Collaboration in reverse: a conceptual framework for reverse logistics operations

Abstract

Purpose
The purpose of this paper is to present the research carried out on the development of a conceptual framework termed as the reverse collaboration framework (RCF) to provide supply chain (SC) visibility and information sharing to practitioners in a reverse logistics (RL) operations.

Design/methodology/approach
The research methodology used in this research is a combination of concept mapping, and the extension of the work of other researchers (deductive approach) to develop a RCF that connects tools, techniques, systems and RL processes.

Findings
This research shows that by integrating tools, systems, tools and techniques with RL processes by means of the RCF will increase performance and productivity of a RL operations. This is demonstrated by applying the RCF to a consumer electronics business that proves that the time taken for the end to end RL operations is reduced by 20%.

Research limitations/implications
The RCF has been demonstrated with the data from a consumer electronics organisation. Literature points out that there are many different mathematical models for RL across a number of industries. Thus, at this stage, it is not clear if the RCF developed in this research will work in other industries, such as the newspaper, plastic bottles and online retailers industry where product returns are high. This research work can be extended in developing an IT solution by future researchers that can be linked to the main ERP system of an organisation.

Practical implications
SC managers can use the RCF in the extended form of an IT solution to manage the RL operations of their organisations.

Originality/value
There is a lack of research in the space of reverse collaboration in the broader field of SC management. This paper has fulfilled that gap.

Full text

International Journal of Productivity and Performance Management
Collaboration in reverse: a conceptual framework for reverse logistics operations
Kumaraguru Mahadevan,
Article information:
To cite this document:
Kumaraguru Mahadevan, (2019) "Collaboration in reverse: a conceptual framework for reverse
logistics operations", International Journal of Productivity and Performance Management, https://
doi.org/10.1108/IJPPM-10-2017-0247
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Collaboration in reverse: a
conceptual framework for reverse
logistics operations
Kumaraguru Mahadevan
School of Business and Law, Central Queensland University, Sydney, Australia
Abstract
Purpose –The purpose of this paper is to present the research carried out on the development of a
conceptual framework termed as the reverse collaboration framework (RCF) to provide supply chain (SC)
visability and information sharing to practitioners in a reverse logistics (RL) operations.
Design/methodology/approach –The research methodology used in this research is a combination of
concept mapping, and the extension of the work of other researchers (deductive approach) to develop a RCF
that connects tools, techniques, systems and RL processes.
Findings –This research shows that by integrating tools, systems, tools and techniques with RL processes
by means of the RCF will increase performance and productivity of a RL operations. This is demonstrated by
applying the RCF to a consumer electronics business that proves that the time taken for the end to end RL
operations is reducred by 2020.
Research limitations/implications –The RCF has been demonstrated with the data from a consumer
electronics organisation. Literature points out that there are many different mathematical models for RL
across a number of industries. Thus, at this stage, it is not clear if the RCF developed in this research will
work in other industries, such as the newspaper, plastic bottles and online retailers industry where product
returns are high. This research work can be extended in developing an IT solution by future researchers that
can be linked to the main ERP system of an organisation.
Practical implications –SC managers can use the RCF in the extended form of an IT solution to manage
the RL operations of their organisations.
Originality/value –There is a lack of research in the space of reverse collaboration in the broader field of SC
management. This paper has fulfilled that gap.
Keywords Supply chain management, Supply chain integration, Reverse logistics, Reverse collaboration,
Supply chain visibility
Paper type Research paper
Introduction
Supply chain (SC) collaboration has received a of lot attention in recent times with advent of
technology. Researchers have examined this topic in depth in the forward SC: organisations
manufacture, store, transport and sell products to customers. In doing so, they operate in a
collaborative manner, where SC partners are integrated and share vital information such as
demand forecasts. However, even with the advent of technology and supply chain
integration (SCI), organisation still face the lack of supply chain visibility (SCV ) and
information sharing (IS) (Bowersox et al., 2007). Popa-Anica (2012) noted that SCI, IS and
SCV increases productivity. Researchers (Mahadevan, 2013; Bowersox et al., 2007) have
addressed the lack of SCV and IS and its impact on business. Mahadevan (2013)’s research
has pointed out the importance of SCI, SCV and IS in the context of the collaboration in the
forward SC. Likewise Bowersox et al. (2007) has emphasised the importance of an SCI in the
forward collaboration with inadequate focus in the reverse SC space. Overall, Mahadevan
(2013) found that according to researchers (Popa-Anica, 2012; Priesmeyer et al., 2012;
Sharma et al., 2011; Yap and Tan, 2012; Li, 2002; Caridi et al., 2010) having the desired levels
of SCI, IS and SCV is a competitive advantage that management can leverage for an
organisation’s performance and profitability.
Simultaneously, the reverse SC for products is growing in prominence due to legislations,
sustainability and the advent of the circular economy (Genovese et al., 2017). The circular
International Journal of
Productivity and Performance
Management
© Emerald Publishing Limited
1741-0401
DOI 10.1108/IJPPM-10-2017-0247
Received 7 October 2017
Revised 9 March 2018
29 July 2018
Accepted 15 September 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1741-0401.htm
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economy will have a major impact on the broader economy as it requires a fundamental
rethink on the role and function of resources (Preston, 2012). Moreover, as metal ores are
becoming scarcer, there is an increasing incentive for upstream industries, such as smelters,
to look for a secondary supply of resources from recycling activities (Di Maio and Rem,
2015), thus raising the bar for sustainability and recycling operations. The reverse SC
predominantly known as reverse logistics (RL) in most of the literature. However, in this
paper, reverse SC and RL is used interchangeably although logistics is part of the main SC
(Li, 2014). The reverse SC is an emerging business practice that supports the objectives of
sustainable production and consumption (Khor et al., 2016). Further, the importance of RL
has gained prominence in the recent years, as there are high returns by customers due to
expansion of product choices and shorter product life cycles (Shaikh and Abdul‐Kader,
2012). Thus, RL has grown in importance over the last decade (Mahadevan, 2013) where the
average household now has 15 discrete CE products (Rupnow, 2017). This in turn further
elevates the prominence of RL in the broader SC operations. The abbreviation CE, in this
paper, refers to “consumer electronics”and not to circular economy.
