Inventory management and demand forecasting are fundamental to supply chain management. Good integration of these two tasks is critical to organizational performance (Balachandra et al., 2020). Within this context, forecasting approaches range from qualitative techniques through time series analysis and, eventually, informal models. However, qualitative and causal forecasts are considered to be ineffective as inputs to inventory and scheduling decisions.
An effective forecasting process allows the company to prepare for all possibilities, estimate the likelihood of attaining forecast values, and assess the risk to the organization of failing to achieve them. In inventory management, it is necessary to assess demand uncertainty, which is often represented in estimates of demand throughout the lead time period (Kourentzes et al., 2020). Predictions in qualitative demand forecasting are based on professional knowledge of how the market functions (Chapman, 2021). Meanwhile, causal forecasting incorporates the past and considers relationships and unique events (Diezhandino, 2022). The ineffectiveness of these approaches in inventory and scheduling decisions can be described by their inability to determine exact data. In other words, as only assumptions can be projected in these approaches, it will not help with inventory and scheduling decisions. Moreover, because it takes a long time to analyze these forecasts and find answers, they cannot be used as inputs for inventory and scheduling choices. Additionally, the data obtained must rely on each other when individual data collection is not possible, so data analysis using the forecast technique cannot be used as an input to inventory.
Forecasting is essential for planning, scheduling, and enhancing supply chain coordination. Proper demand forecasting gives businesses vital information about their prospects in their present and other markets, allowing managers to make educated pricing, corporate expansion strategies, and market potential decisions. However, qualitative and casual forecasting are not useful as inputs to inventory and scheduling management mainly because they cannot predict the exact data and it takes a long time to analyze the obtained information.
References
Balachandra, K., Perera, H., N., & Thibbotuwawa, A. (2020). Human factor in forecasting and behavioral inventory decisions: A system dynamics perspective. Proceedings of the 7th International Conference, 516-526.
Chapman, M. (2021). An introduction to quantitative & qualitative inventory forecasting models.Eazystock. Web.
Diezhandino, E. (2022). Importance and benefits of forecasting customer demand.Keepler. Web.
Kourentzes, N., Trapero, J., R., & Barrow, D., K. ( 2020). Optimising forecasting models for inventory planning. International Journal of Production Economics, 225.