9.2 Use of Technology in Risk Management
Integrating advanced technologies, particularly the Internet of Things (IoT), has revolutionized supply chain and logistics risk management practices. This technological evolution has enabled organizations to transition from reactive to proactive risk management strategies, fundamentally changing how risks are identified, monitored, and mitigated.
Tracking and Monitoring: The IoT Revolution
The Internet of Things has emerged as a game-changer in risk management, offering unprecedented capabilities in tracking and monitoring across the entire supply chain. IoT devices, including sensors, GPS trackers, and smart tags, create a vast network of interconnected objects that continuously collect and transmit data (SmartMakers, 2023).
Sensors
IoT sensors play a crucial role in monitoring various parameters critical to supply chain integrity:
- Environmental Sensors: These devices track temperature, humidity, and light exposure, crucial for perishable goods and sensitive materials. For instance, in pharmaceutical supply chains, temperature-sensitive medications can be monitored in real-time to ensure they remain within safe temperature ranges throughout transportation and storage.
- Pressure and Impact Sensors: These are vital for detecting potential damage to goods during transit. They can alert managers to mishandling or accidents, allowing for immediate intervention.
- Chemical Sensors: In industries dealing with hazardous materials, these sensors can detect leaks or contamination, preventing environmental hazards and ensuring worker safety.
GPS Tracking
GPS technology integrated with IoT provides real-time location data of goods and vehicles:
- Route Optimization: By tracking the movement of delivery vehicles, companies can optimize routes, reducing fuel consumption and delivery times.
- Theft Prevention: Continuous tracking helps in quick detection and recovery of stolen goods.
- Delay Prediction: Real-time location data allows for accurate prediction of delays, enabling proactive communication with customers and stakeholders.
Smart Tags and RFID
Radio-Frequency Identification (RFID) and smart tags offer detailed tracking at the item level:
- Inventory Management: These technologies enable accurate, real-time inventory tracking, reducing the risk of stockouts or overstock situations.
- Counterfeit Detection: In industries prone to counterfeiting, such as luxury goods or pharmaceuticals, smart tags can verify product authenticity throughout the supply chain.
The vast amount of data collected through IoT devices serves as the foundation for optimizing supply chain operations and enhancing risk management strategies (SmartMakers, 2023).
Optimization: Supply chain data collected from various points can drive optimization by providing insights into operational efficiencies, potential risks and areas for improvement.
Automation
Automation is achieving impressive results with minimal human effort. Using technology, programs, robots, or even pre-defined processes to get things done with little to no human intervention is automation.
This powerful tool is rapidly becoming a fixture in our modern world. From running businesses more smoothly to creating conveniences in our homes, automation’s applications are vast. In the business world, automation takes on many forms. It can streamline everyday tasks (business process automation), manage IT operations with artificial intelligence (AIOps), or even automate entire workflows (enterprise automation).
But its reach extends far beyond the office. Factories use robots powered by automation in car manufacturing, while smart home devices bring automation to our living rooms. From finance and healthcare to utilities and defence, there’s hardly an industry untouched by automation’s magic. It can be applied to all aspects of a business, giving organizations that leverage it effectively a significant edge over the competition.
Increased productivity and profits, improved customer service, reduced costs and errors, smoother compliance, and optimized efficiency are just a few benefits. Automation is a key ingredient in the recipe for digital transformation and a powerful ally for businesses looking to scale new heights.
There are different levels of automation to tackle repetitive tasks and streamline workflows (McGrath & Jonker, 2023):
- Basic Automation: This level automates simple, routine tasks like sending invoices or onboarding new employees. It eliminates errors, speeds things up, and frees people for more strategic work.
- Process Automation: This tackles more complex, multi-step processes across different systems. It boosts productivity, helps identify bottlenecks, and even suggests solutions using pre-defined rules. Tools like workflow automation and business process management (BPM) fall under this category.
- Intelligent Automation: This is the ultimate power-up, combining AI with other automation tools. Virtual agents that answer customer questions or AI assistants that help employees. It streamlines decision-making, reduces costs, and creates a smoother experience for everyone.
Key Benefits of Automation in Risk Management
Automation tools and robotics technology are revolutionizing risk management practices by increasing efficiency, reducing human error, and enhancing overall risk mitigation strategies. Key benefits of automation in risk management include:
- Reduced Human Error: Automated systems perform tasks consistently, minimizing errors in critical processes such as data entry, compliance monitoring, and risk assessments.
