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.