Predicting Performance - A Dynamic Capability View

Abstract

Production planning and resource allocation are ongoing issues that organisations face on a day-to-day basis. The study addresses these issues by developing a dynamic performance measurement system (DPMS) to effectively re-deploy manufacturing resources, thus enhancing the decision-making process in optimising performance output. The study also explores the development of dynamic capabilities through exploitation of the organisational tacit knowledge. The study was conducted using 6-stage action research for developing DPMS with real-time control of independent variables on the production lines to study the impact. The DPMS was developed using a hybrid approach of discrete event simulation (DES) and system dynamics (SD) by using the historical as well as live data from the action case organisation. Through the development of DPMS and by combining the explicit and tacit knowledge, this study demonstrated an understanding of using cause and effect analysis in manufacturing systems to predict performance. Such a DPMS creates agility in decision making and significantly enhances the decision-making process under uncertainty. The research also explored how the resources can be developed and maintained into dynamic capabilities to sustain competitive advantage. The present study provides a starting-point for further research in other manufacturing organisations to generalise findings. The originality of the DPMS model comes from the approach used to build the cause and effect analysis by exploiting the tacit knowledge and making it dynamic by adding modelling capabilities. Originality also comes from the hybrid approach used in developing the DPMS.