Big Data Management Using Artificial Intelligence in the Apparel Supply Chain : Opportunities and Challenges

Sammanfattning: Big data management using artificial intelligence in the apparel supply chain:Opportunities and ChallengesOver the past decade, the apparel industry has seen several applications of big data and artificial intelligence (AI) in dealing with various business problems. With the increase in competition and customer demands for the personalization of products and services which can enhance their brand experience and satisfaction, supply-chain managers in apparel firms are constantly looking for ways to improve their business strategies so as to bring speed and cost efficiency to their organizations. The big data management solutions presented in this thesis highlight opportunities for apparel firms to look into their supply chains and identify big data resources that may be valuable, rare, and inimitable, and to use them to create data-driven strategies and establish dynamic capabilities to sustain their businesses in an uncertain business environment. With the help of these data-driven strategies, apparel firms can produce garments smartly to provide customers with a product that closer meets their needs, and as such drive sustainable consumption and production practices.In this context, this thesis aims to investigate whether apparel firms can improve their business operations by employing big data and AI, and in so doing, seek big data management opportunities using AI solutions. Firstly, the thesis identifies and classifies AI techniques that can be used at various stages of the supply chain to improve existing business operations. Secondly, the thesis presents product-related data to create a classification model and design rules that can create opportunities for providing personalized recommendations or customization, enabling better shopping experiences for customers. Thirdly, this thesis draws from the evidence in the industry and existing literature to make suggestions that may guide managers in developing data-driven strategies for improving customer satisfaction through personalized services. Finally, this thesis shows the effectiveness of data-driven analytical solutions in sustaining competitive advantage via the data and knowledge already present within the apparel supply chain. More importantly, this thesis also contributes to the field by identifying specific opportunities with big data management using AI solutions. These opportunities can be a starting point for other research in the field of technology and management.

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