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BIJ
18,6 Supply chain collaboration
performance metrics:
a conceptual framework
856
Usha Ramanathan
Newcastle Business School, Northumbria University, Newcastle upon Tyne, UK
Angappa Gunasekaran
Department of Decision and Information Sciences, Charlton College of Business,
University of Massachusetts, North Dartmouth, Massachusetts, USA, and
Nachiappan Subramanian
Department of Mechanical Engineering, Thiagarajar College of Engineering,
Madurai, India
Abstract
Purpose – Successful implementation of supply chain collaboration (SCC) by Wal-Mart has
encouraged many manufacturing companies, such as Procter & Gamble, Hewlett-Packard Co, and West
Marine Products Inc., to initiate collaboration. Subsequently, collaboration between suppliers and
retailers has become a common practice in many recent supply chains. However, measuring the benefits
of collaboration is still a big challenge. Based on supply chain literature and practice, this paper aims to
propose a conceptual framework and a standard set of metrics to evaluate the performance of SCC.
Design/methodology/approach – The authors discuss two case studies to validate the proposed
model. The case study discussions are appropriate to understand the usage of different performance
metrics in initial and advanced stages of collaboration.
Findings – From the case study it is recognized that the collaborating members in the supply chain
are not able to visualise all possible benefits of collaboration. To surmount this issue, the paper
proposes a framework to study the performance of companies involved in initial and advanced stages
of collaboration.
Originality/value – The classification suggested in this paper on different stages of collaboration
and related metrics can guide researchers and practitioners in manufacturing companies to evaluate
the performance of SCC.
Keywords Collaboration, Performance metrics, Supply chain, Supply chain management,
Manufacturing industries
Paper type Research paper
1. Introduction
Supply chain involves raw material and component suppliers, manufacturers, distributors,
and retailers until the finished products reach end customers. It has been generally agreed
that the performance of the entire supply chain could be improved through collaboration
(Barratt and Oliveira, 2001; Seifert, 2003). The literature reveals that businesses have been
Benchmarking: An International collaborating in general for several decades in many different forms for varied purposes.
Journal Some of the purposes of collaboration are to improve overall business performance,
Vol. 18 No. 6, 2011
pp. 856-872 reduce cost, increase profit, and improve forecast accuracy (McIvor et al., 2003; McCarthy
q Emerald Group Publishing Limited and Golicic, 2002; Matchette and Seikel, 2004). Lucrative benefits of collaboration can
1463-5771
DOI 10.1108/14635771111180734 encourage many supply chain members to initiate the process of collaboration.

2. In general, businesses with similar objectives work closer to achieve excellence in common Supply chain
supply chain processes such as planning, forecasting, and replenishment. The extent and performance
intensity of collaboration may vary greatly based on business objectives, which in turn
decide the success of supply chain collaboration (SCC) (Larsen et al., 2003, ECR Europe, metrics
2002). Owing to cost involved in initiating collaboration, sometimes SCC will be more
viable to suppliers than buyers (Chen et al., 2007) or more viable to buyers than suppliers
(Dong and Xu, 2002). Hence, in the process of SCC, each business needs to weigh their 857
current scenario with past and future. This may include periodic review of performance
of collaboration using a standard set of metrics. Periodic reviews can help improve
collaboration agreement with other supply chain members regularly.
In the literature of SCC, many performance measures have been suggested including
cost, benefits such as profit, lead time, customer satisfaction, inventory, forecast accuracy,
etc. (Chang et al., 2007; Kim and Oh, 2005; Angerhofer and Angelides, 2006; Simatupang
and Sridharan, 2005). Majority of supply chain metrics in the literature are measures of
internal performance of a firm (Lambert and Pohlen, 2001; Barratt, 2004). If information on
performance of supply chain is shared with other partners, then it could possibly improve
the overall efficiency of the supply chain. Simatupang and Sridharan (2004a, b) have
proposed a collaborative performance system consisting of three cycles with respect to
collaborative enablers to improve operational performances. On reviewing the literature of
SCC, we have identified that the performance metrics for SCC were not given adequate
importance as compared to general supply chain performance.
We have also found that there is no specific set of metrics readily available to supply
chain members to measure their performance in SCC at pilot and advanced stages of
collaboration. Hence, we believe that identifying the key metrics to measure the
performance of SCC from suppliers’ or buyers’ viewpoint is indispensable. Therefore, in this
paper, we propose a conceptual framework to measure performance of SCC at initial and
advanced stages of partnership. In line with supply chain literature of collaboration and
performance measurement we have developed a conceptual model for performance metrics.
