Abstract for: Analysing the Impact of Forecasting and Demand Patterns in Supply Chains

System Dynamics has been recognized as an invaluable tool for analysing and designing supply chains since Forrester’s phenomenal study of Industrial Dynamics. Many researchers have proposed numerous studies about different replenishment structures and inventory management policies using system dynamics methodology. Though simulation tools are not designed for precise forecasting and modellers use limited number of functionalities, calibrated system dynamics models are likely to be more informative than those from other approaches. Forecasting is the trigger of material flows in demand-driven supply chains and has always been a popular area for supply chain management researchers and practitioners. However, quantifying the impact of forecast accuracy on system performance is a challenging task. It takes time for the numbers to emerge and managerial decisions to invest in this area in terms of technology, process and organisation may be overruled by delays. In this study, we propose a methodology where we take forecast as a probability distribution of demand and assess the performance over time with various accuracy levels for both unseasonable and seasonal demand. In addition, we enrich the model with strategic growth targets and temporary demand jumps such as promotional occasions and demand decreases and conclude with other possible additions.