A hybrid performance measurement framework for optimal decisions


Journal Article

Purpose: This paper seeks to propose a hybrid performance measurement framework integrating available frameworks and mathematical models. The hybrid framework potentially allows decision makers to move from intuitive decisions to analysis-based decisions by using a complete hierarchy of objectives, mathematical equations and a simulation of increased capabilities. To illustrate the utility of the proposed framework, this paper aims to apply the framework to a hypothetical decision-making scenario in a computer manufacturing company. Design/methodology/approach: In the proposed framework, a developed hierarchy is verified with correlation and regression analyses. Mathematical equations relating performance indicators are defined with a multiple linear regression model. An expected final outcome and uncertainty are evaluated with a Monte Carlo simulation. Findings: An organization can find consistent performance indicators based on correlation and regression analyses. In addition, based on a forecast final outcome, the organization can make proactive decisions about up-front investments in its capabilities. Research limitations/implications: A hierarchy of objectives developed in this paper is not comprehensive. A scenario used for simulating a future outcome is hypothetical. Originality/value: Although some studies illustrate mathematical equations relating objectives, the studies are limited to parts of a hierarchy and there are few practical directions. This paper proposes mathematical equations that represent vertical relationships among objectives in a hierarchy, while evaluating the importance of a performance measurement system in a big picture. Moreover, this paper explains a decision-making procedure based on a forecast outcome. © Emerald Group Publishing Limited.

Full Text

Duke Authors

Cited Authors

  • Fukushima, A; Peirce, JJ

Published Date

  • May 1, 2011

Published In

Volume / Issue

  • 15 / 2

Start / End Page

  • 32 - 43

International Standard Serial Number (ISSN)

  • 1368-3047

Digital Object Identifier (DOI)

  • 10.1108/13683041111131600

Citation Source

  • Scopus