Mahadevan (2013) added that with the ever-increasing competition and growing
sustainability trends in the market place, RL is becoming increasingly important for OEMs
globally. The growth of the sustainability functions, the emerging circular economy,
environmental legislations and product stewardship, all have contributed to its position in
crafting an organisation’s corporate strategy (Preston, 2012). Thus, organisations today
adopt the notion of ReUse, ReMarket, ReDeploy, ReCycle and ReNew their products and
services ( Jayant et al., 2012a). However, often, firms are more inclined to invest in resources
in forward SC processes and are hesitant to adopt RL practices because the economic
benefits are somewhat unclear (Hall et al., 2013). Organisations still consider RL as a
necessary “evil”rather than an opportunity for future performance (Genchev et al.,
2011) Furthermore, the internet has revolutionised shopping for clothes, music, toys, CE and
has been completely redefined in the last five years by Amazon, Alibaba and ebay (Jayant
et al., 2012a, b). Moreover, internet shopping has shaped the forward logistics operations,
and in addition the products returns of the online shoppers have also increased
exponentially ( Jayant et al., 2012b). Govindan et al. (2015) point out RL is crucial for online
purchases that can have 50 per cent rate of return. In the case of consumer electronics, the
return rate is around 6 per cent (Shulman et al., 2010). Furthermore, CE returns are as a
result of product failure within warranty period (Zaarour et al., 2014). In addition, there is a
legal, social and moral requirements for manufacturers of CE to manage the recycling of
their products at the end of its useful life as it is expected to produce e-waste in the order of
millions of tons ( Janse et al., 2010).
RL plays a pivotal role in the broader SCM, however, it is researched in an isolated
fashion, in terms of the problems studied, the methodologies applied and in the context it is
addressed (Narayana et al., 2013). Mahadevan (2013) found that there is also a lack of robust
information systems to manage processes in meeting all these environmental social
obligations (Meade and Sarkis, 2002; Delaney, 2001; Mollenkopf and Weathersby, 2004;
Rogers and Tibben-Lembke, 1999). This lack of information systems translates to an
apparent lack of SCV and IS along the reverse SC (Mahadevan, 2013).
The research in RL is multifaceted and distinguished itself from forward logistics ( Jayant
et al., 2012a, b). Furthermore, SC collaboration in the forwardlogistics is well versed; however,
collaboration in reverse is still in its infancy (Jayant et al., 2012a). The author notes that there
is an apparent lack of collaborative practices in the RL with limited discussions about SCI, IS
and SCV (Mahadevan, 2013; Lau and Wang, 2009). Thus, the author argues that with the
growth in RL functions, organisations would start paying equal attention to both SC
collaboration in both directions: reverse collaboration (RC) in SCs possibly lacks IS and SCV,
which adds to the problem of establishing trends in product returns. Thus, there is a value in
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investigating this research surroundingRC in the CE industry inconjunction with SCI, IS and
SCV that will enable SC practitioners to plan, manage returns and coordinate recycling
operations effectively.
This paper is structured as follows. First, a literature review is presented which covers
the work that relates to both forward and RL in the context of SCI, IS and SCV. The
theoretical perspectives of RL in conjunction with SCI, IS and SCV are discussed. This is
followed by the presentation of the methodology. Next, the conceptualization of the reverse
collaboration framework (RCF) is discussed. Application of the conceptualised framework
with RL data from an organisation in the CE industry. Finally, the conclusion, contribution
to research, limitations and future research are presented.
Literature review
The topic of RL has been researched since the late 1960s, however, in only the last ten years
it has gained prominence ( Jayant et al., 2012a, b; Chan et al., 2010; Chan and Chan, 2008). In
this research, the literature review is divided into two major sections. First, it discusses the
theoretical aspects of RL, and forward collaborative practices: SCI, IS and SCV. Second, it
reviews the current state of RL, which focuses on the work that relates to approaches that
are taken to execute RL operations.
Theoretical examination of RL
The purpose of discussing the theoretical perspectives is to develop an understanding of RL
in the context SCI, IS and SCV. The theoretical examination consists of two parts: the
theoretical aspects of RL and the theoretical aspects of SCI, IS and SCV.
In Mahadevan (2013)’s research, there are many theoretical interpretations of forward
logistics using a number of theories: RBV –resource-based view citied by (Halldorsson et al.,
2007; Barratt and Oke, 2007 and Chen et al., 2009); NT –network theory (Halldorsson et al.,
2007); SNA –social network analysis (Halldorsson et al., 2007); and TCA –transaction cost
analysis (Ketchen and Hult, 2007; Miri-Lavassani et al., 2008).
However, in the case of RL, resource dependent theory (RDT) and RBV imply that
collaboration between members of the SC can bring about better results for the entire SC
(Moubed and Mehrjerdi, 2014). Khor et al. (2016) applies RBV to decompose RL into five
commonly adopted disposition options (repair, recondition, remanufacture, recycle and
disposal) to examine the effects of using each option on measures of environmental
performance, and profitability.