The major objective of this paper is to suggest a specific set of metrics at early and
advanced stages of collaboration. To facilitate this study, we have attempted to
understand the current status of collaboration in SC through literature review. Then we
have conducted two case studies as this approach will be appropriate to have an in-depth
knowledge on the selected cases in achieving our research objective (Yin, 1994). For case
study, we have considered two manufacturing companies at two different stages of
collaboration. One of the case study companies is practicing “collaborative planning
forecasting and replenishment (CPFR)” for the past four years, whereas the other
company is recently involved in CPFR. Choice of these two cases has been instrumental
in relating our literature findings to match with initial and advanced stages of
collaboration. Further in this article SCC with respect to literature review refers to a
combination of different supply chain practices, whereas in the context of case study,
SCC is specific to CPFR practice.
This paper starts with an introduction to role of collaboration in supply chain and a
review of literature related to performance metrics of SCC. Section 3 proposes various
functional drivers and enhancers for constructing SCC. This section also describes
a conceptual framework on performance metrics of SCC. Section 4 describes and
analyses the SCC at two case companies that practice collaboration at two different
stages. The paper concludes with some scope for future research.

3. BIJ 2. SCC and performance
18,6 2.1 Role of collaboration in supply chain
In order to improve supply chain processes and to gain support from other supply chain
partners, several supply chain management practices such as vendor managed
inventory (VMI), efficient consumer response (ECR), continuous replenishment (CR), and
electronic data interchange have been suggested in the literature. In VMI (developed in
858 the mid-1980s) the customer’s inventory policy and replenishment process are managed
by vendor or supplier. However, VMI’s supply chain visibility has not been found
powerful enough to avoid the bullwhip effect completely (Barratt and Oliveira, 2001).
Here, bullwhip effect refers to amplification of demand fluctuations from downstream to
upstream in supply chain. This drawback of VMI has been successfully modified in the
later versions in different sectors and the derived versions are termed as ECR, CR, etc.
Ever increasing supply chain demands have led to the invention of CPFR (introduced in
late 1990s), another supply chain management practice, which incorporates planning,
forecasting, and replenishment under a single framework (Fliedner, 2003). CPFR has
been introduced as a pilot project between Wal-Mart and Warner-Lambert in the
mid-1990s aiming to develop a supply chain responsive to customer demand. CPFR is a
new collaborative business perspective that combines the intelligence of multiple
trading partners in the planning and fulfilment of customer demand by linking sales and
marketing information (VICS, 2002).
The CPFR framework encourages all partners to share sales, inventory, forecast, and
all related information to improve forecast accuracy (VICS, 2002). Such information
sharing believes to avoid bullwhip effect (Lee et al., 2000; Cachon and Fisher, 2000). This
information exchange is made possible through advanced technology in many retail
sectors (for example, Wal-Mart’s electronic point of sale data is made available to all its
collaborating partners). Quality of information being exchanged among SC partners’
influences the supply chain processes and forecast accuracy (Forslund and Jonsson,
2007). Some of the benefits of SCC such as cost reduction, inventory reduction and
forecast accuracy are revealed through many case studies (Smaros, 2007; Danese, 2007)
and some mathematical models (Lee et al., 2000; Aviv, 2007); but the indicators for
measuring the benefits of collaborations are not clear and precise. We have endeavoured
to group the performance metrics of SCC, identified from the literature, in the
next section.
2.2 Performance metrics for SCC
The primary objective of initiating collaboration in any supply chain is to enhance the
overall performance of supply chain and this can be achieved through the collective
effort of all supply chain members (Angerhofer and Angelides, 2006). Barratt (2004)
identified managing change, cross-functional activities, process alignment, joint
decision making, and supply chain metrics as essential elements for successful SCC.
In these five elements, the first two are related to initial front-end agreement among SC
members and their involvement in SCC. Power sharing and leadership issues are also
included in the front-end agreements. Whilst, supply chain processes and joint decision
making are commonly used in all type of SCC, the supply chain metrics are different
for inter- and intra-– organizational collaborations. Internal and logistics performance
measures are also discussed in recent literatures of SCC. In this paper, we have tried to
identify all possible performance metrics from the literature of SCC with respect

4. to different stages of collaboration. In this attempt, first we have checked the motive of Supply chain
SCC as this will indirectly indicate SC processes to be evaluated. performance
Like-minded people or businesses with similar objectives come closer to form a
group. One or more of them take a leading role in initiating formal collaboration. Then, metrics
interested supply chain members make front-end agreement (VICS, 2002). Top
management decides cross-functional activities and involvement of various
departments in collaboration at functional/operational and strategic level (Ireland and 859
Crum, 2005). Performance at this stage of collaboration is measured through operational
efficiency and risk/return ratio. Hence, business strategy is been considered as one of the
metrics as it measures the functional capability of the SC member for varying market
demand (Akkermans et al., 1999; SCOR model). Although CPFR suggests equal
opportunities to the SC members in collaboration, this is not reflected in practice. Hence,
the order of dominance and decision sharing create a win- win or win- or lose-lose
situation in SCC (Kim and Oh, 2005). Partnership revival or inclusion is considered in
case of unexpected loss/profit or reduction in profit. As various processes of supply
chain (namely planning, forecasting, production, and replenishment) have impact on
cost, profit, inventory levels, stock outs and resource measures, these measures have
been deemed important by many academics and practitioners (Angerhofer and
Angelides, 2006; Gunasekaran et al., 2001). Table I lists the measures of SC from the
literature.
