| dc.description.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. | en_US |
