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.

Corporate Governance Research in Asian Countries: A Bibliometric and Content Analysis (2001–2021)

Corporate failure suggests that weak corporate governance leads to frail institutions and exposes them to severe crises. Asian countries have faced financial crises in three different periods, most recently due to the COVID-19 pandemic. A crisis will trigger structural changes in corporate governance to enable firms to either respond to, or prevent, the reoccurrence of potentially similar events. The characteristic of corporate governance practice in Asian countries are also unique due to some institutional and informal factors. These will alter direction and future trend of research in corporate governance in Asian region. The objective of this study is to utilize a bibliometric analysis which focuses on research trends and themes, and citations (with additional inclusive visualization) and perform in-depth content analysis to trace the evolution and identify knowledge of corporate governance in Asian countries from 2001 to 2021. Following bibliometric analysis, a sample of 656 articles on corporate governance in Asian countries has been extracted and analyzed from the Scopus database. The results indicate that there is a growing of interest in corporate governance in Asian countries from 2001 to 2021. Eight major themes have been recognized: corporate governance, corporate social responsibility and financial performance, corporate strategy and performance, agency theory, corporate sustainability, audit and agency problems, firm size, and business ethics. Major findings, shortcomings, and directions for future research are also discussed in this study. In general, most cited articles related to corporate governance theme explain the importance of corporate governance in companies with the focus on preventing financial fraud, impact on earnings management, and cost of equity capital in the market and reporting methods.

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.

Statistical Analysis of University Academic Performance in the Area of Exact Sciences, Before and During the Covid-19 Pandemic

The pandemic caused by Covid-19 at the end of 2019 affected the development of academic activities in educational institutions at all levels. This article focuses its interest on analyzing the academic performance, failure and dropout of the students of the Universidad de las Fuerzas Armadas ESPE, Santo Domingo, before and during the pandemic. The analyzed data was collected from the academic results matrices of the Departamento de Ciencias Exactas of the ordinary academic periods: 201950, 201951, 202050, 202051, 202150 and 202151. The information was analyzed with a descriptive approach, using bar charts, which indicate the evolution of the areas of knowledge in the indicated periods. For the variation of academic performance, the ANOVA method was used, obtaining a p − value = 0.126, which indicated that there is no significant variation between the means of the analyzed data. In addition, it was determined that between academic periods the assumptions of normality and homoscedasty with values p − value > 0.05 are met. In the linear correlation analysis, the Pearson coefficient was calculated, whose value indicated a strong negative correlation between the academic performance and the percentage of failure (values of 0.887, 0.796), while between the academic performance and the percentage of abandonment there was a moderate negative correlation (values of 0.428, 0.636). Finally, models based on linear regression are proposed in the areas of knowledge analyzed, to predict the academic performance of the new academic period 202250. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A discrete mixed distribution: Statistical and reliability properties with applications to model COVID-19 data in various countries.

The aim of this paper is to introduce a discrete mixture model from the point of view of reliability and ordered statistics theoretically and practically for modeling extreme and outliers' observations. The base distribution can be expressed as a mixture of gamma and Lindley models. A wide range of the reported model structural properties are investigated. This includes the shape of the probability mass function, hazard rate function, reversed hazard rate function, min-max models, mean residual life, mean past life, moments, order statistics and L-moment statistics. These properties can be formulated as closed forms. It is found that the proposed model can be used effectively to evaluate over- and under-dispersed phenomena. Moreover, it can be applied to analyze asymmetric data under extreme and outliers' notes. To get the competent estimators for modeling observations, the maximum likelihood approach is utilized under conditions of the Newton-Raphson numerical technique. A simulation study is carried out to examine the bias and mean squared error of the estimators. Finally, the flexibility of the discrete mixture model is explained by discussing three COVID-19 data sets.

Dynamic Group Difference Coding Based on Thermal Infrared Face Image for Fever Screening

Recently, noncontact temperature measurement methods based on infrared face perception have received widely attentions since fever screening plays an important role in the early prediction of respiratory infections, such as SARS, H1N1, and COVID-19. However, the performance of these methods always significantly degrades when facing the changes of environment. Thus, the majority of these methods leverage the block-body and sensors to reduce the influence of environment changes. It is a pity that the increased instrument complexity leads to higher costs and failure rate. To address the aforementioned issues, this article presents a novel fever screening method, named dynamic group difference coding (DGDC), which is based on the analysis about the influencing factors. The key idea of DGDC is to compute the temperature differences between the target person and the recently passed crowd (dynamic group). Specifically, we develop the face temperature encoder (FTE) to describe the face temperature and thus construct the difference matrix of the embedding feature between the target person and the dynamic group. Multilayer perceptions (MLP) are employed to capture the intrinsic information by characterizing the difference matrix in vertical and horizontal directions, respectively. Finally, we provide a dataset of thermal infrared face (TIF) images and conduct extensive experiments to demonstrate the advantages of the proposed method over the competing methods. © 1963-2012 IEEE.

