Comparative Analysis of Forecasting Methods and Integration with Inventory Models for Efficient Demand Management in the Electronics Sector
DOI:
https://doi.org/10.38032/scse.2025.3.158Keywords:
Simple Moving Average, Simple Exponential Smoothing Method, Linear Regression, ForecastAbstract
Demand fluctuates over time, so forecasting future needs based on the previous data is crucial for proper inventory management. This study aims to showcase the proper way of choosing the perfect forecasting technique for a system and utilize it in the inventory model. In this study, the forecasted demands have been determined based on the sales data obtained from an electrical appliance retailer company using three forecasting techniques: Simple Moving Average, Simple Exponential Smoothing method, and Linear Regression. The forecasting results from these three methods are then examined by several error detection techniques: Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Mean Forecast Error (MFE), and Mean Absolute Percentage Error (MAPE). The Linear regression has fewer errors among the forecasting techniques than the other two. The linear regression has 207.37 for MAD, 74679.50 for MSE, 0 for MAPE, and 25.13% for MAPE. Hence, the data obtained from linear regression was used to build an inventory model. The retailer sells electronic appliances, which are finished goods, and there is no evidence of extreme fluctuation or shortage, so the purchase model with instantaneous replenishment without shortage has been chosen as the inventory model. Sensitivity analysis on this inventory model has been conducted based on different carrying costs and ordering costs, which suggested that small changes in the ordering cost or the carrying cost result in remarkable changes in the total cost, and it indicates that the sensitivity of each type of cost on this inventory model is very high. This sensitivity can be mitigated by keeping the ordering and carrying costs within a fixed range. The findings of the study focuses on the importance of robust demand forecasting for effective inventory management.
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Copyright (c) 2025 Md. Ashaduzzaman, Shakil Ahmed Khan, Nahiyan Ishmam Nawar, Atif Abrar Biswas, Md. Limonur Rahman Lingkon, Md. Sanowar Hossain (Author)
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