Further, Lau and Wang (2009) also use RBV to express the firm as a bundle of
resources and assets and emphasise the use of rare, valuable material to gain sustainable
advantage (Barney, 1991; Grant, 1991). Mai et al. (2012) has taken the RBV approach to
identify understudied antecedents of RL. Genchev et al. (2011) examined the RL using the
RBV theory and argued that organisations should allocate their resources to developing
RL programs in order to avoid the potential negative impact on the bottom line. On the
other hand, RBV accounts for the incorporation of RL as part of long-term company
business strategies by large corporations in order to attain sustainable competitive
advantage (Clendenin, 1997; Wells and Seitz, 2005). In addition, Ramirez (2012) used the
RBV theory to examine whether RL improves firm performance. Lau and Wang (2009)
found that whilst TCE and RBV theories can be used to justify the different approaches to
RL implementation at a firm’s level, however, they are inadequate to account for RL
initiatives from a macro perspective.
From the above discussions, it can be argued that there is an apparent lack of a single
theory that completely explains RL. However, in the context of this paper SCI, IS and SCV
and its connection to RL, the author argues that the RBV theory will be most appropriate as
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it concerns about the reuse of resources. The author argues that the RBV theory indirectly
supports RC as it fosters creation of new knowledge and IS.
In the context of collaborative practices, SCM theoretical researchers (Frankel et al., 2008;
Hines and Rich, 1997; Stock, 1992, 1998) have long recognised the value applying theories
from other disciplines such as economics (Chen et al., 2009): using a number of theoretical
frameworks to explain its concepts. Furthermore, researchers (Chen et al., 2009; Rindfleisch
and Heide, 1997; Halldorsson et al., 2007; Ketchen and Hult, 2007; Miri-Lavassani et al., 2008;
Barratt and Oke, 2007) have attempted to explain SCI, IS and SCV with a number of theories
such RBV, NT, TCA and TCE.
Mahadevan (2013) found that SCI, IS and SCV can be explained using RBV, TCA, TCE
and NT, further found a number of researchers have postulated that SCI, IS and SCV can be
described using only the RBV. The author argues that this view can also be applied to the
reverse SC.
In synthesising the discussions surrounding the theoretical perspectives of RL and SCI,
IS and SCV, the author argues there is a commonality between those two areas based on the
argument that RBV supports both those areas.
Collaborative practices in a SC
There is a plethora of discussions with regards to the collaborative practices in the
forward SC. Researchers (Mahadevan, 2013; Chapman et al., 2007; Bowersox et al., 2007;
Wisner et al., 2005) have discussed the collaborative practices in a forward SC. Moreover,
SCI has been continuously evolving ever since its recognition (Chapman et al., 2007). In
addition, Mahadevan (2013)’s research in collaborative practices has pointed out that there
are five levels of SCI, IS and SCV, Mahadevan (2013) also developed the “perceived levels”
of the core variables with five categories based on the work of the researchers (Igbaria,
1993; Chapman et al., 2007; Fawcett et al., 2009; Jeyaraj et al., 2006; Naim et al., 2006).
Mahadevan (2013)’s research work also pointed out that SCI must take place before SCV
and IS can occur in a collaborative forward SC. Thus, this ideology can be applied to the
RL operations or RC in organisations.
Current state of reverse SC
Govindan and Soleimani (2017)’s research on developing RL and closed-loop SCs in both
developed and developing industries is accepted as a vital need in our societies.
In the context of the broader SC, RL is defined as the process of planning, implementing
and controlling the efficient, cost effective flow of raw materials, in-process inventory,
finished goods and related information from the point of origin to the point of consumption
for the purpose of conforming to customer requirements (Waites, 2015; Khor et al., 2016;
Rogers and Tibben-Lembke, 1998). The difference between RL and forward logistics is that
all these activities operate in the reverse. Moreover, the process of planning, implementing
and controlling the flow of raw materials, in-process inventory, finished goods and related
information starts at the point of consumption and ends at the point of origin (manufacturer)
for the purpose of recapturing value or proper disposal (Rogers and Tibben-Lembke, 1998;
Huscroft et al., 2013). Thus, traditional SOP (sales and operations planning) processes can be
applied to the products returned. However, forecasting the rates of product returns can be
very complex.
In recent years, RL has become more prominent in both the business community and
academia, spanning such diverse areas as recycling, remanufacturing, information
technology, warehousing, operations and environmental sustainability, among others
(Dowlatshahi, 2010, 2012; Hazen, 2011; Lee et al., 2009; Pokharel and Mutha, 2009;
Venkatesh, 2010). Abraham (2011) noted that most research on RL is focussed on industries
such as automobiles, metal scraps, sales packaging materials, waste paper recycling as
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reported by Stock (1992) and Flapper and Ron (1999) and in the apparel industry by Tibben-
Lembke et al. (2002) and Svensson (2007).
Overall, RL is driven by factors such as environmental legislations (Lau and Wang, 2009;
Nnorom and Osibanjo, 2008), extended producer responsibility (EPR) (Khetriwal et al., 2009),
economics (Liu et al., 2008) and improved customer service (Wu and Cheng, 2006). In taking
a proactive approach, Lau and Wang (2009) found RL is a part of the company’s long-term
strategy to gain competitive advantage over its competitors.
In recent times, the online shopping sector has grown in leaps and bounds ( Jayant et al.,
2012a, b). Furthermore, the retail RL has emerged as a key management issue within the
field of SCM (Bernon et al., 2011) and has direct impact on the bottom line of organisation
(Stock, 1998; Mason, 2002). The return rates can vary between 5 and 20 per cent (Daugherty
et al., 2001) up to around 50 per cent in some sectors (Rogers et al., 2002; Prahinski and
Kocabasoglu, 2006). It is also noted that online shoppers, typically return items such as
papers and bottles where the return rates are very high ( Jayant et al., 2012a, b). In managing,
such high rates of return the author argues that RL would attract a lot of discussions around
the notion of RC with SCV and IS to meet customer service. However, it appears that
researchers have inadequately discussed frameworks for RL incorporating IS and SCV.
