Artificial Intelligence Assisted Social Failure Mode and Effect Analysis (FMEA) for Sustainable Product Design

Nowadays, the social dimension of product sustainability is increasingly in demand, however, industrial designers struggle to pursue it much more than the environmental or economic one due to their unfamiliarity in correlating design choices with social impacts. In addition, this gap is not filled even by the supporting methods that have been conceived to only support specific areas of application. To fill this gap, this study proposed a method to support social failure mode and effect analysis (SFMEA), though the automatic failure determination, based on the use of a chatbot (i.e., an artificial intelligence (AI)-based chat). The method consists of 84 specific questions to ask the chatbot, resulting from the combination of known failures and social failures, elements from design theories, and syntactic structures. The starting hypothesis to be verified is that a GPT Chat (i.e., a common AI-based chat), properly queried, can provide all the main elements for the automatic compilation of a SFMEA (i.e., to determine the social failures). To do this, the proposed questions were tested in three case studies to extract all the failures and elements that express predefined SFMEA scenarios: a coffee cup provoking gender discrimination, a COVID mask denying a human right, and a thermometer undermining the cultural heritage of a community. The obtained results confirmed the starting hypothesis by showing the strengths and weaknesses of the obtained answers in relation to the following factors: the number and type of inputs (i.e., the failures) provided in the questions;the lexicon used in the question, favoring the use of technical terms derived from design theories and social sustainability taxonomies;the type of the problem. Through this test, the proposed method proved its ability to support the social sustainable design of different products and in different ways. However, a dutiful recommendation instead concerns the tool (i.e., the chatbot) due to its filters that limit some answers in which the designer tries to voluntarily hypothesize failures to explore their social consequences.

Research on Affective Interaction in Mini Public Transport Based on IPA-FMEA

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.

Logistics System Risk Analysis Using FMEA-AHP Methodology: During and after the COVID-19 pandemic

The COVID-19 pandemic broke out, and the global logistics industry suffered severe losses, therefore, the FMEA-AHP (Failure Mode and Effects Analysis-Analytic Hierarchy Process) method is proposed to analyze the failure reasons of the logistics system in the COVID-19 pandemic. In this article, we have made an improvement on the basis of the traditional FMEA method: The AHP is integrated into the FMEA algorithm (referred to as RPWN (risk priority weighted number) in this article). In this algorithm, the AHP is to determine the weights of risk indicators. Meanwhile, in this article, we also consider about the new logistics failures, such as the failure modes and failure reasons of the logistics system under the COVID-19 pandemic. 12 failures have been identified, and corresponding preventive and corrective measures have been suggested to cut off the path of failure propagation and reduce the impact of failures. © 2022 ACM.

Cystic fibrosis learning network telehealth innovation lab during the COVID-19 pandemic: a success QI story for interdisciplinary care and agenda setting.

INTRODUCTION: The Cystic Fibrosis Foundation chronic care guidelines recommend monitoring clinical status of a patient with cystic fibrosis (CF) through quarterly interdisciplinary visits. At the beginning of the COVID-19 pandemic, the Cystic Fibrosis Learning Network (CFLN) designed and initiated a telehealth (TH) innovation lab (TH ILab) to support transition from the classic CF care model of quarterly in-person office visits to a care model that included TH. AIM: The specific aims of the TH ILab were to increase the percentage of virtual visits with interdisciplinary care (IDC) from 60% to 85% and increase the percentage of virtual visits in which patients and families participated in shared agenda setting (AS) from 52% to 85% by 31 December 2020. METHODS: The model for improvement methodology was used to determine the ILab aims, theory, interventions and measures. In the testing phase of the ILab, data related to process and outcome measures as well as learnings from plan-do-study-act cycles were collected, analysed and shared weekly with the TH ILab teams. Participating centres created processes for IDC and AS for TH visits and developed and shared quality improvement tools specific to their local context with other centres during the ILab weekly meetings and via a secure CFLN-maintained platform. RESULTS: Both specific aims were achieved ahead of the expected target date. By August 2020, 85% of the TH ILab visits provided IDC and 92% of patients were seen for CF care by teams from the TH ILab that participated in AS. CONCLUSION: Shared learning through a collaborative, data-driven process in the CFLN TH ILab rapidly led to standardised TH IDC and AS, which achieved reliable and sustainable processes which could be reproduced by other networks.

