by Dimension-based Measurement Systems (DBMS)
DBMS concept is based on the premise that any supply chain can be measured on dimensions (Ramaa et al., 2009). Initially in 1999, Beamon (1999) identified three types of measures as necessary components in supply chain performance measurement systems, namely: Resources (R), Output (O) and Flexibility (F). She believed that each of these types is vital to reflect the overall performance success of a supply chain and that the result of each type affects the others.
Examples of resource performance measures are manufacturing cost, inventory cost and return on investment (ROI). Output measures include total sales, on-time deliveries and fill rate, whereas flexibility measurements measure volume changes and new product introduction.
Another example of DBMS is that identified by Hausman (2003) who suggests that a supply chain needs to perform well on three key dimensions: Service, Assets and Speed.
Service related to the ability to anticipate, capture and fulfil customer demands. Assets involve anything with financial value such as inventory and cash, while speed includes metrics that are time-related to track responsiveness and velocity of execution.
DBMS are generally simple, flexible and easy to implement; however, they don’t reflect the performance of internal functions and operations within the chain since they only focus on top level measures.
Interface-based Measurement Systems (IBMS)
IBMS was primarily put forward in 2001 by Lambert and Pohlen (2001). They proposed a framework in which performance of each stage is linked within the supply chain. The framework begins with the linkages at the focal company and moves outward one link at a time. This link by link approach provides a means for aligning performance from point of origin to point of consumption with the overall objective of maximizing the shareholder value for the entire supply chain as well as for each individual company. The IBMS approach theoretically looks good but in actual business setting, it requires openness and total sharing of information at every stage which is eventually difficult to implement.
Perspective-based Measurement Systems (PBMS)
PBMS look at the supply chain in all possible perspectives and provides measures to evaluate each of them. They were developed in 2003 by Otto and Kotzab (2003) who identified six main perspectives as follows: System Dynamics, Operations Research, Logistics, Marketing, Organization and Strategy. The authors presented six unique sets of metrics, one for each perspective, to measure performance of supply chains.
An example of a PBMS is the Logistics Scoreboard in which recommended performance measures focus only on logistical aspects of the supply chain. They fall into the following general categories: logistics financial performance measures (ex: expenses and return on assets), logistics productivity measures (ex: orders shipped per hour), logistics quality measures (ex: shipment damage) and logistics cycle time measures (ex: order entry time).
PBMS provides different vision to evaluate the supply chain performance. However, there might be a trade-off between measures of one perspective with measures of other perspectives.
Hierarchical-based Measurement Systems (HBMS)
In 2004, Gunasekaran et al. (2004) developed HBMS in which measures are classified as strategic, tactical or operational. The main idea was to assign measures where they can be best dealt with by the appropriate management level, thus facilitating quick and appropriate decisions. The metrics are further distinguished as financial or non-financial. Such systems tie together the hierarchical view of supply chain performance measurement and maps the performance measures specific to organization goals. However in such systems, a clear guide cannot be made to put the measures into different levels that can lead to reduced levels of conflict among the different supply chain partners.
Function-based Measurement Systems (FBMS)
FBMS is one in which measures are combined to cover the different processes in a supply chain. It was originally developed in 2005 by Christopher (2005) to cover the detailed performance measures applicable at different linkages of the supply chain. Though easy to implement and targets can be dedicated to individual departments, it does not provide top level measures to cover the entire supply chain. FBMS are generally criticized for viewing the separate supply chain functions in isolation with the overall strategy and hence results in localized benefits that may harm the whole supply chain.
Efficiency-based Measurement Systems (EBMS)
EBMS are systems that measure the supply chain performance in terms of efficiency. Several approaches were developed in this context provided a framework to study supply chain performance by developing a Data Envelopment Analysis (DEA) model for the internal supply chain performance efficiency using case study applications.
Chen et al. (2006) investigated the efficiency existing between two supply chain members. They proposed several DEA-based supply chain efficiency functions aimed at identifying the inefficiency among the chain members by developing two efficiency functions. They established the existence of several Nash1 equilibriums in the supplier manufacturer game.
