Högskolan i Skövde

Precision engineering companies face increasing demands to improve efficiency, reduce environmental impact, and retain expert knowledge in an increasingly digitalized industrial landscape. This master’s thesis explores how digital technologies—specifically Business Intelligence (BI), Artificial Intelligence (AI), and voice recognition—can be effectively integrated into small and medium-sized precision engineering firms to enhance cost estimation, process monitoring, and decision support. Adopting a mixed-methods approach, the study combines field observations in two industrial SMEs with the design and implementation of tailored digital tools. Three main solutions were developed: (1) Power BI dashboards for real-time monitoring of production and energy efficiency; (2) a quotation support tool in Excel, embedding the reasoning process of an expert estimator through structured formulas and engineering abacuses; and (3) a voice-assisted input system, allowing users to verbally specify part features, which are then processed to estimate machining time automatically. Although a fully autonomous AI-based quotation system could not be deployed due to technical and data limitations, exploratory work was carried out on OCR-enhanced part classification from 2D technical drawings and the leveraging of historical ERP data to support semi-automated quoting. Feedback from company stakeholders confirmed that the implemented tools significantly improved operational transparency, reduced manual workload, and contributed to knowledge formalization.The thesis concludes that small-scale, user-focused digital solutions can create immediate value for precision engineering SMEs while laying the foundation for future integration of advanced AI and data analytics. It provides recommendations for further research in areas such as AI model training with domain-specific datasets, ERP system integration, and the development of sustainability-oriented performance indicators.