Risk management for second-hand clothing imports in least developed countries: Legislations and perception of public-sector corruption.

The second-hand clothing imports are very popular in the least developed countries (LDCs). The social health risk (SHR) associated with second-hand clothing products and the lack of relevant legislations in LDCs, however, bring substantial challenges. This article is therefore developed to explore the sterilization legislation design for second-hand clothing supply chains in LDCs. To address LDCs' different import requirements of fumigation, both the extended exporter responsibility (EER) legislation scheme and the extended importer responsibility (EIR) legislation scheme are considered. We also examine whether the perception of public-sector corruption in LDCs may affect the performance of sterilization legislation schemes. We compare the performance of sterilization legislation schemes under different public-sector corruption cases, different sterilization legislation structures, as well as market competition. Interestingly, our analyses show that the EER and EIR legislation schemes can achieve the same performance under a per unit SHR duty, no matter whether there is public-sector corruption or not. However, these two legislation schemes perform differently under the lump-sum SHR duty. Besides, with the presence of the public-sector corruption perception, the prospect of financial benefits from bribing the regulatory agency can induce the firm to choose a higher optimal sterilization level when the bribe is sufficiently small. These implications complement the extant knowledge on risk management of second-hand clothing in LDCs, and provide an important guidance regarding the design of sterilization legislations on second-hand clothing imports.

Travel bubble policies for low-risk air transport recovery during pandemics.

Global pandemics restrict long-haul mobility and international trade. To restore air traffic, a policy named "travel bubble" was implemented during the recent COVID-19 pandemic, which seeks to re-establish air connections among specific countries by permitting unrestricted passenger travel without mandatory quarantine upon arrival. However, travel bubbles are prone to bursting for safety reasons, and how to develop an effective restoration plan through travel bubbles is under-explored. Thus, it is vital to learn from COVID-19 and develop a formal framework for implementing travel bubble therapy for future public health emergencies. This article conducts an analytical investigation of the air travel bubble problem from a network design standpoint. First, a link-based network design problem is established with the goal of minimizing the total infection risk during air travel. Then, based on the relationship between origin-destination pairs and international candidate links, the model is reformulated into a path-based one. A Lagrangian relaxation-based solution framework is proposed to determine the optimal restored international air routes and assign the traffic flow. Finally, computational experiments on both hypothetical data and real-world cases are conducted to examine the algorithm's performance. The results demonstrate the effectiveness and efficiency of the proposed model and algorithm. In addition, compared to a benchmark strategy, it is found that the infection risk under the proposed travel bubble strategy can be reduced by up to 45.2%. More importantly, this work provides practical insights into developing pandemic-induced air transport recovery schemes for both policymakers and aviation operations regulators.

How COVID-19 pandemic has shaped buyer-supplier relationships in engineering companies with ethical perception considerations: A multi-methodological study.

In business-to-business (B2B) operations, prior studies have mainly explored transaction-based relationships with both buyers and suppliers opportunistic behaviors, driven largely by their intent to maximize their own benefits. These studies have also found that dependency on partners increases when supply materials are scarce. However, research is scant on how this relationship changes in the face of exogenous forces such as the COVID-19 pandemic, keeping in mind the ethical perception considerations. This study aims to bridge this gap in the literature by studying how buyers and sellers leverage collaboration and resource-sharing to tide over pandemic-like situations similar to the current COVID-19 pandemic while considering their ethical perceptions. We conduct a multi-methodological study consisting of an industrial survey and an interview-based thematic analysis. In the first phase, we collect primary data using a structured questionnaire and conduct a covariance-based structural equation modeling (CB-SEM) analysis. In the second phase, we conduct a post-hoc test. We find that non-regular suppliers will share strategic resources with buyers during uncertain times (e.g. COVID-19 pandemic) if they have a high ethical perception of the buying firm and share a candid relationship despite being their irregular customers. Our findings propose that B2B firms should maintain healthy relationships with alternative suppliers to build trust and avoid supply crises in times of disruptions.

Mining voices from self-expressed messages on social-media: Diagnostics of mental distress during COVID-19.

