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Industry 4.0 (I4.0) not only turns traditional industrial activities upside down but also demonstrates its potential to enhance industrial competitiveness and productivity. In this context, technological advancement and I4.0 is a strategy to be pursued. This study aims to consider different I4.0 technologies by analysing Indian small and medium enterprises (SMEs).

Key factors and promising I4.0 technologies were selected using literature analysis and experts’ panel. The appropriate I4.0 technology for Indian SMEs is recommended using the fuzzy complex proportional assessment (COPRAS) method.

Results reveal that ability to expand IT infrastructure, change in the organization’s structure and the capacity to analyse key performance indicators as three crucial key factors in I4.0 implementation. In particular, the smart factory is identified as a better I4.0 for Indian SMEs.

This work has analysed Indian SMEs, but it is appropriate for other developing economies with limited technical resources, financial resources and inadequate skill sets. This work identifies a gap in the current literature, and the findings proposed by this work are oriented to assist decision makers, industrial managers and practitioners in selecting I4.0 technology and enhancing the industrial infrastructure. At the same time, cooperation between the government and industrial community is required to develop programmes for imparting the knowledge of I4.0 among SMEs. The framework used in this study will arm the industrial management in adopting I4.0.

Blood availability is critical for saving lives in various healthcare services. Ensuring blood availability can only be achieved through efficient management of the blood supply chain (BSC). A key component of the BSC is bloodmobiles, which are responsible for a significant portion of blood donation collections. The most crucial factor affecting the efficacy of bloodmobiles is their location selection. Therefore, detailed decision analyses are essential for the location selection of bloodmobiles. This study proposes a comprehensive approach to bloodmobile location selection for resilient BSCs.

This study provides a novel integration of the spherical fuzzy analytical hierarchy process (SF-AHP) and spherical fuzzy complex proportional assessment (SF-COPRAS) methodologies. In this framework, the criteria are weighted using SF-AHP. The alternatives are then evaluated using SF-COPRAS, employing criteria weights obtained from SF-AHP without defuzzification.

The results show that supply conditions and resilience are the most important criteria for a bloodmobile location selection. Additionally, the validation analyses confirm the stability of the solution.

This study presents several managerial implications that can aid mid-level managers in the BSC during the decision-making process for bloodmobile location selection. The critical factors revealed, along with their importance in choosing bloodmobile locations, serve as a comprehensive guide. Additionally, the framework proposed in this study offers decision-makers (DMs) an effective method for ranking potential bloodmobile locations.

This study presents the first application of multi-criteria decision-making (MCDM) for bloodmobile location selection. In this manner, several aspects of bloodmobile location selection are considered for the first time in the existing literature. Furthermore, from the methodological aspect, this study provides a novel SF-AHP-integrated SF-COPRAS methodology.

Evaluations of grey systems and systems with subjective judgements are always like an impasse for science and companies. Especially, calculations of the problems which include various units are really difficult situations. The purpose of this paper is to propose a grey analytic hierarchy process (G-AHP) for engineering and managerial problems with grey systems to make more clear and objective decisions.

Proposed G-AHP approach is applied to project manager selection for a software project of an energy company. The application includes three different units as year, score and assessment. Six engineers are evaluated with 25 criteria in the application. Weights of the factors and assessments are done by three top managers of the company as pairwise comparisons. Other data in the decision matrix are obtained from the personal information and exam results of engineers.

Final weights of the criteria and evaluations of engineers are all done with the proposed G-AHP. Obtained results of G-AHP are also compared with grey “VlseKriterijumska Optimizacija I Kompromisno Resenje” results as a validation of the calculations and proposed approach. Final results of the applications are ranked for the evaluations and comparison. All results of the case study are concluded with the effectiveness and applicability of the proposed G-AHP method both for this study and other fields of science, engineering and management.

This study provides to evaluate and interpret grey systems with different units and subjective judgements for science, engineering and management more clearly and objectively in an easier way.

This paper analyses the voice of customers (VoCs) using a hybrid clustering multi-criteria decision-making (MCDM) approach. The proposed method serves as an efficient tool for how to approach multiple decision-making involving a large set of countrywide customer complaints in the Iranian automotive sector.

The countrywide data comprising 3,342 customer complaints (VoCs) were gathered. A total of seven determinant complaint criteria were identified in brainstorming sessions with three groups (six each) of experts employing the fuzzy Delphi method. The weights of these criteria were assigned by applying the fuzzy best–worst method (FBWM) to identify the severity of the complaints. Subsequently, the complaints were clustered into five categories with respective customer locations (province), car type and manufacturer using the K-mean method and further prioritised and ranked employing the fuzzy complex proportional assessment of alternatives (FCOPRAS) method.

