duplicated.
This study presented an analysis of a set of reverse logistics activities to
support a proposed new product returns process for a major direct retailer in the US.
The objective of the project was to improve customer service quality and reduce
operational costs. To capture the complexity and dynamism of the reverse logistics
activities that support the products returns processes, a computer simulation
modeling technique was used. The simulation model allowed the comparison of
multiple scenarios for both the organizations current product return process as well
as a proposed new product returns process. Analysis of the simulation model
7/30/2019 Re-Engineering a Reverse Supply Chain
27/36
27
facilitated the organizations decision making with respect to the design of its reverse
supply chain for product returns, enabling it to reduce the time for customers to
receive credit or products in exchange for product returns. In addition, the
organizations operational resources in the form of returns processing center staffing
requirements were able to be reduced.
7/30/2019 Re-Engineering a Reverse Supply Chain
28/36
28
REFERENCES
Arena 3.01, 1997, System modeling corporation, Sewicley, PA
Cho, Y., I. Im, and R. Hiltz, 2003, The impact of e-services failures and customer
complaints on electronic commerce customer relationship management, Journal ofconsumer satisfaction, dissatisfaction and complaining behavior, 16, 106-118.
Cohen, M.A., and S. Whang, 1997, Competing in product and service: a product life-
cycle model, Management science, 43 (4), 535-37.
Daugherty, P. J., C.W. Autry, and A. E. Ellinger , 2001, Reverse logistics: therelationship between resource commitment and program performance, Journal ofbusiness logistics, 22(1), 107-123.
Dennis, M.J. and A. Kambil, 2003, Service management: building profits after thesale, Supply chain management review, (January/February), 42-48.
Fitzsimmons, J.A. and M.J. Fitzsimmons, 1998, Service management: operations,strategy, and information technology, Boston, MA: McGraw-Hill.
Handfield, R.B. and E.L. Nichols, 1999, Introduction to supply chain management,Upper Saddle River, NJ: Prentice-Hall.
Heskett, J.L., T.O. Jones, G.W. Loveman, and E.W. Sasser, Jr., 1994, Putting theservice-profit chain to work, Harvard business review, Mar/Apr, 72 (2), 164-74
Lambert, D.M., and J.R. Stock, 1993, Strategic logistics management, 3rd Edition,Irwin, Boston
Law, A. M. and W. D. Kelton, 1999, Simulation modeling and analysis, New York:McGraw-Hill Book Company.
Meyer, H., 1999, Many happy returns, Journal of business strategy, 20 (4), 27-31.
Rabinovich E. and P. Evers, 2003, Product fulfillment in supply chains supporting
internet-retailing operations, Journal of business logistics, 24 (2), 205-236.
7/30/2019 Re-Engineering a Reverse Supply Chain
29/36
29
Retzlaff-Roberts, D.L., 1998, Return customers and profits to your bottom line, AFedEx White Paper.
Retzlaff-Roberts, D.L. and M.N. Frolick, 1997, Reducing cycle time in reverselogistics, Cycle time research, 3(1), 69-78.
Schoenbachler, D. D. and G. L. Gordon, 2002, Multi-channel shopping:
understanding what drives channel choice, The journal of consumer marketing, 19 (1),42-54.
Wagner, K.A., M. Mittal, F. de Rosa, and J.A. Mazzon, 2002, Assessing the service-profit chain, Marketing science, 21 (3), 294-317.
7/30/2019 Re-Engineering a Reverse Supply Chain
30/36
30
RETURNSPROCESSINGCENTER
CUSTOMER
Returns Package
Sort Packages
Process Packages & Sort M erchandise
Assess Quality, PrepareMerchandise &Consolidate Products
DISTRIBUTION CENTER
Re-shelve Products
Receives Refund or Exchange
Ship Exchange Items
Indicates movement of products
Indicates movement of documentation and financial transactions
FIGURE 1A SIMPLIFIED MAP OF REVERSE LOGISTICS
ACTIVITIES FOR THE CURRENT PRODUCT RETURNSPROCESS
Financial Transaction
FirstQuality?
Yes
No
Exchanges OnlyOtherDestinations
7/30/2019 Re-Engineering a Reverse Supply Chain
31/36
31
RETURNS PROCESSINGCENTER
CARRIER
DistributionCenter
Carrier Receives Package & Scans Label
Carrier ShipsPackage toRetailer
Enter Transaction MatchTransaction
Sort by Scanable Label Traditional PackageProcessing
FinancialTransaction
Open Package, ReconcileTransaction, & Prep Merchandise
Traditionalmerchandise prep
FirstQuality?
