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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 integrity assurance is the primary objective of Halal supply chain management. Several halal-related risks are present that have the potential to breach halal integrity. Therefore, this study aims to develop the framework for the assessment of halal-related risk from a supply chain perspective.

Risk related to halal is identified through the combined approach of the systematic literature review and experts’ input. Further, these risks are assessed using the integrated approach of intuitionistic fuzzy number (IFN) and D-number based on their severity score. This integrated approach can handle fuzziness, inconsistency and incomplete information that are present in the expert’s input.

Eighteen significant risks related to halal are identified and grouped into four categories. These risks are further prioritised based on their severity score and classified as “high priority risk” or “low priority risks”. The findings of the study suggests that raw material status, processing methods, the wholesomeness of raw materials and common facilities for halal and non-halal products are more severe risks.

This study only focusses on halal-related risks and does not capture the other types of risks occurring in the supply chain. Risks related to halal supply chain management are not considered in this study. Prioritisation of the risks is based on the expert’s input which can be biased to the experts' background.

The proposed risk assessment framework is beneficial for risk managers to assess the halal related risks and develop their mitigation strategies accordingly. Furthermore, the prioritisation of the risks also assists managers in the optimal utilisation of resources to mitigate high-priority risks.

This study provides significant risks related to halal integrity, therefore helping in a better understanding of the halal supply chain. To the best of the authors' knowledge, this is the first comprehensive study for developing a risk assessment model for the halal supply chain.

This study aims to apply new modifications by changing the nonlinear logarithmic calculation steps in the method based on the removal effects of criteria (MEREC) method. Geometric and harmonic mean from multiplicative functions is used for the modifications made while extracting the effects of the criteria on the overall performance one by one. Instead of the nonlinear logarithmic measure used in the MEREC method, it is desired to obtain results that are closer to the mean and have a lower standard deviation.

The MEREC method is based on the removal effects of the criteria on the overall performance. The method uses a logarithmic measure with a nonlinear function. MEREC-G using geometric mean and MEREC-H using harmonic mean are introduced in this study. The authors compared the MEREC method, its modifications and some other objective weight determination methods.

MEREC-G and MEREC-H variants, which are modifications of the MEREC method, are shown to be effective in determining the objective weights of the criteria. Findings of the MEREC-G and MEREC-H variants are more convenient, simpler, more reasonable, closer to the mean and have fewer deviations. It was determined that the MEREC-G variant gave more compatible findings with the entropy method.

Decision-making can occur at any time in any area of life. There are various criteria and alternatives for decision-making. In multi-criteria decision-making (MCDM) models, it is a very important distinction to determine the criteria weights for the selection/ranking of the alternatives. The MEREC method can be used to find more reasonable or average results than other weight determination methods such as entropy. It can be expected that the MEREC method will be more used in daily life problems and various areas.

Objective weight determination methods evaluate the weights of the criteria according to the scores of the determined alternatives. In this study, the MEREC method, which is an objective weight determination method, has been expanded. Although a nonlinear measurement model is used in the literature, the contribution was made in this study by using multiplicative functions. As an important originality, the authors demonstrated the effect of removing criteria in the MEREC method in a sensitivity analysis by actually removing the alternatives one by one from the model.

The purpose of this paper is to present a conceptual model that defines the essential components shaping the new Digital Supply Chains (DSCs) through the implementation and acceleration of Industry 4.0.

The scope of the present work exposes a conceptual approach and review of the key literature from 1989 to 2019, concerning the evolution and transformation of the actors and constructs in logistics and Supply Chain Management (SCM) by means of examining different conceptual models and a state-of-the-art review of Industry 4.0’s concepts and elements, with a focus on digitization in supply chain (SC) processes. A detailed study of the constructs and components of SCM, as defined by their authors, resulted in the development of a referential and systematic model that fuses the inherent concepts and roles of SCM, with the new technological trends directed toward digitization, automation, and the increasing use of information and communication technologies across logistics global value chains.

