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Harnessing Information Systems in Behavioral Operations Management: Managing Human Factors in Supply Chains

by Md Zahidur Rahman Farazi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 1
Year of Publication: 2025
Authors: Md Zahidur Rahman Farazi
10.5120/ijca2025924783

Md Zahidur Rahman Farazi . Harnessing Information Systems in Behavioral Operations Management: Managing Human Factors in Supply Chains. International Journal of Computer Applications. 187, 1 ( May 2025), 46-56. DOI=10.5120/ijca2025924783

@article{ 10.5120/ijca2025924783,
author = { Md Zahidur Rahman Farazi },
title = { Harnessing Information Systems in Behavioral Operations Management: Managing Human Factors in Supply Chains },
journal = { International Journal of Computer Applications },
issue_date = { May 2025 },
volume = { 187 },
number = { 1 },
month = { May },
year = { 2025 },
issn = { 0975-8887 },
pages = { 46-56 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number1/harnessing-information-systems-in-behavioral-operations-management-managing-human-factors-in-supply-chains/ },
doi = { 10.5120/ijca2025924783 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-05-17T02:45:23.319004+05:30
%A Md Zahidur Rahman Farazi
%T Harnessing Information Systems in Behavioral Operations Management: Managing Human Factors in Supply Chains
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 1
%P 46-56
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper aims at investigating the contribution of human decision making towards the performance of the supply chain focusing on the Behavioral Operations Management perspective. Working with quantitative research methodology, the study investigates the impact of overconfidence and loss aversion over operational decisions in a firm using a Retailer Dataset and regression analysis. The analysis reveals that the Linear Regression model produces a mean squared error of 0.1337 and an R-squared value of 0.8569. In comparison, the Decision Tree Regression model achieves a mean squared error of 1.48 × 10⁻³¹ and an R-squared value of 1.0. The XGBoost Regression model yields a very low MSE of 1.17×10⁻⁸ and an R-squared value approaching 1. This evidence suggests that cognitive biases have dramatic effects on inventory and promotion policies, which cause suboptimal supply chain systems. Thus, the research establishes that behaviorally-updated versions of most supply chain operations management frameworks hold great potential to facilitate better decision making and aid increased supply chain performance. Future work should further extend these findings when creating particular examples of the application of these insights along with the machine learning tools to build the improved SCM models. Some of the solutions include awareness creation by furnishing decision-makers with training programs that improve their understanding of cognitive biases and the creation of conceptual tools for the resolution of such biases within the supply chain, with an aim of enhancing the efficiency of the supply chain systems.

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Index Terms

Computer Science
Information Sciences

Keywords

Supply Chain Management Machine Learning Information Systems Behavioral Operations Management Risk Management Human Decision-Making

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