Supply Chain Decision Making Under Demand Uncertainty and the Use of Control Systems: A Correlational Study.

摘要

Decision making under demand uncertainty, a top priority task, has remained as the most challenging problem to many manufacturing leaders due to lack of sufficient information to establish supply chain management (SCM) standard policies. The problem was that business performance could be impeded because optimization models of existing SCM systems lacked appropriate control mechanisms to optimize inventory levels and reduce the bullwhip effect. The purpose of this quantitative correlational study was to investigate the extent to which SCM control mechanisms predict optimized inventory levels (OPT) and reduced bullwhip effect (BWE) based on the perceptions of supply chain (SC) senior-level managers of medium-size and large manufacturing firms in the United States. Model predictive control-based inventory optimization (MPC), internal model control-based inventory optimization (IMC), postponement (POS), and collaboration (COL) were used as predictor variables, and SCM performance was the criterion variables as measured by OPT and BWE. A survey was used to collect data from SC senior-level managers. Regression analysis resulted in two significant regression models for OPT and BWE that explained 61% and 49.7 % of the variance respectively for OPT (p < .05) and BWE (p < .05). As a result, both null hypotheses 1 and 2 were rejected, and support existed for the alternative hypotheses 1 and 2. Practical recommendations included use of MPC to optimize inventory levels, use of POS and COL strategies to reduce the bullwhip effect and optimize inventory levels, and to combine IMC, MPC, POS, and COL to synergistically reduce the bullwhip effect and optimize inventory levels. Recommendations for future research included a replicate quantitative correlation study with expansion to international manufacturing firms, a quantitative structural equation modeling study to examine relative strength and causal relationships among variables, a quantitative meta-analysis study to critically examine the findings of the study across other studies, a quantitative experimental study to further scrutinize the significant relationships between OPT and BWE, and a quantitative experimental study of archival data to reduce self-selection and self-reporting sampling biases.