Effectiveness of R&D project selection in uncertain environment: An empirical study in the German automotive supplier industry

This paper presents results of an empirical large-scale study on uncertainty reduction of R&D projects and R&D project selection. The empirical field is the German automotive supplier industry. We explore R&D project selection practices in this specific industry and briefly contrast our findings with the academic research and management literature in this field. We concentrate on answering three research questions (with focus on questions no. 1 and 2): I. Which information and related uncertainties are crucial for the product selection decision to the R&D decision makers? II. How do R&D decision makers today cope with typical challenges related to reducing uncertainty? Where do they face major problems and how effective are they? III. What are major implications for managing the Fuzzy Front End (FFE) of innovation process in industry practice and respectively for further academic research in this field? Key findings are that on the one hand certainty about fields of product applications, target markets and production feasibility are most important criteria for initial product selection decisions. On the other hand market and cost related uncertainties (e.g. sales volume, product price, cost per unit) cannot be satisfyingly reduced in practice before project approval for development or definite termination of projects. Although different uncertainty profiles exist within the process of project evaluation, most companies do not systematically choose available product selection methods and tools according to specific uncertainty situations. Intuition still plays a major role in R&D product selection. Some first conclusion drawn from this research are: A sufficient level of resources (including financial and methodological know-how), a systematic use of suitable project selection instruments, and a fit with the company specific as well as the OEMs' product/brand strategies can be potential levers for more effective uncertainty reduction before product decision.