Development of SCOR Database for Digitalisation of Supply Chain Customer Feedback Analysis

Abstract

High-level customer service, with improved quality at a lower cost, is imperative in today's global supply chain, where customers have myriad options across borders. This is in line with the primary objective of supply chain management, which is to enable and deliver high-level customer service with lower costs, reduce lead time, and improve the quality of products/services. The Supply Chain Operations Reference (SCOR) model based on KPI metrics enables an increase in the quality of products/services by monitoring and digitalising involved processes. The current paper suggests the structure of the SCOR database for Supply Chain process improvement by applying the best practices of the SCOR for Business Processes improvement. We recommend the Bayesian Belief Network (BBN) and Case-Based Reasoning (CBR) methods to estimate the influence of these improvements on Supply Chain efficiency through key performance indicators before implementation. Integrating the methods proposed in this article focuses on an approach that minimises supply chain failures, decreases failure elimination time, understands consumer needs, and offers more accurate price proposals and lead times to improve customer satisfaction. Accordingly, the integrated SCOR-based Business Process Modelling (BPM) was not previously combined with BBN to identify the most efficient ways to improve the reliability of a Supply Chain by applying best practices, which impacts the entire supply chain. Our research is limited to SME companies in electronics manufacturing, but our ambition is to develop a universal framework suitable for broader areas. The study aims to analyse how the customers see the company by combining the CDA (Customer Delivery Accuracy) and Customer Complaints measures and find the optimal way to improve business processes by applying best practices. Still, it is limited to SCOR reliability performance metrics.