ABSTRACT
In the promotion of sustainable modes of transport, especially public transport, reasonable failure risk assessment at the critical moment in the process of service provider touch with users can improve the service quality to a certain extent. This study presents a product service touch point evaluation approach based on the importance–performance analysis (IPA) of user and failure mode and effect analysis (FMEA). Firstly, the authors capture service product service touch points in the process of user interaction with the product by observing the user behavior in a speculative design experiment, and perform the correlation analysis of the service product service touch point. Second, the authors use the IPA analysis method to evaluate and classify the product service touch points and identify the key product service touch points. Thirdly, the authors propose to analyze the failure of key product service touch points based on user-perceived affective interaction and clarify the priority of each key touch point. Finally, reluctant interpersonal communication, as the key failure caused by high risk, is derived according to the evaluation report, which leads to establishing new product service touch points and improving the overall user experience to promote sustainable transports with similar forms and characteristics.
ABSTRACT
Failure Mode and Effect Analysis (FMEA) is a highly structured risk-prevention management process that improves the reliability and safety of a system. This article investigates one of the most critical issues in FMEA practice: Clustering failure modes based on their risks. In the failure mode clustering problem, all identified failure modes need to be assigned to several predefined and risk-ordered categories to manage their risks. We model the clustering of failure modes through multi-expert multiple criteria decision making with an additive value function, and call it the additive N -clustering problem. We begin by proposing six axioms that describe an ideal clustering method in the additive N -clustering problem, and find that the EXogenous Clustering Method (EXCM), where category thresholds can be exogenously provided, is ideal (Exogenous Possibility Theorem), whereas any endogenous clustering method, where the clustering is determined endogenously in the given method, cannot satisfy all six axioms simultaneously (Endogenous Impossibility Theorem). In practice, endogenous clustering methods are important, due to the difficulty in providing accurate and reasonable category thresholds of the EXCM. Therefore, we propose the Consensus-based ENdogenous Clustering Method (CENCM) and discuss its axiomatic properties. We also apply the CENCM to the SARS-CoV-2 prevention case and justify the CENCM through axiomatic comparisons and a detailed simulation experiment. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
In the last years, particularly after Covid-19, Health care waste (HCW) has increased significantly due to the increasing population and number of healthcare organizations. HCW produces a significant risk of infectious contamination and injury. Accordingly, healthcare waste management plays a vital role in creating waste management strategies, and policies and implementing waste management plans. To build robust healthcare management systems, the risk assessment process is a key step. This paper assesses the top hazards of healthcare waste at Sultan Qaboos University Hospital (SQUH) in Oman using the Exponential Weighted Geometric Mean-Failure Mode and Effect Analysis (EWGM-FMEA). Fifteen healthcare waste hazards were selected to apply the tool. These hazards are ranked to prioritize the top hazards wastes. This assessment helps in identifying the most crucial hazards,whiche the policymakers should pay attention thus, the main countermeasures could be conducted. These hazards were proposed based on the conducted survey questionnaire and interviews accordingly, and analyses of the data have been carried out. The applied tool examined the importance of quantifying healthcare waste to apply the appropriate corrective actions which can be applied to mitigate the harm and the negative effects of healthcare waste. The results of the assessment tool will help policymakers in developing clear plans for management, disposal of wastes, and segregation. Furthermore, prioritizing healthcare waste explored the importance of integrating tthe raining plans of workers with the healthcare waste management policy. Although the prospective managerial and policy implications of this research, some limitations could be studied by future researchers. Firstly, the sample covered one hospital that may be representative of only one hospital in Oman which constrains the generalization of results. Secondly, the number of identified waste hazards is fifteen so, increased the number of hazards may help policymakers in building a more effective healthcare waste management plan which will reflect in improving the healthcare management system in the organization, mitigating the harmful effects on human health and the negative effects on the environment. © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
ABSTRACT
The COVID-19 pandemic instigated massive production of critical medical supplies and personal protective equipment. Injection moulding (IM) is considered the most prominent thermoplastic part manufacturing technique, offering the use of a large variety of feedstocks and rapid production capacity. Within the context of the European Commission-funded imPURE project, the benefits of IM have been exploited in repurposed IM lines to accommodate the use of nanocomposites and introduce the unique properties of nanomaterials. However, these amendments in the manufacturing lines highlighted the need for targeted and thorough occupational risk analysis due to the potential exposure of workers to airborne nanomaterials and fumes, as well as the introduction of additional occupational hazards. In this work, a safety-oriented failure mode and effects analysis (FMEA) was implemented to evaluate the main hazards in repurposed IM lines using acrylonitrile butadiene styrene (ABS) matrix and silver nanoparticles (AgNPs) as additives. Twenty-eight failure modes were identified, with the upper quartile including the seven failure modes presenting the highest risk priority numbers (RPN), signifying a need for immediate control action. Additionally, a nanosafety control-banding tool allowed hazard classification and the identification of control actions required for mitigation of occupation risks due to the released airborne silver nanoparticles.
