The Demand Forecasting And Aggregate Production Planning: A Case Study Of Nok Vietnam Company

Abstract

One of the most critical industries for both developed and developing economies is the automobile industry. The massive issue of unknown automobile demand significantly affects the manufacturing environment's productivity. Therefore, demand forecasting and production planning are essential elements of supply chain management and are crucial to the efficient operations of companies in a variety of sectors. This research paper provides a method for selecting forecasting models to help a corporation select strategies that are proper for its needs. Three-time series (TS) models—the Exponential Moving Average (EMA), ARIMA, and Holt's Winters—are evaluated for performance. Three errors are used in the evaluation phase to measure how exact the forecasts are: Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Following that, the aggregate production plan model, whose aim is to reduce both inventory and backorder costs, receives the forecasted data with lower inaccuracy from three forecasting techniques. The results show that, in comparison to other models, the EMA approach provides lower error values. The potent software was created to handle the problem of the aggregate production plan. It can deliver high-quality solutions to optimization issues.