Most organisations would have an overlap of the “products returns loop”(Figure 1) and
the “supply chain loop”(Figure 2) (Rogers and Tibben-Lembke, 1998). Furthermore, most
organisations have processes and systems in place to manage the returns of its products
EXTENDED
PRODUCER
RESPONSIBILITY
SUPPLY
CHAIN
LOOP
ECONOMICS LEGISLATION
Figure 1.
Supply chain loop
DISTRIBUTION
PRODUCT
RETURNS
LOOP
CUSTOMER MANUFACTURING
Figure 2.
Product returns loop
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from its end users. However, over time, RL has metamorphasised from the basic
refurbishing, recycling, remanufacturing and meeting EPR guidelines (Lau and Wang,
2009). According to Govindan et al. (2015), there are a total of 382 published papers between
January 2007 and March 2013 in the area of RL and close loop SC. Govindan et al. (2015)
further noted that based on environmental, legal, social and economic factors, RL and
closed-loop SC issues have attracted attention among both academia and practitioners.
The author noted that there are the number of discussions that connects RL with IS.
Who studied related RL activities of nearly 600 US auto companies, found that good IS could
not only enhance the transparency of the entire SC, but also provide a solid basis for
cooperation, thereby, it could help reducing costs and improving customer satisfaction.
Meanwhile, Janse et al. (2010) developed a diagnostics tool for assessing the RL
improvement in the CE industry. Shulman et al. (2010) developed an analytical model to
manage product returns in the CE industry. Zaarour et al. (2014) focuses on discrete and
continuous cases of collection periods for product returns in the sustainable SC: their model
allows the decision maker to make trade-offs between inventory carrying costs and shipping
costs. The author argues that researchers have connected RL with IS.
Thus, it is evident that researchers have extensively researched RL with IS and CLSC.
However, based on Govindan et al. (2015)’s research, the future direction of RL is very open:
their paper suggests that there is a need for different levels of decision making and defining
new variables for future opportunities.
Thus, the author argues the need for collaborative SC practices based on Mahadevan’s
(2013) research on SCI, SCV and IS in RL for effective planning. Hence, there is a need for
further investigations.
The next section reviews the operational aspects of RL.
Operational aspects of RL
The collaboration with SC partners is important in both the forward and reverse SC
(Olorunniwo and Li, 2010). Furthermore, what makes a forward SC successful is the
collaboration, SCV and trust of the various entities in the chain (Olorunniwo and Li, 2010).
This is also true for the reverse SC, especially since RL process is also heavily demand
driven, where the downstream customers make the final decisions in orders and returns
(Olorunniwo and Li, 2010). Simultaneously, the challenges of the closed-loop SC activities
are driven by uncertain forward flows of SC and uncertain returns of backward flows of
SC (Kaya et al., 2014). Meanwhile, Gaur et al. (2017) noted that closed-loop supply chain
management is considered as a strategic response to the call for corporate sustainability.
Moubed and Mehrjerdi (2014), in their conceptual paper, highlighted that collaboration as
a key to success in SCs can be a way to decrease the costs and make reverse SCs
economically attractive. In addition, organisations are attempting to reuse, remanufacture
and recycle end of life products in order to reduce the negative impact on environment
(Chung and Wee, 2008).
In the context of operational planning, just in time ( JIT) and RL are both related in
reducing the impact on the environment, although they sometimes conflict with each other
(Chan et al., 2010). These researchers further added that JIT focuses on moving the materials
smoothly which require a stable demand and supply, but RL is weak in terms of predicting
how many returned products will be processed. Further by applying JIT in RL that seemed
to be separated elements in RL could have a strong relationship with each other (Chan et al.,
2010). The author argues that RBV and RDT theories support the JIT operations.
In the context of operational planning for materials disassembly, Barba-Guiteerrez and
Adenso-Diaz (2009) investigated the reverse materials requirement planning (MRP) that can
be applied to a product structure where there is a certain demand for components. Although
they are considered deterministic data, the real information about the demand of used
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components is often vague and, thus, Barba-Guiteerrez and Adenso-Diaz (2009)
reformulated the reverse MRP algorithm using a fuzzy logic approach. However, their
research does not connect the concepts of reverse MRP with SCV or IS in addressing RC. In
the context of operations management, a decentralised reverse SC is responsive for
maximising asset recovery by fast tracking returns to their ultimate disposition and
minimising the delay cost (Blackburn et al., 2004). However, in a centralised reverse SC,
activities are delegated to one organisation to handle the responsibilities of the collection,
sorting and redistribution of returned products, which are processed in centralised return
centres. The returned products arrive in bulk where they are evaluated and tested, and
based on the result of testing the appropriate disposition alternative is selected (Rogers and
Tibben-Lembke, 1999). Thus, it can be argued that RC in the centralised and decentralised
approaches could behave differently, and likewise could differ in their SCV and IS.
In the space of RL, it is of crucial importance to guarantee the agility and receptiveness of
the return system (Sarder et al., 2009). Furthermore, within a decentralised reverse SC model,
the easiness of the point for control and evaluation of the circumstances of returned
products has been shifted to the point of seller or reseller (Sarder et al., 2009). This suggests
that IS potentially takes place along the reverse SC; however, there is inadequate research
supporting that view.
In the context of operating a SC, several researchers (Coppini et al., 2010; de Souza and
D’Agosto, 2013) have measured the efficiency of forward SCs (Wangphanich et al., 2010).
However, measuring efficiencies in reverse SCs have not received considerable attention
(Wangphanich et al., 2010). Moreover, sharing data and information with retailers improves
the manufacturers order quantity decisions in multi-stage serial systems in the forward SC.
