AMELIORATING CORPORATE PERFORMANCE: BY DESIGNING A RESILIENT
SUPPLY CHAIN MEASURING SYSTEM
ABSTRACT
In today’s highly business competitive environment, most businesses have and continue realizing the value of
investing in supply chain improvements. A good starting point of improvements can be associated with scientific
analysis of their returns. For that reason, most businesses have also created metrics that document practical supply
chain performance and keep track of changes overtime in order to drive their business performances and
profitability. While performance measurement is critical, unfortunately most companies either measure too much or
too little with regard to supply chain. Other shortcomings may include; too many metrics, isolated metrics,
conflicting metrics, outdated metrics, unreliable data, lack of ownership among others. It becomes even worse when
companies measure wrong things. On the other hand, companies continue to pursue supply chain metrics as a means
to increase their line of sight (i.e. that which is visible to them) over areas they do not directly control but have an
impact on their companies’ performance. Problems with current metrics and the need for supply chain performance
measure are discussed. This paper is aimed at establishing universally effective measurement system for global
supply chain. Proposed framework focuses on managing the interfacing customer and supplier relationship
management processes at each link in the supply chain. A unified approach for measuring supply chain system is
presented supported by real life case studies coupled with practical examples.
Keywords: Supply Chain Management, Performance Metris, Customer & Supplier Relationship Management
INTRODUCTION
It is generally believed that a well crafted supply chain system metrics can increase the company’s chances for
profitability and competitiveness. This is possible through alignment of processes across multiple companies,
targeting the most profitable market segment and obtaining a competitive advantage through differentiated services
and lower costs. Equally, it is a very well known fact that lack of proper metrics for supply chain will result in
failure to meet customer expectations, sub-optimal company performance, missed opportunity to outperform the
competition and internal supply chain conflicts. From available supply chain literature it is fair to suggest that there
is no evidence that pre-defined, meaningful performance measures that span the supply chain actually exist. This
call for consolidated efforts among supply chain researchers and practitioners to create a system or an approach that
will among others be able to address such limitations. Common factors that contribute to this problem include;
complexity of capturing metrics across multiple companies, the willingness to share information among companies,
plagued supplier performances, etc. However, a major contributor to the lack of meaningful supply chain
performance measures is the absence of an approach for developing and designing such measure. The most common
metrics that most companies of the globe refer to as supply chain metrics are primarily internally focused measures
that includes, products lead time, on-time delivery performance, etc. In some instances, companies measure financial
aspects which somehow lack insight as to how key business processes have been performed and/or how supply
chain met customer requirements. Some companies on the other hand measure performance outside of the internal
supply chain, but these efforts are limited to evaluating performance of only on tier, i.e. supplier or customer. This
again calls for a unified model that should address such measurements, from the beginning to the end of the chain.
These metrics do not capture how the overall supply chain has performed and fail to identify where opportunities
exist to increase customer satisfaction and shareholder value for each company in the supply chain. Supply chain
metrics are explained below:
Supply Chain Metrics Defined…
Supply chain metrics are measurements of how a business implements its supply chain. Using supply chain metrics
helps organization to track its own success and measure progress toward its goals. One can think of it as a strategic
business planning. Business metrics in general are just measurements of any business component, supply chain
metrics include attention to all areas of business process. Along with supply chain metrics that focus on sourcing,
other supply chain metrics focus on inventory, shipping and other aspects of supply chain or distribution including
warehousing, transportation and customer service.
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Metrics like backorder reporting and inventory accuracy help to ensure that physical products and materials are in
the right place. Other metrics, like a balanced scorecard, help to show how a company is meeting its internal goals
for delivery. Additional supply chain metrics include benchmarking, where a company compares its own progress
against that of the competitors.
The supply chain metrics of a company and its overall supply chain management have a lot to do with the specifics
of a company. A retail supply chain for a garment store supplier may be different that the supply chain for a more
industrial enterprise, or service center. Strategically, consultants look at the bigger picture for a company to see
how it can benefit from more advanced supply chain monitoring. Essentially, the kind of advanced observation that
these metrics provide helps a company to know where they are in terms of their goals and the standards within the
greater industry or field that they operate in.
By the late 1980s, outsourcing in South African industries contributed to nearly 60% of the total gross domestic
product cost (Stats SA, 2012). A survey further showed that 40% of the GDP was spent on distribution and logistics
related activities (Stats SA, 2012). These figures clearly reveal the importance of supply chain in the overall global
economy. In a pursuit and attempt to well manage supply chain activities, many industries employed important
techniques or methodologies such as just in time (JIT), total quality management (TQM), lean production, Kaizen,
computer generated enterprise resource planning (ERP) schedule, etc. The concept of supply chain management
(SCM) represents the most advanced state in the evolutionary development of purchasing, procurement and other
supply chain activities. At the operational level, this brings to the fore functions that are as old as commerce itself:
seeking goods, buying them, storing and distributing them. Whereas at the strategic level, SCM is relatively a new
and rapidly growing discipline that is transforming the way that manufacturing and non-manufacturing
The growth and development of SCM is not only driven by internal measures and motives, but by a number of
external factors such that may be deemed as outside the company’s control, such as increasing globalization,
reduced barriers to international trade, improvements in information availability, environmental concerns and
government regulations. Supply chain integration in needed to manage and the flow of information, material and
products in operating systems. Such flow control is associated with inventory control and activity system scheduling
across the whole range of resource and time constraints. Supplementing to this inventory control, an operating
system must try to meet the broad competitive and strategic objectives of lead time, quality, dependability,
durability, flexibility and cost. Control is as equally important as both customer needs and supply chain performance
might change with time.
