A DATA-DRIVEN FRAMEWORK FOR INCREMENTAL SUPPLY CHAIN OPTIMIZATION

Purpose: This paper develops an incremental supply chain modeling framework using Big Data and Data Science methods to enhance decision-making, adaptability, and operational efficiency.By integrating advanced analytics, it offers a scalable approach to optimize realworld supply chains.Design/methodology/approach: The model was developed utilizing the Design Science Research (DSR) methodology, ensuring a structured and rigorous approach to its creation and validation.I propose a four-phase approach-data collection, analysis, decision-making, and model enrichment-combining theoretical insights from supply chain modeling with practical Data Science methods.This iterative method was tested on a retail supply chain to validate its utility. Findings:The results show that incremental modeling enhances adaptability by enabling quick reconfiguration amid changing conditions and improves decision-making through analytics integration.Each iteration reduces both the time and cost of technical implementation.Research limitations/implications: Because the study focuses on one retail supply chain, the findings may not be fully generalizable.Future research could examine the framework in other sectors or explore its scalability to more complex supply chains.Practical implications: By leveraging Big Data and Data Science, the framework provides deeper supply chain visibility, refined resource allocation, and cost reduction.Real-time insights also strengthen alignment between supply chain strategy and operational execution.Social implications: More efficient resource use and lower waste levels support sustainable supply chain practices.These insights can guide corporate policies on environmental and social responsibility, while promoting data-driven decision-making.Originality/value: This paper presents an innovative incremental model integrating Big Data and Data Science methods.It aids managers, analysts, and policymakers in boosting operational efficiency within dynamic environments.Moreover, it accounts for the resources and skills required at each stage of supply chain modeling.