Ryu et al. (2009) have discussed about demand uncertainty faced by the supplier and further
by sharing point-of-sales demand enables the manufacturer to improve its forecast
accuracy. This argument can be extended to Wangphanich et al. (2010)’s view of the
bullwhip effect of decentralised decision leading to amplify, delay and distort demand
information moving upstream in a make-to-stock forward SC. Further, sharing demand and
or inventory data with retailers can improve the manufacturer’s order quantity decisions in
multi-stage serial systems because knowledge asset specificity reduces the demand
uncertainty faced by the supplier which can be argued that IS is important in a RC but has
not been researched adequately.
Strategies, systems and tools for RC
Mahadevan (2013) points out that the levels of SCI, IS and SCV, strategies, tools, systems
and processes has an impact on the collaborative practices in the forward SC. This view can
be extended to the reverse SC, which justifies the purpose of this section.
The RL strategy is of critical importance in managing the reverse direction in SCs from
consumer to producer (Dowlatshahi, 2005; Autry, 2005). Given the return rate of products
are hard to predict, therefore, an RL strategy is needed to dictate return policies and
procedures and integrate them with forward logistics operations is needed (Vlachos, 2014).
Mahazir et al. (2011) explored the relationship between push-pull manufacturing strategies
and RL strategies. Further, Mahazir et al. (2011) used the link created by Decroix et al.
(2009) in the management of assembled to order systems with product returns in which
inventory is kept at the component level and finished products are assembled in response
to customer demands. Moreover, tracking product based return information provides less
value than tracking demand-based information (Mahazir et al., 2011). Therefore, Mahazir
et al. (2011) has taken into account the characteristics of the push-pull systems in their RL
planning strategies.
In managing the certainty of demand and returned products, different approaches
including stochastic optimisation (SOA) orrobust optimisation (ROA) can be used in a reverse
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SC (Kaya et al., 2014). Further, Olorunniwo and Li (2010) noted in their research of RL that
three distinct dimensions of information systems support in RL (Daugherty et al., 2002):
capability, compatibility and technologies. In addition, one of the most serious problems that
organisations face in the execution of a RL operation is the lack of good information systems
(Rogers and Tibben-Lembke, 1998). In the context of systems, it is apparent that very few
firms have successfully automated the information flow surrounding the return process and
further research indicates that there are no RL information systems that are commercially
available in the market place to optimise RL operations (Daugherty et al., 2002), thus, the
justification to develop a framework to support RC.
Overall, it is apparent that researchers have discussed systems, strategies, techniques such
as JIT, IS, and reverse MRP for the RL in an ad hoc fashion. However, there is an apparent lack
of research that addresses collaboration in reverse: a framework that supports SCI, IS and
SCV for RL and deployment of systems, strategies and tools in the RL operations. According
to Morgan et al. (2016), by means of collaboration of firms can improve their ability to handle
returns, but this research introduces IT as providing a moderating influence over the impact
of collaboration in the advancement of an RL competency.
Moreover, very few frameworks and performance measures have been developed to
evaluate the RL performance (Shaikh and Abdul‐Kader, 2012). Thus, this gap justifies the
need for a framework that will incorporate the various tools (such as ERP) and its functions
to manage the RC. However, before proposing such a framework it is necessary to first
identify a research methodology.
Methodology
The literature review pointed out that there is an inadequate discussion about a framework
that connects individual areas of planning (such as MRP and WMS), tools, techniques,
systems strategies and SCI, IS and SCV in RC. It is apparent that researchers have
attempted to connect RL with ERP, WMS and with strategies in an ad hoc fashion. Thus,
there is a need to conceptualise a framework that will incorporate tools, strategies, systems
and planning methodologies to manage RL operations. The methodology used in this
research paper is twofold. First, the concept mapping is used which provides a useful
framework to organise and represent knowledge on a topic (Novak and Canas, 2008).
Mahadevan (2017) used concept mapping to connect the established relationship between
culture, leadership and organisation learning. Similarly, the extension of the work of other
researchers in SC to develop a framework that connects tools, techniques, systems and RL
processes in this research. Second, the deductive approach to connect industry data to the
application of the framework.
The proposed framework will be referred to as the “Reverse Collaboration Framework”
or RCF and is based on theoretical perspectives discussed earlier, integration of systems and
tools applied in forward SC and strategies. Based on Mahadevan (2013)’s research, it is
noted that SCI, IS and SCV are supported by RBV.
The aim of this framework is to provide SCV and IS of the end to end processes to
practitioners in industry to manage the RC effectively. Given, it is a conceptual paper, that
there is no empirical analysis included. However, there is a need to demonstrate using the
deductive approach on how the proposed framework will impact on the time taken from
collecting the returned product to the end where it is resold or recycled. Thus, the
operational times, weights of products and product return codes are used in demonstrating
the application of the RCF.
The next two sections will present those discussions. First, the conceptualization of
RCF which is broken into the high-level framework, and the detailed framework.
Second, the application of the RCF is demonstrated by using data from an RL operations
of a CE organisation.
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Conceptualisation of the RCF
Several researchers have attempted to conceptualiseRLframeworkswithtoolssuchas
ERP to manage product returns in a collaborative SC. dos Santosa and Marins (2015)
proposed an integrated model for RL to meet the product returns requirements in an
operational manner. However, their framework did not include the integration of various
SC tools, such as ERP and WMS to manage the returns. Likewise, Sharif et al. (2012),
Rogers and Tibben-Lembke (1999) and Shaikh and Abdul Kader (2012) developed
frameworks to address different aspects of RL. Sharif et al. (2012)’s framework leveraged
the factors influencing reverse 3PL based on the information systems. Furthermore, the
response from the customer back to the originator requires accurate and real-time
management of information of shipped or returned orders (Daugherty et al., 2002;
Evangelista and Sweeney, 2006).