The COVID-19 pandemic has had a severe impact on mankind, causing physical suffering and deaths across the globe. Even those who have not contracted the virus have experienced its far-reaching impacts, particularly on their mental health. The increased incidences of psychological problems, anxiety associated with the infection, social restrictions, economic downturn, etc., are likely to aggravate with the virus spread and leave a longer impact on humankind. These reasons in aggregation have raised concerns on mental health and created a need to identify novel precursors of depression and suicidal tendencies during COVID-19. Identifying factors affecting mental health and causing suicidal ideation is of paramount importance for timely intervention and suicide prevention. This study, thus, bridges this gap by utilizing computational intelligence and Natural Language Processing (NLP) to unveil the factors underlying mental health issues. We observed that the pandemic and subsequent lockdown anxiety emerged as significant factors leading to poor mental health outcomes after the onset of COVID-19. Consistent with previous works, we found that psychological disorders have remained pre-eminent. Interestingly, financial burden was found to cause suicidal ideation before the pandemic, while it led to higher odds of depressive (non-suicidal) thoughts for individuals who lost their jobs. This study offers significant implications for health policy makers, governments, psychiatric practitioners, and psychologists.

Seeking survivals under COVID-19: The WhatsApp platform's shopping service operations.

Under COVID-19 outbreak, retail operations are seriously threatened. There are lots of cases in which physical stores basically have to stop operating. This creates problems to the firm, its employees, and consumers. Recently, Timberland in Hong Kong and various other brands such as Joyce Boutiques and The North Face have established the "WhatsApp Shopping Service Operation" (WSO) in which consumers can shop by using the well-established communication tool "WhatsApp." Salespeople in stores provide services via WhatsApp to assist the consumers without them having to visit the stores. We collect primary data from real-world cases and theoretically explore WSO. We build a standard consumer utility based model to derive the firm's optimal pricing and employment decisions under different cases. We evaluate the impacts of COVID-19 and values of WSO implementation from the "Worker-Consumer-Company" (WCC) welfare perspective. Our results interestingly imply that WSO is superior to the traditional online channel in terms of keeping business under the pandemic; meanwhile, implementing WSO can help stimulate demand in the physical store under COVID-19. However, whether WSO is effective to help increase the firm's profit and WCC welfare depends on both consumer type' distribution and consumers' fear of infection. When consumers' fear of infection is very polarized (i.e., extremely low or high), WSO is not recommended. We further propose that the government's subsidy for WSO implementation could be an effective way to help the firm improve its profit and WCC welfare. We also check the robustness of our study by extending the model to consider endogenous consumer type, endogenous service level, and WCC-welfare-oriented firm.

Competitive pricing of substitute products under supply disruption.

There has been an increased interest in optimizing pricing and sourcing decisions under supplier competition with supply disruptions. In this paper, we conduct an analytical game-theoretical study to examine the effects of supply capacity disruption timing on pricing decisions for substitute products in a two-supplier one-retailer supply chain setting. We investigate whether the timing of a disruption may significantly impact the optimal pricing strategy of the retailer. We derive the optimal pricing strategy and ordering levels with both disruption timing and product substitution. By exploring both the Nash and Stackelberg games, we find that the order quantity with the disrupted supplier depends on price leadership and it tends to increase when the non-disrupted supplier is the leader. Moreover, the equilibrium market retail prices are higher under higher levels of disruption for the Nash game, compared to the Stackelberg game. We also uncover that the non-disrupted supplier can always charge the highest wholesale price if a disruption occurs before orders are received. This highlights the critical role of order timing. The insights can help operations managers to proper design risk mitigation ordering strategies and re-design the supply contracts in the presence of product substitution under supply disruptions.

Advances in Risk Analysis with Big Data.

With cloud computing, Internet-of-things, wireless sensors, social media, fast storage and retrieval, etc., organizations and enterprises have access to unprecedented amounts and varieties of data. Current risk analysis methodology and applications are experiencing related advances and breakthroughs. For example, highway operations data are readily available, and making use of them reduces risks of traffic crashes and travel delays. Massive data of financial and enterprise systems support decision making under risk by individuals, industries, regulators, etc. In this introductory article, we first discuss the meaning of big data for risk analysis. We then examine recent advances in risk analysis with big data in several topic areas. For each area, we identify and introduce the relevant articles that are featured in the special issue. We conclude with a discussion on future research opportunities.

Sustainable successes in third-party food delivery operations in the digital platform era.

