Abstract
Global supply chains, specificity, diversified product portfolios and different customers delivery requirements, COVID's lasting impact, disruptive environments, and digitization innovations are creating more and more complexity in supply chains. The purpose of this book chapter is to explore the factors that impact digital supply chain segmentation and provide a best practice transformation approach to ensure a successful implementation of the strategy. The chapter includes a literature review of supply chain segmentation and the application of relevant segmentation transformation steps to specific case studies. Supply chain digital segmentation strategy presents huge opportunities that are being tapped by very few companies who achieved significant benefits and gained competitive advantage. The chapter provides a practical and proven supply chain digital segmentation framework for companies who are about to take the segmentation transformation journey.
TopBackground
Definition of Supply Chain Segmentation Concept
Sabri (2015) defined supply chain segmentation as managing profitably different virtual end-to-end supply chains defined by a combination of channel/customer requirements, product characteristics, business value considerations, and differentiated supply response strategies.
Key Terms in this Chapter
Predictive Analytics: According to Wikipedia: Predictive analytics encompasses a variety of statistical techniques from predictive modelling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.
Governance: Encompasses the establishment of organizational structure, metrics, & policies, and monitoring of their effectiveness.
Transformation: The journey of taking an organization in a new direction and reaching an entirely different level of effectiveness. It is a change to processes, systems, structure, and culture.
Supply Chain Digital Segmentation: Profitably managing different virtual end-to-end supply chains defined by a combination of channel/customer requirements, product characteristics, business value considerations, and differentiated supply response strategies leveraging digital technologies.
Clustering: The process of associating and grouping very similar (but not identical) profiles that reveals patterns, trends, requirements, characteristics, relationships, and structures.
End-to-End Supply Chain: The philosophy of considering all value-added processes from supplier to customer and embracing the concept of eliminating middle steps and barriers to optimize the performance of supply chain.
Machine Learning: According to Wikipedia: Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to “learn” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.
Profiling: The process of learning information about behavior patterns, business characteristics, trends, requirements, relationships, and structures.