Depending on individual company’s objectives, the output of the processes enabled by the supply chain must be
measured and compared with a set of defined standard to meet the objectives. In order to be controlled, the process
parameter values needed to be kept within a set limit and remain relatively constant. This will allow comparison of
planned and actual parameter values, and the outcomes can be influenced by certain reactive measures in order to
improve the performance or realign the monitored value to the defined value. Example of such misalignment may
include; an analysis of the layout of facilities could reveal the cause of long distribution time, high transportation
and movement costs and costs associated with inventory accumulation. Using suitable approaches available in the
literature, for example, reengineering facilities, routing network designs and others, problems can be tackled and
with close monitoring, subsequent improvements can be possible from analysis of the new design. Thus, control of
processes in the chain is essential in improving performance and can be achieved, at least in part through
measurement metrics. Very well-defined and controlled processes are also essential to better supply chain
management.
While substantial financial and human resources have been spent on implementing integrated supply chain
management principles, there has been a little sign of realized benefits. There’s a general lack of formal approaches
on measuring supply chain systems. In addition, while SCM software providers are selling solutions that enables
companies to improve supply chain performances, these same vendors do not adequately provide tools needed to
measure these improvements. This paper seeks to discuss supply chain performance measurements and the results of
research conducted to address the following questions and issues:
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Essentiality of measuring supply chain systems,
General approaches available to measure supply chain systems,
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Factors to consider when selecting performance measurement metrics,
How to set performance target?
How should a company get started?
The role of application vendors in supporting supply chain performance measurement.
There are number of frameworks and discussions on supply chain performance measurements in the literature;
however, there is a lack of empirical analysis and case studies on performance metrics and measurements in a supply
chain environment. Research background on SCM related to performance measurement is discussed, selected related
literature review, and development of a framework based on available literature and empirical analysis, and finally,
summarize the findings and conclusions. In this paper, a unified framework for developing supply chain metrics that
measure performance of key supply chain processes from SC flexibility perspective, identify implications of
individual companies on the overall supply chain system is proposed.
PROBLEMS WITH EXISTING METRICS & RESEARCH BACKGROUND
In this section, problem statement and literature is used in describing the general context within which measurement
of supply chain performance is undertaken. The works of various authors are used in accentuating and/or
establishing the need for supply chain performance measurement and to describe in general terms how it should be
addressed – emphasis is on measurement systems and approaches as opposed to specific measures.
The performance measures used in most companies have several problems that prevent them from effectively
measuring performance of the supply chain. Many measures identified as supply chain metrics are measures of
internal logistics operations as opposed to measures of overall supply chain management. The majority is single
company logistics measures such as on-time delivery, lead times, and are not multi-companies measures that are
necessary to measure the performance of the supply chain. Inventory turn among other measures has been identified
as a common measure of supply chain performance (Durtsche & Ledyard, 1999). It is clearly apparent that inventory
turns and other commonly measures are not an effective measure of supply chain performance, and provides a useful
example of why new metrics are needed for managing the supply chain. They are inadequate for evaluating and
aligning performance across multiple companies in the supply chain. Another problem with metrics stems from the
lack of a widely accepted definition for supply chain management. Until recently, many researchers, academics and
practitioners viewed supply chain management as a mere extension of logistics outside the company in question to
include suppliers and customers (Coyle, Bardi & Novack, 1999). Supply chain has a much broader scope and
considers the effect of functions other than logistics on business processes spanning multiple companies, and as
thus, performance measures should encompass the entire chain, that which this paper seeks to address. Supply chain
management is defined as an integration of key business processes from end user though original suppliers that
provide products, services and information that add value for customers and other stakeholders (Lambert & Cooper,
2000). Whereas, logistics is that part of the supply chain process that plans, implements and controls the efficient,
effective flow and storage of goods, services and related information from point of origin to point of consumption in
order to meet customer requirements.
The strategic, tactical and operational levels are the hierarchies in function, wherein policies and trade-offs can be
distinguished and suitable control exerted. According to Rushton and Oxley (1989), such a hierarchy is based on the
time horizon for activities and the pertinence of decisions to and influence different levels of management. The
strategic level measures influence the top level management decisions, very often reflecting investigation of broad
based policies, corporate financial plans, competitiveness and level of adherence to organizational goals. The tactical
level deals with resource allocation and measuring performance against targets to be met in order to achieve results
specified at the strategic level. Measurement of performance at this level provides valuable feedback on mid-level
management decisions. Operational level measurements and metrics require accurate data and assess the results of
decisions of low level managers. Set targets if met, will lead to the achievement of tactical level objectives.
Evaluation of supply chain performance measures
Cost, activity time, customer responsiveness, and flexibility have all been used as supply chain performance
measures either singly or jointly. Yet the measures used thus far possess some significant weaknesses. This section
evaluates and identifies the limitations of these supply chain performance measures.
