Re-Engineering a Reverse Supply Chain

7/30/2019 Re-Engineering a Reverse Supply Chain

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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

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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]


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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


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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


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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


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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


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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


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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.


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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


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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


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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


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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.


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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


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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


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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


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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.


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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


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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.


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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


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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.


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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.


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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


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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


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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


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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


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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


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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


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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.


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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

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Retzlaff-Roberts, D.L., 1998, Return customers and profits to your bottom line, AFedEx White Paper.

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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


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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


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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


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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


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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


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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%


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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

Documents

Re-Engineering a Reverse Supply Chain