On the identification of architectures for product programs: In a complexity cost perspective

The topic of this thesis is the identification of architectures for product programs with the purpose of improving the competitiveness of industrial companies while reducing complexity. The focus here is reduction of time-tomarket for new product development to enable companies to launch products at a faster pace while continuously reducing the associated costs of complexity.

The thesis proposes a set of frameworks and approaches to expand the body of knowledge within the research areas of product architectures and complexity management from the scientific standpoint of engineering design and product development. The thesis presents frameworks capable of identifying a scalable program architecture for companies in varying situations, namely project-based versus product-based development, architecture for modular versus integrated product structures, and architecture for new versus existing product programs.

For this purpose, the thesis suggests the definition of a program architecture resulting from the purposeful alignment of critical aspects across market, product and production areas including constitutive/structural aspects (what the architecture is) as well as behavioral aspects (what the architecture does).

The thesis also suggests the definition of life cycle complexity cost factors representing the situations or life phase meetings where complexity costs appear. The thesis presents a 5-step approach for detecting, identifying, quantifying and allocating the complexity costs of a product program by means of the life cycle complexity factors.

The approach enables the reduction of complexity reactively by cleaning out unprofitable products, and proactively by guiding product program development towards future reduction of complexity costs. The presented frameworks and approaches have been tested in a number of case studies across a variety of industries demonstrating significant effects in terms of time-to-market reduction, improvement of R&D efficiency, and complexity cost reduction.