Simulation-based assessment of supply chain resilience with consideration of recovery strategies in the COVID-19 pandemic context

Predicting the effects of supply chain resilience and robustness on COVID-19 impacts and performance: Empirical investigation through resources orchestration perspective

This study investigates the effects of supply chain (SCRE) and robustness (SCRO) on COVID-19 super disruption impacts and firm's financial performance by mobilizing the resources orchestration theory (ROT) as the main theoretical framework. We adopt structural equation modeling analysis of data collected from 289 French companies. The findings reveal the significantly positive influence of resources orchestration on SCRE and SCRO and the role of the latter in mitigating the pandemic disruption impacts. Notwithstanding, depending on whether the measures are objective or subjective, the effects of SCRE and SCRO on financial performance vary. Overall, this paper presents empirical evidence of the influence of both of SCRE and SCRO on pandemic disruption impacts and financial performance. Furthermore, this research provides insights to guide practitioners and decision makers regarding resources orchestration and the deployment of SCRE and SCRO.

Assessing last-mile distribution resilience under demand disruptions

The COVID-19 pandemic led to a significant breakdown of the traditional retail sector resulting in an unprecedented surge in e-commerce demand for the delivery of essential goods. Consequently, the pandemic raised concerns pertaining to e-retailers' ability to maintain and efficiently restore level of service in the event of such low-probability high-severity market disruptions. Thus, considering e-retailers' role in the supply of essential goods, this study assesses the resilience of last-mile distribution operations under disruptions by integrating a Continuous Approximation (CA) based last-mile distribution model, the resilience triangle concept, and the Robustness, Redundancy, Resourcefulness, and Rapidity (R4) resilience framework. The proposed R4 Last Mile Distribution Resilience Triangle Framework is a novel performance-based qualitative-cum-quantitative domain-agnostic framework. Through a set of empirical analyses, this study highlights the opportunities and challenges of different distribution/outsourcing strategies to cope with disruption. In particular, the authors analyzed the use of an independent crowdsourced fleet (flexible service contingent on driver availability); the use of collection-point pickup (unconstrained downstream capacity contingent on customer willingness to self-collect); and integration with a logistics service provider (reliable service with high distribution costs). Overall, this work recommends the e-retailers to create a suitable platform to ensure reliable crowdsourced deliveries, position sufficient collection-points to ensure customer willingness to self-collect, and negotiate contracts with several logistics service providers to ensure adequate backup distribution.

Modeling the artificial intelligence-based imperatives of industry 5.0 towards resilient supply chains: A post-COVID-19 pandemic perspective

The recent COVID-19 pandemic has significantly affected emerging economies' global supply chains (SCs) by disrupting their manufacturing activities. To ensure business survivability during the current and post-COVID-19 era, it is crucial to adopt artificial intelligence (AI) technologies to renovate traditional manufacturing activities. The fifth industrial revolution, Industry 5.0 (I5.0), and artificial intelligence (AI) offer the overwhelming potential to build an inclusive digital future by ensuring supply chain (SC) resiliency and sustainability. Accordingly, this research aims to identify, assess, and prioritize the AI-based imperatives of I5.0 to improve SC resiliency. An integrated and intelligent approach consisting of Pareto analysis, the Bayesian approach, and the Best-Worst Method (BWM) was developed to fulfill the objectives. Based on the literature review and expert opinions, nine AI-based imperatives were identified and analyzed using Bayesian-BWM to evaluate their potential applicability. The findings reveal that real-time tracking of SC activities using the Internet of Things (IoT) is the most crucial AI-based imperative to improving a manufacturing SC's survivability. The research insights can assist industry leaders, practitioners, and relevant stakeholders in dealing with the impacts of large-scale SC disruptions in the post-COVID-19 era.

