The Model of Using Artificial Intelligence in Supply Chain Management in Product Production

Abstract

The drive to enhance the effectiveness of the supply chain in the manufacturing of apparel, otherwise known as ECPSC (Efficiency of the Apparel Manufacturing Supply Chain), has gained significant prominence in recent times. This increased focus on efficiency can be attributed to the rising costs associated with logistics as well as the advancements made in Industry 4.0 technologies, such as artificial intelligence. The field of logistics has witnessed noteworthy advancements that have captured the attention of both academics and industry experts, leading them to explore the potential of artificial intelligence tools in the realm of operations management. This particular study adopts a perspective rooted in the concept of dynamic capability (DCV) and theories related to organizational information processing. Its primary objective is to delve into the impact of technologies based on artificial intelligence on both the supply chain and production processes. By doing so, it seeks to minimize the costs associated with the supply chain of apparel products, ultimately aiming to increase the profitability of manufacturing organizations. Furthermore, this study also delves into the analysis of the mediating role played by outbound logistics and distribution network efficiency (DNE) in the relationship between artificial intelligence and ECPSC. In order to achieve these research objectives, the researchers utilized the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. This approach facilitated the analysis and testing of the conceptual model and hypothesis using a dataset comprised of 300 responses collected from managers and executives within Iran's garment industry. The study yielded a novel finding, indicating that the integration of artificial intelligence has a direct and positive impact on ECPSC-mediated distribution network efficiency as well as organizational performance. Intriguingly, the researchers also discovered that as the level of artificial intelligence integration increases, the strength of the relationship between the aforementioned variables weakens. Additionally, the impact of artificial intelligence on production and business was evaluated and compared through the administration of questionnaires to two distinct groups.