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

This paper presents the results of a large-scale empirical study of 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. This paper seeks to answer three research questions (with a primary focus on questions I and II): I. which information and related uncertainties are crucial to R&D decision-makers taking product selection decisions? II. How do R&D decision-makers today deal with typical challenges related to reducing uncertainty? III. What are the major implications for managing the fuzzy front end (FFE) of the innovation process in industry practice as well as for further academic research in this field? The key findings are, on the one hand, that certainty about fields of product applications, target markets, and production feasibility are the most important criteria for initial product selection decisions. On the other hand, market-related and cost-related uncertainties (e.g., sales volume, product price, and cost per unit) cannot be satisfac-torily reduced in practice prior to project approval for the 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 conclusions 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 prior to product decisions