- Real-time Risk Detection and Response: Automation enables continuous critical process monitoring, allowing instantaneous detection and response to potential risks.
- Enhanced Decision Support: AI-powered risk assessment tools can rapidly analyze complex datasets, supporting more informed decision-making.
- Improved Operational Efficiency: Automation streamlines risk management processes, freeing up human resources for more strategic tasks.
- Enhanced Safety in Hazardous Environments: Robotics and automated systems can operate in dangerous conditions, reducing human exposure to risks in industries like manufacturing, oil and gas, and nuclear power.
- Data Analysis and Reporting: Automated systems can generate insights, trends, and detailed reports, aiding in risk analysis and decision-making.
While automation offers significant benefits, organizations must consider challenges such as potential over-reliance on technology, cybersecurity risks associated with connected systems, and the need for skilled personnel to manage these technologies (Valleskey, 2024; Wrobel, 2023).
Blockchain in Risk Management
Blockchain technology is emerging as a powerful tool for enhancing risk management practices across various industries. Its unique features offer several advantages in mitigating and managing risks:
- Immutability and Transparency: Blockchain’s immutable ledger provides an unalterable audit trail of all transactions, enhancing transparency and reducing the risk of fraud or data manipulation.
- Decentralization: The distributed nature of blockchain eliminates single points of failure, improving resilience against cyber attacks and system failures.
- Smart Contracts: Automated, self-executing contracts can reduce operational risks by minimizing human error and ensuring consistent execution of agreed-upon terms.
- Enhanced Data Security: Cryptographic algorithms used in blockchain technology significantly improve data integrity and security, reducing the risk of data breaches.
- Real-time Risk Monitoring: Blockchain enables real-time tracking and monitoring of transactions, allowing for quicker identification and response to potential risks.
- Improved Compliance: The transparent and immutable nature of blockchain can streamline regulatory compliance and auditing processes.
- Supply Chain Risk Management: In supply chains, blockchain can enhance traceability, reducing risks associated with counterfeiting, quality control, and provenance (Deloitte, 2017; Edwards, 2023).
Video: “How will blockchain be used in supply chain logistics ? | Zmodal” by Zmodal [3:06] is licensed under the Standard YouTube License. Transcript and closed captions available on YouTube.
As blockchain technology continues to evolve, its application in risk management is likely to expand. Organizations adopting blockchain for risk management should develop comprehensive strategies that address both the opportunities and challenges presented by this innovative technology.
AI and Machine Learning in Risk Management
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing risk management practices across industries. These technologies offer powerful capabilities to enhance risk identification, assessment, and mitigation strategies.
Key Applications
- Predictive Analytics: AI algorithms can analyze vast amounts of historical and real-time data to predict potential risks and their likelihood of occurrence.
- Fraud Detection: ML models can identify unusual patterns and anomalies in transactions, helping to detect and prevent fraudulent activities.
- Credit Risk Assessment: AI-powered systems can evaluate creditworthiness more accurately by analyzing diverse data points beyond traditional credit scores.
- Market Risk Management: ML algorithms can process market data in real-time, enabling quicker responses to market volatility and potential risks.
- Operational Risk: AI can monitor internal processes, identifying inefficiencies and potential points of failure.
- Compliance and Regulatory Risk: Natural Language Processing (NLP) can assist in monitoring regulatory changes and ensuring compliance.
Benefits
- Enhanced accuracy in risk assessment
- Real-time risk monitoring and alerts
- Improved efficiency in risk management processes
- Better decision-making through data-driven insights (Resolver, 2023; Analyst Prep, 2023)
As AI and ML technologies continue to evolve, their role in risk management is expected to grow, offering more sophisticated and effective tools for managing complex risk landscapes.
Video: “AI Risk Management” by Now Next Later AI — AI Strategy & Transformation [5:20] is licensed under the Standard YouTube License.Transcript and closed captions available on YouTube.
In Supply Chain and Operations Management, AI and machine learning algorithms analyze massive datasets to find the most efficient routes, predict disruptions, and assess environmental impact in transportation. Simulation software complements this by modelling scenarios, aiding risk prediction and contingency planning (McGrath & Jonker, 2023).
