Defensive Future Studies

Defensive Future Studies

Prioritizing Demand Forecasting Methods in the Supply Chain of the 9th Supply Class of the Islamic Republic of Iranian Army Land Forces

Document Type : Original Article

Authors
Nader Shamami 1
Mahdi Saki 2
Hasan Ghzi 3
1 Assistant Professor of Industrial Engineering, IRI Military command and staff university, Tehran, Iran
2 Master's student in Defense Management, IRI Military command and staff university, Tehran, Iran.
3 Assistant Professor of Strategic Defense Sciences, IRI Military command and staff university, Tehran, Iran.
10.22034/dfsr.2025.2061881.1910
Abstract
Objective: Examining and prioritizing different methods of demand forecasting in the supply chain of the 9th Supply Class of the Islamic Republic of Iranian Army Land Forces.
Methods: real-world data related to the demand for these items were collected for two units. Five forecasting methods were used to analyze demand patterns: moving average, weighted moving average, exponential smoothing, adjusted exponential smoothing, and linear regression. The performance of the methods was evaluated using the Mean Absolute Error (MAE) and Mean Squared Error (MSE) metrics, and statistical differences between them were assessed using ANOVA and Tukey's tests. The research method employed is a descriptive comparison, in which we compared demand forecasting methods based on two variables without considering the cause-and-effect relationship between the variables.
Findings: The results indicated that for three items (light tires, heavy tires, and light batteries), the linear regression method had the lowest error, while for the fourth item, heavy batteries, the best performance was related to the moving average method. The ANOVA test for the three items showed that there was no significant difference between the methods, but for heavy batteries, a significant difference was observed.
Conclusion: The results indicated that it is best to choose the appropriate forecasting method based on the type of item and its demand pattern. Although the linear regression method often showed. Therefore, a thorough evaluation of the characteristics of each type of item and its operating environment is key to successfully improving forecasting processes in the supply chain.
Keywords
Demand Forecasting Methods
Supply Chain
Supply Class 9

Subjects

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