Quantitative Ground Risk Assessment for Urban Logistical Unmanned Aerial Vehicle (UAV) Based on Bayesian Network

The Unmanned Aerial Vehicle (UAV) has been used for the delivery of medical supplies in urban logistical distribution, due to its ability to reduce human contact during the global fight against COVID-19. However, due to the reliability of the UAV system and the complex and changeable operation scene and population distribution in the urban environment, a few ground-impact accidents have occurred and generated enormous risks to ground personnel. In order to reduce the risk of UAV ground-impact accidents in the urban logistical scene, failure causal factors, and failure modes were classified and summarized in the process of UAV operation based on the accumulated operation data of more than 20,000 flight hours. The risk assessment model based on the Bayesian network was built. According to the established network and the probability of failure causal factors, the probabilities of ground impact accidents and intermediate events under different working conditions were calculated, respectively. The posterior probability was carried out based on the network topology to deduce the main failure inducement of the accidents. Mitigation measures were established to achieve the equivalent safety level of manned aviation, aiming at the main causes of accidents. The results show that the safety risk of the UAV was reduced to 3.84 × 10−8 under the action of risk-mitigation measures.

AI technologies and their impact on supply chain resilience during COVID-19

Purpose>COVID-19 has pushed many supply chains to re-think and strengthen their resilience and how it can help organisations survive in difficult times. Considering the availability of data and the huge number of supply chains that had their weak links exposed during COVID-19, the objective of the study is to employ artificial intelligence to develop supply chain resilience to withstand extreme disruptions such as COVID-19.Design/methodology/approach>We adopted a qualitative approach for interviewing respondents using a semi-structured interview schedule through the lens of organisational information processing theory. A total of 31 respondents from the supply chain and information systems field shared their views on employing artificial intelligence (AI) for supply chain resilience during COVID-19. We used a process of open, axial and selective coding to extract interrelated themes and proposals that resulted in the establishment of our framework.Findings>An AI-facilitated supply chain helps systematically develop resilience in its structure and network. Resilient supply chains in dynamic settings and during extreme disruption scenarios are capable of recognising (sensing risks, degree of localisation, failure modes and data trends), analysing (what-if scenarios, realistic customer demand, stress test simulation and constraints), reconfiguring (automation, re-alignment of a network, tracking effort, physical security threats and control) and activating (establishing operating rules, contingency management, managing demand volatility and mitigating supply chain shock) operations quickly.Research limitations/implications>As the present research was conducted through semi-structured qualitative interviews to understand the role of AI in supply chain resilience during COVID-19, the respondents may have an inclination towards a specific role of AI due to their limited exposure.Practical implications>Supply chain managers can utilise data to embed the required degree of resilience in their supply chains by considering the proposed framework elements and phases.Originality/value>The present research contributes a framework that presents a four-phased, structured and systematic platform considering the required information processing capabilities to recognise, analyse, reconfigure and activate phases to ensure supply chain resilience.

Development and Simulation of Cyberdisaster Situation Awareness Models

Cyberdisasters require an organization’s disaster team to be prepared. Disaster events are difficult to predict, but the impact of this risk on an organization is large. However, organizations sometimes struggle in being prepared for disaster situations. Here, awareness of disaster situations when analysing priority disasters (e.g., earthquakes and pandemics) and how to mitigate them can help an organization’s preparedness. Mitigation scenarios need to be determined and simulated so that a disaster team is ready to face disaster. Using Endsley’s situational awareness model and a tabletop exercise, this study aimed to help a disaster team determine cyberdisaster risk priority and assess a team’s preparedness for dealing with a cyberdisaster. The situation awareness model was divided into two stages: awareness of cyberdisaster situations and tabletop evaluations. Awareness of a disaster situation was carried out by determining the highest priority for disaster risk using the fuzzy failure modes and effects analysis (FMEA) method. The results of the first study show that the high-risk category contains ransomware attacks during pandemics and earthquakes. The second study performed a tabletop simulation questionnaire survey of earthquakes and ransomware attacks during a pandemic for several disaster teams with 152 respondents. The results of the survey evaluation of the earthquakes and ransomware attacks simulation survey show that the effect factors of cyberdisaster simulation decisions are 95% system capability (p < 0.05), 90% knowledge (p < 0.05), and 90% awareness of a disaster situation (p < 0.05);these factors show the effect of a disaster team’s decision during a tabletop simulation. The novelty of this research lies in building a model for how an organizational process determines the priority of a cyberdisaster tabletop simulation and the factors that contribute to increasing a disaster team’s awareness in dealing with cyberattacks.