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Single supply chain performance measures
The use of a single performance measure is most preferred because of its simplicity by most supply chain partners.
However, one must ensure that if a single performance measure is utilized, this measure adequately describes the
system performance at its entirety. After Beamon (1996) identified and evaluated various individual supply chain
performance measures, concluded that significant weaknesses were present in each of the performance measures
evaluated, based on such criteria as inclusiveness, universality, measurability and consistency. Repeatedly, the most
consistent weakness for these performance measures was inclusiveness. In order for a measure to be inclusive, it
must measure all pertinent aspects of the supply chain. Consider an example in which a company decides to use cost
as the measure of supply chain performance. Although the supply chain may be operating under minimum cost, it
may simultaneously demonstrate poor customer response time performance, or lack flexibility to meet random
fluctuations in demand.
Cost as a single supply chain performance measure
Cost is the performance measure of choice for many supply chain models. Although cost as a resource measure is
important, there are downfalls to relying on cost as the sole performance measure. Maskell (1991) identifies many
shortcomings of traditional management accounting. The problems include a lack of relevance of the cost categories,
cost distortions (especially overheads), and inflexibility, such as reports that are too late to be valuable. Lee and
Billington (1992) identify many pitfalls in supply chain management and one identified pitfall is the incorrect
assessment of inventory costs. The authors identify two commonly omitted inventory costs:
(i) obsolescence; and
(ii) rework due to engineering changes.
This problem is magnified by current cost accounting methods, such as overhead calculations, and omitted inventory
costs. Existing supply chain models have typically restricted themselves to traditional cost measures, and have not
yet utilized the advantages of strategic cost management of the supply chain. Shank and Govidarajan (1992) and
Barker (1996) address strategic cost management issues within the context of supply chains.
Strategic goals and supply chain performance measures
Maskell (1991) suggests that the type of performance measures required for a manufacturing organizations are
directly related to the manufacturing strategy chosen by the company. The two reasons cited for establishing and
maintaining this relationship are:
(i) the company may determine if its performance is meeting its strategic goals; and
(ii) people in the organization will concentrate on what is measured; thus the performance measure will
steer company direction.
Generically, strategic goals seldom imply only one performance measure; they usually point to many (more than one
parameter), and are not always clearly defined. For example, product quality can be measured in many different
ways. Although it may be difficult to choose the individual performance measures, it is vital that the performance
measures are related to the strategic goals of the organization.
Performance measure evaluation summary
Individual performance measures used in supply chain analysis have been shown to be non-inclusive. Consequently,
important supply chain characteristics and their associated interactions have been ignored. Measuring the use of
resources, especially cost, has also been identified as an important part of the supply chain. Many strategic goals of
organizations recognize not only the importance of minimizing resources, but also the overall importance of the
output of the system. Additionally, ignoring the effects of uncertainty on the supply chain results in a system that is
unable to adapt to future changes. Current supply chain performance measurement systems are inadequate because
they rely heavily on the use of cost as a primary (if not sole) measure, they are not inclusive, they are often
inconsistent with the strategic goals of the organization, and do not consider the effects of uncertainty. That is,
although use of multiple supply chain performance measures may be commonplace in real-world settings, it is not
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commonplace in supply chain modeling. A performance measurement system for supply chain analysis must be
developed that addresses these issues. The next step, then, is to develop a framework for measuring supply chain
performance.
IMPORTANCE OF PERFORMANCE MEASUREMENT
Measurements are important as they affect performance of the supply chains. As such, performance measurement
provides the means by which a company can assess whether its supply chain has improved or degraded. Companies
cannot manage what they do not measure because anything measured improves, and that which companies measure
get. The importance of using measures to help ensure that a supply chain is performing well can be illustrated by the
following popular anecdotal case:
“A driver is embarking on a long trip in a car that has a malfunctioning speedometer and a broken gas gauge. This
driver is keeping track of time and constantly referring to his odometer to see how fast he is driving. He is certain
that he has been obeying states’ speed limits when suddenly stopped by a traffic officer and given him a speeding
ticket. He carried on by slowing down, again, keeping track of time and odometer, he is stopped by a traffic officer
and receive a ticket for driving below the acceptable speeds on the highway. During the remainder of the trip, he
keeps track of odometer and suddenly the unthinkable happened – he ran out of petrol.”
Not a very good journey for the driver – primarily because he was missing on the display some important key
measurement devices. Some drivers, those who understand the importance of generic performance measurements
would not have agreed to embark on such a trip with this car. In a similar way, however, some companies run their
supply chains without a good set of measurements in place. Like this driver, the only way they are able to find out if
they are meeting their supply chain objectives is after the fact, by diagnosing poor financial returns, missing
important clients, ran out of perishable raw materials, etc. Translating from this case, what companies should learn:
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Using wrong measurement metrics and leaving relevant and/or important ones could lead to supply chain
degradation, like running out of petrol,
Managing supply chains based on “only after the fact” measures, like losing an important client/account is
not very effective for business success, for example, the way getting speeding tickets and running out of
petrol is an expensive way to drive a car,
Irrelevant measures like “odometer” in this case as translated to supply chains could lead to possible
hindrance in helping to improve supply chain performance,
A few, key measurements metrics, if carefully selected will go a long way keeping company on track with
its supply chain set goals, like a speedometer and petrol gauge.
