A condition-based maintenance approach may be used for planning the maintenance activities of textile machines with satisfactory performance by developing a 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.

The outcomes of industry–university collaboration, in an open innovation context, may be of great support to firms, in their response to the challenges of today’s highly competitive environment. However, there is no empirical evidence on how these outcomes are influenced by their antecedents. Aiming to fill this gap, a research model to investigate the impact of the major antecedents, identified in the literature as motives, barriers and knowledge transfer channels on the beneficial outcomes and drawbacks of open innovation between the two organizations was developed in this study. The research model was empirically assessed, using a dual-stage predictive approach, based on PLS-SEM and soft computing constituents (artificial neural networks and adaptive neuro-fuzzy inference systems). PLS-SEM was successfully used to test the hypotheses of the research model, while the soft computing approach was employed to predict the complex dependencies between the outcomes and their antecedents. Insights into the relative importance of the antecedents, in shaping the open innovation outcomes, were provided through the importance–performance map analysis. The findings revealed the antecedents that had a significant positive impact on both the beneficial outcomes and drawbacks of industry–university collaboration, in open innovation. The results also highlighted the predictor importance in the research model, as well as the relative importance of the antecedent constructs, based on their effects on the two analyzed outcomes.

Within the highly complex ecosystem of industry-university collaboration in open innovation, three specific antecedents typically characterize the patterns of their interaction, i.e., motivations, barriers, and channels of knowledge transfer. However, an investigation of the extent to which these antecedents of opening up innovation impact the perceived importance of universities as an external knowledge source to the industry is still missing in the literature. Based on a research framework developed from a review of the literature, a two-stage ordinal regression, and neural network approach was performed to investigate this impact. In the first stage, the hypotheses of the proposed research framework were tested based on an ordinal regression, and those antecedents that significantly impacted the importance perception were revealed. In the second stage, an artificial neural network analysis was carried out to capture the complex relationships among the significant antecedents and the important perception of universities as an external knowledge source to the industry. On the whole, the findings of our study expand the existing open innovation literature and contribute to a more articulate view of the collaboration between industry and university in this field by providing a first perspective on which of the three antecedents has a significant impact on this perception and how such an impact can be predicted.

Since open innovation between industry–university is a highly complex phenomenon, its orchestration may be of great support for better collaboration between these organizations. However, there is a lack of evidence on how an orchestration framework impacts the collaboration performance between these organizations in such a setting. Based on a research model that investigates the influence of the main orchestration dimensions on the performance of collaboration, this study offers one of the first perspectives of an orchestration process between the industry and university actors in open innovation. The developed research model was assessed using a deep learning dual-stage PLS-SEM and artificial neural network (ANN) analysis. In the first stage, the hypotheses of the research model were tested based on a disjoint two-stage approach of PLS-SEM, and the results reveal the orchestration dimensions that have a significant impact on collaboration performance. In the second stage, a deep learning network approach was successfully employed to capture the complex relationships among the significant orchestration dimensions identified through the PLS-SEM analysis. An importance–performance map analysis provided useful insights into the relative importance of the components of each orchestration dimension based on their effects on the collaboration performance.

According to existing studies the phenomena that occur in the exploitation of the braking system are very complex and an analytical mathematical modeling of braking process it is difficult to be developed [1]. Since these phenomena are also characterized by some uncertainties, a fuzzy logic approach has been employed in this research for the estimation of technical state of the disc brakes. Their technical state was expressed through the thickness variation, which was used as the output linguistic variable. The vibrations and temperature of the disc brakes were used as the input linguistic variables. The fuzzy decision system for the estimation of technical state of the disc brakes has been implemented with the Fuzzy Logic Toolbox of the Matlab software, which can be employed to determine if the thickness of the disc brakes becomes smaller than the limit value prescribed by the manufacturer.

Competing in a complex and interconnected environment, firms are increasingly employing open innovation to search for and collaborate with different partners for better performance. While universities are considered an important source of knowledge for industry, there has been limited literature that investigates patterns of their collaboration in an open innovation context. Moreover, the influence of contextual characteristics such as size and industry classes on these patterns has also received little attention. Aiming to address these research gaps, a research framework was developed from the extant literature. Taking into account the main antecedents integrated into this framework, a fuzzy c-means clustering approach was employed to find a typology of open innovative firms in their collaboration with universities. Using the typical value of the fuzzifier factor of this algorithm equal to 2, three distinct clusters were identified with respect to these antecedents as low, insecure, and responsive open innovators. Then, an econometric model using a multinomial logistic regression was constructed to explore the influence of firms’ size and industry type on the identified patterns of such collaboration. Based on the marginal effects analysis, mixed evidence was found regarding the influence of the firm’s size on the identified clusters, while the impact of industry intensity was in line with other prior studies in the extant literature. The results of our study lead to some meaningful implications from both an empirical and managerial point of view that are discussed alongside with future research recommendations.