Checking whether process and product are satisfying or functioning according to the technical specification is not enough to assure competitiveness. Competition compels organizations to develop efforts to assure that product and process characteristics are on target values and the variability around those targets is minimal. This article proposes an alternative method for optimizing both the mean and standard deviation of a quality characteristic of the process or product. The objective function accommodates all the response types, allowing the practitioner to assign distinct weights to process mean and standard deviation and to find trade-off solutions between them, taking their relative magnitudes into account. Two classical examples from the literature are used to illustrate the feasibility of the proposed method and compare its results with those of other popular methods. A practical procedure for implementing the proposed method is also presented.

Purpose – The purpose of this paper is to focus the application of design of experiments (DOE) using industrial equipments, reinforcing idea that non-statistical aspects in planning and conducting experiments are so important as formal design and analysis. Design/methodology/approach – Two case studies are presented to illustrate typical industrial applications and difficulties. Supported on these case studies and literature, this paper presents guidelines to planning, conducting and analysis involving technical and organizational aspects. Findings – Solving problems in industry, including in companies recognized as competent in the respective industrial sector, is not just a question of applying the right technique. Ceramic industry case study illustrates how important are non-statistical issues in DOE application. Paint industry case study illustrates the strong relationship of the results with incorporating presented guidelines into practice. Moreover, both case studies consolidating a fundamental advantage of DOE: experimentation provides more knowledge about products, processes and technologies, even in unsuccessful case studies. Research limitations/implications – Unsuccessful cases studies are very useful for identifying pitfalls and others limitations. This paper highlight difficulties aroused from non-statistical aspects, although it is possible to find unsuccessful case studies due to statistical issues also. So, papers illustrating inadequate application of statistical techniques are welcome. Practical implications – Successful DOE implementation depends on statistical and non-statistical aspects. Although none of them shall be neglected, technical skills and technological knowledge about processes and products, management understanding of potential possibilities of statistical techniques and statistical fundamentals and knowledge about techniques of DOE must be ensuring to successful case studies in industrial setting. Originality/value – This paper highlights non-statistical aspects instead of the statistical ones. Tob overcome difficulties structured guidelines were designed to support DOE application in industrial setting.