Points above indicate the essentiality of managing and controlling supply chains systems through performance
measurements. Traditionally, companies tracked performance based largely on financial accounting principles.
Financial measures are certainly important in assessing whether or not operational changes are improving the
financial health of a company, but insufficient to measure supply chain performance due to the following reasons:
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Measures tend to be historically oriented, and lack forward-looking perspective,
These measures do not relate to strategic, non-financial performance, like customer service, customer
loyalty and products quality – again limiting the organization to internal measures,
The measures do not in any way tie to operational effectiveness and efficiency,
Focus on short term financial returns such as profits and revenues, providing only little insight into
companies’ future prosperity,
LITERATURE REVIEW
The literature review confirmed that the majority of papers addressing performance measurement in SC are
conceptual, not fieldwork-based and that there is a lack of field research that identifies the measures used to reflect
supply chain performance. Although detailed publications related to supply chain measures are available (for the
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latest review see: Shepherd and Gunter, 2006), they are mainly conceptual or literature based. This paper is centered
around two main categories namely; 1) performance models, and; 2) performance metrics.
Performance Models
In terms of performance models, a well-known model showing important relationships is the balanced scorecard,
created by Kaplan and Norton (1992) in the early 1990s. Besides introducing a concept of balancing four different
dimensions of performance, they use cause-and-effect relationships to describe how the four dimensions of
performance are connected. The model is claimed to be not merely a measurement tool, but moreover a management
system to clarify and translate strategy and vision into strategic objectives. This is important, as managers tend to
develop their own understanding of what the strategy means to individual organization. It is also a tool to
communicate and link strategic objectives and measures in the organization. The process of planning, setting targets
and aligning strategic initiatives is facilitated by the “balanced scorecard”, which ultimately aims at enhancing
strategic feedback and learning. This model has become quite popular, possibly because it provides a long-awaited
tool to relate different dimensions of performance. Critics (for example, Neely et al., 1997) claim, however, that the
model fails to answer one of the most important questions of all: what are the competitors doing?
Whereas Kaplan and Norton (1992) use a linear cause-and-effect relationship in their model, Senge (1992) uses a
different technique in which he turns the linear cause-and-effect relationships into circular loops. The technique of
closing the loop plays an important role as it challenges people's desire to oversimplify relationships. The idea of
closed loops may not be that controversial after all, although people often stick to linear cause-and-effect
relationships. People are likely to accept the idea that improved financial results offer an opportunity to reinvest the
money in the business, and, for example, improve internal processes, reward employees and stimulate innovative
ideas. Thus, there is a link from what seemed like an end, to what can be viewed as engines of success.
Both models serve the same purpose, i.e. to uncover the mechanisms of the business. The models make it possible to
connect different phenomena and thus describe - albeit in vague terms - how they interact. Of course, the number of
links can be high, and the need for simplification obvious. Nevertheless, the models provide a means of
communicating the ideas about such things as how sales can be increased or how overall productivity can improve
by adopting non-obvious solutions. According to this model, improving profitability might be easier if resources are
spent on removing the obstacles to increased profitability, i.e. the increased distribution costs, instead of allocating
resources to increased marketing efforts.
Worth mentioning is an initiative taken to improve supply chain performance through a measurement called the
SCOR model. The model is developed by the Supply-Chain Council (SCC), an independent, not-for-profit, global
corporation, and based on a process view of the supply chain using four distinct management processes:
(i) plan;
(ii) source;
(iii) make; and
(iv) deliver .
The process reference model integrates the well-known concepts of business process re-engineering, benchmarking,
and process measurements into a cross-functional framework. Each of the four processes at the top level is
successively divided into sub-processes, first at a configuration level, then at a process element level. Finally, at the
fourth level and beyond the scope of the SCOR model, activities are defined by companies individually. Measures
are defined for all processes at the three top levels, and firms provide information about how they perform while
receiving a benchmark in return against which they can compare their own performance. This model provides not
only an opportunity to see how the firm is doing, but also a common frame of reference and a common language
across the supply chain.
Performance Metrics
In the literature of supply chain, many performance measures suggested including cost, benefit such as profit, lead
time, customer satisfaction, inventory, forecast accuracy, etc (Chang et al., 2007; Kim & Oh, 2005; Simatupang &
Sridhara, 2005). Majority of the supply chain metrics in the literature are measures of internal performance on a
company (Lambert & Pohlen, 2001; Barrat, 2004). If information on performance of supply chain is shared with
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other stakeholders and partners, then it could improve the overall efficiency of the supply chain. It also proposed a
collaborative performance system consisting of three cycles with respect to collaborative enablers to improve
operational performances (Simatupang & Sridhara, 2004). On reviewing the literature of supply chain collaboration,
it was identified that the performance metrics for supply chain collaboration were given adequate importance as
compared to supply chain performance.
In terms of specific SCM performance metrics, it was 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,
this study identifies the key metrics to measure the performance of supply chain collaboration from suppliers or
buyers perspective is indispensable. Therefore, we propose a conceptual framework to measure performance of
supply chain collaboration at initial and advanced stages of partnership. In line with supply chain literature of
collaboration and performance measurement we developed a conceptual model for performance metrics.