The results indicated that the majority of complaints (1,027) from the various regions of the country belonged to one specific model of car made by a particular producer. The analyses revealed that only a few complaints were related to product quality, with the majority related to service and financial processes including delays in automobile delivery, delays in calculating monthly instalments, price variation, failure to provide a registration ( licence) and failure to supply the agreed product. The proposed method is an efficient way to solve large-scale multidimensional problems and provide a robust and reliable set of results.

The proposed method makes it much easier for management to deal with complaints by significantly reducing their number. The highest-ranked complaints from customers of the car industry in Iran are those related to delivery time, price alternations, customer service support and quality issues. Surveying the list of complaints shows that paying attention to the four most voiced complaints can reduce them more than 54%. Management can make appropriate strategies to improve the production quality as well as business processes, thus producing a significant number of customer complaints.

This paper proposes a comprehensive approach to critically analyse the VoCs by combining qualitative and decision-making approaches including K-mean, FCOPRAS, fuzzy Delphi and FBWM. This is the first paper that analyses the VoCs in the automotive sector in a developing country’s context involving large-scale decision-making problem-solving.

This study aims to find and evaluate the impact of COVID-19 in India’s mining activities. From the literature survey and input from experts, 14 important factors and ten green recovery strategies are identified. These 14 factors and 10 strategies are examined in a real industrial environment.

In this research study, fuzzy-complex proportional assessment is used to compare the identified strategies with the selected factors. Based on the outcome, this study identifies the optimum strategy that could assist the mining industry in handling both COVID-19 and sustainability issues.

The study’s outcome reveals that a large group of workers, collapse of demand and disruption, and suffered contractual workers are the top three factors that need to be considered regarding COVID-19 and sustainability issues. Effective strategies for eco-innovation practices, an emphasis on health and safety, and environmental awareness, education and training are the three primary strategies to be implemented for the greatest impact.

This research study is the first of its kind to coordinate both issues of COVID-19 and sustainability. In this regard, this study sheds a timely light on the precautions that need to be taken in tackling COVID-19 and sustainability issues. This study’s outcome will enhance the managerial capability in developing robust and effective strategies for handling difficult situations.

The purpose of this study is to present a new failure mode and effects analysis (FMEA) approach based on fuzzy multi-criteria decision-making (MCDM) methods and multi-objective programming model for risk assessment in the planning phase of the oil and gas construction projects (OGCP) in Iran.

This research contains multiple steps. First, 19 major potential health and safety executive (HSE) risks in OGCP were classified into six categories with the Delphi method. These factors were distinguished by the review of project documentation, checklist analysis and consulting with experts. Then, using the fuzzy SWARA method, the authors calculated the weights of major HSE risks. Subsequently, FMEA and PROMETHEE approaches were used to identify the priority of main risk factors. Eventually, a binary multi-objective linear programming approach was developed to select the risk response strategies, and an augmented e-constraint method (AECM) was used.

Regarding the project triple well-known constraints of time, cost and quality, which organizations usually confront, the HSE risks of OGCP were identified and prioritized. Also, the appropriate risk response strategies were also suggested to the managers to be adopted regarding the situations.

The present research points at the HSE risks’ assessment integrating the fuzzy FMEA, step-wise weight assessment ratio analysis and PROMETHEE techniques with the AECM. Further to the authors’ knowledge, the quantitative assessment of the HSE risks of OGCP has not been done using the combination of the fuzzy FMEA, MCDM and AECMs.

The proposed hybridized framework provides a new performance optimization-based paradigm for analysing the failure behaviour of paneer unit (PU) in the dairy industry.

A novel fuzzy Jaya-based Lambda–Tau Optimization (JBLTO) approach-based mathematical modelling was developed for calculating various reliability indices of the considered unit. Failure mode and effect analysis (FMEA) was carried using qualitative information gathered from system's expert opinions. Fuzzy-complex proportional assessment (FCOPRAS) approach was integrated within FMEA to recognize the most critical failure causes associated with various subsystem/components.

The availability of the unit falls by 0.053% as the uncertainty level increases from ±15 to ±25% and further decreases to 0.323% as the uncertainty level increases from ±25 to ±60%. Failure causes, namely wearing in gears of gearbox (MST4), an impeller's cavitation and/or corrosion (CFP4), winding failure of electric motor (WS9), were recognized as the most critical failure causes with FCOPRAS final performance scores of 100, 100 and 100 and fuzzy combinative distance-based assessment (FCODAS) resultant assessment score of 0.5997, 1.1898 and 1.6135.

