| Historically, most firms have operated in an anticipatory manner. Today, supply chain design focuses on agility and responsiveness. Regardless, forecasts are often still necessary because of longer and more uncertain lead times, service requirements, and capital constraints.; This dissertation investigates the impact of Forecasting Accuracy on Supply Chain Performance. Specifically, this research evaluates the effects of Forecast Bias, Forecast Skewness, Transit Lead Time Variability, and Demand Variability on Order Fill Rate, Case Fill Rate, and Average System Inventory.; Results from dynamic simulation experiments indicate that Forecast Bias is the primary factor, substantially affecting all performance measures. This impact is amplified at higher levels of demand and transit lead time variability. Lead Time Variability has a substantial impact on service, while Forecast Skewness has medium impact on inventory. Additionally, results suggest a trade-off impact of Forecast Bias on service and inventory.; Managerial guidelines are developed after a cost trade-off analysis is conducted. The framework identifies acceptable levels of Forecast Bias, and advises which actions can be taken to control and minimize its effects. |