Remedies to Avoid Failure Mechanisms of Lithium-Metal Anode in Li-Ion Batteries

Rechargeable lithium-metal batteries (LMBs), which have high power and energy density, are very attractive to solve the intermittence problem of the energy supplied either by wind mills or solar plants or to power electric vehicles. However, two failure modes limit the commercial use of LMBs, i.e., dendrite growth at the surface of Li metal and side reactions with the electrolyte. Substantial research is being accomplished to mitigate these drawbacks. This article reviews the different strategies for fabricating safe LMBs, aiming to outperform lithium-ion batteries (LIBs). They include modification of the electrolyte (salt and solvents) to obtain a highly conductive solid–electrolyte interphase (SEI) layer, protection of the Li anode by in situ and ex situ coatings, use of three-dimensional porous skeletons, and anchoring Li on 3D current collectors.

Topic modeling of maintenance logs for linac failure modes and trends identification.

PURPOSE: Medical linear accelerators (linacs) can fail in a multitude of different manners due to complex structures. An unclear identification of failure modes occurring constantly is a major obstacle to maintenance arrangements, thereby may increasing downtime. This study aims to use natural language processing techniques to deal with the unformatted maintenance logs to identify the linac failure modes and trends over time. MATERIALS AND METHODS: The data used in our study are unformatted narrative maintenance logs recording linac conditions and repair actions. The latent Dirichlet allocation-based topic modeling method was used to identify topics and keywords regarding the failure modes. The temporal analysis method was applied to examine the variation of failure modes over 20 years. RESULTS: Based on the output of the topic modeling, 28 topics and keywords with frequency ranking were generated automatically. The latent failure modes in topics were identified and classified into six main subsystems of linacs. Furthermore, by using the temporal analysis method, the trends of all failure modes over 20 years were illustrated. Half of the topics demonstrated variations with three different patterns, namely periodic, increasing, and decreasing. CONCLUSIONS: The results of our study validated the effectiveness of using the topic modeling method to automatically analyze narrative maintenance logs. With domain knowledge, failure modes of linacs can be identified and categorized quantitatively.

A Proposed Framework for Developing FMEA Method Using Pythagorean Fuzzy CODAS

The purpose of this research article is to develop a hybridization between the Failure Mode and Effect Analysis (FMEA) method and the Combinative Distance-Based Assessment (CODAS) method under Pythagorean Fuzzy environment. The traditional FMEA procedure is based on the multiplication between the parameters of severity, occurrence, and detectability where everyone has equal relative importance;therefore, different combinations of these parameters can generate the same result creating uncertainty in the analysis. In this mode, the hybridization proposed in this research deal with relative importance of each parameter;in the fact to have a more suitable combination which consider the level of knowledge of the experts in the assessment. Finally, a numerical case was carried out concerning the public transportation service to validate our proposal;the results show that 31 failure modes and potential risks can be evaluated using user perceptions, a dominant with high level of knowledge about the public transportation service and an apprentice or common user, as team of experts and exploiting the subjectivity of the information in a mathematical model. Also, we compare the results with a variation of the proposed model with the multi-criteria method multi-objective optimization method by relationship analysis (MOORA);it was observed that the convergence of the failure modes depends on the nature of the mathematical model even under the same conditions at the start.

Holistic risk assessment of container shipping service based on Bayesian Network Modelling

Container shipping makes significant contribution to the global economy and is confronted with various hazards and risks especially during the COVID-19 pandemic. These risks can disrupt resilient container shipping service, leading to further deterioration of the global economy. Hence, it is vital to develop resilient container shipping service, which is associated with being on-time, safe, and hassle-free. Theoretically, this research identifies 28 root risks using the PESTLE framework, conducts risk assessment using a hybrid method comprising failure modes and effects analysis, evidential reasoning, and rule-based Bayesian network. A three-hierarchy Bayesian network model is established. The results reveal that economic, political, and technical risks are the most threatening risks affecting resilient container shipping service. Moreover, the holistic container shipping risk is most sensitive to environmental risks. Managerially, this research provides container shipping companies with guidance of drafting risk mitigation plans with economic risks and political risks as priorities.

Systems engineering analysis of diagnostic referral closed-loop processes.