Liang et al. (2006) developed a new DEA based approach to measure the supply chain efficiency when intermediate measures are built into the evaluation scheme. It aimed at correcting the inadequacies of the conventional DEA model when evaluating multi-member supply chain operations directly. Berrah and Cliville (2007) developed a framework which linked elementary performance expression to the overall performance of a supply chain. Aggregation was done using the Choquet integral Operator. Their approach allowed for the comparison of situations conventionally considered incomparable.
Most of the EBMS are DEA-based. Despite being very useful, they suffer the main limitations of the conventional DEA approaches in any other context. The efficiency measured is only a relative one. It determines the efficiency of different units within the supply chain relative to each other and not versus a previously set target value or a best practice. This might sometimes be misleading to managers and stakeholders.
Generic Performance Measurement Systems (GPMS)
Since the early 1980s, a number of generic performance measurement models and frameworks, i.e. not necessarily specific to supply chains, have been developed. Each of which has its respective benefits and limitations. However, the literature review indicates that only very few of them (Tangen,, 2004; Kurien and Qureshi , 2011) are widely cited and referred to as discussed below.
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Performance Prism
The performance prism is a performance measurement framework that suggests performance should be measured across five distinct, but linked, perspectives of performance as indicated by: stakeholder satisfaction, strategies, processes, capabilities and stakeholder contributions.
The performance prism has a much more comprehensive view of different stakeholders than other frameworks. The major strength of this conceptual framework is that it first questions the company’s existing strategy before the process of selecting measures is started. Hence, it ensures that the performance measures have a strong foundation.
The performance prism also considers new stakeholders (such as employees, suppliers, alliance partners or intermediaries) who are usually neglected when forming performance measures. Although the performance prism extends beyond traditional performance measurement, a main drawback is that it offers little about how the performance measures are going to be identified and selected.
ii. Performance Pyramid
The purpose of the performance pyramid is to link an organization’s strategy with its operations by translating objectives from the top down (based on customer priorities) and measures from the bottom up (Kurien and Qureshi, 2011; Lynch and Cross, 1991). This framework includes four levels of objectives that address an organization’s external effectiveness (left side of the pyramid) and its internal efficiency (right side of the pyramid) as demonstrated in Tangen (2004). The development of a company’s performance pyramid starts with defining an overall corporate vision at the first level, which is then translated into individual business unit objectives. The second-level business units are short-term targets of cash flow and profitability and long-term goals of growth and market position. The business operating system bridges the gap between top-level and day-to-day operational measures such as customer satisfaction, flexibility and productivity. Finally, four key performance measures: quality, delivery, cycle time and waste, are used at departments and work centers on a daily basis.
iii. Medori and Steeple’s Framework In 2000, Medori and Steeple (2000) developed and presented an integrated framework for auditing and enhancing performance measurement systems. The graphical framework of their approach is presented in Medori and Steeple (2000). It consists of six detailed stages. Similar to most frameworks, the starting point begins with defining the company’s manufacturing strategy and success factors. In the next stage, the primary task is to match the company’s strategic requirements from the previous stage with competitive priorities. Then, the selection of the most suitable measures takes place in the following stage. After the selection of measures, the existing performance measurement system is audited to identify which existing measures will be kept. An essential activity is the actual implementation of the measures. The last stage is based around the periodic review of the company’s performance measures. An important advantage is that it can be used both to design a new system and to enhance an existing one. It also contains a unique description of how performance measures should be selected. Its limitations are mainly located in the second stage, where a performance measurement grid is created in order to give the system its basic design. Little guidance is given in this stage and the grid is only constructed from six competitive priorities whereas performance measures can be divided into many other categories.
In an earlier literature survey on SCPM, Ramaa et al. (2009) have previously classified SCPM systems into seven distinct types. However in this review, other two novel groups are added to their classification, namely: EBMS and GPMS. The first refers to the performance measurement systems that aimed at measuring the efficiency of supply chains and groups them into one category, while the latter is composed of the common performance measurement frameworks available in literature that can be used for SCPM.