Having achieved an exploration of the different conceptual frameworks, there is no compelling evidence of the existence of a conceptual SCM that incorporates the basic theoretical constructs and the new roles and elements of Industry 4.0. Therefore, the main components of Industry 4.0 and their impact on DSC Management are described, driving the proposal for a new conceptual model which addresses and accelerates a vision of the future of the interconnectivity between different DSCs, grouped in clusters in order to add value, through new forms of cooperation and digital integration.

This research explores the gap in the current SCM models leading into Industry 4.0. The proposed model provides a novel and comprehensive overview of the new concepts and components driving the nascent and current DSCs. This conceptual framework will further aid researchers in the exploration of knowledge regarding the variables and components presented, as well as the verification of the newly revealed roles and constructs to understand the new forms of cooperation and implementation of Industry 4.0 in digitalized SCs.

The purpose of this paper is to generalize the traditional risk evaluation methods and to specify a multi-level risk evaluation framework, in order to prepare customized risk evaluation and to enable effectively integrating the elements of risk evaluation.

A real case study of an electric motor manufacturing company is presented to illustrate the advantages of this new framework compared to the traditional and fuzzy failure mode and effect analysis (FMEA) approaches.

The essence of the proposed total risk evaluation framework (TREF) is its flexible approach that enables the effective integration of firms’ individual requirements by developing tailor-made organizational risk evaluation.

Increasing product/service complexity has led to increasingly complex yet unique organizational operations; as a result, their risk evaluation is a very challenging task. Distinct structures, characteristics and processes within and between organizations require a flexible yet robust approach of evaluating risks efficiently. Most recent risk evaluation approaches are considered to be inadequate due to the lack of flexibility and an inappropriate structure for addressing the unique organizational demands and contextual factors. To address this challenge effectively, taking a crucial step toward customization of risk evaluation.

A flexible model which is based on a Triangular intuitionistic flexibility ranking and aggregating (TIFRA) operator is proposed for failure detection and reliability management in a Wind Turbine system. The model which is employed when there are limited research data and valid source of information, uses expert-based knowledge/opinion for failure detection and reliability management. The results from the study concludes that, the most important area affected by failure with respect to the failure criteria used, includes; oil level sensor tilt sensors for tower installation and accelerometers for tower sway (A₂), Pressure sensor for blade monitoring (A₃), and the Pitch actuator (A₄). The main advantage of the proposed method is that it provides advanced information about faults that identifies the intensity of the system behavior also; the method provides a more complete view of the reliability management and root cause of failure in the Wind Turbine (WT) system using the flexibility parameter in the model.

The purpose of this paper is to address the issue of knowledge visualization and its connection with performance measurement from an epistemological point of view, considering quantification and measurement not just as technical questions but showing their relevant implications on the management decision-making of knowledge-based organizations.

This study proposes a theoretical contribution that combines two lines of research for identifying the three main meta-choices problems that arise in the multidimensional benchmarking of knowledge-based organizations. The first is the meta-choice problem related to the choice of the algorithm used (Iazzolino et al., 2012; Laise et al., 2015; Daraio, 2017a). The second refers to the choice of the variables to be included in the model (Daraio, 2017a). The third concerns the choice of the data on which the analyses are carried out (Daraio, 2017a).

The authors show the interplay existing among the three meta-choices in multidimensional benchmarking, considering as key performance indicators intellectual capital, including Human Capital, Structural Capital and Relational Capital, and performances, evaluated in financial and non-financial terms. This study provides an empirical analysis on Italian Universities, comparing the ranking distributions obtained by several efficiency and multi-criteria methods.

This study demonstrates the difficulties of the “implementation problem” in performance measurement, related to the subjectivity of results of the evaluation process when there are many evaluation criteria, and proposes the adoption of the technologies of humility related to the awareness that we can only achieve “satisficing” results.