The COVID-19 pandemic as a window of opportunity for more sustainable and circular supply chains

The COVID-19 pandemic is a microcosm for future challenges and crises. The greatest of these challenges is the climate crisis and the potential collapse of our Earth system. However, crises may also provide an opportunity to transition to a more sustainable and resilient future. In our study, we qualitatively analyze statements of a heterogeneous group of 46 experts from academia, industry, government, and organized civil society to explore inasmuch experts perceived the pandemic as a window of opportunity for more sustainable SCs and what they consider opportunities, challenges, and necessary actions for more sustainable circular SCs. Our study contributes to current and future studies on the opportunities in times of crisis and the actions needed to overcome SC vulnerabilities and increase the resiliency, circularity, and sustainability of SCs.

Exploring the Role of Industry 4.0 and Simulation as a Solution to the COVID-19 Outbreak: a Literature Review

The COVID-19 pandemic was an unexpected and disruptive event that significantly affected the performance of manufacturing systems and supply chains in various sectors. In this paper, a literature review is provided, which investigates the role that Industry 4.0 technologies and simulation tools have played in addressing the effects of the pandemic crisis. Specifically, a bibliometric analysis provides an overview of the most influential technologies through a study of the most used keywords. While a document analysis, conducted on critical papers that concern real case studies, shows that so far simulation provided support in four main areas: energy consumption, healthcare supply chain & contact tracing, food supply chain, and in general supply chain management. The main outcome of this research work is that Industry 4.0 technologies and simulation models were particularly important during the pandemic crisis and their properties deserve to be deeply exploited in the near future.

How, When, & Where temporary hospitals fit in turbulent times: A hybrid MADM optimization in the middle east

Governments have been challenged to provide temporary hospitals and other types of facilities to face the COVID-19 pandemic. This research proposes a novel multi-attribute decision-making (MADM) model to help determine how, when, and where these temporary facilities should be installed based on a set of critical success factors (CSFs) mapped in an uncertain environment. We portray the available facilities for temporary hospitals based on the CSFs that must be considered to make critical decisions regarding the optimal position based on the government's strategic decision-making process, thus indirectly providing better services and maximizing resources. In relation to earlier work, this research builds upon hybrid Pythagorean fuzzy numbers to find weights in Best-Worst Methods and rank temporary facilities based on evaluation by an area-based method for ranking. Policy implications and future directions are derived.

Two years of COVID-19 pandemic: Understanding the role of knowledge-based supply chains towards resilience through bibliometric and network analyses

Coronavirus disease (COVID-19) catastrophically disrupted most of the global supply chains (SC). Knowledge-based SC can cope with the pandemic disruptions by the efficient use of data, information, knowledge, human intelligence and emerging technologies. This article aims to critically analyse the SC research during the two years of COVID-19 pandemic to understand the role of knowledge-based supply chain towards SC resilience. A review of the 281 shortlisted articles is presented, along with bibliometric and network analyses in order to create an intellectual map of the domain and to identify the emerging knowledge themes. Bibliometric analysis reveals that the knowledge focus during this short span has migrated from COVID-19 pandemic to SC risk management and finally to risk mitigation strategies. The network analysis identifies five emerging knowledge themes, namely impact of COVID-19 on SC; SC risk mitigation and resilience; supply chain viability; sustainable SC strategies; and food SC. This review also elucidates the strategies to mitigate COVID-19 disruptions for incorporating resilience in SC. Future research directions for a knowledge-based sustainable-leagile-resilient (S-leagilient) supply chain have also been propounded.

Intelligent model for contemporary supply chain barriers in manufacturing sectors under the impact of the COVID-19 pandemic

The COVID-19 pandemic has cast a shadow on the global economy. Since the beginning of 2020, the pandemic has contributed significantly to the global recession. In addition to the health damages of the pandemic, the economic impacts are also severe. The consequences of such effects have pushed global supply chains toward their breaking point. Industries have faced multiple obstacles, threatening the fragile flow of raw materials, spare parts, and consumer goods. Previous studies showed that supply chain barriers have multi-faceted impacts on industries and supply chains, which demand appropriate measures. In this regard, seven major barriers that directly impact industries have been identified to determine which industry is most affected by the COVID-19 pandemic. This paper utilized a hybrid multi-criteria decision-making (MCDM) approach under a neutrosophic environment using trapezoidal neutrosophic numbers to rank those barriers. The Analytical Network Process (ANP) quantifies the effects and considers the interrelationships between the determined barriers (criteria) involved in decision-making. Subsequently, the Measurement Alternatives and Ranking according to the COmpromise Solution (MARCOS) method was adopted to rank six industries according to the impact of those barriers. Results show that the lack of inventory is the largest barrier to influencing industries, followed by the lack of manpower. Sensitivity analysis is performed to detect the change in the rank of industries according to the change in the relative importance of the barriers.