Supply chain delays contribute to broader challenges

Systems engineering often contributes to a business' ability to operate effectively through a crisis. However, as the global economy continued to emerge from the Covid-19 pandemic, recurring systemic failures still ravaged the global supply chain. The supply chain's fragility lingered this year, and the aerospace industry's inability to receive parts when needed may extend beyond 2023. This weakness can be attributed to many issues, including a worker shortage and failure to account for a stressed global transportation system. Systems engineering can often mitigate the longterm effects of a major disruption. However, previous failures to fully model the global supply chain illustrates this problem's complexity. The pandemic demonstrated that the aerospace industry is not immune to global disruptions, even when those disruptions do not appear to involve these products. Aided by better systems modeling, market forces worked to correct failures amplified by the pandemic. Broad and strategic investments are needed to address the long-term workforce shortage.

The AOUI Verona Task Force for Improvement of Post-Market Clinical Surveillance for Medical Device: An International Journal of Medical Toxicology and Drug Experience

Introduction: Post-marketing surveillance (PMS) is the practice of monitoring the safety of a pharmaceutical drug or medical device (MD) after it has been released on market and is an important part of science of pharmacovigilance. PMS is considered one of the most critical aspects of the new EU-MDR 2017/745. In AOUI Verona the pharmacist in charge of MD vigilance reports adverse events to Pharmacovigilance's Regional Service and Ministry of Health. For many years there has been a collaboration between Pharmacy and Hospital Risk Management by sharing clinical information about incidents, failures, serious deteriorations or potential deficiency related to MD safety use. This multidisciplinary collaboration is the fundamental aspect to improve protection of health and safety patients, healthcare professionals and all users reducing the likelihood of reoccurring incidents. Unfortunately during Covid-19 a lack of training and staff awareness significantly reduced spontaneous incident reporting. Objective: Aim of the present study is analyze PMS data and organize hospital staff training to increase PMS and spontaneous incident reporting. Methods: Over the years Pharmacy and Risk Management keep a database for recording and monitoring data on MD adverse events. The Cross-check analysis of databases allows to intercept all incident or failure occurred. Results: From 2019, recorded data show a decrease of 30% related to MD incidents or failures (2019: N = 120;2020: N = 67;2021: N = 45) and some Operating Units are less likely to reporting. In 2021 the clinical risk manager received 56 incident reports and only 45 of these to Pharmacy too. 22 were filled in by surgical departments, of which 4 by pediatricians and 18 by adult specialists. The total number of reports shows that 80% have reached the pharmacy office, while the percentage ratio between the two sectors is expected to be 100%. Conclusion: The PMS management in AOUI requires a strong collaboration of all figures involved in this process. For this reason, training and awareness-raising must be carried out in a widespread and continuous way. In AOUI Hospitals we are organizing training meetings to sharing information between various professional skills so that any problems arising are quickly identified. One target for 2022 is a participation to training events for at least one doctor and nurse for each hospital unit.

RISK AND DISASTER MANAGEMENT AND SOCIAL INEQUALITIES: THE EXPERIENCE OF BRUMADINHO AFTER THE CÓRREGO OF FEIJÃO DAM FAILURE AND THE COVID-19 PANDEMIC

