Strategic Cost Management using AI and ERP Systems: An Expanded Case Study of Composite Textile Factories

Abstract

Ripon Chandra Das

This paper investigates how the convergence of Artificial Intelligence (AI) and Enterprise Resource Planning (ERP) systems reshapes strategic cost management (SCM) in composite textile factories — vertically integrated facilities combining spinning, knitting/weaving, dyeing-finishing, and garment manufacturing. Using a mixed-methods case- study design across six composite mills in Bangladesh (combined turnover ≈ USD 612 M), the study triangulates 142 practitioner surveys, 18 semi-structured executive interviews, 24 months of plant-level ERP transaction data, and a curated open dataset hosted on Mendeley Data. Results show that AI–ERP integration reduced unit conversion cost by 9.4%, lifted forecast accuracy from 68% to 89%, improved Overall Equipment Effectiveness (OEE) by 16 percentage points, and produced an average payback of 13 months. Beyond these headline metrics, the study foregrounds the organisational, informational, and cultural preconditions that separate factories that capture the promised value from those that do not. It offers three principal contributions: (i) a conceptual framework linking AI–ERP capabilities to Activity-Based Costing, Target Costing, and Kaizen Costing; (ii) an empirically validated map of adoption barriers, weighted by severity; and (iii) a practitioner playbook for South-Asian textile manufacturers navigating simultaneous margin pressure from compliance, energy tariffs, and ESG mandates. The paper argues that in the current competitive environment, AI–ERP convergence is no longer a discretionary IT programme but a core strategic-cost-management capability.

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