COVID-19 pandemic: Supply chain risk management by integrating Interpretive Structural Modeling and Bayesian belief network

The paper proposes a theoretical framework, based on a literature review, that analyzes the links between COVID-19 impacts and supply chain risk mitigation strategies, investigating the role of digitalization as a potential key resource to improve the effectiveness of supply chain resilience. Then, the paper empirically tests the framework through a hybrid causal mapping technique using the frameworks of Interpretive Structural Modelling and Bayesian Belief Networks methods to support supply chain decision making approaches. The findings of this paper can support managers in developing simple and traciable models for assessing interdependences among supply chain disruption sources and to invest effectively in resilience strategies.

Enablers of post-COVID-19 customer demand resilience: evidence for fast-fashion MSMEs

Purpose

This study aims to analyse the resilience of customer demand management post-coronavirus disease 2019, using fast fashion as an example. The paper provides insights for potential applications to micro-, small and medium enterprises (MSMEs).

Design/methodology/approach

Based on the qualitative analysis and an integrated Plan-Do-Check-Act (PDCA)-decision making trial and evaluation laboratory (DEMATEL)-fuzzy technique for order of preference by similarity to the ideal solution (TOPSIS) methodology of fuzzy multi-criteria decision-making, we explored and prioritised the enablers of resilience management for fast-fashion MSMEs.

Findings

The results reveal that the highest priority enabler is maintaining customer loyalty. Other enablers are associated with e-commerce endorsement, a customer-focussed assortment of items and flexible store operations.

Research limitations/implications

The study findings will enable fast-fashion MSMEs to develop effective actions and priorities in operations efforts to promote post-pandemic recovery.

Originality/value

Despite the importance of the resilience project and the changing fast-fashion customer patterns, only a handful of studies have explored how resilience can be managed in this field. Thus, the findings can contribute to closing this gap in the context of operations resilience research as well as MSME operations.

Using simulation modelling and systems science to help contain COVID-19: A systematic review

This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved.

Resilience and complexity measurement for energy efficient global supply chains in disruptive events

The whole world is faced with the COVID-19 epidemic that causes major disruptions in global supply chains. The aim of study is to evaluate the effects of COVID-19 on energy efficient global supply chains (SCs) and to model the global supply chain resilience and energy management affected during the COVID-19 considering trade between Turkey and China, and Turkey and the EU. In this study, firstly using System Dynamics (SD) model, the behavior of countries against COVID-19 for a certain period of time is observed, subsequently the increase in complexity is analyzed with entropy measurement to determine whether the systems are resilient or not and to mark the differences arising from reporting in the first and second wave of the pandemic in the developed model. It is determined that the second wave reporting differences is less than first wave reporting differences except Turkey. From the learning effect perspective, it has been seen that the effect on the economy and foreign trade are less than first wave of pandemic even though the number of patients originating in the second wave are higher. It means that countries responded to the second wave of COVID-19 in a more resilient way. It is found that as a major finding of this study, perceived complexity of the system decreases in the second wave because of the resilience of supply chain considering learning effect and centralized decision making ensure increasing resilience and resilience measure in global supply chains. The study is highly helpful for governments, decision makers and managers to understand and manage the impacts of COVID-19 on global supply chains being resilient and energy efficient.

