Occupational risk assessment (ORA) is a process that consists of evaluating, ranking, and classifying the hazards and associated risks arising in any workplace from the viewpoint of occupational ...health and safety. Many ORA methods have been proposed in the literature, from a single independent expert to participatory methodologies made by group decision and simple to complex ones. In this paper, a holistic ORA is presented, which uses two important multi-attribute decision methods named Bayesian Best-Worst Method (Bayesian BWM) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Bayesian BWM is used to determine the importance weights of six different assessment criteria, which are the probability of hazardous event (P), frequency (F), severity (S), detectability (D), cost (C) and sensitivity not to use personal protective equipment (SNP). Since the classical BWM finds solution to the weights of a number of criteria from only one expert's judgment, Bayesian BWM is preferred in this paper (1) to enable participation of a group of experts, (2) to aggregate the preferences of these multiple experts into consensus without loss of information and (3) to follow a probabilistic way for solving the ORA problem. The hazards are then ranked by VIKOR. The approach is implemented in the ORA process of a textile production plant. Results of risk analysis showed that electricity hazard and associated risks constitute the highest risk ratings. These hazards arise from the product, process, human and working environment. The associated risks are evaluated, prioritized, and detailed control measures are proposed. This study made comparisons with the classical BWM-VIKOR approach to demonstrate the proposed approach's difference and practicality. Results can also help practitioners and risk analysts in formulating the improvement measures to increase the overall safety of the working environment further.
Although environmental awareness has reached a high level, enterprises—regardless of their working domains—follow the concept of greenness for their practices. This awareness among the stakeholders ...and supply chain experts has a positive impact on the purchasing departments of enterprises in various sectors to consider greenness in their procurement processes. The critical decision that must be made in green supply chain management (GSCM) is supplier selection. In the textile industry, a highly competitive market in recent years, suppliers for this industry have crucial roles in business activities considering environmental issues. Therefore, green supplier selection (GSS) in the textile industry is considered a must-be process for the stakeholders. In this study, a GSS problem is tackled as a multi-criteria decision process. Best worst method (BWM) and TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) methods are merged under an improved fuzzy concept of interval type-2 fuzzy sets (IT2FSs). In determining green suppliers’ evaluation criteria, BWM with interval type-2 fuzzy numbers (IT2F-BWM) is used. In selecting green suppliers, an interval type-2 fuzzy TODIM (IT2F-TODIM) is applied. Considering the characteristics of IT2FSs, BWM, and TODIM methods either individually and in integrated style, the proposed approach can handle uncertainty in the decision-making of GSS. To demonstrate the applicability of the approach, a case study in the Turkish textile industry is performed. Three green supplier alternatives (S1, S2, and S3) are assessed under forty-two sub-criteria. The study shows the most significant sub-criteria are recognized as dye and print quality, product design and pattern suitability, profit on the product, variation in price, and purchase cost. S2 green supplier has been selected as the most appropriate one. A sensitivity analysis is also fulfilled to check variation in the ranking of green suppliers.
The Fine
−
Kinney is a risk assessment method widely used in many industries due to its ease of use and quantitative risk evaluation. As in other methods, it is a method that recommends taking a ...series of control measures for operational safety. However, it is not always possible to implement control measures based on the determined priorities of the risks. It is considered that determining the priorities of these measures depends on many criteria such as applicability, functionality, performance, and integrity. Therefore, this study has studied the prioritization of control measures in Fine − Kinney-based risk assessment. The criteria affecting the prioritization of control measures are hierarchically structured, and the importance weights of the criteria are determined by the Bayesian Best–Worst Method (BBWM). The priorities of control measures were determined with the fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (FVIKOR) method. The proposed model has been applied to the risk assessment process in a petrol station’s liquid fuel tank area. According to the results obtained with BBWM, the most important criterion affecting the prioritization of control measures is the
applicability
criterion. It has an importance weight of about 42%. It is followed by
performance
with 31%,
functionality
with 18%, and
integrity
with 10%, respectively. FVIKOR results show that the “Periodic control of the ventilation device” measure is the top priority for Fine − Kinney risk assessment. “The absence of any ducts or sewer pits that may cause gas accumulation in the tank area and near the dispenser; Yellow line marking of entry and exit and vehicle roads; Placing of speed limit warning signs” has been determined as a secondary priority. On conclusion, this proposed model is expected to bring a new perspective to the work of occupational health and safety analysts, since the priority suggested by Fine − Kinney risk analysis methods is not always in the same order as the one in the stage of taking action, and the source, budget, and cost/benefit ratio of the measure affect this situation in practice.