In closure, given the current literature survey, it is fair to suggest that no effective, unified approach has been
developed to date to assist in managing the supply chain systems through measurement metrics. Thus, this paper
serves to address such a shortcoming by developing a model suitable to assist companies in managing their supply
chain value systems.
METHODOLOGY
A new view of measurement systems
The literature often deals with measurement system design focused on either what metrics to deploy in certain
situations and their inherent characteristics, or on the process of developing a proper measurement system. Whereas
measurement activities at the lowest stages of systems thinking have been depicted as fragmented, it is suggested
that some kind of model building take place at the higher stages of systems thinking. This type of model building,
however, is not often reflected in literature, but has been suggested by Eccles and Pyburn (1992). One consequence
of the notion that a supply chain must be viewed as one entity is that the measurement system should span the entire
supply chain. Thus, each of the components in the measurement system must be considered throughout the entire
supply chain.
The advancement in information technology and database techniques makes it interesting to develop new solutions
for measurement methods. Yet, designing common methods for measurements will most likely be a difficult nut to
crack. One reason is that many firms possess a blend of old and new computer systems, computer software and
database structures that will obstruct them from making changes without great effort.
A proposed framework for performance measurement
A supply chain performance measurement system that consists of a “single performance” measure is inadequate
since it is not inclusive. It ignores the efficient interactions among important supply chain characteristics, and
ignores critical aspects of organizational strategic goals. Strategic goals involve key elements that include the
measurement of resources, output and flexibility. An efficient system is that is inclusive and/or encompasses all
these factors. Resources and output measures have been widely used in supply chain models, and many researchers
neglected “flexibility”. Although flexibility has been limited in its application to systems of supply chains, many
advantages exist to a flexible supply chain.
The use of resources, the desired output and flexibility (how well the system reacts to uncertainty) have been
identified as vital components to supply chain success. Therefore, a supply chain measurement system must place
emphasis on three separate types of performance measures: resource measures (R), output measures (O), and
flexibility measures (F). Each of these three types of performance measures has different goals, as illustrated in
Table 1 below.
TABLE 1
GOALS OF PERFORMANCE MEASURE TYPES
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Performance Measure Type
Resources
Output
Goal
Optimal Utilisation
Customer Satisfaction
Flexibility
Ability to respond to ever-changing
environment: system robustness
Purpose
Direct proportion to profitability
Without acceptable output rate
customers turn to other supply
chains
In a random/stochastic environment,
supply chains must be able to
respond to change unplanned
The supply chain performance measurement system must equally measure each of the three types (R, O & F), as
each type is vital to the overall performance success of the supply chain. Each of the three types of measures has
important characteristics and the measure of each of these affects the others. The interrelationship among these three
types of measures is illustrated in Figure 1 below.
FIGURE 1
THE SUPPLY CHAIN MEASUREMENT SYSTEM
Therefore, the supply chain performance measurement system must contain at least two if not all individual
measures from each of the three identified types. The individual measures chosen from each type must coincide with
the organization's strategic goals. This measurement system can then allow study of the interactions among the
measures or can at least ensure a minimum level of performance in different areas. Each type of performance
measure is discussed in the following sub-sections, with an emphasis of that which we believe has been discounted
or ignored by most researchers.
Resources (R)
Resource measures include: inventory levels, personnel requirements, equipment utilization, energy usage, and cost
(return on investment - ROI). Resources are generally measured in terms of the minimum requirements (quantity) or
an efficiency measure. Efficiency measures the utilization of the resources in the system that is used to meet the
system’s objectives. Resource measurement is an important part of the measurement system. Too few resources can
negatively affect the output and the flexibility of the system, while the deployment of too many resources artificially
increases the system's requirements. System efficiency is mathematically given as:
𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 =
𝑂𝑢𝑡𝑝𝑢𝑡 (𝑤𝑜𝑟𝑘−𝑜𝑢𝑡)
𝐼𝑛𝑝𝑢𝑡 (𝑤𝑜𝑟𝑘−𝑖𝑛)
𝑥100%
(1)
Where Output function is the work done by a system and Input function is the amount of work exerted on a
machine.
One general goal of supply chain analysis is resource minimization. Although a minimum level of output is often
specified, the effect of reducing resources on the flexibility of the supply chain is not often considered. A supply
chain may be reconfigured with reduced resources while present demands are met, but such short-term analyses do
not account for the dynamic nature of demand. In this way, resources are directly related to the system's output and
flexibility performance.
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The following is a list of supply chain resource performance measures:
(i) Total cost: total cost of resources used.
(ii) Distribution costs: total cost of distribution, including transportation and handling costs.
(iii) Manufacturing cost: total cost of manufacturing, including labor, maintenance, and re-work costs.
(iv) Inventory: costs associated with held inventory:
(v) Return on investment (ROI): measures the profitability of an organization. The return on investment is generally
given by the ratio of net profit to total assets.
Output (O)
Output measures include among others: customer responsiveness, quality, and the quantity of final product
produced. Output function is somehow interrelated with resource efficiency; refer to the expression (1) above. Many
output performance measures are easily represented numerically, such as:
 number of items produced;
 time required to produce a particular item or set of items;
 number of on-time deliveries (orders).
