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This study presents the development of a supply chain (SC) observatory, which is a benchmarking solution to support companies within the same industry in understanding their positioning in terms of SC performance.

A case study is used to demonstrate the set-up of the observatory. Twelve experts on automatic equipment for the wrapping and packaging industry were asked to select a set of performance criteria taken from the literature and evaluate their importance for the chosen industry using multi-criteria decision-making (MCDM) techniques. To handle the high number of criteria without requiring a high amount of time-consuming effort from decision-makers (DMs), five subjective, parsimonious methods for criteria weighting are applied and compared.

A benchmarking methodology is presented and discussed, aimed at DMs in the considered industry. Ten companies were ranked with regard to SC performance. The ranking solution of the companies was on average robust since the general structure of the ranking was very similar for all five weighting methodologies, though simplified-analytic hierarchy process (AHP) was the method with the greatest ability to discriminate between the criteria of importance and was considered faster to carry out and more quickly understood by the decision-makers.

Developing an SC observatory usually requires managing a large number of alternatives and criteria. The developed methodology uses parsimonious weighting methods, providing DMs with an easy-to-use and time-saving tool. A future research step will be to complete the methodology by defining the minimum variation required for one or more criteria to reach a specific position in the ranking through the implementation of a post-fact analysis.

This paper aims to classify journal papers in the context of hybrid quality function deployment QFD and multi-criteria decision-making (MCDM) methods published during 2004–2021.

A conceptual classification scheme is presented to analyze the hybrid QFD-MCDM methods. Then some recommendations are given to introduce directions for future research.

The results show that among all related areas, the manufacturing application has the most frequency of published papers regarding hybrid QFD-MCDM methods. Moreover, using uncertainty to establish a hybrid QFD-MCDM the relevant papers have been considered during the time interval 2004–2021.

There are various shortcomings in conventional QFD which limit its efficiency and potential applications. Since 2004, when MCDM methods were frequently adopted in the quality management context, increasing attention has been drawn from both practical and academic perspectives. Recently, the integration of MCDM techniques into the QFD model has played an important role in designing new products and services, supplier selection, green manufacturing systems and sustainability topics. Hence, this survey reviewed hybrid QFD-MCDM methods during 2004–2021.

Halal is an emerging business sector and is steadily gaining popularity among scholars and practitioners. The purpose of this paper is to critically evaluate and review the reported literature in the broad area of Halal using bibliometric technique and network analysis tools. Moreover, this paper also proposes future research directions in the field of Halal.

This paper employed a systematic review technique followed by bibliometric analysis to gain insight and to evaluate the research area associated with Halal. Furthermore, data mining techniques are used for analysing the concerned article title, keywords and abstract of 946 research articles obtained through the Scopus database. Finally, network analysis is used to identify significant research clusters.

This study reports top authors contributing to this area, the key sub-research areas and the influential works based on citations and PageRank. We identified from the citation analysis that major influential works of Halal are from the subject area of biological science and related areas. Further, this study reports established and emerging research clusters, which provide future research directions.

Scopus database is used to conduct a systematic review and corresponding bibliometric study; the authors might have missed some peer-reviewed studies not reported in Scopus. The selection of keywords for article search may not be accurate for the multi-disciplinary Halal area. Also, the authors have not considered the banking/financial aspects of Halal. The proposed four research clusters may inform potential researcher towards supporting the industry.

The novelty of the study is that no published study has reported the bibliometric study and network analysis techniques in the area of Halal.

A significant number of studies have been conducted to analyze and understand the relationship between gas emissions and global temperature using conventional statistical approaches. However, these techniques follow assumptions of probabilistic modeling, where results can be associated with large errors. Furthermore, such traditional techniques cannot be applied to imprecise data. The purpose of this paper is to avoid strict assumptions when studying the complex relationships between variables by using the three innovative, up-to-date, statistical modeling tools: adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANNs) and fuzzy time series models.

These three approaches enabled us to effectively represent the relationship between global carbon dioxide (CO₂) emissions from the energy sector (oil, gas and coal) and the average global temperature increase. Temperature was used in this study (1900-2012). Investigations were conducted into the predictive power and performance of different fuzzy techniques against conventional methods and among the fuzzy techniques themselves.

A performance comparison of the ANFIS model against conventional techniques showed that the root means square error (RMSE) of ANFIS and conventional techniques were found to be 0.1157 and 0.1915, respectively. On the other hand, the correlation coefficients of ANN and the conventional technique were computed to be 0.93 and 0.69, respectively. Furthermore, the fuzzy-based time series analysis of CO₂ emissions and average global temperature using three fuzzy time series modeling techniques (Singh, Abbasov–Mamedova and NFTS) showed that the RMSE of fuzzy and conventional time series models were 110.51 and 1237.10, respectively.

The paper provides more awareness about fuzzy techniques application in CO₂ emissions studies.

These techniques can be extended to other models to assess the impact of CO₂ emission from other sectors.

The objective of the study is to develop a methodology to strategically rank store locations using criteria such as population, store site characteristics, economic considerations, competition and so on to select the most optimal retail convenience store location.

A case of National Capital Region, India, for a 24-h convenience store was considered for the study and the major criteria that affect the performance of a convenience store are identified, such as population characteristics, economic criteria, competition, consumer accessibility, store size, total cost, site attractiveness and security. Fuzzy AHP is utilized to find the weightage for each criteria and a combination of fuzzy TOPSIS and grey relational analysis (GRA) is applied to rank the alternative using these criteria weight. Further, results obtained are compared with results from fuzzy TOPSIS and fuzzy VIKOR methods. Sensitivity analysis is also performed for ensuring the robustness of the framework.

