Conclusion

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start with, optimizing a maturity dimension (a process with a certain level of maturity) does not automatically result in increased company success. Therefore, insight in the path from the optimization project to company success is required. Hence, the optimization project can be validated objectively for effectively and efficiently contributing to company success. Furthermore, if the PD process is identified as the ‘appropriate’ maturity dimension to optimize regarding to achieving company success, insight in the path from the current PD process into a more effective and efficient PD process is required. This research results in a vision on maturity and two concept models that combined achieve the twofold research objective. They form a starting point and illustrate what is possible in the future regarding PD maturity optimization in industry.

Based on the knowledge on product development processes, environments, process optimization and existing PD process structures (Chapter 1: Introduction) the need for a strong vision as directive for maturity optimization model development is identified. A maturity vision is developed (Chapter 2: Maturity vision), which states that higher objectives – as represented by the company strategy for company success as highest objective – form the directive for selecting processes for optimization. Success on a maturity factor (a process that contributes to higher objectives) is a required but not sufficient condition for success on a maturity dimension (the dimension that strives for achieving the higher objective). In other words, optimizing a process does not automatically lead to success on higher objectives. Therefore, identifying maturity dimensions for effective and efficient optimization uses a top-down approach, where the optimization acts bottom-up, from process optimization to company success.

For providing insights in the influences of maturity optimization on company success, based on the vision, the Product Development Maturity Optimization (PDMO) concept model is designed (Chapter 3: The PDMO concept model). It provides insights in maturity contexts advocating PD process Maturity optimization. This design contains three components (the MLC, MRC and MOC) to support decision makers for optimization projects in selecting an appropriate maturity factor to optimize, which contributes effectively and efficiently to company success as highest objective.

Specifically for PD processes, knowledge on maturity optimization is limited. For PD processes, no effective and efficient maturity audit models exist. Therefore, in this research a concept model is developed for optimizing PD process maturity (Chapter 4: The PDPMA concept model). The PDPMA model forms one possible solution within the solution domain of the PDMO model.

7.4 Conclusion

The PDPMA model starts with validating if accreditation of the complete model is valuable for the specific context (Chapter 4.3: PDPMA model design: MTP). After positive results, the PDPMA model relates specific context parameters to relevant model and method parts through corresponding functions, forming a relation profile (Chapter 4.4: PDPMA model design: OSP). The results from the relation profiles are the input for a change and implementation advice of which a design brief is stated in this research (Chapter 4.5: PDPMA model design: CIP).

The PDMO model is capable of providing insights in the value of optimizing a specific process in the company context, guiding decision makers to optimization process selection while aiming for company success. The PDPMA model is capable of proposing appropriate solutions for PD process structures, tailored to the company context. First ideas for iterations are stated, recommending (1) research for other maturity factors influencing PD process success (other than PD process structure) and (2) proactively including ‘changeability’ of the company context as criterium for successful PDPMA model execution.

The valuable combination of practical and theoretical knowledge is made accessible to industry with developing the PDMO and PDPMA model into practical tools. Combined, they provide the industry with insights in improvement of their PD maturity. The designs enable stakeholders in optimization projects to make deliberate decisions regarding optimization projects and PD process optimization. The versatile designs that use network visualization and information structuring methods allow availability, flexibility and sustainability of the models’ content. Optimization project execution with use of the PDMO model and / or PDPMA model allows tailoring focus and effort to the (often fluctuating) needs of industrial stakeholders, contributing to the industry’s aim for company success.

This research contributes to the Design Science domain and builds on standards for model development and validation. Research results are presented as generic semantic ontologies – independent of specific instances – exploring maturity assessments for a new application: PD optimization. The PDMO and PDPMA model have three unique char- acteristics: the focus on Product Development practices, the use of company success and the strategy used for relating context with solutions based on overlapping functions. By providing the industry with insights in the potential value of them, utilization of developed artefacts by the academy is promoted, contributing to the success of the Design Science domain. This research does not present fully developed results, but identifies, illustrates and contextualizes interesting opportunities for PD maturity optimization in industry. It describes a proof-of-concept model development success, providing tailored insights in improvement of industry’s PD maturity.

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