Objetivo: Discutir como as vulnerabilidades, principalmente no que tange as desigualdades sočiais, estäo atreladas aos desastres e como elas se potencializam diante da ocorrencia dos mesmos, agravando ainda mais a situaçâo de grupos e comunidades em condiçöes de fragilidade. Referencial Teórico: O desastre do rompimento da barragem da Vale foi analisado a partir da proposta de Gestäo de Riscos de Desastres (GRD) apresentada pelo Sendai Framework, que se baseia no fortalecimento de açöes e medidas de prevençâo de desastres, bem como no aumento a preparaçâo para respostas e recuperaçâo diante de desastres que possam vir a ocorrer. Metodologia/abordagem: O método utilizado foi o estudo de caso. Foram realizadas pesquisas documentais, observaçâo participante de grupos de WhatsApp e tres entrevistas com representantes do setor público e da sociedade civil, além de sete entrevistas com cidadäos de Brumadinho. A análise de conteúdo foi a estratégia para análise dos dados. Principais resultados: A experiencia do municipio de Brumadinho demonstra como a recuperaçâo de um desastre é difícil de ser realizada e torna ainda mais vulnerável e exposta ao risco a populaçâo atingida, potencializando fragilidades e desigualdades. Implicates da pesquisa: A situaçâo da cidade mineira evidencia a construçâo processual dos desastres e como o atual modelo de desenvolvimento económico e produtivo precisa ser repensado. As criticas a gestâo do desastre e a negligencia quanto a sua prevençâo podem servir para nortear decisőes do poder público no sentido de desenvolver açöes de prevençâo e reduçâo de danos. Originalidade/valor: Estudos empíricos que discutam a relaçâo entre desastres, vulnerabilidades e desigualdades sociais sâo fundamentais para se obter uma melhor compreensâo sobre complexidade que envolve a gestâo de desastres, e, principalmente, mostrar como as desigualdades sâo potencializadas a partir da ocorrencia de eventos dessa magnitude, agravando ainda mais a situaçâo de grupos e comunidades em condiçöes de fragilidade O artigo também contribui para a literatura da área na medida em que analisa os efeitos de superposiçâo de desastres - rompimento de barragem e pandemia - na populaçâo mais vulnerável.Alternate :Objective: To discuss how vulnerabilities, especially in terms of social inequalities, are linked to disasters and how they become more potent when disasters occur, further aggravating the situation of groups and communities in fragile conditions. Theoretical framework: The Vale dam failure disaster was analyzed from the Disaster Risk Management (DRM) proposal presented by the Sendai Framework, which is based on strengthening actions and measures to prevent disasters, as well as increasing preparedness for responses and recovery from disasters that may occur. Methodology/approach: The method used was the case study. Documentary research, participant observation of WhatsApp groups, and three interviews with representatives of the public sector and civil society were conducted, as well as seven interviews with citizens of Brumadinho. Content analysis was the strategy for data analysis. Main Results: The experience of the municipality of Brumadinho demonstrates how recovery from a disaster is difficult to accomplish and makes the affected population even more vulnerable and exposed to risk, potentiating fragilities and inequalities. Implications of the research: The situation of the city in Minas Gerais highlights the processual construction of disasters and how the current model of economic and productive development needs to be rethought. The criticism of disaster management and the negligence regarding its prevention can serve to guide decisions by the public authorities to develop prevention and damage reduction actions. Originality/value: Empirical studies that discuss the relationship between disasters, vulnerabilities and social inequalities are fundamental to obtain a better understanding of the complexity involved in disaster management, and es ecially to show how inequalities are enhanced by the occurrence of events of this magnitude, further aggravating the situation of groups and communities in fragile conditions.

Challenges of Education In Energy and Environment: Comparison before and after Pandemic Constraints

Energy systems present a complex and dynamic interrelation between energy, environment, and society. Therefore, properly educating new professionals for the renewable energy sector is a challenging endeavor by itself. The COVID-19 pandemic has imposed an additional challenge on how to engage students in energy and environment education through distance learning. In this paper, we present the methodology applied at the Federal University of São Paulo (UNIFESP) for students of the discipline "Energy and Environment". The graduate student interns developed an integrated methodology of disseminating knowledge about renewable energy and environment for those students and society as a whole. A positive feedback over 95% was obtained from the enrolled students in the period of 2019-2020. It was also noticed a failure rate of 24% in 2020 in contrast to zero occurrences in 2019, when face-to-face activities were in place. Finally, we present a brief discussion on the primary challenges and lessons learned during the studied period. © 2021. The Authors. Published by International Solar Energy Society Selection and/or peer review under responsibility of Scientific Committee.

