The recent increase in digitalization of industrial systems has resulted in a boost in data availability in the industrial environment. This has favored the adoption of machine learning (ML) methodologies for the analysis of data, but not all contexts boast data abundance. When data are scarce or costly to collect, Design of Experiments (DOE) can be used to provide an informative dataset for analysis using ML techniques. This article aims to provide a systematic overview of the literature on the joint application of DOE and ML in product innovation (PI) settings. To this end, a systematic literature review (SLR) of two major scientific databases is conducted, retrieving 388 papers, of which 86 are selected for careful analysis. The results of this review delineate the state of the art and identify the main trends in terms of experimental designs and ML algorithms selected for joint application on PI. The gaps, open problems, and research opportunities are identified, and directions for future research are provided.

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The main goal of the paper is to provide a statistical categorization of small and micro knowledge-intensive business service (KIBS) companies, based on their knowledge management (KM) attitude. Since knowledge is the main production factor and output of these companies, it is essential to achieve a better understanding of how they manage this resource. A questionnaire-based survey was conducted on a sample of Polish small and micro KIBS operating in various service sectors. A cluster analysis of the data was performed, to categorize the sample according to the KM attitude of the companies. Three main groups of companies were identified, varying in terms of their levels of "knowledge needs", "intensity of use" of KM practices and "perceived barriers to KM implementation". This classification is shown to characterize attitudes towards KM to a higher level of statistical significance than do structural characteristics. The survey was based on a single country sample. On the one hand, this provides consistency to the analysis. On the other hand, further insights can be obtained by a multi-national study. In addition, cluster analysis is exploratory in nature. The results provide useful insights for policy makers (to formulate policies for facilitating KM implementation in small KIBS) and managers (to reflect on the KM attitudes of their company). The statistical categorization of small and micro KIBS in terms of their KM attitude has been very rarely undertaken. Even the most recent investigations of KM issues used samples from large companies.