Re-shelve Items Ship Exchange Item(s)
CUSTOMER Customer Ships Package Customer ReceivesRefund/Exchange
Yes
No
To OtherDestinations
Customer CallsRetailer
FIGURE 2A SIMPLIFIED MAP OF REVERSE LOGISTICS
ACTIVITIES FOR THE PROPOSED PRODUCT RETURNSPROCESS
Indicates movement of documentation and financial transactions
Indicates movement of productsIndicates movement of products
7/30/2019 Re-Engineering a Reverse Supply Chain
32/36
32
Step 1Objective: Reduce Cycle Time
& Operational Costs
Step 8Presentation of
Results andRecommendations
Step 7ScenarioAnalyses
Step 6Model
Experimentation
Step 2Preparation of Maps
for Current &Proposed ProductReturns Processes
Step 3Model DataCollection &
Analysis
Step 4Simulation
ModelDevelopment
Step 5Model
Verification
&Validation
FIGURE 3
COMPUTER SIMULATION MODELING PROCESS
7/30/2019 Re-Engineering a Reverse Supply Chain
33/36
33
NITE MS
AFW P ROCE S S
WithE lse
0.779
NITE MS
AFW TY P MAIL
A P FP rocesstm
AFW TY P MAIL
A P FP rocesstm
A FW TY P MA IL .E Q. 1
IfE lse
WHITEMAI L PROCESS TIME
ORDERSET PROCESS TIME
AFW P ROC E S S ING
C. PROCESS APPAREL & FOOTWEAR(AFW)FW
APACKAGESPROCESSED&
BATCHED
ITEM
SCONVEYED
With
WithWith
1.00.000
0.0
REPAIRS?
PACKAGES CONVEYED TO REPAIRSP KGRE P
BA TCHE D
NOT BATCHED
ORDERSET
WHITEMAIL
MAIL TYPE?
P KGTY P0.
CONVEY TIME
0.
Duplicate
Enter Chance
Duplicate
Assign
Assign
ChooseAdvServer
Chance
LeaveAssign
LeaveAssign Delay
Delay
FIGURE 4A SECTION OF THE ARNEA SIMULATION DEPICTINGTHE PRESENT PRODUCT RETURNS SUB-PROCESS
FOR APPAREL AND FOOTWEAR
7/30/2019 Re-Engineering a Reverse Supply Chain
34/36
34
TABLE 1
SIMULATION MODEL DATA REQUIREMENTS
DATA REQUIREMENTSProcessing Time
Distributions
Delay Time
Distributions Splits
Product 69 101 105
Paperwork 16 13 8
Total 85 120 113
7/30/2019 Re-Engineering a Reverse Supply Chain
35/36
35
TABLE 2
CURRENT RETURN PROCESS RELATIVE AVERAGE PERCENTAGE
(RAP) INCREASE IN CUSTOMER REIMBURSEMENT ANDPRODUCTRESELF CYCLE TIMES
Customer
Purchase
Method
Scenario B
Versus A
Scenario C
Versus A
1 0.47% 5.75%
2 0.17% 10.45%
3 0.13% 6.19%
Customer
Reimbursement
4 0.12% 4.81%
ProductCategory Scenario BVersus A Scenario CVersus A
1 1.64% 72.42%
2 3.81% 8.18%
3 2.51% 2.66%
4 5.37% 5.43%
5 3.04% 93.49%
Product Reshelf
6 2.44% 0.17%
7/30/2019 Re-Engineering a Reverse Supply Chain
36/36
TABLE 3
NEW PRODUCT RETURNS PROCESS RELATIVE AVERAGE
PERCENTAGE (RAP) CHANGES IN CUSTOMER REIMBURSEMENT
AND PRODUCT RESELF CYCLE TIMES
a (-) Suggests reduction in RAP cycle time.
Customer
Purchase
Method
Scenario D
Versus B
Scenario E
Versus B
1 -19.91% a -24.75% a
2 -35.39% a -44.52% a
3 -20.02% a -24.93% a
Customer
Reimbursement
4 -21.77% a -25.22% a
Product
Category
Scenario D
Versus B
Scenario E
Versus B
1 3.31% 3.49%
2 10.05% 10.45%
Product Reshelf
3 -5.31% a -4.87% a