ABSTRACT
The emergence of the COVID-19 pandemic led several organizations around the world and in the most varied areas of activity, to move from the intention to implement a digital transformation in the medium/long-term, to an instant obligation to apply the digital transformation. The organizations’ ability to adapt immediately meant their survival and even in some cases a positive evolution of their business. The digital transformation applied in an abrupt way has uncovered some critical factors for its success. One of the most relevant factors will be information security. Many of the digital systems put into operation more intensively during the pandemic, have shown to be highly fragile on issues related to information security. One relevant problem of the organizations is the low effectiveness and efficiency of financial, human, and material resources, allocated to the reduction or mitigation of the risks identified in their information systems. This study aims to offer a new method for prioritizing security risks. The new proposed method directs the organizations resources to more effectively and efficiently actions to reduce or mitigate the identified vulnerabilities of the information system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Failure mode and effect analysis (FMEA) is a highly structured risk-prevention management process that improves the reliability and safety of a system. This paper investigates one of the most critical issues in FMEA practice: Clustering failure modes based on their risks. In the failure mode clustering problem, all identified failure modes need to be assigned to several predefined and risk-ordered categories to manage their risks. We model the failure mode clustering through multi-expert multiple criteria decision making with an additive value function and call it the additive N -clustering problem. We begin by proposing six axioms that describe an ideal clustering method in the additive N -clustering problem, and find that the exogenous clustering method (EXCM), where category thresholds can be exogenously provided, is ideal (Exogenous Possibility Theorem), while any endogenous clustering method, where the clustering is determined endogenously in the given method, cannot satisfy all six axioms simultaneously (Endogenous Impossibility Theorem). In practice, endogenous clustering methods are important because of the difficulty in providing accurate and reasonable category thresholds of the EXCM. Therefore, we propose the consensus-based endogenous clustering method (CENCM) and discuss its axiomatic properties. We also apply the CENCM to the SARS-CoV-2 prevention case and justify the CENCM through axiomatic comparisons and a detailed simulation experiment. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
PURPOSE: The COVID-19 pandemic has presented challenges to delivering safe and timely care for cancer patients. The oncology community has undertaken substantial workflow adaptations to reduce transmission risk for patients and providers. While various control measureshave been proposed and implemented, little is known about their impact on safety of the radiation oncology workflow and potential for transmission. The objective of this study was to assess potential safety impacts of control measures employed during the COVID-19 pandemic. METHODS: A multi-institutional study was undertaken to assess the risks of pandemic-associated workflow adaptations using failure mode and effects analysis (FMEA). Failure modes were identified and scored using FMEA formalism. FMEA scores were used to identify highest-risk aspects of the radiation therapy process. The impact of control measures on overall risk was quantified. Agreement among institutions was evaluated. RESULTS: Thirty three failure modes and 22 control measures were identified. Control measures resulted in risk score reductions for 22 of the failure modes, with the largest reductions from screening of patients and staff, requiring use of masks, and regular cleaning of patient areas. The median risk score for all failure modes was reduced from 280 to 168. There was high institutional agreement for 90.3% of failure modes but only 47% of control measures. CONCLUSIONS: COVID-related risks are similar across oncology practices in this study. While control measures can reducerisk, their use varied. The effectiveness of control measures on risk may guide selection of the highest-impact workflow adaptions to ensure safe care in oncology.