Supply chain analytics and post-pandemic performance: mediating role of triple-A supply chain strategies

PurposeThe abrupt outbreak of coronavirus disease (COVID-19) hit every nation in 2020–2021, causing a worldwide pandemic. The worldwide COVID-19 epidemic, described as a "black swan”, has severely disrupted manufacturing firms' supply chain. The purpose of this study is to investigate how supply chain data analytics enable the effective deployment of agility, adaptability and alignment (3As) strategies, resulting in improving post-COVID disruption performance. It also analyses the indirect effect of supply chain data analytics on disruption performance through the 3As supply chain strategies.Design/methodology/approachThe hypothesis and theoretical framework were tested using a questionnaire survey. The authors employed structural equation modelling through the SMART PLS version 3.2.7 to analyse data from 163 textile firms located in Pakistan.FindingsThe results revealed that the supply chain data analytics contributed positively and significantly to the agility and adaptability, while all 3As supply chain strategies impacted the PPERF substantially. Further, the connection between supply chain data analytics (SCDA) and disruption performance has substantially been influenced through 3As supply chain strategies.Practical implicationsThe results imply that in the event of low likelihood, high effect disruptions, managers and decision-makers should focus their efforts on integrating data analytics capabilities with 3As supply chain policies to ensure long-term company success.Originality/valueThis research sheds fresh light on the importance of data analytics in effectively implementing 3As strategies for sustaining company performance amid COVID-19 disruptions.

Ethical moments and institutional expertise in UK Government COVID-19 pandemic policy responses: where, when and how is ethical advice sought?

Background:The emergency response to the COVID-19 pandemic has required a rapid acceleration of policy decision making, and raised a wide range of ethical issues worldwide, ranging from vaccine prioritisation, welfare and public health ‘trade-offs', inequalities in policy impacts, and the legitimacy of scientific expertise.Aims and objectives:This paper explores the legacy of the pandemic for future science-advice-policy relationships by investigating how the UK government's engagement with ethical advice is organised institutionally. We provide an analysis of some key ethical moments in the UK Government response to the pandemic, and institutions and national frameworks which exist to provide ethical advice on policy strategies.Methods:We draw on literature review, documentary analysis of scientific advisory group reports, and a stakeholder workshop with government ethics advisors and researchers in England.Findings:We identify how particular types of ethical advice and expertise are sought to support decision making. Contrary to a prominent assumption in the extensive literature on ‘governing by expertise', ethical decisions in times of crisis are highly contingent.Discussion and conclusions:The paper raises an important set of questions for how best to equip policymakers to navigate decisions about values in situations characterised by knowledge deficits, complexity and uncertainty. We conclude that a clearer pathway is needed between advisory institutions and decision makers to ensure ethically-informed debate.

Framing blockchain-integrated digital twins for emergent healthcare management at local and city levels: A proof of concept

It has been witnessed that digital technology has the potential to improve the efficiency of emergent healthcare management in COVID-19, which however has not been widely adopted due to unclear definition and configuration. This research aims to propose a proof of concept of digital twins for emergent healthcare management through configuring the cyber and functional interdependencies of healthcare systems at local and city levels. Critical interdependencies of healthcare systems have been firstly identified at both levels, then the information and associated cyber and functional interdependencies embedded in seven critical hospital information systems (HISs) have been identified and mapped. The proposed conceptual digital twin-based approach has been then developed for information coordination amongst these critical HISs at both local and city levels based on permissioned blockchain to (1) integrate and manage the information from seven critical HISs, and further (2) predict the demands of medical resources according to patient trajectory. A case study has been finally conducted at three hospitals in London during the COVID-19 period, and the results showed that the developed framework of blockchain-integrated digital twins is a promising way to provide more accurate and timely procurement information to decision-makers and can effectively support evidence-based decisions on medical resource allocation in the pandemic. © 2023 ICE Publishing: All rights reserved.