In synthesising the work of researchers (Sharif et al., 2012; Rogers and Tibben-Lembke,
1999; Shaikh and Abdul‐Kader, 2012) and the concepts supporting forward SC, a framework
for RC is proposed. Unlike the forward logistics system, the RL system is not driven
by a SOP process and, therefore, is not pressured by customer demands to deliver
the products on time. However, the space optimisation is an important parameter in a RL
logistics process and very often products are returned back to the manufacturer by the
reseller in batches.
The RCF is presented at a high level and a detailed level. The high-level framework
covers the schematics, whilst the detailed framework demonstrates how the schematics link
into the various systems/modules of ERP and WMS.
The tools, techniques, systems, processes and strategies deployed by organisations are
related to the SCI, IS and SCV and that it helps to minimise SC demand uncertainty
(Mahadevan, 2013). The author argues that these three fundamentals of collaborative SCM
can be mirrored in a reverse SC and be connected to the logic shown in Figures 3 and 4.
The next two sections present the high-level and detailed RC framework.
High-level framework
The high-level view of RCF demonstrates the end to end of the return process is
shown in Figure 3. The RCF incorporates the three key components that includes
receive, store and sort functions. Further, once the products are sorted, they are either
repaired, remanufactured or disposed off. The repaired products will go back into
stock, whilst the remanufactured products are returned to store and exchanged
with faulty products or used as demonstrations. The products that are dead on arrival
and those that cannot be repaired are then disassembled, segregated into the various
waste streams and recycled. Govindan et al. (2012)’s research has similar high-level
setup to manage RL.
Receive Store Sort
Dispose
Repair
Remanufacture
Demanufacture Segregation
of waste Recycle
Resell
Figure 3.
High-level view of
reverse collaboration
framework
Collaboration
in reverse
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The detailed framework
The author points out that relationships can be formed in one of two dimensions in ICT; either
vertically or horizontally (Mason et al., 2007). Similarly, Barratt (2004) presented a concept in
which he identified the four different potential relationship partners, suppliers and customers
on the vertical axis and competitors on the horizontal axis. Furthermore, one of the main
catalysts to improved vertical and horizontal relationships have been the developments inICT
which have led to the sharing of information (Mason et al., 2007) enabling the core processes
such as forecasting, production, distribution and product development to become
considerably more visible to SC partners leading to collaborative possibilities. Proposes a
real-time information driven dynamic optimisation for sustainable RL using IOT (Internet of
Things) connecting RFID, logistics and vehicles to achieve logistics services. Shulman et al.
(2010)’s reverse channel structure optimises product returns.
Based on the work of Shulman et al. (2010), Mason et al. (2007) and Barratt (2004), the
RCF is conceptualised, as shown in Figure 4, the detailed framework: an ERP system
comprising of MPS, the reverse MRP and capacity planning are integrated with a WMS and
demand planning system. By vertically integrating the individual systems as shown in
Figure 4, a JIT operation can be achieved for the product returns process based on Barba-
Guiteerrez and Adenso-Diaz (2009)’s work on reverse MRP.
Receive
Store
Sort
Dispose
Repair
Remanufacture
Demanufacture
Segregation
of waste Recycle
Demand
Planning/
Forecasting
WMS
MPS
Reverse MRP
Capacity
Planning
Capacity and
Resource Plans
Reverse logistics
Costs report
Material Output
report
Warehouse space
utilization report
Returns
Process
Integrated
Systems
Return Processes
Outputs/
Reports
Outputs and
Reports
Execution
Individual Systems
Horizontal Integration
Vertical Integration
Figure 4.
Schematic of reverse
collaboration
framework (RCF)
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The demand planning system or the forecasting model calculates the rate of return of
products from the customer based on Barba-Guiteerrez and Adenso-Diaz (2009)’s work on
reverse MRP and demand management. The RCF will also include the mathematical tool of
moving averages, and time series mathematical models that can calculate trends in the
return process: the rate of product return from customer is often unknown and is difficult to
predict (Shaikh and Abdul‐Kader, 2012). The repair model manages the repair process of the
products returned and it includes spares management and time taken to repair the faulty
products. The products after disassembly are then sorted and kept in stock based on
Shulman et al. (2010) reverse channel optimisation model. The remanufacture of the
products is the process when several faulty products are disassembled and the good parts
from these products are used to produce one good working product. The remanufacture
model links up to the bill of material (BOM) structure of each product.
Samaranayake’s (2000) unitary structuring technique allows the integration in the RCF
that includes planning, control and execution of a number of strategies. Further, Mahadevan’s
(2013) research has discussed about the integration of number of strategies, systems and
processes ina collaborativeSC. The integration of systems allows the process of simultaneous
planning and forward planning for the RCF and based on the work of Samaranayake’s(2009)
unitary structuring technique that has following relationships: parent –component,
component–component and Network link (activity –precedence). At the same time, using the
above relationships, the functions in the product return process are linked horizontally to the
integrated systems to achieve a JIT operation. The linkage to systems of each function in the
product return process may be described in Table I and Figure 4.
Table I provides a summary of the vertical and horizontal integration processes and
systems. It also identifies the links between the individual processes in a vertical fashion
and, at the same time, the links between the systems with the application of the unitary
structuring technique that provides a robust framework.
Thus, by integrating the various systems, ERP and WMS, and linking the high-level RL
functions that include receive, store, sort, repair, demanufacture, dispose and recycle, a JIT
operation through the integration process can be expected from the proposed RCF (as
shown in Figure 3) leveraging Shulman et al.’s (2010) work. As the integration is based on
the unitary structuring (Samaranayake, 2009), the RCF will be able to carry out forward and
simultaneous planning which will enable a streamlined system that allows organisations to
reduce their overall cycle times in the return process. In addition to providing organisations
Functions in the return
process
Horizontal and vertical integration –linking of product return processes to the
disparate systems
Receive This function to be linked to the demand planning and WMS. The demand
planning (DP) provides the forecasting mechanism while the WMS system will
calculate the space required for of the returned products
Store WMS system calculates the storage costs and how much of the returned products
can be stored for a given period of time
Sort The MPS/MRP module categorise, calculate and sort into the appropriate work
buckets which products are remanufactured, repaired or de-manufactured
Remanufacture The capacity-planning module will calculate the resources, time required or labour
capacity to remanufacture the returned products
Repair The capacity-planning module will calculate the resources and time required to repair
the returned products and update WMS module updated for floor space optimisation
Demanufacture The capacity-planning module will calculate the resources, time required or labour
capacity to demanufacture the returned products
Dispose The MRP module through its linkage with the BOM structure will calculate the
quantity of material from each waste stream that will be recycled
Table I.
