Development of prediction model for information technology equipment procurement as the basis of knowledge for an Intelligent Decision Support System based on carbon emissions and End-of-Life phase

The high quality of Information Technology (IT) equipment undoubtedly contributes to the seamless functioning of various industries in today’s digital era. As organizations strive to increase their IT equipment procurement, there is growing concern about its negative environmental impact. This increased environmental consciousness has made it crucial to adopt a sustainable approach to IT equipment procurement that considers factors such as carbon emissions and End-of-Life (EOL) cycle of equipment. Therefore, this research developed a prediction model for IT equipment procurement as the basis of knowledge for an Intelligent Decision Support System based on carbon emissions and EOL phase. The primary aim of this study is to develop a prediction model for IT equipment procurement that allows for the estimation of carbon emissions associated with the equipment. Several models, including K-Nearest Neighbors, Decision Tree, Polynomial Regression, Autoregressive Integrated Moving Average applied to historical procurement data, and Long Short-Term Memory, were tested to determine the most effective. The developed model has proven successful in predicting IT equipment procurement for future periods, achieving an impressive R-squared score of 0.80. This high accuracy demonstrates the model’s effectiveness in assisting organizations to make well-informed and sustainable decisions regarding IT equipment procurement based on precise predictions and estimated environmental impacts. The developed prediction model is expected to optimize the procurement process by considering environmental aspects like carbon emissions and equipment lifecycle.