Digitalization of the supply chain: transformation factors

PurposeThere is an increasing interest in the supply chain's digitalization, yet the topic is still in the preliminary stages of academic research. The academic literature has no consensus and is still limited to research assessing the supply chain's digitalization of organizations. This study aims to explore the supply chain digitalization drivers to understand the emerging phenomena. More specifically, the authors devised from the literature the most common factors in assessing the readiness in scaling supply chain digitalization.Design/methodology/approachThis study followed a five-phased systematic literature review (SLR) methodology in this research: designing, analyzing, conducting, writing and assessing the quality of the review. The SLR is beneficial for justifying future research regardless of the complex process that requires dealing with high-level databases, information filtering and relevancies of the content. Through analysis of 347 titles and s and 40 full papers, the authors showed and discussed the supply chain digitalization: transformation factors.FindingsThe results generated three main themes: technology, people and processes. The study also generated ten subthemes/primary drivers for assessing the readiness for supply chain digitalization in organizations: IT infrastructure, cybersecurity systems, digitalization reskilling and upskilling, digitalization culture, top management support, digitalization and innovation strategy, integrated supply chain, digital innovation management, big data management and data analytics and government regulations. The importance of each factor was discussed, and future research agenda was presented.Research limitations/implicationsWhile the key drivers of the supply chain digitalization were identified, there is still a need to study the statistical correlation to confirm the interrelationships among factors. This study is also limited by the articles available in the databases and content extraction.Practical implicationsThis study supports decision-makers in understanding the critical drivers in digitalizing the supply chain. Once these factors are studied and comprehended, managers and decision-makers could better anticipate and allocate the proper resources to embark on the digitalization journey and make informed decisions.Originality/valueThe digitalization of the supply chain is more critical nowadays due to the global disruptions caused by the Coronavirus (COVID-19) pandemic and the surge of organizations moving toward the digital economy. There is a gap between the digital transformation pilot studies and implementation. The themes and factors unearthed in this study will serve as a foundation and guidelines for further theoretical research and practical implications.

The effect of increasing health disaster risk and public spending on economy conditions: a DSGE perspective

PurposeThe main motivation of the present study is to understand the severity of the effect of health shock on Iran's oil economy and analyze the role of government under these conditions.Design/methodology/approachDynamic stochastic general equilibrium (DSGE) models can show the precise interactions between market decision-makers in the context of general equilibrium. Since the duration of the virus outbreak and its effect on the economy is not known, it is more appropriate to use these models.FindingsThe results of the survey of hands-on policies scenarios compared to the state of hands-off policy indicate that the effect of government expending shocks on the economy under pandemic disease conditions has much less feedback on macroeconomic variables.Originality/valueAs a proposed policy, it is recommended that the government play a stabilizing role under pandemic disease conditions.Key messages There is no study regarding health shock and its economic effects in Iran using DSGE models. Also, in foreign studies, the health shock in an oil economy has not been modeled.The general idea in the present study is how the prevalence of a pandemic infectious disease affects the dynamics of macroeconomic variables.In three different scenarios, according to the persistence of health disaster risk and the deterioration rate of health capital due to this shock, the model is simulated.In modeling pandemic diseases, quarantine hours are considered as part of the total time of individuals.According to the research findings, it is recommended that the government, as a policy-maker, play a stabilizing role under pandemic crises conditions.

LSTM-based Forecasting using Policy Stringency and Time-varying Parameters of the SIR Model for COVID-19

Accurate forecasting of the number of infections is an important task that can allow health care decision makers to allocate medical resources efficiently during a pandemic. Two approaches have been combined, a stochastic model by Vega et al. for modelling infectious disease and Long Short-Term Memory using COVID-19 data and government's policies. In the proposed model, LSTM functions as a nonlinear adaptive filter to modify the outputs of the SIR model for more accurate forecasts one to four weeks in the future. Our model outperforms most models among the CDC models using the United States data. We also applied the model on the Canadian data from two provinces, Saskatchewan and Ontario where it performs with a low mean absolute percentage error. © 2023 IEEE.