A preliminary framework for managing airport capacity and demand from an economic perspective

Purpose>Airport capacity constraints lead to operational congestion and delays, which have become major threats to the aviation industry. They impose large costs on airlines and their passengers. Uncertainty in demand or unexpected events can cause a mismatch between capacity and demand, resulting in either capacity oversupply, with a decrease in efficiency, or airport congestion over an extended period. Moreover, airport capacity is rather difficult to define due to its multifaceted and dynamic nature, and it depends both on the available infrastructure and on operating procedures. Additionally, traditional capacity management methods do not consider relevant behavioral economic challenges to conventional analysis, particularly failure of the expected utility hypotheses and dependence of valuations on reference points. This study aims to develop a preliminary framework to include economic concepts when evaluating expansions of airport capacity.Design/methodology/approach>This paper reviews major opportunities in airport demand and capacity management from an economic perspective while appraising the challenges involved in airport capacity expansion processes that have not been fully completely in past studies. Although welfare economics provides the conceptual foundations for demand/capacity analyses, the authors integrate the findings regarding capacity definition, uncertainty management and behavioral economics into standard economics to guide the measurement of the airport capacity expansion problem.Findings>The authors obtain several insights regarding airport capacity and demand management. First, airport capacity is a complex metric when evaluating airport expansion, and it depends both on the available infrastructure and on operating procedures. Furthermore, airport throughput is highly conditioned by factors that shape capacity and delay and shows significant variability when these factors are modified. Second, a marginal change in capacity at congested airports may have a great impact on demand distribution, airline competition, aircraft types, fares, operating revenues, route map and other characteristics of a given airport. Behavior after capacity expansion is highly reliant on the slot allocation models. Additionally, overall social welfare is usually affected after changes in infrastructure in terms of increased connectivity, economic benefits and negative externalities, including noise and local pollution. Third, on-time performance is clearly nonlinear, and thus sensitive to variations in demand and capacity. Finally, airport capacity and demand management involve a trade-off between mitigating congestion and maximizing capacity utilization, so decision-making tools are required to support and enhance policy and managerial choices. Three main challenges arise when developing new methods for evaluating airport expansions: the definition of capacity, the management of uncertainty in demand and the need to consider economic concepts.Originality/value>This paper explores and produces an in-depth understanding of the problem of airport capacity and demand balance. The authors propose a preliminary framework that considers the challenges that have been previously identified and that, particularly, provides an economic perspective for airport capacity expansion processes. This framework is completed with a theoretical model to help policymakers and airport operators when faced with a capacity development decision.

Towards Reliable Remote Health Monitoring in Fog Computing Networks

As the World is still facing the COVID-19 pandemic, several researchers and industry players have proposed technological solutions to help fight the pandemic and pave the way for post-pandemic era precautions. In this matter, the potential benefits of remote health monitoring have been brought back to the spotlight. Indeed, with current advances in wireless communications, core network virtualization, and computing architectures as enablers, consistently guaranteeing the stringent quality-of-service (QoS) requirements of remote health monitoring, e.g., ultra-low latency, may be achievable. Notably, the fog computing (FC) paradigm has been advocated as a potential solution for remote health monitoring. However, the unreliability of fog nodes in FC networks is a critical aspect often overlooked despite its significant impact on vital latency requirements. This paper proposes a reliable fog-based remote health monitoring framework operating under uncertain fog computing conditions. Specifically, we formulate the problem of assigning tasks of remote sensors attached to patients to their adequate applications deployed in fog nodes aiming to maximize the number of satisfied tasks with respect to the fog nodes’availability and communication latency constraints. Due to the problem’s NP-hardness, we leverage a differential evolution-based algorithm enhanced by reinforcement learning to deploy applications in fog nodes. Numerical results demonstrate the superior reliability performance of our proposed solution, in terms of the average success ratio of tasks, compared to benchmarks. Specifically, our simulations show up to 60 % performance improvement compared to benchmarks in specific scenarios. Moreover, by investigating the impact of several key parameters, we identify a design trade-off between the number of fog nodes and the latter’s intrinsic failure rates. IEEE

Study on Invulnerability of Material Emergency Transportation System Based on Three-Layer Interdependent Network

The material transportation capacity under emergency conditions is an important guarantee for the country to deal with war, epidemic outbreak, and other crisis situations. Under emergency conditions, some nodes of the material transportation system may fail to work normally, which may lead to the collapse of the whole system. Based on analyzing the characteristics of the material emergency transportation system, this article builds a three-layer interdependent network model and uses the improved M-L model to describe the failure propagation mechanism of node damage in the three-layer network. Then, the network model is attacked randomly, and the relationship between invulnerability of the three-layer network and the three main indexes of network flow, average degree, and probability of interdependence are studied. Afterwards, the propagation of cascading failure among three subnetworks in the interdependent network is analyzed and compared. This article provides a theoretical basis for building an efficient and robust material emergency transportation system.

