Even though interest in open innovation (OI) both as a research field and as an industrial practice for creating competitively advantageous innovation through collaboration has grown exponentially over the last decades, the issue of how to transform OI strategy into a sustainable competitive advantage is still an open research question. Selecting partners capable of operationally and strategically contributing to the OI project goals is a strategic decision for companies striving to effectively implement the OI concept. The study is aimed at defining a structured and methodology-supported decision-making process for OI partner selection based on a novel hybrid Multi-Criteria Decision-Making (MCDM) model which is enhanced by interval type-2 fuzzy sets (IT2F) to deal with the inherent uncertainty. The model combines IT2F Delphi (IT2FD), IT2F Analytical Hierarchy Process (IT2F AHP), and IT2F Preference Ranking Organization METHod for Enrichment of Evaluations (IT2F PROMETHEE). The study provides a comprehensive framework of the OI partner performance indicators; additionally, it provides a contingent approach to identifying evaluation criteria depending on the nature of the company’s innovation processes, contextual conditions, and innovation strategy. The case study is used to verify the feasibility and effectiveness of the proposed process. The study’s results highlight the significance of specific factors related to the partners’ technological competencies.

In recent decades, many researchers and practitioners have believed that reaching a high level of business excellence leads to the continuous realization of a set of business goals. In the literature, a vast number of models for business excellence evaluation that contain different criteria depending on the cultural, technological, organizational, and socio-economic factors can be found. The aims of the proposed fuzzy two-stage model are to address some of the main shortcomings of the EFQM2020 model and to adapt it to the needs of process manufacturing. The relative importance of quality criteria and their values are presented by pre-defined linguistic expressions modeled by the triangular fuzzy numbers. The determination of the weight vector of criteria is stated as a fuzzy group decision-making problem and determined by using the fuzzy best-worst method. The proposed fuzzy multi-objective optimization by ratio analysis is implemented for determining the rank of enterprises. The management initiatives that should lead to the improvement of business excellence should be based on the business practices of enterprises that are highly placed in the rank. Testing and verification of the proposed model are performed on real data originating from enterprises operating in the same economic sector.

Product Lifecycle Management (PLM) provides a paradigmatic model that enables companies to operate more effectively in the face of shorter product lifecycles, global networking, and increasing complexity. However, despite strengthening the PLM initiative, companies still struggle to implement this concept. The limited results of current PLM implementations often stem from a lack of unique indicators or consistent methodologies that help companies prioritize their implementation efforts. This article proposes an approach to set a PLM strategy, focusing on enhancing company innovation potential by introducing a structured methodology capable of (i) capturing latent needs based on the normative-contingent New Product Development (NPD) evaluation model and (ii) quantifying the influence of various PLM functional aspects on NPD capability. The proposed methodology is based on the Quality Function Deployment (QFD) method, modified to overcome the limitations of the conventional approach, employing the Analytic Hierarchy Process (AHP) for prioritizing request attributes and the Evaluation based on Distance from Average Solution (EDAS) method for quality attribute importance ranking. Motivated by the arbitrary and vague nature of the decision-making environment in the PLM implementation projects, which introduces uncertainties that could be effectively managed by fuzzy logic, the study introduces Interval Type-2 Fuzzy Sets (IT2FSs) to minimize ambiguity and inconsistency in expressing and modeling preferences. The main study contribution pertains to generating quantitative and objective guidelines for adequately grounding a PLM strategy from the perspective of enhancing the company’s innovation potential. The findings of this study ultimately contribute to establishing an optimal model of the PLM concept implementation process, tailored to specific company requirements. Finally, an empirical case study demonstrates the effectiveness and practicality of the proposed approach.

New industrial initiatives and innovations are being brought forward at an increasingly rapid rate, radically transforming job profiles and imposing the need for new paradigms in engineering education. Over the last few decades, PLM has evolved into the key business paradigm for supporting companies’ efforts to achieve long-term sustainable competitiveness by balancing innovation and efficiency growth on the one hand and cost optimization on the other. New labor market demands, shaped by rapid PLM evolution, require a shift in current engineering education to adapt to new realities and better prepare engineers for future challenges. This paper aims to provide a comprehensive review of the state of the art in PLM education, highlight issues facing PLM education, and discuss possible future trends and developments. The paper aims to increase awareness of the importance of PLM education development and encourage active debate about potential actions as responses to current issues.

The problem of personnel selection in the logistics process is one of the most important tasks of human resource management, and its relationship has a critical effect on achieving the organization’s business goals. The considered problem can be stated as a two-stage multi-attribute decision problem that includes both quantitative and qualitative criteria. The attribute weights are determined by applying the modified CRiteria Importance Through Intercriteria Correlation (CRITIC) method. The proposed fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to rank the personnel. The proposed model is illustrated by an example using literature data. It is shown that the proposed two-stage MADM model is highly suitable as a decision-making tool for making decisions about personnel selection in the logistics process.