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In an open innovation (OI) paradigm, universities are considered as important sources of external scientific knowledge for industry, and comparative study of such collaboration can result in more effective and efficient employment of OI. Within this framework, this study explores how the determinants of collaboration between industry and universities in an open context of innovation are addressed by firms within industrial areas. For this purpose, a conceptual framework of industry–university determinants in an open context of innovation is developed from the related literature. Taking into consideration the determinants integrated into the framework, this study compares motives, barriers, channels of knowledge transfer, benefits and drawbacks of such collaboration in two Italian and Romanian industrial areas. Comparative differences in each OI determinant between the firms from the two Italian and Romanian industrial areas are analysed. The associations among the study determinants are also investigated based on correlation matrices among the five determinants in both Italian and Romanian firms. An artificial intelligence approach based on fuzzy logic was developed to predict the impact of the study determinants on the perception of universities as a source for OI activities of firms.

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Key Words: reliability improvement, design of experiments, Taguchi approach, renewal policy SUMMARY & CONCLUSIONSAs we are moving to more emerging global markets, one of the most important goals of a manufacturer is to improve the reliability of its products. While the reliability may be affected by many potentially factors, some factors are more important and that they have to be identified. The values of the significant factors that can improve reliability are also important to be recommended. Taguchi's robust design experiments provides an efficient way to achieve these goals and the concepts of the Taguchi's method in the context of the reliability improvement are emphasized at the beginning of the paper.The studies available in the literature demonstrate the importance of robust design experiments in improving products reliability. However, not so much work has been done on applying robust design experiments for reliability improvement of deformation tools used in automotive industry. This is mainly due to the difficulty to obtain the necessary data for experiments design. In this paper, the renewal policies are proposed to gather the data for experiments design and the minimization of the average maintenance cost rate was used as the criterion in formulating renewal policies.For this purpose, the reliability modeling of deformation tools used in automotive industry was performed by a goodness-of-fit test. A data acquisition system, composed by resistive strain gages-strain indicator-connector block-board acquisition-personal computer, was used for the identification of the tools failure. A case study illustrates the application of Taguchi's robust design paradigm.In conclusion, using robust designed experiments provides a proactive means by identifying important factors that affect product's reliability. These influential factors can then be set at levels that generate reliability improvement. The results of the case study demonstrate that the Taguchi approach of robust design is a method which can be successfully used in improving the reliability of deformation tools used in automotive industry.Abbreviations ARP -Age Replacement Policy BRP -Block Replacement Policy F(t) -Cumulative distribution function(cdf) R(t) -Reliability function m -Mean time-to-failure z(t) -Failure rate η -The signal-to-noise ratio

A condition-based maintenance approach may be used for planning the maintenance activities of textile machines with a satisfactory performance by developing maintenance decision-making support based on fuzzy logic and vibration monitoring. Since textile machines are systems with moving parts operating at relatively high-speed, vibration monitoring was used to indicate their failure development. At the same time, the characterization of the degradation phenomenon of textile machines involves some degree of uncertainty and vagueness. Within this context, a knowledge-based approach that employed fuzzy logic and vibration monitoring was developed. Deterioration symptoms do announce future failures of industrial machines, therefore building a maintenance decision-making support for scheduling maintenance actions of textile machines based on the estimation of their condition becomes a resourceful way to prevent their further deterioration.