Advanced Approaches to Risk Management in Modern Supply Chain Networks

Globalization and the interconnectedness of our economy have made supply chain networks more susceptible to a wider range of risks than ever before, all of which can disrupt operations, undermine business continuity, and put financial stability in jeopardy. These can be anything from traditional threats, such as a lack of supplies or delays in transportation, to more contemporary problems like cyberattacks and political instability (both domestic and foreign). Conventional supply chain risk management methods — which are built on recognizing and counteracting risks that companies face at an organizational level — will not be sufficient for the growing required adaptability of modern, complex supply chains. In this paper, we discuss several more advanced methods for risk totaling, including a proactive approach in identifying risks and modeling only those specific scenarios focused on a supply chain network, data analytics used to identify worst-case supply chain scenarios, and, finally, collaborative risk management approaches. New technologies like Blockchain, Artificial Intelligence (AI), and Internet of Things (IoT) are explored that could be used to optimize the supply chain resilience and scope for real-time risk factor monitoring. The challenges and opportunities of these technologies to reduce risks across the supply chain, from raw material acquisition to the final delivery of products, are also elaborated in several parts of this paper. This paper offers a novel approach to contemporary supply chain risk management, drawing on best practices, case studies, and theoretical frameworks to provide guidance on how organizations can optimize their risk management processes in order to maintain continuity of operations amid both dynamic and evolving risks. It also assesses where AI, IoT, and blockchain can play into existing or innovative risk management strategies, allowing companies to become more nimble to disruptable events. In conclusion, it illustrates that an integrated approach to risk management through technology provides organizations the opportunity to take a proactive stance while maintaining organizational agility under uncertainty and competition — improving supply chain efficiency and customer service. Keywords Supply Chain Risk Management, Risk Mitigation, Supply Chain Resilience, Blockchain, Artificial Intelligence, Internet of Things, Proactive Risk Identification,

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2024-12-10
DOI
https://doi.org/10.55041/ijsrem16697
Primary Topic
Supply Chain Resilience and Risk Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Advanced Approaches to Risk Management in Modern Supply Chain Networks

Faiz Mohiuddin Mulla
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Supply Chain Resilience and Risk Management
article

Advanced Approaches to Risk Management in Modern Supply Chain Networks

Faiz Mohiuddin Mulla
article en

Abstract

Globalization and the interconnectedness of our economy have made supply chain networks more susceptible to a wider range of risks than ever before, all of which can disrupt operations, undermine business continuity, and put financial stability in jeopardy. These can be anything from traditional threats, such as a lack of supplies or delays in transportation, to more contemporary problems like cyberattacks and political instability (both domestic and foreign). Conventional supply chain risk management methods — which are built on recognizing and counteracting risks that companies face at an organizational level — will not be sufficient for the growing required adaptability of modern, complex supply chains. In this paper, we discuss several more advanced methods for risk totaling, including a proactive approach in identifying risks and modeling only those specific scenarios focused on a supply chain network, data analytics used to identify worst-case supply chain scenarios, and, finally, collaborative risk management approaches. New technologies like Blockchain, Artificial Intelligence (AI), and Internet of Things (IoT) are explored that could be used to optimize the supply chain resilience and scope for real-time risk factor monitoring. The challenges and opportunities of these technologies to reduce risks across the supply chain, from raw material acquisition to the final delivery of products, are also elaborated in several parts of this paper. This paper offers a novel approach to contemporary supply chain risk management, drawing on best practices, case studies, and theoretical frameworks to provide guidance on how organizations can optimize their risk management processes in order to maintain continuity of operations amid both dynamic and evolving risks. It also assesses where AI, IoT, and blockchain can play into existing or innovative risk management strategies, allowing companies to become more nimble to disruptable events. In conclusion, it illustrates that an integrated approach to risk management through technology provides organizations the opportunity to take a proactive stance while maintaining organizational agility under uncertainty and competition — improving supply chain efficiency and customer service. Keywords Supply Chain Risk Management, Risk Mitigation, Supply Chain Resilience, Blockchain, Artificial Intelligence, Internet of Things, Proactive Risk Identification,

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 08(12)
Openalex Percentile: Top 60%
Supply Chain Resilience and Risk Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.