Enhancing Supply Chain Performance through Comparative Ranked SCOR Digital Standard Practices

The demand and supply chains are complex, multi-tiered and multifaceted systems that operate in unpredictable environments involving continuous feedback. Therefore, organizations must act dynamically to address these fluctuations resulting from large and emerging markets across borders. Although a lot of information is available on performance and practices, practitioners often lack a comprehensive framework to enhance the performance of the entire supply chain. In order to achieve this goal, this article screens 106 analytics and technology-centric (A&Tcentric) practices and then ranks 23 selected practices of the SCOR Model 14.0 (Digital Standard or SCOR DS). The practices were ranked using several multi-criteria decision-making techniques, namely, Relative to an Identified Distribution (RIDIT), Grey Relational Analysis (GRA), and a hybrid approach consisting of Analytical Hierarchy Process-Technique for Order of Preference by Similarity to Ideal Solution (AHP-TOPSIS). The AHP was employed with the help of eight industry and academic experts with a minimum of 10 years of experience. Whereas, for RIDIT, GRA and TOPSIS, the data were collected from 400 operational managers and supervisors working in the manufacturing sector. The top five practices that emerged through the ranking are Demand Planning and Forecasting, Big Data Analytics, Digital Supply Chain, Artificial Intelligence, and the Internet of Things (IoT). These data-driven findings are valuable for decision-makers of manufacturing organizations who can concentrate on these top-ranked A&T-centric practices of the SCOR Digital Standard framework in order to enhance their supply chain performance.