JBLTO approach-based reliability results were compared with traditional particle swarm optimization-based Lambda–Tau (PSOBLT) and traditional fuzzy Lambda–Tau (FLT) approaches for confirming the downward trend in the system's availability. The ranking results of qualitative analysis are compared with the implementation of FCODAS technique. Sensitivity analysis was executed to evaluate the robustness of the proposed hybridized framework.

This paper investigates the endogenous relationships between banking performance and social welfare in Southern African Development Community (SADC) countries.

A comprehensive three-stage multi-criteria decision-making (MCDM) approach based on alternative informational assumptions is applied.

Results indicate that banking performance is paradoxically associated with stagnant economic activity and higher wealth concentration for the minority. The authors found that SADC banking performance promotes higher Human Development Index (HDI) standards possibly via efficient financial intermediation, dissemination of best managerial practices and other forms of positive spillovers in these countries.

This paper contributes to the MCDM literature by simultaneously exploring the key concepts of “utility functions” (using COPRAS) and “distance to ideal solutions” (using TOPSIS) in mapping and explaining the feedback and cause-effect processes between banking performance and social welfare that may exist. Another distinctive aspect is related to the computation of bias-free criteria weights, using a robust SWARA order-rank based on information entropy. Finally, this paper is concerning the endogeneity measurement, using a novel stochastic structural relationship non-linear programme.

Given the critical role of project prioritization and selection process in Six Sigma efforts, this study aims to analyse the relevant literature to answer this question: What types of project prioritization and selection methods have been used in Six Sigma research?

The study implemented the systematic literature review (SLR) method to identify and review all relevant previous studies.

The study revealed that 59 articles focused on the topic used 111 methods, analytic hierarchy process appeared as the most frequently used method with 12 articles (20%) and one-third of the methods used in the current Six Sigma project selection literature contained multi-criteria decision-making methods. In total, 61% of 59 articles were not published in the journals ranked by the ABDC’s list. Only 17% of the articles reviewed in this study were published in journals ranked as B category and 12% of the articles were published in A category journals.

The findings of this literature review may help Six Sigma practitioners and researchers accurately identify project prioritization and selection methods, considering that qualitative and quantitative scientific methods guarantee to make better decisions than “gut feelings” of the decision makers in this process.

Although a variety of studies focused on the topic, an SLR is lacking in the area of Six Sigma project prioritization and selection. Therefore, this study was constructed using the SLR method to analyse the topic.

In the present research work electrochemical grinding (ECG) process is applied to machine Al₂O₃/Al interpenetrating phase composite. The purpose of this paper is to present a new approach to optimize the ECG process parameters while machining alumina-aluminum (Al₂O₃ – Al) interpenetrating phase composites (IPC) used in automotive, aircraft and manufacture of space ships applying Taguchi-based experimental studies and fuzzy multi-criteria decision-making techniques.

The present work identifies the process variables that have significant consequences during ECG of Al₂O₃/Al IPC. The Taguchi L₉ orthogonal array is selected for design of experiments and the analysis is carried out following signal to noise ratio. The analysis of variance is carried out to establish the factors that significantly influence the responses. The present work also investigates the multi objective optimization of ECG process parameters using VIseKriterijumsa Optimizacija I Kompromisno Resenje (VIKOR) and Grey relational analysis (GRA) to establish the reference ranking from a set of alternatives in the presence of conflicting criteria.

Material removal rate, surface finish, overcut and cutting force are shown to depend on the type of electrolyte, supply voltage, depth of cut and electrolyte flow rate. It is found that voltage and electrolyte concentration are important. The optimal machining parameter combination for ECG process is determined using fuzzy set theory, VIKOR and GRA. Substantial improvement in machining performance takes place.

A variety of manufacturing techniques are available for processing of Al₂O₃ – Al metal matrix composites. Generally manufacturers favor low cost modus operandi. Therefore ECG process is the best alternative for processing of MMCs in the present commercial sectors. The experimental investigation approach can act as useful and an efficient guideline for manufacturing.

The characteristic features of the ECG process are reflected through Taguchi design-based experimental studies with various process parametric combinations. Application of multi-response optimization technique for evaluation of best parametric combination for machining Al₂O₃ – Al IPC material using ECG process is a first-of-its-kind approach in literature.