A supplier selection model for hospitals using a combination of artificial neural network and fuzzy VIKOR

A decision-making framework for evaluating medical equipment suppliers under uncertainty

The procurement of medical equipment is a critical concern for healthcare organizations striving to deliver comprehensive patient care. Thus, the procurement process, including performance evaluation and selection of medical equipment suppliers, poses a significant challenge for healthcare organizations. The decision-making process also involves multiple decision-makers making subjective judgments about various quantitative and qualitative criteria for several alternative suppliers. This paper presents a framework for medical equipment supplier evaluation under uncertain assessment information by integrating rank order centroid (ROC) and fuzzy analytic hierarchy process (fuzzy AHP) techniques. The first stage involves identifying the key criteria influencing the performance evaluation of medical equipment suppliers for healthcare organizations. The ROC technique is used to assign weights to the important criteria, reducing uncertainty of weight assignment and subjective judgment information of the decision-makers. The fuzzy AHP method is then applied to evaluate and rank potential suppliers based on their overall performance. The approach is validated through a case study in a hospital setting, demonstrating the practical applicability of the proposed approach in a real-world scenario. Results indicate that the proposed hybrid method effectively supports group decision-making under uncertainty, providing healthcare organizations with a systematic and logical approach for selecting the most suitable medical equipment supplier. This framework enhances procurement efficiency and supports better resource allocation in healthcare.

Power equipment supplier evaluation under a q-rung orthopair fuzzy set based decision making model

The quality performance of power equipment suppliers is directly related to the stable and safe operation of the grid. This study presents a decision-making model based on q-rung orthopair fuzzy sets (q-ROFS) to evaluate suppliers, focusing on quality as the key criterion. To assess the objectivity and comprehensiveness of the results, we provide an innovative information fusion method that integrates the four dimensions of supply risk, supplier quality capability, profit impact, and willingness into the decision-making process. Considering the uncertainty and inconsistency in the decision-making process, in the weight determination stage, the q-ROFS-FWZIC method is used as the standard to allocate weights accurately. In the ranking stage, the q-ROFS-MABAC method was constructed to improve the consistency of evaluation results, and suppliers were ranked based on summarized performance data. A real-world case study involving power transformer suppliers illustrates the effectiveness of the proposed model. This research offers valuable insights for decision-makers in the power sector to optimize supplier selection, improve quality control measures, and ensure the ongoing reliability of the grid. Furthermore, this method can also be extended to other fields to solve various MCDM problems.

Supplier selection in healthcare supply chain management during the COVID-19 pandemic: a novel fuzzy rough decision-making approach

Due to the high necessity of medical face masks and face shields during the COVID-19 pandemic, healthcare centers dealing with infected patients have faced serious challenges due to the high consumption rate face masks and face shields. In this regard, the supply chain of healthcare centers should put all of their efforts into avoiding any shortages of masks and shields as these products are considered as primary ways to prevent the spread of the virus. Since, any shortages in these products would lead to irrecoverable and costly consequences in terms of the mortality rate of patients and medical staff. Therefore, healthcare centers should decide on best supplier to supply required products, considering technical, and sustainability measures. Dynamicity and uncertainty of the pandemic are other factors that add up to the complexity of the supplier selection problem. Therefore, this paper develops a novel decision-making approach using Measuring attractiveness through a categorical-based evaluation technique (MACBETH) and a new combinative distance-based assessment method to address the supplier selection problem during the COVID-19 pandemic. Due to high uncertainty, vague and incomplete information for decision-making problems during the COVID-19 pandemic, the developed decision-making approach is implemented under fuzzy rough numbers as a superior uncertainty set of the traditional fuzzy set and rough numbers. Extensive sensitivity analysis tests are performed based on parameters of the decision-making approach, impacts of weight coefficients, and consistency of results in comparison to other MCDM methods. A real-life case study is investigated for a hospital in Istanbul, Turkey to show the applicability of the developed approach. Based on the results of MACBETH method, job creation and occupational health and safety systems are two top criteria. Results of the case study for five suppliers indicate that supplier (A1) is the best supplier with a distance score of 3.308.

An integrated quality and resilience engineering framework in healthcare with Z-number data envelopment analysis

Supplier selection for medical equipment is a major challenge for hospitals in healthcare supply chains. The primary reason for measuring medical equipment supplier efficiency is to achieve the highest level of overall performance and productivity in healthcare supply chains. This study presents an integrated quality and resilience engineering (QRE) framework for evaluating medical equipment suppliers' performance using structural equation modeling and Z-number data envelopment analysis (Z-DEA). Noise analysis is used to select the best α-cut for the Z-DEA model, and fuzzy data are used to handle uncertainties. We show that flexibility, conformance to standards, redundancy, cost, quality certifications, and delivery time significantly affect the medical equipment suppliers' performance. In addition, we demonstrate that the proposed integrated QRE framework is more efficient and informative than stand-alone quality engineering or resiliency engineering. We present a case study in a cardiovascular hospital to illustrate the applicability of the proposed framework for medical equipment supplier evaluation and selection. To the best of our knowledge, this is the first study to integrate QRE and Z-DEA for supplier performance evaluation in healthcare.

Integrated Multicriteria Decision-Making Methods to Solve Supplier Selection Problem: A Case Study in a Hospital

In supply chain literature, supplier evaluation and selection problem is one of the most studied subjects because of the significant roles of suppliers in terms of the chain's sustainability and profitability. Therefore, it is important for organizations to adopt a systematic way to evaluate and select the best supplier according to their respective criteria in today's competitive environment. Multicriteria decision-making methods provide for this need of organizations because determination of an appropriate supplier selection is a multicriteria decision-making (MCDM) problem essentially. Although a lot of applications of these methods for supplier evaluation and selection can be seen in the literature, studies in the health-care sector are insufficient. Hospitals in the health-care sector also have to consider their supplier-related decisions to decrease risks and threads which affect their effectiveness. The aim of this study was to fill this gap by providing different hybrid models for selecting the best supplier for hospitals. Supplier evaluation and selection process start with recognizing the related criteria according to the studies in the literature. Analytic hierarchy process (AHP) method is deployed to weight the criteria, and suppliers are listed via technique for order preference by similarity to ideal solution (TOPSIS), elimination and choice translating reality English (ELECTRE), grey relational analysis (GRA), and simple additive weighting (SAW) methods. The main aim of this study was to present different hybrid MCDM methods and show their efficiency and consistency with each other. In this study, hybrid multicriteria decision-making models (AHP-TOPSIS, AHP-ELECTRE, AHP-GRA, and AHP-SAW) are presented and compared. The results show that the presented hybrid methods in this study are consistent with each other and give the same ranking for the selection of the best supplier. It can be considered as a useful guideline for hospitals.