Supply chain models developed after inception of CPFR incorporated some
improvement to the original CPFR framework by measuring its performance
Essential
Role of SCC elements for SCC Performance metrics Authors
Collaborative Cross-functional Business strategies (functional Akkermans et al. (1999) and
planning and activities capabilities), processes SCC (2001) – SCOR model
production, (operational efficiencies), stake
decision making holders view (risk/return ratio)
SCC leadership Order of dominance and decision Kim and Oh (2005),
and power sharing Simatupang and Sridharan
sharing (2004a,b), and Aviv (2007)
Process Cost, profit, excess inventory, Beamon (1999), Lambert
alignment stock-out, resource measure and Pohlen (2001), Dong
and Chen (2005), and
Emmet and Crocker (2006)
Information Joint decision Impact of information quality on McCarthy and Golicic
sharing, making forecasting (2002), Forslund and
forecasting Information Jonsson (2007),
decision making sharing and Raghunathan (2001), and
forecasting Chang et al. (2007)
Managing Reliability, reactivity/flexibility Forme et al. (2007),
changes Angerhofer and Angelides
(external and (2006), and Barratt and
internal) Oliveira (2001)
Replenishment, Internal and Inventory and stock position, Cachon (2001); Ettl et al. Table I.
decision making logistics stock out, lead time, internal (2000), Aviv (2007), Simchi- Supply chain
performance service rate, cross-functional Levi and Zhao (2005), and performance metric and
capability, logistics efficiency Chen and Paulraj (2004) its correlation with SCC

5. BIJ and identifying areas of improvement. Stank et al. (2001) and Rowat (2006) attempted to
18,6 relate internal and external collaboration with logistical service performance. McCarthy
and Golicic (2002) used responsiveness along with other basic measures – cost and
revenue. Chang et al. (2007) claimed that “Augmented CPFR” (an improved model with
third party information) is a better model with improved forecast accuracy and
inventory. In a recent literature on SCC, capacity utilization and supply chain flexibility
860 have also been considered as measures of performance (Angerhofer and Angelides, 2006
and Aviv, 2007).
In the literature, flexibility, and reactivity are used as synonyms to represent ability
of the supply chain in adapting to the changes (Forme et al., 2007; Angerhofer and
Angelides, 2006; Barratt and Oliveira, 2001). Normally, only the changes internal to an
organization have been considered for this purpose. Responsiveness of the SCC is
another metric that has not been discussed adequately in the literature. In recent years,
information exchange has become integral part of SCC processes and hence it also needs
to be measured periodically. Though quality of information is important (Forslund and
Jonsson, 2007; Forme et al., 2007), use of technology for improving quality of information
has not been adequately stressed in the literature. If one could measure the
responsiveness of SC on timely information (timely information to act upon), this will
measure the importance of information exchange in SCC to a larger extent.
Measures of responsiveness and flexibility can reflect a wider perspective of supply
chain performance incorporating suppliers and buyers. Hence, comprehensive view
of performance metrics of SCC need to involve all the metrics mentioned in Table I along
with a few elemental measures such as managing change (use of technology), sharing
performance metrics with customer (responsiveness), and sharing performance metrics
with suppliers (flexibility). While, flexibility measures the ability of adapting to the
changes effectively with available resources, responsiveness can measure the response
of the supply chain for any unexpected changes in demand. Responsiveness is usually
related with innovative products or products with short lead time which decides the level
of collaboration needed (Lee, 2002). Recently, many companies have started giving more
emphasis on the use of information technology and hence IT has become an integral part
of SCC (VICS, 2002). For example, use of barcode and radio frequency identification
technology in the retail sector helps to track point of sale, which in turn makes supply
chain more responsive (Ireland and Crum, 2005). Such technological advancement
makes communication between retailer and manufacturer easier. Hence, in this paper we
have included the use of technology as one of the performance metrics of SCC.
We augment the metrics suggested in the literature with three other important measures
namely flexibility responsiveness and technology in our comprehensive view of
performance metrics of SCC (Figure 1).
Applying all the above measures identified from the literature into a single model to
evaluate a SCC will be a complicate task. However, the objective of SCC and front-end
agreements between SC partners can help to decide on which measures need to be used.