Supply chain disruption during the COVID-19 pandemic: Recognizing potential disruption management strategies

The COVID-19 pandemic has made a significant impact on various supply chains (SCs). All around the world, the COVID-19 pandemic affects different dimensions of SCs, including but not limited to finance, lead time, demand changes, and production performance. There is an urgent need to respond to this grand challenge. The catastrophic impact of the COVID-19 pandemic prompted scholars to develop innovative SC disruption management strategies and disseminate them via numerous scientific articles. However, there is still a lack of systematic literature survey studies that aim to identify promising SC disruption management strategies through the bibliometric, network, and thematic analyses. In order to address this drawback, this study presents a set of up-to-date bibliometric, network, and thematic analyses to identify the influential contributors, main research streams, and disruption management strategies related to the SC performance under the COVID-19 settings. The conducted analyses reveal that resilience and sustainability are the primary SC topics. Furthermore, the major research themes are found to be food, health-related SCs, and technology-aided tools (e.g., artificial intelligence (AI), internet of things (IoT), and blockchains). Various disruption management strategies focusing on resilience and sustainability themes are extracted from the most influential studies that were identified as a part of this work. In addition, we draw some managerial insights to ensure a resilient and sustainable supply of critical products in the event of a pandemic, such as personal protective equipment (PPE) and vaccines.

Investigating the relationship between digital technologies, supply chain integration and firm resilience in the context of COVID-19

The outbreak of the COVID-19 pandemic has significantly augmented the complexity of information, adding to the challenges that firms face in effectively processing and grasping accurate information. As a result, the production uncertainty of firms has been seriously intensified during the pandemic, disrupting the normal operation of firms and their supply chains. Digital technologies serve as salient tools that help firms to process and analyse information, consequently enhancing firm resilience in the face of supply chain disruptions. This study aims to examine how digital technologies affect firm resilience in the context of COVID-19 through the lens of information processing theory and a large-scale survey conducted among Chinese manufacturers. Specifically, our study evaluates the mediating effect of supply chain integration (internal integration, customer integration and supplier integration) and the moderating effect of information complexity. The results show that supply chain integration plays a mediating role in the effect of digital technologies on firm resilience, and the mediation effect is particularly significant for customer integration. Furthermore, digital technologies have a stronger impact on firm resilience when information complexity is high. The findings advance our understanding and recognition of the resilience implications of digital technologies and provide important managerial implications for improving firm resilience in the context of COVID-19.

Measuring the Supply Chain Performance of the Floricultural Sector Using the SCOR Model and a Multicriteria Decision-Making Method

This study aims to highlight the usefulness of studying the performance of supply chains (SC) at the sectoral level in greater detail through the combination of a disaggregated supply chain operations reference (SCOR) model, with a multicriteria decision-making approach, specifically using an AHP, to adjust the analysis to the particularities of the sector under study by stakeholders’ judgements. The methodology was applied to the Ecuadorian flower industry, and the data for the analysis was from a survey of a group of companies that represent this sector. In addition, a focus group of SC experts weighted the model constructs as part of the analytic hierarchy process (AHP), and then the performance level for each construct was determined. According to the results methodologies, this model allows the classification of companies by their performance, as well as the performance of the aggregate sector. The processes that Ecuadorian flower companies need to improve on are planning, procurement, and manufacturing. The study’s main contribution is developing a general framework for measuring the overall performance of SCs and how the results are obtained. This tool could help managers, consultants, industries, and governments to assess the performance of SCs, as well as improving SC management in order to increase the sector’s competitiveness in the international market.

The application of industry 4.0 technologies in pandemic management: Literature review and case study

The Covid-19 pandemic impact on people's lives has been devastating. Around the world, people have been forced to stay home, resorting to the use of digital technologies in an effort to continue their life and work as best they can. Covid-19 has thus accelerated society's digital transformation towards Industry 4.0 (the fourth industrial revolution). Using scientometric analysis, this study presents a systematic literature review of the themes within Industry 4.0. Thematic analysis reveals that the Internet of Things (IoT), Artificial Intelligence (AI), Cloud computing, Machine learning, Security, Big Data, Blockchain, Deep learning, Digitalization, and Cyber-physical system (CPS) to be the key technologies associated with Industry 4.0. Subsequently, a case study using Industry 4.0 technologies to manage the Covid-19 pandemic is discussed. In conclusion, Covid-19,is clearly shown to be an accelerant in the progression towards Industry 4.0. Moreover, the technologies of this digital transformation can be expected to be invoked in the management of future pandemics.