Identifying AI Opportunities in Operations: A Case Study of Aimpoint AB

The increasing complexity of global supply chains, fueled by fluctuating demand, geopolitical uncertainties, and rapid technological advancements, has heightened the need for innovative solutions. While Artificial Intelligence (AI) holds great promise for transforming Supply Chain Management (SCM) through improved efficiency and better decision-making, organizations encounter significant challenges in determining where and how to implement these technologies effectively. This thesis aims to assess the potential for AI implementation within the manufacturing sector and develop a systematic framework for prioritizing AI opportunities based on organizational readiness and anticipated impact. The research was conducted as a two-phase case study at Aimpoint AB, a red dot sight manufacturer. The approach utilized semi-structured interviews, production walkthrough, systematic literature review, and Enterprise Resource Planning (ERP) data to identify nine AI application areas spanning quality assurance, production optimization, and process enhancement. A comprehensive evaluation framework was developed, incorporating five criteria: problem complexity, system potential, implementation complexity, data availability, and business impact. Each identified area was scored across these dimensions to create an implementation priority matrix. The evaluation reveals that AI potential exists at Aimpoint across multiple domains, but implementation feasibility varies significantly. AI vision-based applications, particularly glue application by a robot, but also correlation analysis between scrap and component characteristics, demonstrate among the highest implementation readiness with scores of 5/5 or 4/5 across most criteria. On the other hand, advanced human-machine collaboration systems such as Augmented Reality (AR) assistance face implementation barriers at Aimpoint when writing this thesis, despite high theoretical potential. The framework identifies that successful AI implementation requires matching technology complexity to organizational readiness. The findings suggest a strategic pathway where Aimpoint should prioritize certain vision-based applications, such as optical lens inspection and glue application by a robot, before advancing to more sophisticated automation and human-augmentation systems, where existing data at Aimpoint is limited. The evaluation framework itself proves applicable across precision manufacturing contexts, though specific scoring criteria require looking over again for different organizational maturity levels.