Quantitative models for performance measurement systems—alternate considerations
Introduction
Suwignjo, Bititci and Carrie (SBC; Suwignjo et al., 2000) and Bititci, Suwignjo and Carrie (BSC; Bititci et al., 2001) provide an innovative framework and supporting system that allows organizations to incorporate and map performance measures in a hierarchical fashion. Central to this approach is the development and application of their tool defined as the quantitative model for performance measurement system (QMPMS) that relies on the analytic hierarchy process (AHP) to quantify factors (tangible and intangible) for performance. They decompose their process into three steps (p. 231 of SBC):
- 1.
identification of factors affecting performance and their relationships,
- 2.
structuring the factors hierarchically,
- 3.
quantifying the effect of the factors on performance.
These three steps are appropriate for use within an AHP framework as described by Saaty (1980), Saaty (1996). Yet, the hierarchy they form in SBC is more of a network hierarchy, which incorporates a number of inter-relationships. Using this network formation, we recommend the use of a technique developed by Saaty (1996) that incorporates various “feedbacks” for the generation of a stable set of weights incorporating the three effects detailed by SBC. This feedback model has also been defined as the analytical network process (ANP) (Hamalainen and Seppalainen, 1986; Saaty, 1996). SBC do mention the use of ANP for reducing the rank-reversal problem. Yet, the advantages of the ANP approach goes even further allowing for a direct calculation of the combined effects of all the factors, utilizing a Markovian process and a more complete set of relationships that are allowed to flow through the network.
To show how these inter-relationships can be modeled, we will review the various relationships (effects), their formation into a supermatrix and then their calculation. This alternative approach can provide a single coherent model without the many, separate identification and iterations of various hierarchies, paths, and detailed factor aggregations.
In addition, Bititci et al. (2001) (BSC), apply their QMPMS technique for a “dynamic environment” which can be used for manufacturing strategy evaluation and management. We also extend the BSC model for manufacturing performance evaluation to incorporate the feedback mechanisms that can form one coherent long-term strategy for the organization as the current and future dynamics are considered.
Thus the contributions of this paper are to: (1) show how the supermatrix approach can be applied to the QMPMS process with fewer requirements of path (or cognitive map) identification through one aggregate model, and (2) show an alternative modeling of the dynamic nature of strategic decisions based on performance measurement.
Section snippets
Factor relationships and effects
SBC defined three relationships of factor influences. They did state that this was a critical and important step in the process. The definition and identification of these relationships are also part of the ANP model, which still requires pairwise comparisons as input. The more relationships that are modeled, the more questions that need to be answered by decision makers, auditors, and/or managers, which adds to the complexity of the model. There were three effects identified when describing
Using ANP with strategy management
To further show the application of ANP, and as an extension to the QMPMS approach, we take the example of the BSC paper. The BSC paper seeks to evaluate the performance of alternative manufacturing strategies using QMPMS. They recommend using sensitivity analysis for dealing with dynamic environments. Here, we show an extension of the decision model of BSC by explicitly modeling a multiperiod time horizon that lessens the need for sensitivity analysis by explicitly modeling the multiperiod or
Summary and conclusion
In this paper, we have shown an alternative method to quantify the combined effects of factors on organizational performance measures using the supermatrix approach. The technique still has some of the difficulties of the AHP approach mentioned in SCB, such as the perceptual subjectivity of decision-maker input and slight problems with rank reversal, even though some have posited that the rank-reversal problem can be mitigated through the supermatrix approach (Saaty, 1994; Schenkerman, 1994).
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