Horizontal and
vertical integration of
process to the systems
Collaboration
in reverse
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a system that will enable a streamlined RL planning process, the RCF will also provide the
users’reports on warehouse space utilisation, material and RL costs as indicated in Figure 4.
In synthesising the findings from the literature on the CLSC models and the work of
other researchers, the author argues that RCF is next level of CLSC based on Govindan
et al.’s (2015) research which points out RL demands different levels of decision making in
the future. Thus, there is value in using RCF in relation to the other models.
Application of RCF
The RCF will enable organisations to execute the reverse SC in a collaborative fashion.
Further using the data from a global CE business, namely, Caycon (fictitious name), which
imports CE products from Japan and sells within Australia to demonstrate the RCF shown
in Figure 3. The products sold by Caycon include printers, scanners, multi-function devices,
digital cameras, photocopiers and consumables. Caycon is an AUD $2.0bn business in
Australia, and has an approximate 4 per cent return of products.
The SC partners involved in the return process at Caycon are shown in Figure 5. There
are 200 major resellers Australia wide from which Caycon can expect to receive the return of
products back to its National Distribution Centre (NDC) in Sydney transported by five major
organisations. The Product Marketing managers authorises the returns by providing a
return material authorisation (RMA) and issues number to NDC.
In the current state, the consumer protection acts in Australia allow the customers to
return purchased goods back to the reseller for various reasons within 14 days.
Furthermore, it will take at least 14 days for the product to get back to NDC at Caycon from
the reseller and in addition it takes the NDC at least 10 days to sort out the category of each
product (repair, or refurbish or recycle). Once this process is completed, it will take five days
to disassemble a batch of returned products or five days to repair the product and return it
to the stock. Further, it will take the NDC two days to prepare for shipment to the recycler,
and, further, it takes three days to be transported to the recycler.
Caycon can expect to receive at the NDC from all over Australia approximately 5 tonnes
of CE products that are to be written-off each month. These products are written-off due to
obsolesce or faults. The written-off products are disassembled into various waste streams
that are then sent to be recycled at a facility offsite. It takes approximately seven working
days with six casual staff working 8 h to disassemble the 5 tonnes of written-off products:
68 man-hours to dissemble 1 tonne of the written-off product.
Table II provides a breakdown (BOM) of material and its weights derived through
disassembly process for an individual product. The total weight for an individual product
can range from 2 to 150 kg. The standard waste derived from disassembling these products
TRANSPORTERS
RESELLERS
CUSTOMERS
NATIONAL
DISTRIBUTION
CENTRE
RECYCLING
ORGANIZATION
200 MAJOR
CUSTOMERS
5 MAJOR
TRANSPORTERS
RESELLERS
CUSTOMERS
RESELLERS
CUSTOMERS
RESELLERS
CUSTOMERS
TRANSPORTERS
TRANSPORTERS
TRANSPORTERS
Figure 5.
Supply Chain partners
in the product return
process at Caycon
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includes: plastic, steel, aluminium and glass. The wiring and electrical components when
disassembled will mainly produce copper.
The reasons for product returns at Caycon are shown in Table III. Further products
returned with codes 91, 92, 93 and 94 can be resold as there is a high probability it may not
have faults. In addition, most of the Status “2”returns are generated from the retailer or
transport organisations, and Status “4”is generated from the product failure.
The data from Caycon include the times, resources and capacity in the various functions
of the RL operations. In the “as is”situation (Table IV ), using the disparate systems, the
times for each function can be added up which will provide the total time taken for the end to
end of the return process. However, there will be a time lag incurred due to delays when
information from one system is manually loaded into another by an operator during the
return process. In addition, if there are bottlenecks in the return process, this will further
increase in the total time of the return process.
The application of the RCF is to demonstrate that integration of the disparate systems
can help in the reduction of the total time of the process, identify and plan for bottlenecks in
the RL process.
The “As-Is”and the “To-be”deployment of the systems at the various stages of the return
process and the individual time each process will take are shown in Table IV. The total time
taken for the end to end of the RL process is 55 days and anadditional 12 days for the manual
data entry at the various stages, thus making it a total of 65 days. Thus, integrating the
individual systems and linking the functions would provide Caycon SCV of the returns
process and could potentially identify some ofthe following: time the returned products spend
in the RL process; possible bottlenecks in the system; resource allocation to the various
functions; and how the framework will behave for different demand level or return rates.
In the “To-Be”scenario, the total end to end RL time is reduced to 52 days from 65 days
shown in Table IV. The reduction in time is as result of the RCF enabling simultaneous
planning and data entry time. The vertical and horizontal integration of processes and
Bill of material Weight in KG for one product
Plastic/plastic mixture 25.06
Steel 89.00
Aluminum 1.8
Lamp/fluorescent tube 0.3
Wiring 1.53
Glass 0.25
Electrical components 3.4
Copper 0.5
Printed circuit board 11
Total weight of salvaged material 132.84
Table II.
