Quantifying the effectiveness of SCOR measures in make-to-forecast supply chains
Effective management of an internal supply chain is a complex process whereby individuals make a conscious choice about how best to align business operations to customer demand. Reference models such as the supply-chain operations reference (SCOR) model have become popular within industry because they propose various pre- and post - performance metrics to support more effective supply chain decision making. Despite the merits and popularity of the SCOR model, further research is required to empirically verify the performance metrics that best align to specific customer demand patterns. Using theoretically derived customer demand patterns that provide the stimuli for a lean, agile or mass production supply chain strategy, this study evaluates the effectiveness of common SCOR performance metrics. The metrics characterise each of the four primary building blocks in the SCOR reference model plan, make, source and deliver. Empirical evidence is based on a simulation model that mimics the BlueScope Steel supply chain; a large make-to-forecast process industry in the Asia Pacific region. The simulation model provides a test bed where the most optimal combinations of supply chain strategy and performance metrics can be experimentally derived. Results indicate that a performance metric's ability to capture the alignment between business operations and customer demand can be augmented by aggregation with additional performance measures. The study concludes with a discussion on the methodological difficulties that researchers face when attempting to empirically examine multidimensional reference models such as SCOR.