Supply chain risk management in the COVID-19 pandemic: strategies and empirical lessons for improving global logistics service providers’ performance

PurposeThe enormous impact of the COVID-19 pandemic showcases the key role of supply chain risk management (SCRM) in achieving and maintaining business performance, competitiveness and survival in the “new normal”. The purpose of this paper is to explore what impact the COVID-19 pandemic has had and may yet have on supply chains (SCs), which SCRM approaches have proved successful and how logistics service providers (LSPs) have applied the knowledge they have gained to improve their SCRM practices and resilience so as to prepare better for the next major disruption.Design/methodology/approachThis paper combines an extensive literature review with a multiple-case study of 10 internationally operating LSPs and how they have handled the impact of the COVID-19 pandemic so far. To bridge the research-practice gap, this study draws on the dynamic-capabilities view and provide insights that are valuable to both academia and practice.FindingsThis study provides empirical evidence on the severe impact of the COVID-19 pandemic on SCs, which has posed several challenges to LSPs. The study identifies eight factors that are critical to the adaptive capabilities of LSPs and, therefore, to their resilience in extreme conditions. The findings of this study show that these factors determine whether an SCRM system is robust and agile enough to allow an LSP to anticipate potential disruption and to respond fast enough when disruption occurs. Specifically, this study finds that robustness and agility demonstrably strengthen business performance, while learning from experience proves key to reconfiguring an SCRM design in response to acute disruption.Originality/valueThis paper is among the first to provide rich, empirical and practically applicable insights into the impact of the COVID-19 pandemic on business in relation to SCRM. These novel insights offer inspiring opportunities for further research.

Bayesian Belief Network Approach for Supply Risk Modelling

Today’s global and complex world increased the vulnerability to risks exponentially and organizations are compelled to develop effective risk management strategies for its mitigation. The prime focus of research is to design a supply risk model using Bayesian Belief Network bear in mind the tie-in of risk factors (i.e. objective and subjective) those are critical to a supply chain network. The proposed model can be re-engineered as per new information available at disclosure, so risk analysis will be current and relevant along the timeline as so situation is strained. The top three factors which influenced profitability were transportation risk and price risks. Netica is the platform used for designing and running simultaneous simulations on the Bayesian Network. The proposed methodology is demonstrated through a case study conducted in an Indian manufacturing supply chain taking inputs from supply chain/risk management experts. .

IS YOUR SUPPLY CHAIN READY FOR THE NEXT DISRUPTION? BUILDING RESILIENT CHAINS

ABSTRACT The ability to recover from disruptions is important for organizations and supply chains. Empirical data were used to investigate factors that affect supply chain recovery from disruptions, including collaboration, visibility, flexibility, analytical orientation, and supply chain risk management. A literature review was conducted to build an online questionnaire that was applied to manufacturing firms in Brazil. This work’s statistical method includes confirmatory factor analysis and structural equation modeling. Our results indicate that a package of resilience capabilities - collaboration, flexibility, visibility, and analytical orientation - positively affect supply chain resilience. Improving such capabilities, therefore, will allow supply chains to recover better from disruptions. It was also discovered, however, that supply chains do not recover from disruptions by way of supply chain risk management alone. Mutual impacts also exist between the group of resilience capabilities and supply chain risk management.

Opinions of the Software and Supply Chain Assurance Forum on Education, Training, and Certifications

This article provides an overview of discussions held at the Software and Supply Chain Assurance (SSCA) forum held May 1-2, 2018, in McLean, Virginia. The two-day event focused on education and training for software assurance (SwA) and Cyber-Supply Chain Risk Management (C-SCRM). Attendees discussed questions such as “What are some challenges facing industry, academia, and government organizations in this area?” “Who needs education or training?” “What needs to be taught?” and “What strategies do or do not work?” Discussions related to the current environment, hiring and retaining qualified employees, defining roles and responsibilities, and the knowledge, skills, and abilities (KSAs) that are most in-demand.

SUA CADEIA DE SUPRIMENTOS ESTÁ PREPARADA PARA A PRÓXIMA INTERRUPÇÃO? CONSTRUINDO CADEIAS RESILIENTES

ABSTRACT The ability to recover from disruptions is important for organizations and supply chains. Empirical data were used to investigate factors that affect supply chain recovery from disruptions, including collaboration, visibility, flexibility, analytical orientation, and supply chain risk management. A literature review was conducted to build an online questionnaire that was applied to manufacturing firms in Brazil. This work’s statistical method includes confirmatory factor analysis and structural equation modeling. Our results indicate that a package of resilience capabilities - collaboration, flexibility, visibility, and analytical orientation - positively affect supply chain resilience. Improving such capabilities, therefore, will allow supply chains to recover better from disruptions. It was also discovered, however, that supply chains do not recover from disruptions by way of supply chain risk management alone. Mutual impacts also exist between the group of resilience capabilities and supply chain risk management.

The contemporary state of big data analytics and artificial intelligence towards intelligent supply chain risk management: a comprehensive review

PurposeThis paper aims to conduct a systematic literature review of the research in the field of Artificial Intelligence (AI) and Big Data Analytics (BDA) in Supply Chain Risk Management (SCRM). Finally, future research directions in this field have been suggested.Design/methodology/approachThe papers were searched using a set of keywords in the SCOPUS database. These papers were filtered using the Title abstract keywords principle. Further, more papers were found using the forward-backward referencing method. The finalized papers were then classified into eight categories.FindingsThe previous papers in AI and BDA in SCRM were studied. These papers emphasized various modelling and application techniques for AI and BDA in making the supply chain (SC) more resilient. It was found that more research has been done into conceptual modelling rather than real-life applications. It was seen that the use of AI-based techniques and structural equation modelling was prominent.Practical implicationsAI and BDA help build the risk profile, which will guide the decision-makers and risk managers make their decisions quickly and more effectively, reducing the risks on the SC and making it resilient. Other than this, they can predict the risks in disasters, epidemics and any further disruption. They also help select the suppliers and location of the various elements of the SC to reduce the lead times.Originality/valueThe paper suggests various future research directions that fellow researchers can explore. None of the previous research examined the role of BDA and AI in SCRM.

Application of multi-attribute decision-making methods based on normal random variables in supply chain risk management

For the multi-criteria group decision-making problem where the criterion value is a normal interval number and the weight information is incomplete, the normal interval number and its compromise expected value, compromise mean square error, algorithm, weighted arithmetic average of normal interval number (ININWAA) Operator, the ordered weighted average (ININOWA) operator of normal interval numbers and the mixed weighted average (ININHA) operator of normal interval numbers, and a multi-criteria group with incomplete information based on normal interval numbers is proposed. Decision-making methods. This method uses ININWAA operator and INNHA operator to integrate criterion values, uses the compromise mean square error of criterion values, establishes an optimisation model to solve the optimal criterion weights and uses the expectation variance criterion to determine the order of the schemes. The case analysis shows the effectiveness and feasibility of this method.