A fuzzy multi-objective decision-making model for global green supplier selection and order allocation under quantity discounts

The selection of potential suppliers has recently become a big challenge for the manufacturing industries due to the rapid spread of covid-19 and the escalating frequency of natural calamities such as earthquakes and floods. When decision-makers (DMs) consider quantity discounts from multiple sources, things get much more complicated. Although previous studies have looked at selecting suitable suppliers from economic and environmental aspects, no one has considered foreign transportation risks while evaluating the textile industry's global green suppliers. In this regard, for the first time, this study combines economic and environmental factors with the foreign transportation risk criterion to develop a holistic model for global green supplier selection and order allocation (SS&OA) in the textile industry under all-unit quantity discounts. Initially, the fuzzy analytical hierarchy process (FAHP) method is used to calculate the relative weights of the criteria. Second, a multi-objective linear programming (MOLP) model is developed to reduce the total procurement cost, quality rejection rate, delivery lateness rate, greenhouse gas emissions from product procurement, and foreign transportation risks. Subsequently, the developed MOLP model is transformed into a fuzzy compromise programming (FCP) model to obtain order allocation quantities among selected suppliers with their offered quantity discount rates. A real-life case study of the Pakistani textile industry is presented to validate the proposed methodology's applicability by determining the optimal order allocation quantities among multiple suppliers based on two decision-making attitudes of DMs (neutral and risk-averse). Finally, sensitivity and comparative analyses are carried out to guarantee that the proposed technique produces accurate and optimal solutions. The final results of the proposed methodology show that it can effectively manage data uncertainties during SS&OA compared to other existing approaches. The suggested integrated methodology's outcomes can assist the supplier organization in overcoming its current shortcomings and developing a long-term relationship with the buyer organization. © 2023 Elsevier Ltd

Leverage points to address climate change risk in destinations

Transformational system change is required to respond to the current climate emergency and the COVID-19 induced structural break presents an opportunity to progress such change. While the tourism industry accepts the need for change, how this may look like remains unclear. This article contributes to identifying pathways by presenting critical reflections on the research process and findings from a three-year research project on reducing climate change risk in Vanuatu. The approach is anchored in systems thinking and draws on the concept of leverage points. Seven points are identified for intervening in the tourism system to reduce climate change risk and achieve varying levels of systemic change. Each is explored in the context of Vanuatu before its broader relevance is discussed. The findings highlight the importance of engaging with deeper influences of risk and unsustainable system outcomes. This has implications for how decision-makers approach crisis management and what ‘tourism recovery' means, especially when considering that system resilience might stand in the way of more profound transformational change required to address long-term risks.Alternate :中文摘要为了应对当前的气候突发事件, 需要进行转变性的制度变革。新型冠状肺炎引发的结构性突破为推动这种变化提供了机会。虽然旅游业接受了有必要进行改变, 但这可能会变成什么样子仍然是未知数。该文通过对一项为期三年的关于减少瓦努阿图气候变化风险研究项目过程和结果的批判性反思, 提出对气候变化进行转变性制度变革的路径。本文方法以系统思维为基础, 并借鉴杠杆点的概念, 提出对旅游系统进行干预的七个要点, 以减少气候变化风险, 实现不同程度的系统性变化。每个要点都是先在瓦努阿图的范围内进行探讨, 然后再讨论其更广泛的启发意义。研究结果强调应对风险和不可持续系统的更深层次影响因素的重要性。该研究结果对决策者如何处理危机管理和理解"旅游业复苏”的意义有启发, 尤其当决策者考虑到系统的弹性可能会阻碍解决长远风险所需要的更深远的转变性变革。

Towards Predicting Supplier Resilience: A Tree-Based Model Approach

With looming uncertainties and disruptions in today's global supply chains, such as lockdown measures to contain COVID-19, supply chain resilience has gained considerable attention recently. While decision-makers in procurement have emphasized the importance of traditional risk assessment, its shortcomings can be complemented by resilience. However, while most resilience studies are too qualitative in nature and to inform supplier decisions, many quantitative resilience studies frequently rely on complex and impractical operations research models fed with simulated supplier data. Thus there is the need for an integrative, intermediate way for the practical and automated prediction of resilience with real-world data. We therefore propose a random forest-based supervised learning method to predict supplier resilience, outperforming the current human benchmark evaluation by 139 percent. The model is trained on both internal ERP data and publicly available secondary data to help assess suppliers in a pre-screening step, before deciding which supplier to select for a specific product. The results of this study are to be integrated into a software tool developed for measuring and tracking the total cost of supply chain resilience from the perspective of purchasing decisions. © 2022 IEEE Computer Society. All rights reserved.