Hospitals are the first point of contact for people in the face of disasters that interfere with the daily functioning of life and endanger health and social life. All preparations should be made ...considering the worst possible conditions and the provided service should continue without interruption. In this study, a multi-criteria decision-making model was developed to evaluate disaster preparedness of hospitals. This decision model includes Bayesian best–worst method (BBWM), the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and technique for order preference by similarity to ideal solution (TOPSIS) methods. With the proposed decision model, six main criteria and 34 sub-criteria related to disaster preparedness of hospitals were considered. The criteria and sub-criteria evaluated in pairwise comparison manner by the experts were weighted with BBWM. These weight values and the data obtained from the six Turkish hospitals were combined to provide inputs for VIKOR and TOPSIS. In addition, a comparative study and sensitivity analysis were carried out using weight vectors obtained by different tools. BBWM application results show that the “Personnel” criterion was determined as the most important criterion with an importance value of 26%. This criterion is followed by “Equipment” with 25%, “Transportation” with 14%, “Hospital building” and “Communication” with 12%, and “Flexibility” with 11%. Hospital-2 was determined as the most prepared hospital for disasters as a result of VIKOR application. The VIKOR Q value of this hospital was obtained as 0.000. According to the results of the comparative study, Hospital-2 was determined as the most disaster-ready hospital in all six different scenarios. This hospital is followed by Hospital-4 (Q = 0.5661) and Hospital-5 (Q = 0.7464). The remaining rankings were Hospital-6, Hospital-3 and Hospital-1. The solidity of the results was checked with TOPSIS. Based on TOPSIS application results, Hospital-2 was again found the most-ready hospital. The usage of BBWM in this study enabled the expert group’s views to be combined without loss of information and to determine the criteria and sub-criteria weights with less pairwise comparisons in a probabilistic perspective. Via the “Credal ranking”, which is the contribution of BBWM to the literature, the interpretation of the hierarchy between each criterion has been performed more precisely.
The importance of risk assessment in the context of occupational health and safety by manufacturing operators strengthens their hands in solving the problems they may encounter in business processes ...related to health and safety. One of the most important phases of conducting an exhaustive occupational risk assessment is to analyze potential hazards and associated risks quantitatively. Since manufacturing is one of the industries that require workers to be highly exposed to work, creating a safer environment to reduce occupational injuries is an important task. This study proposes a novel fuzzy risk assessment approach developed by integrating Fermatean fuzzy sets (FFSs) and technique for order preference by similarity to ideal solution (TOPSIS) method for ranking potential hazards in manufacturing. FFSs are a new version of fuzzy set theory that covers the intuitionistic fuzzy sets and Pythagorean fuzzy sets. This version of the fuzzy set is crucial in the decision-making process to handle uncertain information more easily and reflect uncertainty better. A linguistic scale under Fermatean fuzzy documentation has also been developed for experts/decision makers to disclose their judgments easily. Occupational risk analysts can benefit from this approach since FFSs are used for the first time in occupational risk assessment, and the approach is presented in integration with TOPSIS. The proposed approach is applied in the aluminum plate-manufacturing process risk assessment. In the conclusion of the implementation, risks arising in the production are prioritized. In addition, this study made comparisons with other fuzzy methods to demonstrate the proposed approach’s difference and practicality. This study’s results can support practitioners and risk analysts in formulating the improvement measures to increase the safety of the work environment further.
Failure mode and effect analysis (FMEA) is a risk analysis tool widely used in the manufacturing industry. However, traditional FMEA has limitations such as the inability to deal with uncertain ...failure data including subjective evaluations of experts, the absence of weight values of risk parameters, and not considering the conditionality between failure events. In this paper, we propose a holistic FMEA to overcome these limitations. The proposed approach uses the fuzzy best–worst (FBWM) method in weighting three risk parameters of FMEA, which are severity (
S
), occurrence (
O
), and detection (
D
), and to find the preference values of the failure modes according to parameters
S
and
D
. On the other side, it uses the fuzzy Bayesian network (FBN) to determine occurrence probabilities of the failure modes. Experts use a procedure using linguistic variables whose corresponding values are expressed in trapezoidal fuzzy numbers, and determine the preference values of the failure modes according to parameter
O
in the constructed BN. Thus, the FBN including expert judgments and fuzzy set theory addresses uncertainty in failure data and includes a robust probabilistic risk analysis logic to capture the dependence between failure events. As a demonstration of the approach, a case study was conducted in an industrial kitchen equipment manufacturing facility. The results of the approach have also been compared with existed methods demonstrating its robustness.