Output performance measures must not only correspond to the organization's strategic goals, but must also
correspond to the customers' goals and values, since strategic goals generally address meeting customer
requirements. For example, Corbett (1992) identifies a furniture manufacturer that discovered that their customers
actually valued delivery reliability more than fast delivery. For the customer, short lead times were secondary to
having the product delivered on time. Although lead times may be extremely important to the manufacturer, ontime
delivery was more important to the customer. In this case, both of these output performance measures should be
utilized. The following is an example list of supply chain output performance measures:
(i) Sales: total revenue.
(ii) Profit: total revenue less expenses.
(iii) Fill rate: proportion of orders filled immediately:
 Target fill rate achievement. To what extent a target fill rate has been achieved.
 Average item fill rate. Aggregate fill rate divided by the number of items.
(iv) On-time deliveries: measures item, order, or product delivery performance:
 Product lateness: delivery date minus due date.
 Average lateness of orders: aggregate lateness divided by the number of orders.
 Average earliness of orders: aggregate earliness divided by the number of orders.
 Percent on-time deliveries: percent (%) of orders delivered on or before the due date.
(v) Backorder/stockout: measures item, order, or product availability performance:
 Stockout probability: instantaneous probability that a requested item is out of stock.
 Number of backorders: number of items backordered due to stockout.
 Number of stockouts: number of requested items that are out of stock.
 Average backorder level: number of items backordered divided by the number of items.
(vi) Customer response time: amount of time between an order and its corresponding delivery.
(vii) Manufacturing lead time: total amount of time required to produce a particular item or batch.
(viii) Shipping errors: number of incorrect shipments made.
(ix) Customer complaints: number of customer complaints registered.
Resources affect the output of a supply chain, and the output of the supply chain system (quality, quantity, etc.) is
important in determining the flexibility of the system. Flexibility function is discussed below.
Flexibility
Some advantages of flexible supply chain systems are:
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Reductions in the number of backorders.
Reductions in the number of lost sales.
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Reductions in the number of late orders.
Increased customer satisfaction.
Ability to respond to and accommodate demand variations, such as seasonality.
Ability to respond to and accommodate periods of poor manufacturing performance (machine breakdowns).
Ability to respond to and accommodate periods of poor supplier performance.
Ability to respond to and accommodate periods of poor delivery performance.
Ability to respond to and accommodate new products, new markets, or new competitors.
Flexibility, which is seldom used in supply chain analysis, can measure a system's ability to accommodate volume
and schedule fluctuations from suppliers, manufacturers, and customers. Indeed, flexibility is vital to the success of
the supply chain, since the supply chain exists in an uncertain environment. Slack (1991) identifies two types of
flexibility: range flexibility and response flexibility. Range flexibility is defined as to what extent the operation can
be changed. Response flexibility is defined as the ease (in terms of cost, time, or both) with which the operation can
be changed.
Although there will be a limit to the range and response flexibility of a supply chain, the chain can be designed to
adapt adequately to the uncertain environment. For example, a reduction in system resources may negatively affect
the supply chain's flexibility. A supply chain may be currently utilizing its resources efficiently, and producing the
desired output, but will the supply chain be able to adjust to changes in, for example: product demand,
manufacturing unreliability, the introduction of new products, or supplier shortages? Thus, flexibility is an important
consideration in supply chain performance.
A quantitative approach to flexibility measurement
Numerous flexibility measures for flexible manufacturing systems (FMSs) on the machine and plant levels exist and
have been well-studied. The interested reader is referred to Sethi and Sethi (1990) and Gupta and Goyal (1989) for
comprehensive reviews of the literature in manufacturing system flexibility. However, as previously stated, the
measurement of flexibility in larger, more complex systems, such as supply chain systems, has rarely been
addressed. Flexibility measures are distinctly different from resource and output measures. Slack (1983) indicates
that flexibility measures potential behavior, whereas other operational objectives are actually demonstrated by the
system's operating behavior (performance). Therefore, flexibility does not have to be demonstrated by the system in
order to exist. Slack (1983) identifies factors that cause difficulty in measuring the flexibility of an entire production
system.
These factors are:
 flexibility is a measure of potential;
 flexibility must be applied to other production objectives, such as volume or delivery; and
 the multiple dimensions of flexibility (range and response).
Das (1996) concludes that since every manufacturing facility experiences different changes to different degrees, and
the diversity of these possible changes is large, several different types of flexibilities may be appropriate.
Given the complexity of assessing a system's flexibility, various measures for flexibility in manufacturing systems
have been developed. Slack (1991) defines system flexibility as the flexibility of the entire operation. The author
further identifies four types of system flexibility, as shown in Table 2 below. Each of these types of flexibility can
be measured in terms of range and response followed by their respective mathematical expressions in the following
sections.
Each of these types of system flexibility could be applied to supply chain systems. However, each type may not be
appropriate for every supply chain. Examining historical data for the system can indicate which flexibility measure
types are appropriate for the system of interest. Table 2 below identifies characteristics of the supply chain and their
corresponding appropriate flexibility types. Although these system flexibility types are applicable to supply chains,
many different types of FMS flexibilities have been identified in the literature. Many of these types may have some
application to specific supply chain systems, these include machine, routing, process, product, expansion, market,
production, and program flexibility. However, given the universality of the uncertain environment in which supply
10
chain systems exist, volume flexibility is commonly desirable. Even supply chains with relatively stationary demand
experience some variation.
