Tackling Supply Network Complexity with Customer Segmentation

Companies’ strategies to generate superior customer value to grow profits and ensure long-term viability often increase the complexity of supply chains. The literature discusses concepts of segmentation and shows numerous parameters for segmentation to better manage complexity. Still, research on real-world cases are rare and the link to better apply segmentation is lacking. This research develops and tests a procedure for segmenting customers based on logistics demands, examining the impact on supply chain performance.
Using a design science research (DSR) approach within an explorative single-case study, the study developed an artifact to understand customer segmentation behavior. Pattern-matching logic was used to compare segmentation attempts over time and at various maturity levels. Results show that a structured segmentation procedure based on logistics criteria can reduce complexity and improve resource allocation, leading to lower inventory levels and higher planning accuracy. The presented DSR approach produces actionable knowledge that prescribes how decision-makers should use an artifact to manage supply chain complexity.
The study provides supply chain managers with a method for segmenting customers by logistics demands, offering actionable insights for managing complexity. This research contributes a practical method for adjusting supply chain complexity, bringing in a new twist for the discipline.