Analysis and Evaluation of the Impact of Artificial Intelligence on Value Creation in the Supply Chain.
Authors:   , Getto Joachim 
Published: December 2022

Problem Statement: Significant increase of applying artificial intelligence (AI) will substantially change the way supply chain (SC) entities and their subsystems consisting of human experts and technical agents collaborating in the 2020s and beyond to create sustainable competitive advantages. However, the literature review has revealed that insufficient coverage of appropriate conceptual frameworks (CF) for adequately assessing the performance of future SC scenarios in the context of AI and its contribution to competitive value creation (VC). Research focus has been primarily placed on the ability of isolated AI applications to contribute with natural language processing (NLP), computer vision, machine learning (ML), or rational decision-making to increasing SC effectiveness. However, there is more need to assess the mutual interoperability of these AI abilities and other SC descriptors across the entire SC which will contribute with new knowledge to interdisciplinary academic research and practitioners’ strategic, tactical, and operational evolvement. So far, there is the lack of a CF which can be used to build propositions on the impact of AI on inter-organisational SC structures and the ability of AI to support emergence in the SC. Therefore, the overall aim of this thesis is to analyse and evaluate the impact of AI on VC in the SC. Research Methodology: Based on a critical realism ontology, the thesis applies an abductive research approach using mixed methods for data collection and analysis. The research design has the following stages: (1) semi-structured interviews and Delphi Study, (2) cross-impact balance (CIB) analysis, (3) propositions and theory building, (4) verification with cooperative game theory. Main Achievements: (1) Literature review allows to contextualise the application of AI in SC and reveals SC mechanisms as a foundation to develop an appropriate CF. (2) Gaps of the existing research are identified. (3) A CF is developed for proposing a network of relationships between relevant SC descriptors in this research. (4) Relationships between these SC descriptors are evaluated and analysed with the purpose to identify future SC scenarios and their performance. (5) Exploring these SC scenarios allows for establishing propositions on future SC mechanisms and their ability to create value. (6) A theory about the impact of AI on VC in the SC is developed. (7) Verification of the proposed CF and the developed theory. Results: (1) The theory based on the CIB-analysis proclaims that AI creates value for the SC through improving ordinary and dynamic SC capabilities. (2) Sustainable competitive advantages will only be achieved with the combination of widely implemented use of AI in forecasting and fully adopted autonomous planning techniques along the entire SC. This combination of knowledge creation and knowledge distribution is the only feasible future concept to leverage sufficient value through the inevitable data-centric approach across the SC. (3) Isolated application of AI-enabled descriptors of the CF leads to the unavoidable long-term demise of SC. (4) The recommended inter-organisational structure to support controlled self-organisation is built on clusters that connect the inter-organisational subsystems at the interfaces between SC entities. (5) AI applications only indirectly contribute to emergence of new SC structures but create value by strengthening the collective behaviour of human experts to find a new equilibrium. (6) Self-learning AI ability in combination with big data allows for improved SC responsiveness and SC efficiency by turning demand-driven SC into forecast-driven SC. (7) AI creates value through keeping SC resources valuable, imperfectly imitable, and non-substitutable. Contributions: The main contribution of this PhD project from the theoretical perspective is the development of a theory that allows academics to evaluate the impact of AI on VC in the SC in a fact-based manner. The system-theoretical structure of the underlying CF allows academics to explore the aspect of SC learning by extended Resource-based View (RBV) and to explain phenomena of the SC reality by scientifically justified propositions. From practical application perspective, practitioners can apply the CF to derive logical dependencies beyond the proposed descriptors to decide on SC resource mix and to initiate studies and practical projects to synchronise process-orientation, decentral coordination, and decision autonomy to leverage first-mover competitive advantages. Limitations: Due to the resource and time constraints of this PhD project, the findings and results of this thesis are only as good as the knowledge and the experiences of the participating experts and the deductive capabilities of the author of this thesis.