The Contribution of artificial intelligence in demand forecasting

Author

Marufu, Dylan T.

Title

The Contribution of artificial intelligence in demand forecasting

Abstract

This study investigated the contribution of Artificial intelligence in Demand forecasting using a case study of Kenac computer systems. According to Chase(2013) forecasting in the last twenty years has become more complex due to fast paced business environment. The study seeks to unveil how the new processes of demand forecasting, from traditional methods to new AI technologies have contributed to change in Kenac Computer systems towards reaching their goals. The study sought to describe how practically AI has affected or improved demand forecasting. Due to high competitiveness in the market Kenac needed to be able to improve their demand forecasting using AI as it records less errors and can be more accurate, hence reducing obsolesce or providing incorrect data. This study, however will point out how the introduction of AI has improved the company‟s sale and have more customer satisfaction. Despite the development of AI, however the role of AI remains understudied which bring about the research of this particular project. The study involved proper planning of literature in order to identify what improvements are expected from using AI. More so interviews were carried out at Kenac Computer systems with individuals from the institution. The findings presented gave an overview of the contributions Artificial Intelligence had in the various processes of demand forecasting. Issues pertaining to performance objectives were discussed vis a vi the findings and the study conclusions were mirrored with existent literature. The study also concluded that in order for AI to play a significant role in demand forecasting there need to be complementing technologies. The study revealed that necessary hardware and software was a requirement needed to achieve accuracy and proper demand forecasting outcomes. The issue in handling large data sets and also clean data that will reflect true market dynamics. Conclusively the study highlighted the contribution of AI in demand forecasting and how it corresponds to issues that deal with demand forecasting itself as an activity.

Date

June 2024

Publisher

BUSE

Keywords

Artificial intelligence

Demand forecasting

Supervisor

Mr. Chiguiswa