Due to the recent surge in interest in AI, the term has been used inflationary for many IT applications, obscuring whether AI is truly utilized. Additionally, recent reports from various large consulting firms and scientific papers suggest that AI systems often do not meet expectations and achieve the impact managers hoped for. Many papers suggest capabilities and readiness models, which should improve the ability of businesses to implement AI successfully, yet empirical evidence on which skills and capabilities have the strongest influence and should, therefore, be focused on is still missing. To gain insight into these issues, this thesis examines the adoption of AI in SCM of Austrian companies, the expected and realized impact of the AI systems on organizational performance, and the extent of AI capabilities developed within these companies. First, a literature review was conducted to gather information on possible AI application areas in SCM, AI's impact on organizational performance, and AI capability models. These were subsequently used in constructing an online survey, gathering data among Austrian SCM professionals. The data of 44 respondents was then statistically analyzed and presented to answer the proposed research questions. The results show that half of the surveyed companies already use AI in SCM, most prevalently in planning and distribution processes. These systems are expected to enhance data-based decisionmaking, reduce cost, and increase productivity. Comparing the expectations to the realized impact, AI falls short on all surveyed impact dimensions. When examining their AI capabilities, Austrian companies are rated well in inter-departmental coordination and organizational change capacity. They are average regarding their data capability while showing lower business and technical skills and risk-proclivity grades. Examining the influence of these capabilities, the results suggest that data, risk proclivity, organizational change capacity, and business skills are the most influential ones. Three of these four are rated low to average for the surveyed companies. Many respondents could not answer questions regarding technology, their data scientists, and the AI projects in their companies. This thesis concludes that AI capabilities have a positive influence on AI adoption in SCM and the impact of AI on organizational performance, confirming theoretical AI capability and readiness models. Practitioners should foster the abovementioned capabilities to ensure successful AI implementations and operations. These results also allow businesses to benchmark their capabilities. Further research is needed to gather longitudinal data to examine the effects of AI capabilities over time and assess the validity of the used survey instrument. The generalizability of this research is limited, as the sample consisted mostly of large Austrian companies in the manufacturing and retail sectors. An increased sample size is needed to allow more statistically significant conclusions about the realized impacts of the used AI systems on organizational performance, as only a few systems were already in operative use. Furthermore, the methodology or the survey design needs to be adjusted to gather information on technical questions, as the respondents often weren’t able to provide answers.
AI in Austrian Supply Chain Management: Adoption, Impact and Capabilities
Raab, C. R. (Author). 2024
Student thesis: Master's Thesis