This paper describes a methodology for the development of a proactive balanced scorecard (PBSCM). The balanced scorecard is one of the most popular approaches developed in the field of performance measurement. However, in spite of its reputation, there are issues that require further research. The present research addresses the problems of the balanced scorecard by utilizing the soft computing characteristics of fuzzy cognitive maps (FCMs). By using FCMs, the proposed methodology generates a dynamic network of interconnected key performance indicators (KPIs), simulates each KPI with imprecise relationships and quantifies the impact of each KPI to other KPIs in order to adjust targets of performance.
Today, companies are evolving in turbulent and equivocal environments (Drucker, 1993, Grove, 1999 and Kelly, 1998). This requires companies to be alert and watchful so as to detect weaknesses (Ansoff, 1975) and discontinuities in regard to emerging threats and opportunities and to initiate further probing based on such detections (Glykas, 2004). The strategic role of performance measurement systems has been widely stressed in management literature. These systems provide managers with useful tools to understand how well their organisation is performing and to assist them in deciding what they should do next (Neely, 1998 and Glykas and Valiris, 1999).
Performance measurement systems have grown in use and popularity over the last twenty years. Organisations adopted performance measurement systems for a variety of reasons, but mainly to achieve control over the organisation in ways that traditional accounting systems do not permit (Kellen, 2003). A review of the literature shows that traditional performance measurement systems (based on financial measures) have failed to identify and integrate all those factors that are critical in contributing to business excellence (Eccles, 1991, Fisher, 1992, Hayes et al., 1988, Kaplan, 1983, Kaplan, 1984 and Maskell, 1992).
During the last decade, a number of frameworks, that help in designing and implementing performance measurement systems, has been identified in the literature, such as the balanced scorecard (Kaplan & Norton, 1992), the performance prism (Kennerley & Neely, 2000), the performance measurement matrix (Keegan, Eiler, & Jones 1989), the results and determinants framework (Fitzgerald et al., 1991), and the SMART pyramid (Lynch & Cross, 1991). These frameworks aim to assist organisations in defining a set of measures that reflects their objectives and assesses their performance appropriately. The frameworks are multidimensional, explicitly balancing financial and non-financial measures (Kennerley & Neely, 2002). Furthermore, a number of researchers have proposed a wide range of criteria for designing performance measurement systems (Globerson, 1985, Maskell, 1992 and Morris, 2002).
Despite, the existence of numerous approaches (frameworks, criteria, etc.) it is evident, from the literature, that the need for a broader research in the field of performance measurement is required. The criticism about the static nature of performance measurement systems as well as the relationships and trade-offs that exist among different measures is the catalyst for this research. Furthermore, the software applications that have been developed so far lack of an analytic capability and they cannot do predictive modelling (Morris, 2002). Despite the many attempts in this area (EIS, decision support tools), it is claiming that these tools do not necessarily advance the decision-making process.
The main objective of this research is to propose a methodology (not a new performance measurement framework) that will support existing measurement framework(s) during the process of performance measurement systems’ design, implementation and use, and to advance the decision-making process. Conforming to the most favoured approach, we have adopted the balanced scorecard, to explore the existence of trade-offs among measures within the dynamic nature of performance measurement systems that provide predictive modelling capabilities. The use of FCMs in the development of a Balanced Scorecard, will allow prospective decision-makers to incorporate their insights into the model. They may select the most preferable measures, add new ones, test the relationships between them, and visualise holistic outcomes.
This paper consists of five sections. Section 2 provides a literature review and research background; Section 3 presents the proposed methodology. Section 4 discusses the applicability of the proposed methodology. Finally, Section 5 concludes this paper.