It is observed that outcomes do not change under various settling coefficient values, demonstrating that the methodology is very robust. The developed framework will be quite useful to diverse retailers looking to expand and generate substantial profits.

A large sample size of number of locations encourages generalization of results. Strategic ranking of the selected locations is carried out on a few selected criteria. The study was limited by the designated geographical area.

The study contributes to the few available articles on convenience store selection using combination of fuzzy AHP, fuzzy TOPSIS and GRA for a developing country.

This study aims to determine and investigate the main causes of construction project delays. Construction projects are more intricate and associated with significant levels of risk owing to cost overruns. These overruns frequently lead to delays, incomplete work or other related challenges. Building delays are a prevalent problem in the building sector of developing nations. These delays prolong the duration of projects and result in increased costs and conflicts among stakeholders. A conceptual model consisting of the factors causing the delays in heating, ventilation and air conditioning (HVAC) projects was developed and tested in this study.

A comprehensive data collection process was undertaken. A meticulously designed survey was distributed to a diverse cohort of 294 participants, including contractors and sub-contractors from Chennai, Tamil Nadu. The data was collected using stratified sampling, ensuring a representative sample. The data was then analysed using ordinary least squares multiple regression.

The findings of this study have significant implications for the construction industry. They indicate that factors related to sales, clients, design, procurement, finance and labour all contribute to delays in HVAC projects. Understanding these factors can help stakeholders in the industry to better manage and mitigate project delays.

This study is unique because it is a perceptual study of stakeholders. It provides valuable information for analysing and assessing project performance by identifying the primary causes of HVAC project delays. To the best of the authors’ knowledge, the study conducted on HVAC projects is the first of its kind and hence makes a pivotal contribution to the literature on construction projects. Additionally, the study will assist policymakers and consultants in taking necessary steps to minimize delays.

Heart diseases have become one of the most causes of death among Egyptians. With 500 deaths per 100,000 occurring annually in Egypt, it has been noticed that medical data faces a high-dimensional problem that leads to a decrease in the classification accuracy of heart data. So the purpose of this study is to improve the classification accuracy of heart disease data for helping doctors efficiently diagnose heart disease by using a hybrid classification technique.

This paper used a new approach based on the integration between dimensionality reduction techniques as multiple correspondence analysis (MCA) and principal component analysis (PCA) with fuzzy c means (FCM) then with both of multilayer perceptron (MLP) and radial basis function networks (RBFN) which separate patients into different categories based on their diagnosis results in this paper, a comparative study of the performance performed including six structures such as MLP, RBFN, MLP via FCM–MCA, MLP via FCM–PCA, RBFN via FCM–MCA and RBFN via FCM–PCA to reach to the best classifier.

The results show that the MLP via FCM–MCA classifier structure has the highest ratio of classification accuracy and has the best performance superior to other methods; and that Smoking was the most factor causing heart disease.

This paper shows the importance of integrating statistical methods in increasing the classification accuracy of heart disease data.

This paper presents the particle swarm optimization (PSO) algorithm in conjuction with the fuzzy logic method in order to achieve an optimized tuning of a proportional integral derivative controller (PID) in the DTC control loops of dual star induction motor (DSIM). The fuzzy controller is insensitive to parametric variations, however, with the PSO-based optimization approach we obtain a judicious choice of the gains to make the system more robust. According to Matlab simulation, the results demonstrate that the hybrid DTC of DSIM improves the speed loop response, ensures the system stability, reduces the steady state error and enhances the rising time. Moreover, with this controller, the disturbances do not affect the motor performances.

Intuitionistic linguistic fuzzy information (ILFI), characterized by linguistic terms and intuitionistic fuzzy sets (IFSs), can easily express the fuzzy information in the process of muticriteria decision making (MCDM) and muticriteria group decision making (MCGDM) problems. The purpose of this paper is to provide an overview of aggregation operators (AOs) and applications of ILFI.

First, some meaningful AOs for ILFI are summarized, and some extended MCDM approaches for intuitionistic uncertain linguistic variables (IULVs), such as extended TOPSIS, extended TODIM, extended VIKOR, are discussed. Then, the authors summarize and analyze the applications about the AOs of IULVs.

IULVs, characterized by linguistic terms and IFSs, can more detailed and comprehensively express the criteria values in the process of MCDM and MCGDM. Therefore, lots of researchers pay more and more attention to the MCDM or MCGDM methods with IULVs.

The authors summarize and analyze the applications about the AOs of IULVs Finally, the authors point out some possible directions for future research.

The purpose of this investigation is to develop a hybrid fuzzy TOPSIS methodology in order to understand in a practical and integrated way, the consuming and buying behavior of EU residents towards Fishery and Aquaculture Products (FAPs), with an emphasis in the consumption and buying frequency.

Data were obtained from the Special Eurobarometer Survey (European Union, 2018b), which is a survey of 27,732 EU residents with different socio-demographic characteristics that represent the 28 EU countries. A hybrid fuzzy TOPSIS methodology that synthesizes the consuming and buying behavior of the EU residents toward FAPs was developed.

The results show that among the countries, Spain has the highest consumption and buying patterns of FAPs, while among the generations it corresponds to the residents born between 1928 and 1945. In addition, there are important differences that depend on the country of residence as well as the generation of the residents. The elasticity analysis evidenced that marketing strategies would have the biggest impact in the countries located in the Central-Eastern zone of the EU and on the generation formed by the people born after 1980.

Although in the literature there are many studies that aimed to understand the behavior of consumers for FAPs, few investigations have focused on analyzing and integrating both the consumption and buying behavior, and to our best knowledge, there are no studies providing a methodology that allow making comparisons between different countries regarding the consumption and buying behavior of FAPs.