Why is not design of experiments widely used by engineers in Europe?

Abstract

An extensive literature review was carried out to detect why design of experiments (DoE) is not widely used among engineers in Europe. Once 16 main barriers were identified, a survey was carried out to obtain first-hand information about the significance of each. We obtained 101 responses from academics, consultants and practitioners interested in DoE. A statistical analysis of the survey is introduced, including: (a) a ranking of the barriers, (b) grouping of barriers using factorial analysis, (c) differences between characteristics of respondents. This exploratory analysis showed that the main barriers that hinder the widespread use of DoE are low managerial commitment and engineers’ general weakness in statistics. Once the barriers were classified, the most important resultant group was that related to business barriers.

Keywords:

• barriers
• design of experiments
• engineers
• industry
• survey

Acknowledgements

We are grateful to the three anonymous referees for helpful comments that improved this paper significantly. Moreover, the authors would like to thank Enrique del Castillo and Tony Greenfield for their valuable comments and suggestions regarding survey elaboration. Finally, we would like to thank Ron Kennett, past president of ENBIS, for his support and effort in distributing the survey among ENBIS members.

Notes

B10 was identified only during the brainstorming session.

ENBIS Mission: “(a) Foster and facilitate the application and understanding of statistical methods to the benefit of European business and industry, (b) provide a forum for the dynamic exchange of ideas and facilitate networking among statistical practitioners (a statistical practitioner is any person using statistical methods whether formally trained or not) and (c) nurture interactions and professional development of statistical practitioners regionally and internationally”.

Although some proclaim the superiority of common factorial analysis over the full components analysis, Velicer’s results 27 show that they usually obtain similar results.

Sixty per cent is sufficient in social sciences 14.

Since coefficients are the correlation between the variable and the factor, the square of the coefficients is the amount of total variance of the variable explained by the factor. Therefore, loads of 0.30, 0.50 and 0.70 explain approximately 10%, 25% and 50% of the variable.