In his famous book Design and Analysis of Experiments, Montgomery describes Design of Experiment (DOE) as a broad approach to an experiment, starting from the recognition of and statement of the problem, going through the experimental design and to the possible solution, ending to conclusion and recommendations. Specifically, DOE is known to be a powerful instrument based on statistics to design and analyze experiments. Potentiality of DOE is well known and appreciated among scholars. In some fields its potentiality is recognized and appreciated also by practitioners. That’s why there is an extensive use of Design of Experiment in improvement of industrial process quality.
According to the definition given by Bisgaard, innovation is the complete process of development and eventual commercialization of new products and services, new methods of production or provision, new methods of transportation or service delivery, new business models, new markets, or new forms of organization. While the use of DOE is well spread in industrial experimentation to improve quality and robustness of processes, the advantage of using DOE for innovation is debated among scholars and among practitioners.
The idea of investigating the use of DOE for production process innovation arose from this debate. Different perspectives have been investigated. The effectiveness of DOE to support and enhance the innovation of a production process is highlighted by means of a case study in which a strategy to innovate a thermoforming process for the production of a functional packaging has been developed. DOE enhanced innovation capability allowing reduction of systematic errors and distortions, full exploration of factorial space, and reduction of number of tests. DOE allowed to identify and overcome the mismatch between control factors in laboratory and in production line. Another perspective was the management of the innovation process. The positive impact on innovation process management of adoption of DOE is shown by means of a case study. DOE proved to be helpful providing proper instruments, and impacting on five dimensions typical of managerial field. Namely: decision making, integration, communication, time and cost, and knowledge management.
Concerning the data analysis, some nonparametric methods of analysis have been investigated. A simulation study was used to compare some advanced univariate nonparamentric tests in a crossed factorial design. The study revealed that certain methods of analysis perform better than others depending on the data set and on the objective of the analysis. As a consequence, there does not emerge a unique approach in the design phase of the experiment, but various aspects have to be taken into account simultaneously. A thoughtful choice of the most suitable test enhances the positive impact that DOE has on the innovation of a production process. Furthermore, a novel multivariate nonparametric approach based on NonParametric Combination (NPC) applied to Synchronized Permutation (SP) tests for two-way crossed factorial design was developed. It revealed to be a good instrument for inferential statistics when assumptions of MANOVA are violated. A great advantage given by the adoption of these tests is that they well perform with small sample size. This reflects the frequent needs of practitioners in the industrial environment where there are constraints or limited resources for the experimental design. Furthermore, there is an important property of NPC of SP tests that can be exploited to increase their power: the finite sample consistency. Indeed, an increase in rejection rate can be observed under alternative hypothesis when the number of response variables increases with fixed number of observed units. Properties of this multivariate test make of it a useful instrument when using DOE to innovate a production process and some specific conditions are verified