In the digital era, third-party food delivery operations are very popular all around the world. However, to achieve a sustainable operation for food delivery businesses is a challenging issue. Motivated by the fact that there is a lack of consolidated view towards the topic in the literature, we conduct a systematic literature review to identify how to achieve a sustainable operation for third-party food delivery and highlight the recent advances in this important area with the discussion of real-world practices. In this study, first, we review the relevant literature and apply the triple bottom line (TBL) framework to classify prior studies into economic sustainability, social sustainability, environmental sustainability, and multi-dimensional sustainability. We then identify three major research gaps, including inadequate investigation on the restaurant's preferences and decisions, superficial understanding on the environmental performance, and limited examination on the multi-dimensional sustainability in the third-party food delivery operations. Finally, based on the reviewed literature and observed industrial practices, we propose five future areas that deserve an in-depth further investigation. They are namely applications of digital technologies, behaviors and decisions of the restaurants, risk management, TBL, and post-coronavirus pandemic.

Values of blockchain for risk-averse high-tech manufacturers under government's carbon target environmental taxation policies.

Today, high-tech industries such as consumer electronics commonly face government rules on carbon emissions. Among the rules, carbon emission tax as well as extended producer responsibility (EPR) tax are two important measures. Using blockchain, the policy makers can better determine the carbon target environmental taxation (CTET) policy with accurate information. In this paper, based on the mean-variance framework, we study the values of blockchain for risk-averse high-tech manufacturers who are under the government's CTET policy. To be specific, the government first determines the optimal CTET policy. The high-tech manufacturer then reacts and determines its optimal production quantity. We analytically prove that the CTET policy simply relies on the setting of the optimal EPR tax. Then, in the absence of blockchain, we consider the case in which the government does not know the manufacturer's degree of risk aversion for sure and then derive the expected value of using blockchain for the high-tech manufacturers. We study when it is wise for the high-tech manufacturer and the government to implement blockchain. To check for robustness, we consider in two extended models respectively the situations in which blockchain incurs non-trivial costs as well as having an alternative risk measure. We analytically show that most of the qualitative findings remain valid.

Examining collaborative buyer-supplier relationships and social sustainability in the "new normal" era: the moderating effects of justice and big data analytical intelligence.

The COVID-19 pandemic has resulted in a slew of new business practices that have put the society and environment under strain. This has drawn the attention of supply chain researchers working to address the COVID-19 pandemic's looming social sustainability issues. Prior literature has indicated that collaborative relationships improve organizational performance. Over the past years, problems related to justice are reported (e.g., between Walmart Canada and the Lego group), which might negatively affect the buyer-supplier relationship. In the new normal, the effect of justice on collaborative buyer-supplier relationships on social sustainability in the COVID-19 context is obviously essential but under-explored. The current study examines buyer-supplier collaborative relationships' influence on social sustainability under the moderating effect of justice and big data analytical intelligence. In this paper, we employ the stakeholder resource-based view, loose coupling theory, and resource dependency theory as the theoretical lens to establish the research hypotheses. Using primary survey data collected from supply chain practitioners in South Africa, hypothesis testing is done using a covariance-based structural equation modelling technique. To enhance research rigor, we have checked the dyadic perspectives of both buyers and suppliers. Our empirical results reveal that collaborative buyer-supplier relationships positively influence supplier social sustainability in the new normal era. However, it is relatively stronger from the suppliers' perspective when compared with the buyers' perspective. Secondly, the moderating effect of perceptions of organizational justice and big data analytical intelligence on the relationship between collaborative buyer-supplier relationships and supplier social sustainability is also statistically significant. However, it is relatively stronger from the buyers' perspective when compared with the suppliers' perspective. These are major findings of this study. Theoretical and managerial implications are further discussed.

Fighting against COVID-19: what operations research can help and the sense-and-respond framework.

COVID-19 is affecting all walks of life. To deal with it, we need to make use of scientifically sound tools and models. Operations research (OR), as a well-established field which focuses on deploying analytical tools to solving decision making problems, comes to the rescue. In this paper, by examining the OR literature and practices related to pandemics (including COVID-19), we discuss what OR can help to tackle challenges under COVID-19. We classify the literature into three stages, namely "before pandemic", "during pandemic" and "after pandemic". We examine the related literature and reveal the respective research areas and OR methods employed. Then, we propose a future research agenda. Finally, we establish the sense-and-respond OR framework regarding what specific actions should be taken to cope with COVID-19 from the perspectives of governments, healthcare and non-profit-making organizations, and businesses. We believe that the findings of this paper lay the solid foundation to stimulate further OR studies to combat COVID-19.