To our knowledge, none of the models listed in Table I has discussed performance
metrics at different stages of SCC. Hence, in this paper, we have attempted to align all
the identified performance measures at two different stages of SCC. In this line, we have
developed a conceptual framework on performance measurement of SCC.

6. Technology Supply chain
performance
metrics
Supplier Manufacturer Retailer
Flexible Responsive
861
Cost, profit, stock-out, and resource measure , Business strategies (functional capabilities), processes
(operational efficiencies), stake holders view (risk/ return ratio), Impact of information quality on
Figure 1.
forecasting, Order of dominance and decision sharing, Inventory & stock position, stock out, lead time,
internal service rate or cross functional capability, logistics efficiency, Reliability, reactivity
Comprehensive view
of performance metrics
Metrics from the literature of SCC
3. Conceptual framework for SCC and related metrics
SCC transforms the partnership from narrower perspective of intra-organizational level
to wider perspective of inter-organizational level (Barratt, 2004). This also incorporates
all or many personnel in strategic- tactical- and operational level. Long-term business
plan is generally decided at strategic level, short-term planning and forecasting is made
at tactical level and day-to-day operations are planned and executed in operational
level. Performance measurement will be complete only if it is conducted at all these three
levels (Gunasekaran et al., 2001, 2004).
Generally, all the companies practicing SCC initially test their performance under
collaboration in a pilot stage. Successful pilot stage may facilitate in further
collaboration (Cassivi, 2006). This is evident from several cases such as Wal-Mart and
Procter and Gamble, and also through our case study analysis of two manufacturing
firms, discussed in the next section. The companies need to have different set of
performance metrics specific to their stage of collaboration. At the same time, the stage
of collaboration is decided by various elements. The elements which form the basis for
initiation of collaboration are common business objectives and supply chain processes
and can be termed as functional drivers. Other elements such as degree of involvement
(joint decision making), use of technology (managing change) and incentive sharing,
which enhance or support the collaboration can be classified as enhancers. We feel that
SCC has two distinct stages – pilot stage when the initial attempts are made to test
SCC, and advanced stage when all the partners are convinced of SCC and are fully
committed. Accordingly, the metrics to measure performance of SCC should be
different for pilot and advanced stages. Measuring functional drivers can give
comprehensive idea on performance of SCC at pilot stage. If the company had other
business goals of achieving responsive supply chain for changing demands, it might
have enhancers in collaboration and related metrics. Measuring functional drivers and
enhancers collectively will represent the performance of SCC in its advanced stage. The
essential elements of SCC suggested by Barratt (2004) serve as a backbone for
proposing this conceptual framework and related metrics.
3.1 Metrics to measure “functional drivers”
As mentioned earlier, functional drivers of SCC include business objectives and
SC processes. Business objectives, such as financial and operational, are main factors to
SCC. Supply chain members who intend to establish their business are keen in
identifying partners with similar objectives to have long-term collaboration.

7. BIJ As the first step for collaboration, the companies form a front-end agreement; this needs
18,6 to be reviewed periodically for any changes and can be measured through cost-benefit
analysis.
Supply chain processes in CPFR framework are divided into four main stages namely
planning, forecasting, production, and replenishment (VICS, 2002). But in the recent
years, handling product returns has also become one of the foremost reasons for SCC
862 (Lambert and Cooper, 2000). Hence, we have included “return” as one more stage in the
supply chain processes. These supply chain processes can be measured through
different possible measures suitable to the adopting company. Some of the suggested
metrics in the literature are capacity utilization, adherence to plan, inventory, stock-outs,
and feedback on returns (Aviv, 2007; Cachon and Fisher, 2000). The feedback from
retailers will be one of the effective measures, as it provides opportunity for
manufacturer to improve the product quality or avoid future error or improve sales
based upon feedbacks of returned items. The flexibility, which measures the efficiency
of SCC with upstream members (suppliers), can be measured through timely delivery of
raw material, availability of material at the time of production on urgent orders, and
service rate.
3.2 Metrics to measure “enhancers”
It is generally agreed that collaboration among supply chain members is built
encompassing their business objectives. When the top management support more
collaboration the company will establish collaboration with more partners and may
invest more on SCC. Hence, degree of involvement is the first enhancer of SCC. Degree to
which supply chain partners involve in collaboration is captured through investment on
collaboration and sharing decision making.
A great deal of business is based on the information sharing and proper use of data.
Accelerated information sharing among all supply chain will increase the reliability of
the order generation (VICS, 2002). Improved forecast accuracy is another motivating
factor of SCC. Achieving forecasting accuracy is mainly through information sharing
among members of SCC. Quality of information adds more value to the process of
forecasting and hence it needs to be measured periodically. Improved forecasting
accuracy will be an indicator of effective information exchange. If technology is used
for exchanging information, its efficiency can be measured through accessibility of
information by supply chain members. Based on this, any business can make decisions
on investment on technology.
