Implication of Artificial Intelligence adoption for Key Performance Indicators in the manufacturing industry

Abstract

The integration of Artificial Intelligence (AI) into the manufacturing industry holds transformative potential for redefining operational efficiency and strategic management. This thesis explores the multifaceted impact of AI on Key Performance Indicators (KPIs) in the manufacturing sector. The research is motivated by the significant gap in consolidated studies addressing AI's influence on specific industry metrics critical to operational success. The study employs a mixed-methods approach, encompassing both qualitative and quantitative analyses to comprehensively assess AI's effects on traditional and emerging KPIs.Phase one involves the identification and prioritization of relevant KPIs through extensive literature review and expert consultations. A survey of industry experts across various manufacturing sub-sectors reveals a prioritization of KPIs most susceptible to AI-driven transformation. Phase two entails empirical research, utilizing data from manufacturing companies that have integrated AI technologies, supported by detailed statistical analysis.The findings demonstrate that AI brings significant improvements of certain KPIs of both the primary and secondary processes within the manufacturing industry. Certain KPIs for the primary processes are significantly impacted, allowing improvements in inventory reduction but also increasing Net Operating Profits. The bigger and even transformational impacts by AI can be seen for secondary process KPIs, within the areas of Human Resources, Marketing and Information Technology.This thesis concludes that AI's integration not only optimizes existing KPIs but also necessitates the evolution of new, more nuanced metrics that capture the additional capabilities, which AI brings into the picture. The study provides actionable insights for industry practitioners, emphasizing the strategic implementation of AI to achieve enhanced productivity, cost-efficiency, and overall operational excellence. Several challenges and potential downsides associated with AI implementation are also highlighted, such as data privacy concerns, technical integration issues, and the necessity for new skill sets within the workforce. Ethical considerations and the risk of reinforcing existing biases through AI systems are also examined, underscoring the need for robust regulatory frameworks and ethical guidelines.