Change Management Strategies for Establishing Algorithm-Based Decision-Making Systems in Manufacturing Enterprises: A Case Study of MVP-Based Change Management for Implementing a Demand Forecasting System in a Food & Beverage Company

This study analyzes the impact of advancements in artificial intelligence (AI) technology on corporate decision-making systems, focusing on the challenges that arise during the transition to algorithm-based decision-making. (1) Background/Purpose: AI replaces traditional decision-making that relied on human experience and intuition by enabling more precise and faster judgments through data analysis. Although AI-based forecasting and optimization algorithms play a crucial role in supply chain management (SCM) within the manufacturing sector, there are few successful implementation cases. The primary reasons for failure are insufficient change management and inadequate user trust. (2) Study Design/Methodology/Approach: This study employs an MVP (Minimum Viable Product)-based change management model, grounded in a case study of a food and beverage (F&B) company’s implementation of a demand forecasting system. The Simple Exponential Smoothing (SES) algorithm was chosen as the MVP model, supported by Davis’s (1989) Technology Acceptance Model (TAM), which identifies perceived usefulness and perceived ease of use as key factors in technology acceptance. (3) Findings: The MVP-based demand forecasting system led to a 30% improvement in forecasting accuracy by building initial user trust through comparison of model forecasts with sales team forecasts. Workforce burden was reduced by 25%, and company revenue increased by 5% after system adoption. (4) Originality/Value: The MVP-based change management model provides a replicable methodology for companies across industries to reduce adoption risks, build user trust, and achieve successful AI-driven SCM transformation through incremental implementation and continuous improvement.