TABLE 2
SYSTEM FLEXIBILITY TYPES
System Flexibility Type
Volume Flexibility
Delivery Flexibility
Volume-mix Flexibility
New Product Flexibility
Definition
Ability to change output level of products produced – variable demand
Ability to change planned delivery dates – delivery dates change
unannounced, and costs are associated with not meeting new delivery dates
Ability to change products variety – constant demand for multiple product
types
Ability to introduce and produce new products, incl. products modifications
– products with short cycle times
Volume flexibility (Fv )
Sethi and Sethi (1990) point out that a generalization of volume flexibility is to measure the range of volumes in
which the organization can run profitably. For manufacturing systems, the development of volume flexibility
measures has generally considered the costs associated with volume changes. For the development of a supply chain
volume flexibility measure, we are interested in how much of the demand can be met considering only the range of
volumes that are profitable.
The volume flexibility measure, Fv, measures the proportion of demand that can be met by the supply chain system.
First, we assume as in the practical setting that demand volume (D) is a random variable with an approximate
normal distribution, i.e., D ~ (𝑁(𝜇𝐷, 𝜎 2 𝐷 ) and define Omin and Omax as the minimum and maximum output volume
during any period, respectively. Now, assuming that the supply chain has sufficient data regarding demand volumes,
then the parameters of the distribution for D(𝜇𝐷, 𝜎 2 𝐷 ), corresponding to the mean demand and demand variance, can
̅ and 𝑆𝐷2 , respectively, where:
be effectively estimated as 𝐷
̅=
𝐷
𝑆𝐷2 =
∑𝑇𝑡=1 𝑑𝑡
𝑇
∑𝑇𝑡=1(𝑑𝑡 − 𝑑̅)2
𝑇−1
where dt is the demand during period t, and T is the number of periods considered.
From table 2 above it then follows that volume flexibility (Fv) can scientifically be given/ defined as follows:
𝐹𝑣 = 𝑃 (
̅
̅
𝑂𝑚𝑖𝑛 − 𝐷
𝑂𝑚𝑎𝑥 − 𝐷
≤𝐷≤
)
𝑆𝐷
𝑆𝐷
or
𝐹𝑣 = ∅ (
̅
̅
𝑂𝑚𝑎𝑥 − 𝐷
𝑂𝑚𝑖𝑛 − 𝐷
) −∅(
)
𝑆𝐷
𝑆𝐷
Where 𝐹𝑣 ∈ [0,1], and 𝐹𝑣 represents the long run proportion of demand that can be met by the supply chain system.
The relationship is illustrated in the figure below, in which the demand is standardised and represented as a standard
normal curve with mean 𝜇𝐷 and standard deviation 𝜎𝐷 .
11
Case Study
Suppose a particular supply chain has 32 weeks of weekly demand volume data available, these are confirmed
demand from clients and manufacturer produced based on customer demand – or manufacture to order. These data
are given below in Table 3.
TABLE 3
WEEKLY DEMAND – PUMP PRODUCTION
Period
(t)
1
2
3
4
5
6
7
8
Demand
(units)
16
21
32
5
18
26
40
31
Period
(t)
9
10
11
12
13
14
15
16
Demand
(units)
12
43
8
29
33
39
7
15
Period
(t)
17
18
19
20
21
22
23
24
Demand
(units)
38
19
29
12
34
49
16
30
Period
(t)
25
26
27
28
29
30
31
32
Demand
(units)
24
17
36
11
28
23
32
17
Then, for this system:
̅=
𝐷
∑𝑇𝑡=1 𝑑𝑡
≅ 24.69
𝑇
Normal distribution curve below in Figure 3 is represented by expression D ~ (𝑁(𝜇𝐷, 𝜎 2 𝐷 )
FIGURE 3
STANDARD NORMAL DISTRIBUTION
+
𝑆𝐷 = √𝑆𝐷2
From express above it follows that:
+
𝑆𝐷 = √
∑𝑇𝑡=1(𝑑𝑡 − 𝑑̅ )2
≅ 11.35
𝑇−1
If the system has a maximum profitable output of 50 units per time period, and a minimum profitable output volume
of 5 units per time period, then the volume flexibility can be given as:
50 − 24.69
5 − 24.69
𝐹𝑣 = ∅ (
)− ∅(
)
11.35
11.35
12
= ∅(2.23) − ∅(2.23) = 0.9453
Delivery Flexibility (𝑭𝑫 )
The system’s ability to move planned delivery dates forward may be important to the supply chain system. This
flexibility allows system’s ability to respond timeously to order changes, rush orders, and other late changes, and is
described in this paper as delivery flexibility. It can be expressed as the percentage of slack time by which delivery
time can be reduced. More specifically, define t as the current time period, 𝐿𝑗 as the late due date period (or the latest
time period during which the delivery can be made) for job j, and 𝐸𝑗 as the earliest time period during which the
delivery can be made for job j. If there are 𝑗 = 1, . . . , 𝐽 jobs in the system, then the total slack time for all jobs j is
given by the quantity
𝐽
∑(𝐿𝑗 − 𝑡),
𝑗=1
And he minimum delivery time for all jobs j is given by:
𝐽
∑(𝐸𝑗 − 𝑡).
