DIGITAL TRANSFORMATION AND AI-DRIVEN SUPPLY CHAIN IMPROVEMENT IN THE INDONESIAN AGROCHEMICAL INDUSTRY

Overcapacity and bullwhip effect in the agrochemical industry represent the increasing structural pressures on its supply chains. This study investigates the strategic use of Artificial Intelligence (AI) and digital transformation initiatives in Company realizing significant and quantifiable enhancements on supply chain performance, demand planning accuracy and capacity utilization. The research used a qualitative single case study design that obtained data from 10 semi-structured in-depth interviews, 14 days of field observations, and documentation analysis. A deductive-inductive thematic analysis using the analysis framework of dynamic capabilities, the sensemaking and technology-organization-environement (TOE) framework were undertaken. Four major themes emerged: (a) progressive and organization-wide digital readiness developed by staged investment and leadership commitment, (b) successful AI adoption in demand forecasting creating forecast accuracy improvements from 64% to 91% and enabling proactive decisions in the supply chain, (c) embedding informal relationship intelligence into AI-based demand sensing platforms using a ‘verified channel intelligence’ protocol - a novel mechanism of formalization, reducing bullwhip amplification from 2.7 to 1.4, and (d) data-driven capacity planning enabled by AI that increased capacity utilization from 61% to 79% and decreasing average overcapacity from 39% to 18% within 18 months. This study offers a three-phase theoretical model of digital-AI transformation based on the Indonesian agrochemical context, and practial implications for managers, industry leaders and policy makers to harness AI for competitive resilience.