Pharmaceutical plant machine availability prediction using Artificial Neural Network

During the 2nd phase of COVID-19 pandemic, pharmaceutical plant industry is facing lot of production pressure and machine availability plays vital role in maximizing the manufacturing pharmacy product output. In this paper, Artificial Neural Networks (ANNs) based information processing algorithm has been used to provide a solution to this problem and it has been found suitable to predict machines availability as a prediction function. The considered pharmaceutical plants are dealing with production of medicines related common symptoms in case of COVID-19 (fever, coughing, and breathing problems). The pharmaceutical plant data corresponding to different values of repair and failure rates of different subsystems is collected from plant and analyzed with the help of validated neural network value of availability. This configuration of ANNs approach developed in this research allowed simplifying computational complexities of conventional approaches to solve a large plant machines availability problem. The ANNs methodology in the paper permitted making no assumption, no explicit coding of the problem, no complete knowledge of system configuration, only raw input and clean data found to be sufficient to determine the value of machine availability function for different value of failure and repair rates considered in the paper. The results obtained in the paper are useful for the plant leadership, as the value of failure and repair rates of various subsystems can be fine-tuned at a require clear-cut level to achieve higher availability, and avoid considerably loss of production, loss of man power, and by-pass complete breakdown of concerned system.

Configurational paths to medical crowdfunding success and failure based on a crisp-set qualitative comparative analysis

Purpose>With the increasing use of crowdfunding platforms in raising funds, it has become an important and oft-researched topic to analyze the critical factors associated with successful or failed crowdfunding. However, as a major subject of crowdfunding, medical crowdfunding has received much less scholarly attention. The purpose of this paper is to explore how contingency factors combine and casually connect in determining the success or failure of medical crowdfunding projects based on signal theory.Design/methodology/approach>The paper adopts the crisp-set qualitative comparative analysis to analyze the causal configurations of 200 projects posted on a leading medical crowdfunding platform in China “Tencent Donation.” Five anecdotal conditions that could have an impact on the outcome of medical crowdfunding campions were identified. Three relate to the project (funding duration, number of images and number of updates) and two relate to the funding participants (type of suffer and type of fund-raiser).Findings>The results show that diversified configurations of the aforementioned conditions are found (six configurations for successful medical crowdfunding projects and four configurations for failed ones).Originality/value>Despite the fact that there are a considerably large number of medical crowdfunding projects, relatively few researches have been conducted to investigate configurational paths to medical crowdfunding success and failure. It is found that there are certain combinations of conditions that are clearly superior to other configurations in explaining the observed outcomes.

Resilience Analysis of Australian Electricity and Gas Transmission Networks

Given they are two critical infrastructure areas, the security of electricity and gas networks is highly important due to potential multifaceted social and economic impacts. Unexpected errors or sabotage can lead to blackouts, causing a significant loss for the public, businesses, and governments. Climate change and an increasing number of consequent natural disasters (e.g., bushfires and floods) are other emerging network resilience challenges. In this paper, we used network science to examine the topological resilience of national energy networks with two case studies of Australian gas and electricity networks. To measure the fragility and resilience of these energy networks, we assessed various topological features and theories of percolation. We found that both networks follow the degree distribution of power-law and the characteristics of a scale-free network. Then, using these models, we conducted node and edge removal experiments. The analysis identified the most critical nodes that can trigger cascading failure within the network upon a fault. The analysis results can be used by the network operators to improve network resilience through various mitigation strategies implemented on the identified critical nodes.

Selected Papers from 1st Croatian Conference on Earthquake Engineering (1CroCEE)

The surprisingly heavy social impacts were amplified by a combination of circumstances such as damage concentrated in the dense urban city center, the number of people requiring alternative accommodation and the relatively cold weather. Interdisciplinary research and training in particular in the fields of Earthquake Engineering and Seismic Risk assessment and management is crucial for improving the design of new structures, retrofitting the existing ones and efficiently respond and recover from disastrous earthquakes. The analysis is conducted on a tramway network scale to identify critical locations by performing continuous monitoring on the tramway network and risk analysis based on the distance of buildings from the track, vibration amplitude at source, and building damage. The paper presents a failure analysis of the bell tower of the church of St. Francis of Assisi on Kaptol in Zagreb subjected to seismic activity using the finite-discrete element method—FDEM.

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.