In the face of global crises, such as pandemics and geopolitical conflicts, organizations worldwide are confronted with complexities in planning their supply chains. In order to overcome these challenges, they are turning to innovative solutions that are supported by Artificial Intelligence (AI). Nonetheless, the considerable potential of AI in this area remains largely untapped, leaving the industry struggling to utilize the technology effectively. This master thesis aims to bridge this gap by developing a structured framework to assist supply chain practitioners in identifying AI application potentials in Supply Chain Planning (SCP) processes, including Purchasing & Material Requirements Planning, Production Planning & Scheduling, Distribution & Transport Planning, and Demand Planning & Fulfillment. The AI applications and projected results for each process are identified through a systematic literature review and visually represented within the framework. To evaluate the designed framework, a focus group discussion is conducted with supply chain experts from the SKF Group, a global leader in bearings and seals production that is in the initial stages of implementing AI. The focus group provides valuable feedback, reinforcing the initial framework's design, practicality, correctness, completeness, business relevance, usefulness and applicability in defining future actions. The highlighted recommendations are subsequently incorporated into a revised framework version. The presentation of the framework also facilitates an increase in participants' perceived level of comfort and knowledge regarding AI in SCP. In addition, the study provides a number of insights in the explored field: Firstly, SCP is a relatively minor topic in the existing literature compared to the generic term of Supply Chain Management. It seems that research is currently conducted predominantly at the managerial level rather than at the operational level, which may be a contributing factor. Secondly, the study demonstrates that AI has been extensively employed in the field of demand prediction, while other SCP processes still lack theoretical foundation and practical applications. It can be stated that the potential of AI in SCP has yet to be fully realized, and that further research is required to extend the existing literature and support the business. Besides that, the necessity for future research is also derived from the fact that the thesis has its limitations; these include the identification of only a limited number of relevant papers for developing the framework, the execution of only a single focus group with one case company for evaluating the framework and the potential for the framework to become outdated due to the evolving advancements in AI research. In conclusion, the principal objective of this study is to make theoretical and practical contributions by designing a hands-on framework that should demonstrate the currently available application potentials of AI within SCP operations and encourage organizations to initiate the conceptualization process for AI implementation.
Artificial Intelligence in Supply Chain Planning: Designing a Framework to Identify Application Potentials
Holzer, M. (Author). 2024
Student thesis: Master's Thesis