(untitled) (International Journal of Engineering and Computer Science, 2019)
Demand forecasting is one of the critical functions of supply chain management since it determines the stock level of inventories and factors affecting the entire operations. This paper examines the effect of accurate forecasting procedures on inventory control and the enhancement of the supply chain through the usage of enhanced methods of forecasting. The research also analysis the industry case through both the quantitative data and qualitative research. Measures of an accurate forecast include forecast error metrics (Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE)), turnover rate of inventories, and cost of the supply chain. Findings also show that it is possible to bring down the forecast error by 10% resulting into a 20% optimization of inventories hence bringing down the levels of stock out and over stock situations. Furthermore, enhanced forecasting models are illustrated to cut total supply chain costs by up to a maximum of 15 per cent while at the same time improving flexibility to demand changes. Lastly, the sort of technology, including machine learning and real-time data analysis, is identified as highly prominent for accurate forecasts. It offers suggestions for organizations that are going to implement sophisticated forecasting techniques to improve the supply chain’s robustness and performance.