Internal and External Benchmarking

As the broad categorisation for the variety of performance measurement systems shows, there is a trend to consider external influences starting around the year 2000. However, just because external influences (customers, suppliers, employees, society) are taken into account, one cannot conclude that the means for external benchmarking are available at the same time. In fact, as framework for analysing supply chain performance evaluation models by Estampe, Lamouri, Paris and Brahim-Djelloul (2013) points out, most models are only suitable for internal benchmarking.

Thus our analysis of the state of the art also covers recent performance measurement approaches like SCOR® or ENAPS, providing a consistent supply chain orientation. The

2. Measuring Supply Chain Performance SCOR® reference model is one of the most popular approaches to include supply chain partners. SCOR® is one of few models including an internal and external focus.

Aside from the possibility of standardising the supply chain as referred to above, the SCOR® model also includes ways of measuring overall effectiveness. In their review, Huan, Sheoran and Wang (2004) point out that the SCOR® framework has the potential to become industry standard. It is one of the most common frameworks (Huan et al., 2004, p. 28). There are 12 performance measures classified into the categories (i) delivery reliability, (ii) flexibility and responsiveness, (iii) costs and (iv) assets. Table 1 provides an overview and classifies the performance measures.

Table 1: SCOR® Model Performance Measures (Huan et al., 2004, p. 25)

Delivery reliability Flexibility Costs Assets

Delivery performance Supply chain responsiveness

Total logistics management cost

Cash-to-cash cycle time

Fill rate Production flexibility Value-added employee productivity

Inventory days of supply

Order fulfilment lead

time Warranty costs Asset turns Perfect order

fulfilment

To explain network dynamics across the supply chain, it is possible to integrate software tools. The previously mentioned supply chain management software aims to support supply chain decision-making and profitability. However, as Huan et al. (2004, p. 26) point out, supply chain management software is criticised as being:

 very expensive,  hard to implement,  difficult to use,

 sensitive to compatibility problems.

Compatibility issues between the different companies can only be avoided if all tools, such as for example company-specific ERP solutions, are consequently integrated into SCOR® (Huan et al., 2004, pp. 25–26). From the initial aim to improve supply chain performance, the question arises if, based on the given 12 performance measures, quantifiable measures for supply chain performance can be derived. According to Huan et al. (2004, pp. 26–28), this is possible either by determining absolute priority or by setting the relative importance of different metrics.

2. Measuring Supply Chain Performance The ENAPS process performance model is another model with both an internal and external focus and was developed by the European Network for Advanced Performance Studies (Estampe et al., 2013), (Rolstadas et al., 2004). The ENAPS approach is based on a network of agents in most European countries. The aim of the project is to introduce a solution for advanced business process performance within the process oriented industry (Rolstadas et al., 2004, p. 1). The development of a benchmarking database is essential to ENAPS, the actual benchmarking of companies itself being performed by agents (Rolstadas, 1998, p. 994). The system includes performance indicators on three different levels:

 the enterprise level is very general and suits every manufacturing enterprise.

 the process level emerges out of functions or sub-processes. Exemplary indicators are product development efficiency, outgoing delivery quality and average time to solve complaints.

 the function level is company specific and grouped under the process levels.

The independent network agents measure indicators on the different levels throughout the company including accounts, product development, marketing and sales, planning and production, customer services, purchasing, personnel and others (Rolstadas et al., 2004, p. 18). Then follows the development of quantitative indicators with regard to time, cost, quality, volume, flexibility and environment (Rolstadas et al., 2004, pp. 19–21).

ENAPS consists of the questionnaire-based approach advanced from the TOPP framework described above. The framework is a top down approach used to develop measures and indicators. In case of the same industry and a comparable manufacturing environment, it becomes possible to compare the own company to others. Thus, the underlying assumption of ENAPS is the existence of an optimum that allows comparability within a particular industry. The optimum is given by the company with the most desirable levels of performance. Following this proposal, all similar companies should use the same set of performance measures that focus on the company itself and its position as compared to competitors. However, this approach does not include aspects related to a network orientation or any collaborative elements.

Several other approaches like VICS (2004), Hieber and Schönsleben (2002) or Papakiriakopoulos and Pramatari (2010) build on cooperation across specific supply chains. In this context, each supply chain consists of different companies willing to cooperate. Although strong cooperation helps to improve performance and to reduce the bullwhip effect (fluctuation across the supply chain), the supply chain network itself, being the latest evolution step, remains unconsidered.

2. Measuring Supply Chain Performance Moving beyond the described models, the change from dyadic relationships to the formation of complex supply chain networks raises new issues on performance measurement. Clearly, recognising the importance of dyadic relationships alone is not enough. The expansion of the company’s own boundaries towards supply chain integration may only be a first step. Known trends like global sourcing, the internationalisation of distribution and the search for cheap manufacturing labour are factors stimulating the change from dyadic relationships to the design of complex networks (Lambert & Pohlen, 2001). Companies are part of many different supply chains that form a supply chain network. Therefore we point out the transition from a hierarchical structure to a network structure. This means that there seems to be a demand to extend performance measurement in the light of the supply chain network.