BACKGROUND: Closing loops to complete diagnostic referrals remains a significant patient safety problem in most health systems, with 65%-73% failure rates and significant delays common despite years of improvement efforts, suggesting new approaches may be useful. Systems engineering (SE) methods increasingly are advocated in healthcare for their value in studying and redesigning complex processes. OBJECTIVE: Conduct a formative SE analysis of process logic, variation, reliability and failures for completing diagnostic referrals originating in two primary care practices serving different demographics, using dermatology as an illustrating use case. METHODS: An interdisciplinary team of clinicians, systems engineers, quality improvement specialists, and patient representatives collaborated to understand processes of initiating and completing diagnostic referrals. Cross-functional process maps were developed through iterative group interviews with an urban community-based health centre and a teaching practice within a large academic medical centre. Results were used to conduct an engineering process analysis, assess variation within and between practices, and identify common failure modes and potential solutions. RESULTS: Processes to complete diagnostic referrals involve many sub-standard design constructs, with significant workflow variation between and within practices, statistical instability and special cause variation in completion rates and timeliness, and only 21% of all process activities estimated as value-add. Failure modes were similar between the two practices, with most process activities relying on low-reliability concepts (eg, reminders, workarounds, education and verification/inspection). Several opportunities were identified to incorporate higher reliability process constructs (eg, simplification, consolidation, standardisation, forcing functions, automation and opt-outs). CONCLUSION: From a systems science perspective, diagnostic referral processes perform poorly in part because their fundamental designs are fraught with low-reliability characteristics and mental models, including formalised workaround and rework activities, suggesting a need for different approaches versus incremental improvement of existing processes. SE perspectives and methods offer new ways of thinking about patient safety problems, failures and potential solutions.

Home spirometry utilisation in telemedicine clinic for cystic fibrosis care during COVID-19 pandemic: a quality improvement process.

IntroductionThe Cystic Fibrosis (CF) Foundation chronic care guidelines recommend monitoring spirometry during quarterly multidisciplinary visits to identify early lung function decline. During the COVID-19 pandemic, the CF adult clinic at University of Virginia (UVA) transitioned from the classic CF care model to a model that included quarterly multidisciplinary telemedicine visits. While using telemedicine, CF care needed to include spirometry monitoring. Only a fraction of adult CF patients at UVA owned and used home spirometers (HS) in March 2020. AIM: The specific aims of this quality improvement (QI) project were to increase the percentage of eligible adult CF patients who owned an HSs from 37% to 85% and to increase the percentage of adult CF patients seen at UVA with available spirometry in telemedicine from 50% to 95% by 31 December 2020. METHODS: Following the Model for Improvement QI methodology, a standardised process was developed for monitoring forced expiratory volume in 1 s with HS during multidisciplinary telemedicine visits during the COVID-19 pandemic. INTERVENTION: (1) HSs were distributed to eligible patients and (2) Home spirometry was monitored in eligible patients with each telemedicine visit and results were used for clinical care decisions. RESULTS: Both specific aims were achieved ahead of expected date. In March 2020, the beginning of the pandemic, 37% (49/131) of patients owned an HS and 50% (9/18) of patients seen via telemedicine performed spirometry at home. By September 2020, 97% (127/131) of adult patients at UVA owned an HS and by October 2020, 96% (24/25) of patients provided spirometry results during their telemedicine encounters. CONCLUSION: Employing QI tools to standardise the process of monitoring spirometry data with home devices via telemedicine is reliable and sustainable and can be replicated across centres that provide care for patients with CF.

Use of failure modes and effects analysis to mitigate potential risks prior to implementation of an intravenous compounding technology.

PURPOSE: The purpose of this study was to identify potential failure points in a new chemotherapy preparation technology and to implement changes that prevent or minimize the consequences of those failures before they occur using the failure modes and effects analysis (FMEA) approach. METHODS: An FMEA was conducted by a team of medication safety pharmacists, oncology pharmacists and technicians, leadership from informatics, investigational drug, and medication safety services, and representatives from the technology vendor. Failure modes were scored using both Risk Priority Number (RPN) and Risk Hazard Index (RHI) scores. RESULTS: The chemotherapy preparation workflow was defined in a 41-step process with 16 failure modes. The RPN and RHI scores were identical for each failure mode because all failure modes were considered detectable. Five failure modes, all attributable to user error, were deemed to pose the highest risk. Mitigation strategies and system changes were identified for 2 failure modes, with subsequent system modifications resulting in reduced risk. CONCLUSION: The FMEA was a useful tool for risk mitigation and workflow optimization prior to implementation of an intravenous compounding technology. The process of conducting this study served as a collaborative and proactive approach to reducing the potential for medication errors upon adoption of new technology into the chemotherapy preparation process.