Incentive sharing is another important enhancer of SCC, which attracts more
members in collaboration and hence incentive sharing agreement needs periodic
revision. Regular contacts among members of SCC and feedback on performance of
supply chain will help to revive incentive sharing agreement. Responsiveness, which
measures the efficiency of SCC with changing demand in downstream (retailers), could
be measured through product availability.
3.3 Conceptual framework for the whole SCC
Every company taking part in SCC needs to decide on the performance metrics on
functional drivers and/or enhancers to track its success. The conceptual model
developed based on the above discussions is shown in Figure 2. The desired metrics
essential for measuring SCC is listed out in Figure 2 under categories functional

8. Supply chain
Functional Drivers performance
Processes
Plan, Forecast, Produce,
Business objectives
Metrics to measure the performance of SCC metrics
Financial & Operational
Replenish and Return Measuring Functional drivers
- Front end agreements (mutual agreements)
- Business strategy (Profit and loss)
Initiate Collaboration (Initial stage) - Processes (production, forecast accuracy,
replenishment and handling of returned products) 863
- Capacity utilization (production efficiency)
- Adherence to plan (plan vs. actual)
- Availability of material (resource planning
efficiency)
Manufacturer - Inventory (Stock outs /Excess)
(Evaluator) - Service rate (Product lead time measure)
Strategic - Feedback
Supplier Retailer Measuring Enhancers
Tactical
- Decision making sharing (involvement of partners,
Operational involvement in information exchange and
forecasting)
- Investment on communication technologies (support
and financial measure)
- Use of technology (communication, information
exchange & forecasting)
Support Collaboration (advanced stage) - Information sharing &communication (Frequency and
access)
Degree of Information sharing, forecasting - Information quality (accuracy)
Incentive - Forecasting
involvement and technology
- Product availability
- Feedback
Enhancers
Overall effectiveness of SCC
Responsiveness + Flexibility + Technical excellence Figure 2.
Proposed metrics
for SCC framework
drivers and enhancers. In addition, measures on responsiveness, flexibility and
technical excellence can help the company to assess the overall effectiveness of SCC.
Based on this assessment, further changes to the collaboration can be incorporated if
needed.
4. SCC in practice – case study observations
Collaboration and its suitability with the retail sector have been rigorously examined
by numerous researchers (Smaros, 2007; Holweg et al., 2005; Rowat, 2006). In the recent
literature, design for SCC is suggested by Simatupang and Sridharan (2008). But,
research on performance metrics suitable to manufacturing companies is still in its
infancy. This paper studies the performance metrics used in a packaging firm at their
initial (pilot) stages of collaboration. Case of textile company has been used to analyze
the use of metrics in advanced stage of collaboration. The choice of a case is important
as it explores the research question (Eisenhardt, 1989; Yin, 1994) namely the metrics to
measure performance of SCC.
In this research, case studies aim to understand SCC and performance metrics used
at various stages of collaboration. Case 1 is a packaging firm has been involved in SCC
with their downstream members for the past 18 months to control inventory and to
avoid obsolescence. Case 2 is a textile company initiated collaboration before four years
and has well established SCC with their buyers mainly for promotional sales and
forecasting. Although, both these companies are in SCC, the level of collaboration is
different and hence their practice on performance measurement is also different.

9. BIJ We have conducted case studies in two stages. The first stage has been intended to
18,6 study existing SCC and assess its reliability. The second stage of case study is mainly
for the purpose of understanding the metrics used in SCC. Interviews and frequent
visits are the methods adapted to perform the above case studies. Interviews have been
conducted with dependable officers responsible for collaborative relationship among
partners, information exchange, forecasting, and operations. A few interviews have
864 also been conducted with decision makers. The first author has visited the company
several times in the span of two years in order to observe the changes in current
collaborative arrangement in comparison with the sales and order data. Initially, Nvivo
tool has been used to analyse the interview transcripts. Brief description of the case
companies will help readers to understand the SCC in practice.
4.1 Description of case 1
Company background. The packaging company (Case 1) considered for this case was
established in 1966. In its early years, Case 1 produced waterproof packaging materials
and gradually expanded its production base to produce flexible intermediate bulk
containers (FIBC). In the local market, Case 1 is the first manufacturer introducing
FIBC and has nearly 50 percent market share. After 1996, the company has started to
export its products to many international companies in petrochemical industry,
mineral industry, dyes industry, and selected products in pharmaceutical industry.
Case 1 has maintained quality and durability of the packaging material by treating it
with ultra violet (UV) radiation. The company’s global operation requires them to have
partnership with their supply chain partners to survive in the competitive international
business.
