IMPROVING SALES FORECAST ACCURACY USING LEAN SIX SIGMA: A CASE STUDY IN THE FMCG INDUSTRY
Published:
2026-02-10Downloads
Abstract
Sales forecasting accuracy is crucial for operational efficiency, inventory optimization, and service reliability in commodity and logistics industries. ABC Company faces persistent challenges in achieving reliable forecasts, leading to frequent rescheduling, shipment adjustments, and cross-functional misalignment. This research aims to identify the major factors affecting forecast accuracy at ABC Company, develop solutions to address root causes, and determine the most appropriate forecasting approach. Using the Lean Six Sigma DMAIC framework, it analyzes historical data (Jan 2024-Dec 2025) with MAPE, Forecast Bias, and RMSE metrics. Qualitative data from stakeholder interviews reveal primary drivers: weak forecasting governance, insufficient integration of execution feasibility, lack of stable forecasts, and unstructured expert judgement. The study proposes a four-stream improvement framework focusing on forecast governance stabilization, feasibility integration, quantitative forecast improvement, and structured expert judgement integration via Expert Knowledge Elicitation. A control mechanism with SOPs, RACI, KPI monitoring, and regular reviews ensures sustainable improvement.
Keywords:
Sales Forecasting Forecast Accuracy Lean Six Sigma DMAIC Holt-Winters Expert Knowledge Elicitation Supply Chain Planning.References
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