Predictive Supplier Risk Modelling in Wholesale Distribution: An SPSS-Based Quantitative Study
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I4P116Keywords:
Supplier Risk Management, Predictive Risk Modelling, Wholesale Distribution, Supply Chain Resilience, SPSS Quantitative Analysis, Supplier Performance Evaluation, Multiple Regression Analysis, Factor Analysis, Risk Segmentation, Proactive Risk MitigationAbstract
Wholesale distribution is subject to operating interruptions when suppliers fail to perform adequately. Stock-outs, unexpected delays, premeditated quality defects, etc. affect profits, reputations and adherence to agreed service-levels. The classical “scorecard” means of evaluating suppliers rely heavily on memory, subjective responses or “star” rating systems that cannot forecast evolving risk levels nor blend well into an increasingly “multi-tier” distribution channel. This paper attempts to investigate the predictive modelling of supplier risk based on quantitative analytic methods using SPSS_. Utilising a real-world case-study taken from a wholesale distributor, the research delves into the development of an experimental model using actual responses to survey-scales, supplier indicators of performance and constructs of risk. Using other risk models as a base, a statistically validated predictive risk model is developed in the context of a supply chain using reliability assessment, factor reduction, correlation analysis and multiple regression modelling procedures. The consequent findings show that supplier risk is predicted from delivery consistency, defect rates, responsiveness, financial stability and communication efficiency. The research develops a “refined” predictive model that explains a significant portion of variance in the overall risk scoring of wholesaler suppliers, used as a basis for supplier “segmentation” and for “pro-active” risk mitigation. Such research can be easily replicated on behalf of wholesale distributors who wish to develop user-defined downside-risk modelling against an up-to-date notion of resilience
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