The impact of artificial intelligent (AI) on inventory management and cost efficiency in the supply chain.

Abstract

This dissertation discusses the impact of artificial intelligence on inventory management and cost efficiency. The research shows how AI improves in the supply chain such as forecasting, replenishment, and warehouse operations. At the same time, it shows the challenges facing the small and medium sized enterprises (SMEs). Furthermore, the research investigates the importance of trust and satisfaction that can affect the success of AI adoption. The study uses a quantitative survey to collect data from professionals in the supply chain from different organisations and industries. Various analyses were employed including descriptive statistics, reliability testing, t-tests, correlation, regression, and ANOVA tests. According to the findings, AI adoption has improved the inventory turnover, reduced delays, and lowered labour and logistics costs. However, there are some unexpected findings such as demand forecasting and automated replenishment, which did not show statistically significant evidence. This shows that system integration and the maturity of adoption are important to achieve all the benefits. The findings also show that trust and satisfaction have an important role. The trust showed to reduction in stockouts and the satisfaction improved as the company size and usage time increased. These findings match the Technology Acceptance Model (TAM) which shows user perception affects the adoption and the outcome of AI. In conclusion, the study shows improvement in the supply chain operations after AI adoption. However, the success of AI depends on some factors such as the company's resources, the level of adoption and how employees trust the technology. These results give the manager the full view of how to use AI effectively in the operations.