In the global environment where industrial enterprises strive for competitiveness, the ability to adjust quickly to changing conditions is important. This global industrial context challenges the companies to develop new capabilities. Capacity flexibility is an important measure of competitiveness and one important capability for improving productivity and effectiveness. Available resources contribute to capacity (output) and influence capacity flexibility. Thus, the way resources are managed is important. One way to manage resources within the manufacturing system is to choose resources that are the most suitable for the task performed by adopting task allocation. Task allocation between human and technology therefore becomes central for design of workplaces with optimal performance. This becomes the challenge of automation. However, to make the right decisions on automation and the skills required for the handling of tools and technology is a complex process of decision making for managers.

In the light of this, the objective of this thesis is to develop a methodology for analysing and choosing levels of automation with the purpose to formulate automation strategy in manufacturing systems. The analysis is based on measurement of levels of automation and alignment between levels of automation and the business and manufacturing strategies. The application area of the research is the manufacturing industry and in particular assembly lines or cells because of the mixture of human and technological resources.

As indicated by the objective, the outcome of this thesis is a structured methodology that analyses possible alternatives of levels of automation weighted against competitive priorities. The methodology consists of five stages: (1) preparation, (2) business and manufacturing strategy, (3) estimation of levels of automation for critical subtasks, (4) analysis of levels of automation, and (5) completion. The methodology supports visibility of results. Depending on where the company has its greatest improvement potential, different starting points in the methodology can be applied. Validation of the methodology indicates that usefulness, use, and satisfaction with the methodology can be seen as good.

The issue of considering both humans and technology is critical for the success of the system, as it builds the resources of the manufacturing function. Overcoming barriers in measuring LoA and in aligning resources with market needs is crucial for developing long term automation strategies. Certain criteria of the manufacturing system influence the choice of LoA. Those criteria are production volume and specific product characteristics. Proposed improvements for formulating manufacturing strategy involve a focus on communication and knowledge sharing, introducing measures for learning and knowledge, enhancing interactions between inside and outside partners, and closing knowledge gaps. Those improvements should be seen primarily as research opportunities in the area of manufacturing strategy processes.