Experiences and challenges with pandemic online learning - a Pforzheim University survey

The objective of the work presented is to highlight experiences, impacts and challenges which students had to face during online learning and the corona-pandemic. This paper reports on findings of a survey conducted at a German university of applied sciences. An online survey was developed and administered. Responses were analyzed, using mostly descriptive statistics, and key findings are shared. These relate to experiences and challenges encountered by students during the time of online learning and revealing insights to the student perspective on online learning. These findings inform decision makers in considering future options regarding the use of technology for distance learning too. © 2022 IEEE.

Gender Diversity and Firm Profitability, before and during the Covid-19 Pandemic

The intense competition in business will lead company managers to ensure their business is performing at its best. The board of directors' choice will always impact whether the company's value rises or falls. The argument that companies with female executives make better judgments for shareholders has led to a noticeable trend of raising the participation of women on boards in several nations throughout the world during the past decade. Male directors tend to be risk-takers, whereas female directors are more risk-averse, making them more effective decision-makers in some situations. When making complicated decisions, women on the board typically digest information more effectively and efficiently than a board made up entirely of men. This study examines if having more women on boards of directors increases company profitability. This study examines how gender diversity influences the impact of the board of directors (BOD) and independent directors (ID) on business profitability throughout two time periods - before and during COVID-19 - Pandemic. This study's sample comprises 40 Food and Beverage firms listed on the Indonesian Capital Market between 2011 and 2021. The F & B industry was chosen as a research topic because it is considered to be able to survive in difficult times. Panel data regression was used to estimate the research models. Before Covid 19 - Pandemic, BOD size significantly negatively influenced firm profitability, whereas ID positively impacted firm profitability. The presence of women on the board significantly mitigates the negative impact of the BOD on firm profitability. During the Pandemic, BOD had no significant impact on corporate profitability. Nonetheless, ID has a negative impact on company profitability. Surprisingly, this condition demonstrates that the presence of women on the BOD strengthened the influence of the BOD and ID on firm profitability. Increasing women's participation on boards of directors is one way to enhance their performance. © 2023 IEEE.

Analytics Saves Lives During the COVID-19 Crisis in Chile

During the COVID-19 crisis, the Chilean Ministry of Health and the Ministry of Sciences, Technology, Knowledge and Innovation partnered with the Instituto Sistemas Complejos de Ingeniería (ISCI) and the telecommunications company ENTEL, to develop innovative methodologies and tools that placed operations research (OR) and analytics at the forefront of the battle against the pandemic. These innovations have been used in key decision aspects that helped shape a comprehensive strategy against the virus, including tools that (1) provided data on the actual effects of lockdowns in different municipalities and over time;(2) helped allocate limited intensive care unit (ICU) capacity;(3) significantly increased the testing capacity and provided on-the-ground strategies for active screening of asymptomatic cases;and (4) implemented a nationwide serology surveillance program that significantly influenced Chile's decisions regarding vaccine booster doses and that also provided information of global relevance. Significant challenges during the execution of the project included the coordination of large teams of engineers, data scientists, and healthcare professionals in the field;the effective communication of information to the population;and the handling and use of sensitive data. The initiatives generated significant press coverage and, by providing scientific evidence supporting the decision making behind the Chilean strategy to address the pandemic, they helped provide transparency and objectivity to decision makers and the general population. According to highly conservative estimates, the number of lives saved by all the initiatives combined is close to 3,000, equivalent to more than 5% of the total death toll in Chile associated with the pandemic until January 2022. The saved resources associated with testing, ICU beds, and working days amount to more than 300 million USD.

