Demand Forecasting for a Consumer Goods Manufacturer in Benue State

This research investigates demand forecasting practices at a consumer goods manufacturing company in Benue State, North Central Nigeria, focusing on improving forecast accuracy to support production planning and inventory management decisions. Accurate demand forecasting is a foundational requirement for operational efficiency in consumer goods manufacturing, yet many Nigerian manufacturers continue to rely on informal judgement-based forecasting methods that produce high forecast errors and consequent stock-out or overstock situations. This study compares the performance of three quantitative forecasting methods: simple moving average, exponential smoothing, and linear regression trend analysis, applied to twelve months of historical sales data across five product categories. Forecast accuracy is evaluated using Mean Absolute Percentage Error and Mean Squared Error as performance metrics. Results demonstrate that exponential smoothing with an optimal smoothing constant consistently outperforms the other methods for seasonal and trend-influenced product categories, achieving an average Mean Absolute Percentage Error of nine-point-four percent compared to fifteen-point-two percent for the moving average method. The study recommends the adoption of exponential smoothing as the baseline forecasting model, supplemented by collaborative forecasting meetings with key distribution partners. A forecast review calendar and escalation protocol for significant demand deviations are also proposed. Keywords: demand forecasting, exponential smoothing, consumer goods, Benue State, forecast accuracy