Infection Control Measures, in situ Simulation, and Failure Modes and Effect Analysis to Fine-Tune Change Management during COVID-19.

Coronavirus disease 2019 (COVID-19) was an impetus for a multitude of transformations - from the ever-changing clinical practice frameworks, to changes in our execution of education and research. It called for our decisiveness, innovativeness, creativity, and adaptability in many circumstances. Even as care for our patients was always top priority, we tried to integrate, where possible, educational and research activities in order to ensure these areas continue to be harnessed and developed. COVID-19 provided a platform that stretched our ingenuity in all these domains. One of the mnemonics we use at SingHealth in responding to crisis is PACERS: P: Preparedness (in responding to any crisis, this is critical) A: Adaptability (needed especially with the ever-changing situation) C: Communications (the cornerstone in handling any crisis) E: Education (must continue, irrespective of what) R: Research (new opportunities to share and learn) S: Support (both physical and psychological). This article shares our experience integrating the concept of simulation-based training, quality improvement, and failure mode analysis.

Algorithmic prediction of failure modes in healthcare.

BACKGROUND: Preventing medical errors is crucial, especially during crises like the COVID-19 pandemic. Failure Modes and Effects Analysis (FMEA) is the most widely used prospective hazard analysis in healthcare. FMEA relies on brainstorming by multi-disciplinary teams to identify hazards. This approach has two major weaknesses: significant time and human resource investments, and lack of complete and error-free results. OBJECTIVES: To introduce the algorithmic prediction of failure modes in healthcare (APFMH) and to examine whether APFMH is leaner in resource allocation in comparison to the traditional FMEA and whether it ensures the complete identification of hazards. METHODS: The patient identification during imaging process at the emergency department of Sheba Medical Center was analyzed by FMEA and APFMH, independently and separately. We compared between the hazards predicted by APFMH method and the hazards predicted by FMEA method; the total participants' working hours invested in each process and the adverse events, categorized as 'patient identification', before and after the recommendations resulted from the above processes were implemented. RESULTS: APFMH is more effective in identifying hazards (P < 0.0001) and is leaner in resources than the traditional FMEA: the former used 21 h whereas the latter required 63 h. Following the implementation of the recommendations, the adverse events decreased by 44% annually (P = 0.0026). Most adverse events were preventable, had all recommendations been fully implemented. CONCLUSION: In light of our initial and limited-size study, APFMH is more effective in identifying hazards (P < 0.0001) and is leaner in resources than the traditional FMEA. APFMH is suggested as an alternative to FMEA since it is leaner in time and human resources, ensures more complete hazard identification and is especially valuable during crisis time, when new protocols are often adopted, such as in the current days of the COVID-19 pandemic.

Assisting beginners in root cause analysis operations: analysis and recommendations regarding the spread of COVID-19 in nursing facilities for the elderly.

BACKGROUND: To analyse a medical accident, much time and experience are needed. However, people without experience in analysis have difficulty understanding its conditions and methods, and as a result it takes longer to establish countermeasures. It must be noted that understanding conditions by simply aligning occurrences in the accident in a chronological order is difficult. PURPOSE: A workflow chart that considers time was proposed so that individuals without adequate experience in analysis could easily carry out root cause analysis. METHODS: In the 'workflow chart (WFC)', the time sequence was described horizontally. On the vertical axis, the business manual, the occurrence of the accident, and the time of the occurrence are displayed. In the bottom column of patient event, information regarding damage to patients was written in accordance with time axis. Regarding the degree of damage, the time of error until the accident was identified was connected using a straight line (when the patient was not affected, a dotted line was used) in order to show the overall picture of the accident. RESULTS: According to the time flow chart, hints to identify potential risks were proposed. Focus was placed not only on the error event, but also on keywords such as manual inadequacy, time gap, degree of error and so on to easily lead to the question 'why?' To visualise this, I proposed an operation flow chart. By using time-WFC, even beginners can easily develop accident countermeasure strategies. CONCLUSION: Using a WFC that considers time, time of error and the occurrence of accident could be visualised. As a result, even individuals without experience in analysis could easily perform an analysis.