Conditional Conservatism in Islamic Banks During the COVID-19 Pandemic

This study explores conditional conservatism (CC) in listed Islamic banks (IB) during the COVID-19 pandemic. The author collects data manually over the period from 2019 to 2020. In order to capture CC, the author uses the C_score measurement in the main model. As predicted, the author finds an increase in CC level within IB during the COVID-19 pandemic. This finding enriches the current literature on CC, IB, and the economic outcomes of the COVID-19 pandemic. On the other hand, decision-makers (investors, creditors, etc.) can benefit from the governance role of CC experienced in IB during crises such as the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Tri-Model Prediction Approach for COVID-19 ICU Bed Occupancy: A Case Study

The impact of COVID-19 and the pressure it exerts on health systems worldwide motivated this study, which focuses on the case of Greece. We aim to assist decision makers as well as health professionals, by estimating the short to medium term needs in Intensive Care Unit (ICU) beds. We analyse time series of confirmed cases, hospitalised patients, ICU bed occupancy, recovered patients and deaths. We employ state-of-the-art forecasting algorithms, such as ARTXP, ARIMA, SARIMAX, and Multivariate Regression models. We combine these into three forecasting models culminating to a tri-model approach in time series analysis and compare them. The results of this study show that the combination of ARIMA with SARIMAX is more accurate for the majority of the investigated regions in short term 1-week ahead predictions, while Multivariate Regression outperforms the other two models for 2-weeks ahead predictions. Finally, for the medium term 3-weeks ahead predictions the Multivariate Regression and ARIMA with SARIMAX show the best results. We report on Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), R-squared ((Formula presented.)), and Mean Absolute Error (MAE) values, for one-week, two-week and three-week ahead predictions for ICU bed requirements. Such timely insights offer new capabilities for efficient management of healthcare resources. © 2023 by the authors.

FACS-CHARM: A Hybrid Agent-Based and Discrete-Event Simulation Approach for Covid-19 Management at Regional Level

Pandemics have huge impact on all aspect of people's lives. As we have experienced during the Coronavirus pandemic, healthcare, education and the economy have been put under extreme strain. It is important therefore to be able to respond to such events fast in order to limit the damage to the society. Decision-makers typically are advised by experts in order to inform their response strategies. One of the tools that is widely used to support evidence-based decisions is modeling and simulation. In this paper, we present a hybrid agent-based and discrete-event simulation for the Coronavirus pandemic management at regional level. Our model considers disease dynamics, population interactions and dynamic ICU bed capacity management and predicts the impact of various public health preventive measures on the population and the healthcare service. © 2022 IEEE.

What can managers learn from professional poker players about decision-making?

This paper aims to investigate how past decision-making experiences can improve future decisionmaking. Nine semi-structured interviews were conducted with profitable professional Poker players. The results point out that it is the knowledge background of the decision-maker that makes him make sense of the situations he experiences. The research findings allowed the identification of three mechanisms that facilitate and make future decisions faster and more appropriate based on past experiences: (1) memory, (2) reflection, and (3) tools and analytical approach. The research contributes by showing evidence that, when supported by analytical tools, decision-makers can improve the quality and speed of the decision-making process. For organizations and supply chains, the paper highlights the importance of recognizing patterns based on the past to make sense of the future. For operations management, in events like COVID-19, companies can take advantage of memory to enact over unprecedented scenarios, prevent disruptions, and recover. © 2023 IGI Global. All rights reserved.

The Public Investment Fund and Salman's state: the political drivers of sovereign wealth management in Saudi Arabia

Saudi Arabia's Public Investment Fund (PIF) has grown from marginal player to the most important economic actor in the Kingdom since 2015. Nevertheless, we know surprisingly little about the political economy of the PIF revamp. Against an unfavorable macroeconomic backdrop, I argue that shifts in PIF organization and orientation are fundamentally driven by the centralization of power within the circles of the Saudi ruling family since the rise of Mohammed bin Salman (MBS). The fund's governance framework and network of insiders forming the board of directors mirror the concentration of authority around MBS. In turn, PIF domestic activities shifted from scattered investment patterns associated with the competing influence of senior decision-makers to a highly authoritative and personalized strategy. Moreover, the PIF's response to COVID-19 further exemplifies the turn from a conservative strategy toward a short-term oriented approach to sovereign wealth management. Going beyond macro-level economic and institutional dynamics, the article introduces the role of political agency in SWF choices by stressing how political actors and the distribution of power within ruling elites shape SWFs. The article thus adds to the scholarship on domestic drivers of SWFs and contributes to debates surrounding the interplay between states and markets under processes of financialization.