Supply chain at Case 1 before collaboration. Raw material suppliers to packaging
industry are available in plenty and hence competition to become a partner in supply
chain is very fierce. Though many raw material suppliers are available, the company
prefers to have collaboration with a few local markets. In this case,
supplier-manufacturer collaboration is simple and straight forward.
As the company has been maintaining a good relationship with their clients, supply
chain members exchanged information related to inventory and demand. The
company builds their demand forecast based on those information from SC members
and resulted in poor forecast accuracy. This has promoted the company to focus on
information accuracy and related problems. Owing to lack of formal agreement among
SC members, the information accuracy has always been uncertain. Without clear vision
on incentive of SCC, no supply chain member has been committed for success of supply
chain performance. As a consequence, forecasted demand from downstream member is
25-30 percent higher than the actual orders. The company essentially produces to
order, though it also produces a limited amount to stock. About 50 percent of the basic
common production process used to be completed based on initial forecast made
through available information. As a result, the company has been facing a problem of
excess inventory of finished and unfinished products. Recently, Case 1 has realized the
importance of collaborative agreement to improve the information quality and
accuracy. New government environmental regulation has forced the company to make
use of raw materials and UV treatment of bags. This has necessitated the company to
upgrade their products or to sell their product quickly before implementation of new
sales regulation. Ultimately the company has incurred a loss at the end of 2006.

10. As-is scenario. In the beginning of the year 2007, the top management of the Supply chain
company engaged in formal supply collaboration to revive its performance. The performance
company has adopted vertical collaboration with suppliers and customers as part of
their external collaboration and also has maintained internal collaboration among metrics
various departments. The company has adopted a transparent profit sharing policy for
SCC and also assured timely delivery for their clients. These two features of SCC have
helped them to get committed involvement of other members. Decision on profit 865
sharing has been bound to the duration of collaboration and proportion of share in SC
activities. Front-end agreement among SC partners has clearly mentioned the role of
each member in SCC. The company has incorporated 40 percent of their clients in
collaboration in its pilot stage of SCC. Partners with similar business objectives and
with further interest in future collaboration have worked together. At the same time,
the company has not invested much on information technology in its pilot stage of SCC.
Most of Case 1’s communication with their customers has been carried out through
iMail Server (iMail is one of the advanced recent communication technology that works
well even in the presence of other servers such as e-mail server, SMTP, POP3, and
IMAP). The company has used information from other partners to make their demand
forecast. This has been fundamental in minimizing forecast errors. Periodically, the
company has measured performance of collaboration through simple measures such
as cost, profit, timely delivery of goods to customers, inventory level, and forecast
accuracy. The above given information on various performance metrics of SCC in
Case 1 and their purposes are further detailed in Table II.
At the end of the next 12 months (end of 2007), the company has achieved 20 percent
inventory reduction and 10 percent overall cost reduction. Improved forecast accuracy
has helped the company on production plan and expansion. Case 1 has reduced their
safety stock level to 10 percent of expected demand as against its earlier 30 percent.
4.2 Description of case 2
Company background. Case 2 is a leading textile manufacturing and exporting firm
located in the main lands of Asia. Case 2 exports to various countries across the globe.
Customized products are embroidered dress materials with exclusive design, and
made-to-measure finished cushions, pillows, and curtains. Standard products are
embroidered material with multiple repeated designs and curtain materials. The
company generally follows make-to-order strategy for its exports and local business of
customized products. A small part of the business (standard products to local markets)
follows make-to-stock strategy with very limited stock that minimizes inventory and
obsolescence cost. Like Case 1, Case 2 also has a strong uninterrupted supplier base for
raw materials. In order to compete with ever growing challenges, the company has
been involved in SCC with other downstream members.
Supply chain at Case 2 at initial stage of collaboration. Like any other company, Case 2
has intended to improve inventory and reduce obsolescence and hence it has involved
in SCC with their suppliers and buyers. Its collaboration with suppliers signifies
a confirmation of availability of material/resources at the time of production. Initial
collaboration with buyers has been very successful to the company in terms of profit.
Case 2 measures their performance every month and analyzes the area of improvement.
Accordingly, at the end of every year (for the first two years) the company revives their
front-end agreement with customers. Except the measure of handling product returns,

11. BIJ
Metrics in use
18,6 Purpose Desired metrics Case 1 Case 2
Initial stage
Initiate and maintain collaboration Front-end agreements x x
Business objective (financial) Business strategy (profit or cost) x x
866 Supply chain process and business Processes
processes On time production – x
Forecast accuracy x x
Timely replenishment x x
Handling product returns – –
Production process Capacity utilization – x
Planning execution Adherence to plan – x
Supplier collaboration Availability of material on time – x
Inventory control Inventory (stock outs/excess) x x
Production/replenishment Service rate – x
Improvement of SCC Feedback – x
Advanced stage
Investment decision in Technology Use of technology –
Future involvement in Decision making sharing
collaboration x
Investment in the state-of-the-art Investment on technologies (IT and
technologies communication) x
Improve SC processes and Information sharing No
collaboration collaboration x
Improve forecast accuracy and SC Information quality (accuracy)
processes –
Table II. Improve forecast accuracy Forecasting x
Purpose of desired Improve inventory position Product availability x
metrics in SCC for case Improvement of SCC Feedback x
companies Efficient use of SCC Managing change of whole SCC x
all the other measures suggested in our conceptual framework have been measured by the
company during their initial period of SCC. On success of initial SCC, the company
intends – to involve in further collaboration with long-term agreements and to engage in
advanced collaboration.
