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The purpose of this paper is threefold: first, to present a structural competency model; second, to remark new criteria for personnel selection in Industry 4.0 environment; and third, to contribute to the operations management literature by focusing on recruitment process in Industry 4.0 environment and supporting human resources activities with Industry 4.0 related criteria and point out a new research field in Industry 4.0.

Fuzzy DEMATEL has been used in the implementation. The study is conducted in a high-tech firm, which has started to modify its processes according to Industry 4.0, and introduces a new specific department that is responsible of this transformation. In total, 11 personnel selection criteria were presented and then assessed by experts through a fuzzy linguistic scale. Both importance order and causal relation between criteria are presented at the end of the study.

According to the results, the most important criteria in the selected firm are the ability of dealing with complexity and problem solving, thinking in overlapping process, and flexibility to adapt new roles and work environments. While cause group includes criteria such as knowledge on IT and production technologies, awareness of IT security and data protection, and ability of fault and error recovery, effect group includes flexibility to adapt new roles and work environments, organizational and processual understanding, and the ability to interact with modern interfaces.

Analytical thinking and system approach are the key topics for new supporting personnel selection criteria, which lead to the need for the skills and qualifications in decision making and process management. Results of the cause group criteria also indicate the importance of technical abilities such as coding, IT security and human-machine interfaces. On the other hand, effect group of the study emphasizes on the flexibility and interdisciplinary working structure that suggests the suitability of matrix organization in the companies which follow the Industry 4.0 trends. Moreover, team work comes forward as another key concept for organizations transforming to Industry 4.0.

The originality of this study appears on modeling of a competency structural model for Workforce 4.0 which is proposed as a road map, including the suggested set of related criteria and the fuzzy MCDM-based methodology for companies which alter their organizations according to Industry 4.0.

Performance assessment of green supply chain management (GSCM) requires a systematic approach because of its interdisciplinary and multi-objective nature. The purpose of this paper is to propose a model to the performance assessment of GSCM.

A model is proposed, grounded on a literature review on GSCM performance, after which the causal relationships and prioritization of the sub-criteria are analyzed by fuzzy Decision Making Trial and Evaluation Laboratory technique in a company operating in the cement industry.

An integrated holistic performance assessment model incorporating specifically six criteria and 21 sub-criteria is applied, which represents causal relationships and prioritization of sub-criteria.

The proposed model can be generalized, because an integrative framework can be used in future empirical studies to analyze performance of GSCM. However, the causal relationships and prioritization among sub-criteria are analyzed based on the needs and capabilities of the individual company; therefore, the causal relationships found are company specific.

The proposed model can be hired and implemented by companies striving for GSCM. This model allows companies to assess their current GSCM performance, analyze causal relationships, and prioritize sub-criteria.

Several studies have analyzed performance assessment in green supply chains; however, to the best of the authors’ knowledge, no study has taken an approach to performance assessment in GSCM that combines environmental, economics/financial, logistics, operational, organizational and marketing in the same framework. In addition, the cause-effect relationships identified will be the base for performance improvement.

This research aims to investigate and define the eight wastes of lean philosophy in higher education institutions (HEIs) by proposing a multi-stage model.

The authors have used a specific multi-criteria decision-making method, fuzzy decision-making trial and evaluation laboratory, to investigate the cause–effect relationships and importance order between criteria for wastes in HEIs. In total, 22 criteria were categorized under eight wastes of lean. The study was implemented in a business school with the participation of faculty members from different departments.

The results showed that the most important wastes in the business school selected were repeated tasks, unnecessary bureaucracy, errors because of misunderstanding/communication problems, excessive number of academic units and creation of an excessive amount of information. Another important result was that all the sub-wastes of talent were in the causes group, while motion and transportation wastes were in the effect group.

A road map to guide lean transformation for HEIs is proposed with a multi-stage model and potential areas for improvement in HEIs were presented.

This study proposes a multi-stage structure by applying multi-criteria decision-making to HEIs, focussing on wastes from a lean perspective.

Nowadays, many firms are finding ways to enhance the survivability of sustainable supply chains (SUSSCs). The present study aims to develop a model for the SUSSCs of small and medium enterprises (SMEs) during the COVID-19 pandemic.

With the help of exhaustive literature review, constructs and items are identified to collect the responses from different SMEs. A total of 278 complete responses are received and 6 hypotheses are developed. Hypotheses testing have been done using structural equation modeling (SEM).

Major constructs identified for the study are supply chain (SC) performance measurement under uncertainty (SPMU), supply chain cooperation (SCCO), supply chain positioning (SCP), supply chain administration (SCA), supply chain feasibility (SCF) and the SUSSCs. From statistical analysis of the data collected, it can be concluded that the considered latent variables contribute significantly towardsthe model fit.

The present study contributes to the existing literature on disruptions and survivability. The study can be further carried out in context to different countries and sectors to generalize the findings.

The research findings will be fruitful for SMEs and other organizations in developing strategies to improve survivability during uncertain business environments.

