How AI can help forecasting in purchasing

Abstract

Since the industrial revolution, there has been notable progress in technological innovation, leading to the transformation of many manual tasks and processes that had previously been limited by human physical capabilities for decades. In today's AI-dominated landscape, businesses heavily rely on AI and machine learning technologies. Within various industries, AI has become inseparable from operations, particularly in enhancing business forecasting- the process of predicting sales, managing stock levels, and optimising product and service sales timing. Despite the widespread adoption of AI in business, there are concerns about its potential negative impacts. Before the advent of AI forecasting, traditional methods were employed for business forecasting. This thesis aims to explore both the positive and negative aspects of AI forecasting in procurement, comparing it with traditional methods to determine which yields more accurate forecasts. The study utilises qualitative analysis and a documentary review approach, drawing data from various online sources. The findings indicate that AI-based forecasting outperforms traditional methods in accuracy. Additionally, while searching data for the study, it was found that many companies are investing in AI to streamline their overall business operations.