As-is scenario of Case 2. In the advanced collaboration, the company involves all
SCC members into information sharing and collaborative forecasting. Transparent and
timely information has helped them to arrive at a single forecast figure which
improved the forecast accuracy. As production and resource planning are directly
linked to this single forecast figure, the company has reported improved product
availability and adherence to production plan. Case 2’s investment on information
technology and communication devises has helped them to secure exclusive network
for receiving and sending information on sales, inventory and production processes.
This has effected in considerable reduction of logistics difficulties during the time of
replenishment. The company expects to be benefited more from SCC and related
metrics. The measures of performance of SCC in Case 2 and their purposes are given in
Table II.

12. 4.3 Possible scenario with advanced SCC and related metrics Supply chain
Although Case 1 was successful in terms of controlling inventory and related cost, the performance
top management was not sure on further benefits of CPFR as performance metrics
were not clear to them. In its pilot stage of collaboration, Case 1 aimed to improve their metrics
inventory to avoid loss. In this stage, the company must check their efficiency in SCC
through the list of metrics given under “measures of functional drivers”. But Case 1
used only four performance metrics, namely forecast accuracy, inventory level, timely 867
replenishment, and cost, during their pilot stage of SCC.
We have suggested our proposed conceptual framework of performance metrics to
identify the performance of SCC. The first result after implementing the suggested
framework for performance metrics, the company has reported that they could identify
their strength and weakness in SCC under evaluation of each metrics. After calculation,
Case 1 officials have confirmed that they are in a good position after SCC and hence
intended to continue further collaboration with most of the existing partners. They
have also considered revising front-end agreement with some of the SCC members. The
company has also showed their interest in adopting our proposed metrics for SCC
framework as their standard measure.
When the company moves to the advanced stage of collaboration, they need to
measure the effectiveness of enhancers. Collective consideration of functional drivers
and enhancers will help the company to identify its areas of improvement. This
exercise should be repeated periodically to review the front-end agreement on
collaboration.
The cost-benefit analysis of both the companies at the end of 2007 has encouraged
them to invest more on SCC. Hence, in the next stage of collaboration, Case 1 has
decided to invest more on technology to gain access to their clients’ data on real time
basis. They have believed that this could improve quality and visibility of information.
So the company has decided to have set of metrics as given in Table II to measure
performance of SCC at its second stage. However, Case 2 had a well established basic
collaboration and now they are in an advanced stage of collaboration.
Substantial benefit of SCC has encouraged Case 2 to involve in further collaboration
at its next stage. They have also shown interest in exploring the suggested performance
metrics in the advanced stage of collaboration. The company has measured almost all
the measures suggested in our framework. “Product returns” have not been included
in the inventory and hence product has not been realigned. In the advanced stage “use of
technology” and “quality of information” have not been measured. But later during our
discussion, the company has understood the importance of these two measures in their
decision making. The performance of overall SCC through responsiveness, flexibility
and technical excellence for managing changes is another metric that has been viewed
important by the case company to improve their SCC. Table II represents the list of
measures currently being used by the companies for measuring their performance
in SCC. This table also lists the desired set of metrics at pilot and advanced stages of
collaboration. By comparing these two columns of desired metrics and metrics in use
in the Table II, it is clear that the company (Case 2) that aims to have advance
collaboration use more number of metrics than Case 1 that practices pilot stage of
collaboration. However, before establishing further collaboration, Case 1 has been
advised to measure all the desired metrics to evaluate their SCC performance. This
approach can be used as basic guidelines by any firm that is interested in SCC

13. BIJ to measure its performance. Based on the level of collaboration, the top management can
18,6 choose the metrics to evaluate its benefits of SCC.
5. Conclusion and scope for future research
In this paper, we have identified several performance metrics from the existing literature
and through two case studies. We have proposed a set of metrics to measure SCC at its
868 initial (pilot stage) and advanced stages. We have suggested including flexibility,
responsiveness and use of technology as important measures in comprehensive view of
performance metrics of SCC. While, flexibility measures the ability of adapting to the
changes effectively with available resources, responsiveness can measure the response
of the supply chain for any unexpected changes in demand. Evaluating the collaboration
at the time of initiation is suggested through measurement of functional drivers.
