Purpose Medical equipment’s supply chains play a vital role in performance of national 1healthcare systems. This supply chain is confronted with different internal and external risks. The purpose of this study is to investigate and find the key factors affecting the resilience of the supply chain of medical equipment and to examine the dynamic relations among these factors. Design/methodology/approach A hybrid methodology is used for meeting the purpose of this study. First, the Delphi method is extended by using hesitant fuzzy linguistic term sets to identify the key factors of supply chain resilience. At the second phase, using the system dynamic methodology, the dynamic relations among identified resilience factors are analyzed. Findings Using the Delphi method, agility, collaboration among actors, sharing of information, trust among actors, explicitness of supply chain, risk management culture, adaptability, structure, funding and environment conditions are identified as ten major factors affecting medical equipment’s supply chain resilience. Also, four scenarios are simulated along with their impacts on the system. Originality/value The main contribution of this study is extending a hesitant fuzzy linguistic term sets-based Delphi and applying it along a system dynamic analysis to identify the key factors affecting resilience of medical equipment’s supply chain for the first time.
Purpose The purpose of this paper is to identify critical equipment by dynamically ranking them in interval-valued intuitionistic fuzzy (IVIF) circumstances. Accordingly, the main drawbacks of the conventional failure mode and effects analysis (FMEA) are eliminated. To this end, the authors have presented the interval-valued intuitionistic fuzzy condition-based dynamic weighing method (IVIF-CBDW). Design/methodology/approach To realize the objective, the authors used the IVIF power weight Heronian aggregation operator to integrate the data extracted from the experts’ opinions. Moreover, the multi-attributive border approximation area comparison (MABAC) method is applied to rank the choices and the IVIF-CBDW method to create dynamic weights appropriate to the conditions of each equipment/failure mode. The authors proposed a robust FMEA model where the main drawbacks of the conventional risk prioritization number were eliminated. Findings To prove its applicability, this model was used in a case study to rank the equipment of a HL5000 crane barge. Finally, the results are compared with the traditional FMEA methods. It is indicated that the proposed model is much more flexible and provides more rational results. Originality/value In this paper, the authors have improved and used the IVIF power weight Heronian aggregation operator to integrate information. Furthermore, to dynamically weigh each equipment (failure mode), they presented the IVIF-CBDW method to determine the weight of each equipment (failure mode) based on its equipment conditions in the O, S and D criteria and provide the basis for the calculation. IVIF-CBDW method is presented in this study for the first time. Moreover, the MABAC method has been performed, to rank the equipment and failure mode. To analyze the information, the authors encoded the model presented in the robust MATLAB software and used it in a real sample of the HL5000 crane barge. Finally, to evaluate the reliability of the model presented in the risk ranking and its rationality, this model was compared with the conventional FMEA, fuzzy TOPSIS method, the method of Liu and the modified method of Liu.
Today entrepreneurship is considered as the economic driving engine for developed and developing countries and most countries have invested considerably on entrepreneurship development. The entrepreneurship development in a community could provide sustainable employment and economic development. It should be noted that entrepreneurship development has always been encountered different challenges and barriers. The present article is aimed to detect and classify the barriers of entrepreneurship development in Iranian SMEs and determine the importance of each barrier. Analyzing 28 detected barriers in the present study show that lack of sufficient knowledge in management skills, business management, lack of adequate investment to start and retain a business, difficulty in finding information about markets, products and prices, troublesome rules obtaining bank loans, and the difficulty in recruiting good and reliable staff are the most important barriers and challenges of corporate entrepreneurship development in Iranian SMEs.
In the design of the supply chain, the use of the returned products and their recycling in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network (SCN), and determine the amount and location of facilities and the planning of transportation in conditions of demand uncertainty. Thus, maximizing the total profit of operation, minimizing adverse environmental effects, and maximizing customer and supplier service levels have been considered as the main objectives. Accordingly, finding symmetry (balance) among the profit of operation, the environmental effects and customer and supplier service levels is considered in this research. To deal with the uncertainty of the model, scenario-based robust planning is employed alongside a meta-heuristic algorithm (NSGA-II) to solve the model with actual data from a case study of the steel industry in Iran. The results obtained from the model, solving and validating, compared with actual data indicated that the model could optimize the objectives seamlessly and determine the amount and location of the necessary facilities for the steel industry more appropriately.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.