A Framework for Assessing Higher Education Courses Employability

The 4th Industrial Revolution is causing profound and accelerated changes to work, bringing new opportunities and challenges as new technologies impact practically all occupations. The transformations in the labor market were accelerated even more due to the COVID-19 pandemic. In the scenario where old careers cease to exist, and new occupations are being created, Higher Education Institutions (HEIs) need to be prepared to educate professionals capable of getting and keeping qualified jobs. To do so, HEIs need tools to evaluate their undergraduate courses in the face of the changing demands of the labor market. We propose a novel approach to employability from the perspective of HEIs, creating a framework- called Higher Education Courses Employability (HECE). The framework can help HEI decision-makers to make decisions based on employability data. The framework allows for mitigating the reported gap between the theory taught in HEIs and the labor market demands. We evaluated the HECE framework as useful and relevant by HEI decision-makers and Employability experts from Brazil, a continental country with great social differences between and within its regions, and where the unemployment and underemployment rates demonstrate the mismatch between the labor market demands and the undergraduate course's curricula. The applicability of HECE in different Brazillian regions provides evidence that we can apply the framework in most contexts. This study provides tools to facilitate the implementation of the framework by HEIs. The evaluators reported the innovative nature of the approach of this research. © 2013 IEEE.

A Systematic Review of COVID-19 Geographical Research: Machine Learning and Bibliometric Approach

The rampant COVID-19 pandemic swept the globe rapidly in 2020, causing a tremendous impact on human health and the global economy. This pandemic has stimulated an explosive increase of related studies in various disciplines, including geography, which has contributed to pandemic mitigation with a unique spatiotemporal perspective. Reviewing relevant research has implications for understanding the contribution of geography to COVID-19 research. The sheer volume of publications, however, makes the review work more challenging. Here we use the support vector machine and term frequency-inverse document frequency algorithm to identify geographical studies and bibliometrics to discover primary research themes, accelerating the systematic review of COVID-19 geographical research. We confirmed 1,171 geographical papers about COVID-19 published from 1 January 2020 to 31 December 2021, of which a large proportion are in the areas of geographic information systems (GIS) and human geography. We identified four main research themes—the spread of the pandemic, social management, public behavior, and impacts of the pandemic—embodying the contribution of geography. Our findings show the feasibility of machine learning methods in reviewing large-scale literature and highlight the value of geography in the fight against COVID-19. This review could provide references for decision makers to formulate policies combined with spatial thinking and for scholars to find future research directions in which they can strengthen collaboration with geographers.Alternate :2020年, 新冠肺炎流行病迅速席卷全球, 对人类健康和全球经济造成了巨大影响。这次流行病激发了各个学科研究的爆炸性增长。其中, 地理学研究以独特的时空角度, 为流行病治理做出了贡献。对有关研究进行综述, 有助于理解地理学对新冠肺炎研究的贡献。然而, 海量的文献使得这个综述更具挑战性。为了加快对新冠肺炎地理研究的系统性综述, 我们利用支持向量机和词频-反文档频率算法寻找文献中的地理学研究, 利用文献计量学发掘主要研究题目。本文确认了2020年1月1日至2021年12月31日发表的1,171篇新冠肺炎地理学论文, 其中多数文章属于地理信息系统和人文地理学领域。确定了体现地理学贡献的四个主要研究题目：流行病传播、社会管理、公众行为和流行病影响。研究结果表明了利用机器学习方法去开展海量文献综述的可行性, 强调了地理学在抗击新冠肺炎的价值。该文献综述有助于决策者制定具备空间思维的政策, 也有助于学者们寻求加强与地理学者合作的未来研究方向。

Analysis of a decision support system for supplier selection in glove industry

Sensitivity Analysis is a method to determine how possible changes or errors in parameter values affect model outputs. This study evaluates the AHP based decision support system used for supplier selection in a glove manufacturing industry by performing a sensitivity analysis. Uniqueness of this study is that it deals with the sensitivity analysis of criteria used for supplier selection during COVID pandemic. The pandemic has altered the weightages of factors considered for supplier selection in a normal times. Expert Choice software is used to analyze the sensitivity of these parameters. The study facilitates the decision maker to understand and experiment on the effect of criterion weights on ranking of the suppliers. This makes the decision maker confident about the decisions in both favorable and unfavorable conditions. © 2022