How small-and-medium transportation companies handle asymmetric customer relationships under COVID-19 pandemic: A multi-method study.

The COVID-19 outbreak has created major challenges for transportation companies. Grounded in the dynamic capabilities view theory, this paper adopts a multi-methodological operations management approach to derive scientifically sound insights with regard to handling key customer relationships in the crisis. To be specific, first, qualitative interviews with representatives of small carriers and forwarders as well as an examination of their social media posting are conducted. The qualitative research reveals the customer relationships situation of transportation companies during the COVID-19 pandemic. What specific actions build relational capabilities during the COVID-19 pandemic is uncovered. The research model is then tested based on the survey with one hundred Polish SME carriers. Several insights are generated. First, this study provides evidence that among various available networking routines, companies should concentrate on relationship monitoring, conflict handling, and selective relationship downsizing, while initiating new partnerships does not appear to be beneficial. Second, this study suggests that the positive influence of relational caps on company performance is moderated positively by contracts signed between partners and negatively by the financial debt of focal companies. Finally, this study discusses its results with regard to other studies on business relationships in dramatic environmental changes and highlights the corresponding implications.

Infection vulnerability stratification risk modelling of COVID-19 data: a deterministic SEIR epidemic model analysis.

Basic Susceptible-Exposed-Infectious-Removed (SEIR) models of COVID-19 dynamics tend to be excessively pessimistic due to high basic reproduction values, which result in overestimations of cases of infection and death. We propose an extended SEIR model and daily data of COVID-19 cases in the U.S. and the seven largest European countries to forecast possible pandemic dynamics by investigating the effects of infection vulnerability stratification and measures on preventing the spread of infection. We assume that (i) the number of cases would be underestimated at the beginning of a new virus pandemic due to the lack of effective diagnostic methods and (ii) people more susceptible to infection are more likely to become infected; whereas during the later stages, the chances of infection among others will be reduced, thereby potentially leading to pandemic cessation. Based on infection vulnerability stratification, we demonstrate effects brought by the fraction of infected persons in the population at the start of pandemic deceleration on the cumulative fraction of the infected population. We interestingly show that moderate and long-lasting preventive measures are more effective than more rigid measures, which tend to be eventually loosened or abandoned due to economic losses, delay the peak of infection and fail to reduce the total number of cases. Our calculations relate the pandemic's second wave to high seasonal fluctuations and a low vulnerability stratification coefficient. Our characterisation of basic reproduction dynamics indicates that second wave of the pandemic is likely to first occur in Germany, Spain, France, and Italy, and a second wave is also possible in the U.K. and the U.S. Our findings show that even if the total elimination of the virus is impossible, the total number of infected people can be reduced during the deceleration stage.

Innovative "Bring-Service-Near-Your-Home" operations under Corona-Virus (COVID-19/SARS-CoV-2) outbreak: Can logistics become the Messiah?

The corona virus (COVID-19/SARS-CoV-2) outbreak has created serious disruptions to many business operations. Among them, many service operations, which require customers to travel and visit a place indoor, become almost infeasible to run in a crowded city like Hong Kong. Motivated by a recent reported real case on an innovative service operation in Hong Kong, we build analytical models to explore how logistics and technologies together can transform the "static service operations" to become the "bring-service-near-your-home" mobile service operations. We also highlight how the government may provide the subsidy to support the above mentioned mobile service operation (MSO) to make it financially viable. We specifically show that the government may adopt the fixed-cost-subsidy (FCS) scheme, operations-cost-subsidy (OCS) scheme or safety-technology-support (STS) scheme to help. We further uncover that the OCS scheme would bring a larger consumer surplus than the FCS scheme and is hence more preferable. In the extended models, we first study the case when service fee cannot be changed because of corona virus outbreak (CVO). We then explore the feasibility of adopting MSO in the long run as a financially self-sustainable service operation and derive the analytical conditions under which MSO is a win-win business model for both the service provider and consumers. Finally, we study the optimal safety technology investment problem.