Supplier selection is one of the most important multi-criteria decision-making (MCDM) problems for decision-makers in the competitive market. Today’s organizations are seeking new ways to reduce the ...negative effects they have on the environment and to achieve a greener system. Currently, the concept of green supplier selection has gained great importance for its ability to incorporate environmental or green criteria into classical supplier selection practices. Therefore, in this study, a multi-phase MCDM model based on the best-worst method (BWM) and the interval type-2 fuzzy technique for order preference by similarity to ideal solution (IT2F TOPSIS) is proposed. A case study in a plastic injection molding facility in Turkey was carried out to show the applicability of the proposed integrated methodology. The paper offers insights into decision-making, methodology, and managerial implications. Results of the case study are examined and suggestions for future research are provided.
Making decisions require increasingly more sophisticated methods. The Best-Worst method was developed recently to find a good balance between the amount of information required and its consistency. ...The Fuzzy Best-Worst method was then proposed to integrate the uncertainty and imprecision of evaluations. In this paper, we propose the Calibrated Fuzzy Best-Worst Method, which enables us to define personalized fuzzy numbers. This new method was applied to the real case study of a global fashion supply chain suffering a dramatic drop of demand due to the COVID-19 shocks. Since its upstream was leading to a ripple effect on the rest of the supply chain stages, our study focused on evaluating the viability of its distribution channels based on weighted resilience capabilities and performance indicators. The proposed method allowed being more precise than the traditional Best-Worst method because it personalizes the uncertainty and subjectivity. The results revealed that cash flow, revenues, and inventory turnover were the most important performance indicators in the supply chain resilience. Financial strength, adaptability, and market position were shown to be the most critical resilience capabilities in preserving supply chain survivability with low-demand items and longer disruptions. Their effects explain why e-commerce was the most viable distribution channel.
Providing better hospital service quality is one of the major concerns of healthcare industry in the world. Since health services in Turkey are provided in a very competitive environment, for making ...a better choice, the services delivered by the public and private hospitals should be evaluated according to the viewpoint of stakeholders in terms of satisfaction. In this study, a model proposal is presented based on the concept of Pythagorean fuzzy analytic hierarchy process and Pythagorean fuzzy technique for order preference by similarity to ideal solution method to provide an accurate decision-making process for evaluating the hospital service quality. We study under fuzzy environment to reduce uncertainty and vagueness, and use linguistic variables parameterized by Pythagorean fuzzy numbers. The proposed approach is separated from others with the integration of the methods in a way providing a systematic fuzzy decision-making process. A case study including 32 service quality criteria and two public and one private hospitals in Turkey assessed by 32 evaluators by medical staff, hospital executives, auxiliaries, and patients is performed to demonstrate the applicability and validity of the proposed approach. On conclusion, integrated model produces reliable and suggestive outcomes better representing the vagueness of decision-making process.
The rapid development of innovative technologies, population growth, shifting resource management patterns, and decarbonization challenges are expected to increase the demand for raw materials ...labeled as critical in the following years. The increasing concerns have led to evaluate the critical raw materials (CRMs) with high economic importance and supply risk. In this context, this paper aims to develop a conceptual methodology to identify CRMs in Turkey. The methodology integrates a multi-criteria decision-making method (MCDM) to provide the raw material criticality matrix regarding various criteria, the EU (European Union) method, which represents supply risk and economic importance indicators, and the time series analysis method, in which dynamic evaluation of materials over time instead of the static indicators. We first provide a Bayesian Best-Worst method (B-BWM) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) based model for the criticality matrix, which enables evaluations of participated experts to be combined to reveal the weights of the criteria and sub-criteria from a probabilistic point of view without loss of knowledge. The main and sub-criteria include supply risk and economic importance, which are frequently studied in the literature, but also environment, price, secondary production, and socio-economic indicators are evaluated in the criticality matrix. Then, exponential smoothing forecast, one of the Time Series Analysis models, which determine the element criticality in the perspective of future years with the data of import figures of Turkey, is superior to the other models. Finally, with some modifications, the EU criticality methodology is used to quantify the raw materials regarding each material associated with the sector applications and their value-added and supply disruptions. Thus, a comprehensive criticality list integrating the results of the three mentioned methods is important because Turkey's raw materials characterize many sectors. The findings should also interest practitioners and researchers as the detailed assessment of raw material criticality ensures a sufficient database for future studies.
•A comprehensive criticality assessment is first proposed in Turkey.•The material criticality is evaluated using three different methods.•Bayesian Best-Worst method is applied to evaluate the weights of dimensions.•Discussions are presented to compare the present work in the context of criticality assessments.
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