𝑗=1
Thus 𝐹𝐷 , the instantaneous delivery flexibility may be measured as the proportion of excess slack across all job j,
which can be formally defined as:
𝐹𝐷 =
∑𝐽𝑗=1 ((𝐿𝑗 − 𝑡)) − (𝐸𝑗 − 𝑡)
∑𝐽𝑗=1(𝐿𝑗 − 𝑡)
Above expression can be simplified as follows:
𝐹𝐷 =
∑𝐽𝑗=1(𝐿𝑗 − 𝐸𝑗 )
∑𝐽𝑗=1(𝐿𝑗 − 𝑡)
Mix Flexibility (𝑭𝒎 )
Mix flexibility is similar to process and job flexibility. It generally measures either the range of different product
types that ma be produced during a particular time period, or the response time between product mix changes. More
specifically, Slack (1991) explain measuring mix flexibility as follows:
-
the number of different products that can be produced within a given time period; or
the time required to produce new product mix. Product mix can be mathematically expressed as:
𝐹𝑚 = 𝑁(𝑡)
Where 𝑁(𝑡) is the number of different product types that can be produced with time period t, with 𝑡 >
0 𝑎𝑛𝑑 𝑁(𝑡) ∈ 𝐼 + . The product mix flexibility response, then, may be given as:
𝐹𝑚 = 𝑇𝑖𝑗
Where 𝑇𝑖𝑗 is the changeover time required from product mix i to product mix j, with 𝑇𝑖𝑗 ≥ 0 for any i and j.
New product flexibility (𝑭𝒏 )
13
New product flexibility (𝐹𝑛 ) is defined in this paper as the ease with which new products are introduced to the
system. The introduction of new products generally involve some time for development and set-up. Sethi and Sethi
(1990) discuss measuring product flexibility as either time or cost required to add new products to existing
production operations. Time-based new product flexibility may be formally expressed as:
𝐹𝑛 = 𝐶
Where 𝐶 is the time required to add new products, with 𝐶 ≥ 0.
CONCLUDING DISCUSSION
The purpose of this article is to explain common measurement problems from a systems perspective, and to show if
and how the problems are a result of insufficient systems thinking. Among the problems described were the weak
link between strategy and actions, a heavy reliance on financial measures causing reactive behavior, and a confusing
multitude of isolated measures. Together or collectively the problems made it difficult for companies to understand
and act upon the information provided by their measurement systems. It was also argued in the article that
measurement systems seem fragmented regarding both the notions of performance and how measurements are
conducted across the supply chain.
Summary and conclusion
Performance measurement selection is a critical step in the generic design and evaluation of any system. Generally,
the larger and more complex the system, the more challenging it becomes to measure effectively. While there is an
ever increasing number of supply chain models presented in the literature, there is very little available in supply
chain performance measure selection. As such, many of the existing models use inappropriate or ineffective
performance measures that are limited in scope (non-inclusive). Of course, the use of simple performance measures
is tempting, since simple measures are more easily implemented into numerical models; however, by limiting the
scope of the performance measurement, these models ignore important performance tradeoffs.
The effects of these performance trade-offs are magnified when the supply chain is reconfigured on the basis of a
non-inclusive measurement system. In order to improve the effectiveness of supply chain models, performance
measures must be selected that will allow for a more complete and accurate analysis.
This research discusses the importance of a supply chain system to achieve simultaneously a high level of
efficiency, a high level of customer service and the ability to respond effectively to a changing environment.
Previous work in performance measurement has generally focused on:



developing new performance measures for specific applications;
benchmarking, as in Camp (1989); and
categorizing existing performance measures, as in Neely et al., (1995).
The research presented here goes beyond this previous work by establishing a foundation toward the development of
a universal framework for the selection of performance measures for supply chain systems. The categorization of
supply chain performance measures resulted in the identification of three types of performance measures that are
necessary components in any supply chain performance measurement system: resource, output and flexibility.
Although many individual supply chain performance measures exist for resources and output, the number of
flexibility measures actually applied to supply chains is few. Therefore, this paper also develops volume flexibility
and delivery flexibility measures for supply chains, and presents existing measures for mix flexibility and new
product flexibility. Supply chain models that utilize this framework can more completely characterize the supply
chain system and the resulting reconfiguration effects, thus enabling the development of models that are more
complete, accurate and therefore more effective.
In closure, given the limitations of the research underlying this article, suggestions for future research are primarily
directed at developing a better understanding of whether and how performance models should be used in order to
facilitate understanding and learning in a supply chain context. Furthermore, the adoption of systems thinking and
14
the development of more sophisticated explanations based on system structures, suggest that measurement activities
might facilitate the integration process across supply chains. More research is needed in this area also in order to
support the development of supply chain relationships.
ACKNOWLEDGEMENTS
This material is based upon work supported financially by the National Research Foundation (NRF). Any opinion,
findings and conclusions or recommendations expressed in this material are those of the author and therefore the
NRF does not accept any liability in regard thereto.
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