The study has developed a model that shows that the identified latent variables and their indicators contribute significantly toward the dependent variable, i.e. survivability. It contributes significantly in bridging the research gaps existing in context to the survivability of SMEs.

This paper aims to create a new decision-making procedure that uses “Lot-by-Lot Acceptance Sampling Plan by Attributes” methodology in the production processes when any production interruption is observed in tobacco industry, which is a significant example of batch production.

Based on the fish bone diagram, the reasons of the production interruptions are categorized, then Lot-by-Lot Acceptance Sampling Plan by Attributes is studied to overcome the reasons of the production interruptions. Furthermore, managerial aspects of decision making are not ignored and hence, acceptance sampling models are determined by an Analytical Hierarchy Process (AHP) among the alternative acceptance sampling models.

A three-phased acceptance sampling model is generated for determination of the reasons of production interruptions. Hence, the necessary actions are provided according to the results of the proposed acceptance sampling model. Initially, 729 alternative acceptance sampling models are found and 38 of them are chosen by relaxation. Then, five acceptance sampling models are determined by AHP.

The current experience dependent decision mechanism is suggested to be replaced by the proposed acceptance sampling model which is based on both statistical and managerial decision-making procedure.

Acceptance sampling plans are considered as a decision-making procedure for various cases in production processes. However, to the best of our knowledge Lot-by-Lot Acceptance Sampling Plan by Attributes has not been considered as a decision-making procedure for batch production when any production interruption is investigated.

In India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI’s influence on supply chain risk mitigation (SCRM).

This study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses.

This study’s findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence.

This study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.

The main aim of this study is to understand the role of smart technologies and show the rankings of various smart technologies in collection and classification of electronic waste (e-waste).

This study presents a framework integrating the concepts of collection and classification mechanisms and smart technologies. The criteria set includes three main, which are economic, social and environmental criteria, including a total of 15 subcriteria. Smart technologies identified in this study were robotics, multiagent systems, autonomous tools, smart vehicles, data-driven technologies, Internet of things (IOT), cloud computing and big data analytics. The weights of all criteria were found using fuzzy analytic network process (ANP), and the scores of smart technologies which were useful for collection and classification of e-waste were calculated using fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR).

The most important criterion was found as collection cost, followed by pollution prevention and control, storage/holding cost and greenhouse gas emissions in collection and classification of e-waste. Autonomous tools were found as the best smart technology for collection and classification of e-waste, followed by robotics and smart vehicles.

The originality of the study is to propose a framework, which integrates the collection and classification of e-waste and smart technologies.

With the rapid change that has taken place with digitalization and data-driven approaches in supply chains, business environment become more competitive and reaching sustainability in supply chains become even more challenging. In order to manage supply chains properly, in terms of considering environmental, social and economic impacts, organizations need to deal with huge amount of data and improve organizations' data management skills. From this view, increased number of stakeholders and dynamic environment reveal the importance of data-driven technologies in sustainable supply chains. This complex structure results in new kind of risks caused by data-driven technologies. Therefore, the aim of the study to analyze potential risks related to data privacy, trust, data availability, information sharing and traceability, i.e. in sustainable supply chains.

A hybrid multi-criteria decision-making (MCDM) model, which is the integration of step-wise weight assessment ratio analysis (SWARA) and TOmada de Decisao Interativa Multicriterio (TODIM) methods, is going to be used to prioritize potential risks and reveal the most critical sustainability dimension that is affected from these risks.

Results showed that economic dimension of the sustainable supply chain management (SSCM) is the most critical concept while evaluating risks caused by data-driven technologies. On the other hand, risk of data security, risk of data privacy and weakness of information technology systems and infrastructure are revealed as the most important risks that organizations should consider.

The contribution of the study is expected to guide policymakers and practitioners in terms of defining potential risks causes by data-driven technologies in sustainable supply chains. In future studies, solutions can be suggested based on these risks for achieving sustainability in all stages of the supply chain causes by data-driven technologies.

The manufacturing sector is highly competitive and operationally complex. Therefore, the strategic alignment between operational excellence methodologies and Industry 4.0 technologies is one of the issues that need to be addressed. The main aim of the study is to determine the critical factors of strategic alignment between operational excellence methodologies and Industry 4.0 technologies for manufacturing industries and make comparative analyses between automotive, food and textile industries in terms of strategic alignment between operational excellence methodologies and Industry 4.0 technologies.

First, determining the critical factors based on literature review and expert opinions, these criteria are weighted, and analytical hierarchy process is run to calculate the weights of these criteria. Afterward, the best sector is determined by the grey relational analysis method according to the criteria for the three manufacturing industries selected for the study.

As a result of AHP, “Infrastructure for Right Methodology, Techniques and Tools, is in the first place,” Organizational Strategy, is in the second place, while the third highest critical factor is “Capital Investment”. Moreover, based on grey relational analysis (GRA) results, the automotive industry is determined as the best alternative in terms of strategic alignment between operational excellence (OPEX) methodologies and I4.0 technologies.

This study is unique in that it is primarily possible to obtain the order of importance within the criteria and to make comparisons between three important manufacturing industries that are important for the economies of the world.