Material breakdown
of a standard
electronic product
Reason for return Code Reason for return Code Reason for return Code
No power 1 Box damage 6 Over supply 92
Display of function error 2 Damaged parts 7 Cancelled order 93
Communication error 3 Missing items 8 Customer changed
mind
94
Printing, paper feed, scanner fault 4 Other faults/machine errors 9
Intermittent fault 5 Incorrect goods or duplicate
order keyed
91 Table III.
Return categories at
Caycon
Collaboration
in reverse
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Systems
in use Activities
Stages in
product return
Time
(days) for
activities
as-is
Time
(days)
for data
entry
as-is Activities
Stages in
product return
Time (days)
for activities
and data
entry to-be
DP Consumer purchases products Consumer purchases products
Manual data entry for the
products sold to the customer
2 Single Manual data entry for products sold to the
customer
2
The consumer can return a
product within 14 days of
purchase back to the reseller
14 The consumer can return a product within 14 days
of purchase
14
WMS It takes 14 days for the product
get back to the NDC from the
reseller
Receive 14 It takes 14 days for the product get back to the
reseller
Receive 14
The NDC receives and moves
product from receiving dock to
inspection area
2 The NDC receives and moves product from
receiving dock
2
Manual data entry for the
products has been moved into
the inspection area
2 No entry required 0
MPS NDC sort out the category of the
returned batch of product, i.e. to
repair/refurbish/recycle
Sort and
storage
10 NDC sort out the category of the returned batch
of product
Sort and
storage
10
Manual data entry for each
category of the products
2 No entry required 0
MRP/CP 5 days to disassemble a batch of
returned products/5 days to
repair the product and return it
to the stock
Demanufacture
repair
5 The WMS and MPS can predetermine the space
required in the warehouse, the capacity and time to
disassemble, repair and return to stock or dispose
a batch of products
Demanufacture
repair
5
Manual data entry 2 No entry required 0
MRP/CP 5 days to recycle disassemble
and recycle a batch of products
5
(continued )
Table IV.
“As is”and “To be”
times and activities
for product returns
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Systems
in use Activities
Stages in
product return
Time
(days) for
activities
as-is
Time
(days)
for data
entry
as-is Activities
Stages in
product return
Time (days)
for activities
and data
entry to-be
MRP/CP 2 days to prepare for shipment
to the recycler for written-off
products
Storage and
disposal
2 2 days to prepare for shipment to the recycler for
the written-off products
Storage and
disposal
2
PMS The travelling time door to door
is 3 days from the NDC to the
recycler
3 Travelling time door to door is 3 days from the
NDC to the recycler
3
Manual data entry 2 No entry required 0
Number of days taken for the
end to end cycle
53 12 52
Total number of days (activities
and data entry)
65 52
Table IV.
Collaboration
in reverse
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systems, as indicated in the RCF (Figure 4), will, in addition, provide the weights of the
products throughout the RL process and capacity in man-hours to perform each function:
de-manufacturing times.
In the RCF, returns are identified by their individual product numbers, and the creation
of an RMA number that will immediately identify the quantities of products with product
numbers which will be returned. The product numbers and their quantities will be
immediately reflected in the RCF in which the individual systems can calculate and forecast
the resources required as demonstrated in Figure 3. Once the products are physically
received in the warehouse, the planning scenarios will be firmed up as the exact numbers of
products that are to be remanufactured, repaired and recycled will be known, and therefore
the capacity to carry out the various operations can be determined. Hence, the exact amount
of man-hours of labour required can be identified as there will be no further manual loading
of data upon the receipt of the products at the warehouse. Thus, the total time the products
spent in the return process will be less than that of the sum of the times in the individual
processes due to simultaneous planning. Moreover, the bottlenecks, constraints,
warehousing and recycling requirements can be determined with the RCF.
Thus, overall by using simultaneous planning aided SCV and IS, the number of man-hours
taken in the RL operations at Caycon has been reduced from 65 to 52 days. This includes the
reduction in data loading time of ten days and three days saved in the capacity-planning process
by using the various systems. The author points out a relationship between RL and IS, and
between the various SC partners (transporter, recycling organisation) in the improvement of the
return process.
Conclusion
The RCF enables seamless planning in a reverse SC operations with one single data entry
supported by RBV theory, hence real-time data visibility and reduction in processing time.
The RCF is the extension of the ad hoc work of previous researchers in the space of RL,
collaboration, simultaneous planning, SC management, framework development,
performance measurement, strategy and operations. RCF provides SCV and IS for SC
partners along the reverse operations, and enables collaboration, sales and operations
planning and integrated business planning in the reverse. Those organisations that need to
manage product stewardship, engage in circular economy and resources recovery can use
the RCF to plan their reverse SOP effectively to ensure sustainability. The research on the
RCF can be commercialised and can be integrated to an existing ERP system.
Contribution to research
This research has identified a RCF leveraging ERP and WMS systems, operational strategies,
reverse MRP, simulation exercise or models and tools (JIT and SOP) to demonstrate material
flow in a reverse SC. The author has connected several components from each of those
systems, strategies, models and tools to conceptualise with the horizontal and vertical
integration of information systems and processes, addressing the gaps of other researchers in
RL enabling SCI, IS and SCV in RL. In the theoretical aspects of this research, RBV theory
supports the RL operations. Furthermore, the RCF can be connected to the forward logistics
with IOT based on work on dynamic optimisation for RL.
Limitations
The RCF has been demonstrated with using the data from a CE organisation based in
Australia. Literature points out that there are a number of different mathematical models for
RL developed across a number of industries. Thus, at this stage it is not clear if the RCF will
work effectively in other industries such as the newspaper, plastic bottles and online
retailers where product returns are high.
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Future research
Future researchers can incorporate the levels of SCI, SCV and IS developed by Mahadevan
(2017) in the RCF. This will enable managers to effectively plan their product returns:
providing varying levels of visibility in their reverse sales and operations planning.
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Corresponding author
Kumaraguru Mahadevan can be contacted at: K.Mahadevan@cqu.edu.au
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