Tracking the benefits of collaborative arrangement by measuring enhancers would be
ideal for decision makers to revisit their agreement on SCC. While analysing the case of
packaging firm, we have identified that the technology is not necessarily a key obstacle
but effective communication is vital. Proper uses of technology, flexibility, and
responsiveness have been considered as important criteria for successful SCC by the
case companies. Measures of evaluating these three SCC criteria are termed as overall
performance metrics in the conceptual framework.
Another important observation from the case analysis is that ample availability of
raw material supply or suppliers will engage manufacturers in simple collaboration
with their suppliers mainly for on time material availability. Meanwhile they try to
establish strong collaboration with their buyers in order to improve the product sale,
inventory control, etc. Incentive alignment in collaboration will be beneficial to all
partners involved. One of the observations about utility of production facilities reveals
that the support from suppliers helps to provide raw material on time to make use of
the production capacity to its maximum. Meanwhile, relationship with buyers does
have an indirect impact on production capacity utilization and planning as job
allocation is based on demand. Both the case companies did not have close relationship
with its suppliers compared to buyers. Further research is indeed necessary to identify
the impact of closer partnership with suppliers.
Manufacturers with high degree of collaboration may or may not perform well. But
consistent intervention and necessary changes as required by the system will aid to
improve the performance. In case of no improvement in the performance, the
collaboration can be withdrawn or revamped with new set up. This case study reveals
that the manufacture-to-order type of business requires more support from their buyers
than their suppliers to exchange information, to improve forecasting accuracy, to avoid
inventory and also to achieve overall performance in the supply chain. The same kind
of research can be extended to manufacture-to-stock business or assemble-to-order
type of businesses. Detailed survey-based analysis is also essential to validate the
above framework in future and to standardise for various sectors other than
manufacturing. The case study did not consider number of suppliers as an important
factor due to the availability of sufficient suppliers and their readiness to serve. The
main reason for such attitude is products from packaging industry have got more life
and have more opportunity to sell in the other market’s before their value got eroded.
But collaborative relationship with suppliers will help to reduce excess raw material
inventory. By the way of allotting incentive, manufacturer can involve supplier in SCC.

14. Incentive can be considered as the indirect motivating factor for involvement of supply Supply chain
chain members in collaboration in order to get overall performance lift in the supply performance
chain process. Further research in this line will help to identify some more metrics
related to performance measurement of collaborative supply chain. metrics
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About the authors
Dr Usha Ramanathan is a Senior Lecturer in Logistics and Supply Chain Management in
Newcastle Business School, Northumbria University, UK. Her research interest includes supply
chain collaboration, collaborative planning forecasting and replenishment (CPFR), value of
information sharing and forecasting, structural equation modeling, simulation, AHP and
SERVQUAL. She has published in leading journals such as International Journal of Production
Economics, Expert Systems with Applications and Omega: The International Journal of
Management Science.
Dr Angappa Gunasekaran is a Professor in, and the Chairperson of, the Department of Decision
and Information Sciences at the Charlton College of Business, University of Massachusetts,
Dartmouth. He teaches undergraduate and graduate courses in operations management and
management science. He has over 190 articles published in 40 different peer-reviewed journals,
has presented about 50 papers and published over 50 articles in conferences, and has given a
number of invited talks in about 20 countries. Dr Gunasekaran is on the editorial board of
over 20 journals. He is the editor of several journals in the field of operations management
and information systems. Dr Gunasekaran is currently interested in researching information
technology/systems evaluation, performance measures and metrics in new economy,
technology management, logistics and supply chain management. He actively serves on several
university committees. He is also the Director of the Business Innovation Research Center (BIRC).
Dr Nachiappan Subramanian is an Associate Professor at Thiagarajar College of
Engineering, Madurai, India. Nachiappan (Nachi) has published over 75 refereed papers which
include journal articles and international conference papers. Currently, he is on the editorial
board of the International Journal of Integrated Supply Management and International Journal of
Applied Industrial Engineering. He also serves as a reviewer for many leading operations

17. BIJ and supply chain management journals. In September 2011 he is joining as an associate
professor in operations management at the University of Nottingham Ningbo, China. Previously,
18,6 Nachi conducted his post-doctoral research at University of Nottingham, UK, under BOYSCAST
fellowship program and received the Australian Endeavour Research Fellowship Award to
conduct research on complexity, risks and low-cost country sourcing (with special reference
to India). His research interests are supply chain operations, modeling and analysis of
manufacturing systems, sustainable supplier selection, low-cost country sourcing, supply chain
872 complexity and resilience and reverse logistics. Nachiappan Subramanian is the corresponding
author and can be contacted at: spnmech@tce.edu
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