A United Nations' Sustainable Development Goals perspective for sustainable textile and apparel supply chain management.

Motivated by United Nations' Sustainable Development Goals (SDGs) and the importance of sustainability, this study examines how the textile and apparel (TA) supply chains can comply with the SDGs. By examining the literature as well as industrial practices, we show that the current sustainable operations in TA industry are far away from realizing the goals of economic growth going hand-in-hand with the social and environmental sustainability. For instance, among the SDGs, the goals of "Responsible Consumption and Production", "Clean Water and Sanitation", and "Climate Action" receive a considerable amount of attention, while goals of "No Poverty", "Reduced Inequalities", "Life below Water" and "Life on Land" have the least attention. Balanced sustainable development actions from the stakeholders' perspective are proposed. Managerial implications and future studies are discussed.

Blockchain technology in supply chain operations: Applications, challenges and research opportunities.

Blockchain is a technology with unique combination of features such as decentralized structure, distributed notes and storage mechanism, consensus algorithm, smart contracting, and asymmetric encryption to ensure network security, transparency and visibility. Blockchain has immense potential to transform supply chain (SC) functions, from SC provenance, business process reengineering to security enhancement. More and more studies exploring the use of blockchain in SCs have appeared in recent years. In this paper, we consider a total of 178 articles and examine all the relevant research done in the field associated with the use of blockchain integration in SC operations. We highlight the corresponding opportunities, possible societal impacts, current state-of-the-art technologies along with major trends and challenges. We examine several industrial sectors such as shipping, manufacturing, automotive, aviation, finance, technology, energy, healthcare, agriculture and food, e-commerce, and education among others that can be successfully revamped with blockchain based technologies through enhanced visibility and business process management. A future research agenda is established which lays the solid foundation for further studies on this important emerging research area.

A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines.

This paper investigates the feasibility of applying a relatively novel neural network technique, i.e., extreme learning machine (ELM), to realize a neuro-fuzzy Takagi-Sugeno-Kang (TSK) fuzzy inference system. The proposed method is an improved version of the regular neuro-fuzzy TSK fuzzy inference system. For the proposed method, first, the data that are processed are grouped by the k-means clustering method. The membership of arbitrary input for each fuzzy rule is then derived through an ELM, followed by a normalization method. At the same time, the consequent part of the fuzzy rules is obtained by multiple ELMs. At last, the approximate prediction value is determined by a weight computation scheme. For the ELM-based TSK fuzzy inference system, two extensions are also proposed to improve its accuracy. The proposed methods can avoid the curse of dimensionality that is encountered in backpropagation and hybrid adaptive neuro-fuzzy inference system (ANFIS) methods. Moreover, the proposed methods have a competitive performance in training time and accuracy compared to three ANFIS methods.

Recent Development in Big Data Analytics for Business Operations and Risk Management.

"Big data" is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., wireless sensor networks, Internet-based systems, etc.). Big data research, however, is still in its infancy. Its focus is rather unclear and related studies are not well amalgamated. This paper aims to present the challenges and opportunities of big data analytics in this unique application domain. Technological development and advances for industrial-based business systems, reliability and security of industrial systems, and their operational risk management are examined. Important areas for future research are also discussed and revealed.

Optimal Bi-Objective Redundancy Allocation for Systems Reliability and Risk Management.

In the big data era, systems reliability is critical to effective systems risk management. In this paper, a novel multiobjective approach, with hybridization of a known algorithm called NSGA-II and an adaptive population-based simulated annealing (APBSA) method is developed to solve the systems reliability optimization problems. In the first step, to create a good algorithm, we use a coevolutionary strategy. Since the proposed algorithm is very sensitive to parameter values, the response surface method is employed to estimate the appropriate parameters of the algorithm. Moreover, to examine the performance of our proposed approach, several test problems are generated, and the proposed hybrid algorithm and other commonly known approaches (i.e., MOGA, NRGA, and NSGA-II) are compared with respect to four performance measures: 1) mean ideal distance; 2) diversification metric; 3) percentage of domination; and 4) data envelopment analysis. The computational studies have shown that the proposed algorithm is an effective approach for systems reliability and risk management.