Analyst Insight: The Gartner® Market Guide for IT Vendor Risk Management Solutions
Machine learning (ML) is defined as “the capability of a machine to imitate intelligent human behavior.” This artificial intelligence (AI) technology is often embedded in reporting and analytics tools to make sense of large amounts of seemingly disconnected data. ML can have a significant impact on vendor risk management and supplier risk management as organizations look to better predict and mitigate potential cyber and physical risks.
This post describes seven ways to use ML in your third-party risk management (TPRM) program, and it examines common types of data to include in your analysis.
ML analytics are transforming the way companies manage their vendors and supply chains. These technologies enable companies to better understand supply chain risks and opportunities, make more informed decisions, and respond quickly to potential disruptions. Below are some of the more well-known uses of ML and AI within supplier risk management.
Machine learning algorithms can analyze large volumes of data to identify patterns and anomalies that may indicate potential supply chain risks. For example, you can use ML algorithms to detect anomalies in network traffic, Dark Web chatter, and paste site activity to examine unusual patterns of data transfer, exposed data, or unauthorized access attempts. This can help you identify potential threats associated with vendors or suppliers who provide critical products and services to your company or have access to its systems.
Machine learning algorithms can be used to predict future risks in the supply chain. For example, you can use predictive analytics to analyze supply chain data for potential risks, such as disruptions from natural disasters or political instability. Collecting, collating and analyzing a rich set of third-party data can help your company proactively predict and manage risks and take action to mitigate the impact of any disruptions. This data can include physical locations of providers, manufacturing location, geo-political events, shipping patterns, weather patterns, cyber events and more.
Machine learning algorithms can help your company make more informed decisions about its vendor management and supply chain strategies. For example, you can use ML algorithms to analyze a wide range of data sources to identify potential ESG risks associated with third-party vendors or suppliers. By analyzing news articles and social media posts, ML can identify suppliers with poor environmental records or those that have been involved in controversial social issues, which can influence your company's decisions to contract with those suppliers.
Machine learning automate many of the tasks involved in vendor management and supply chain management. For example, you can use ML algorithms to automate supplier onboarding, supplier qualification, and third-party risk assessments.
Your supply chain specialists can use ML and AI to predict demand for specific products or services, enabling them to adjust inventory levels and production schedules accordingly.
Your operations teams can use historical analysis and ML to predict equipment failures or maintenance requirements, enabling supply chain managers to address issues before they become major problems.
You can use machine learning algorithms to identify suspicious patterns or anomalies in financial data, enabling procurement teams to detect and prevent fraud.
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Machine learning and AI can enable you company to identify potential risks from third-party vendors and suppliers more quickly and accurately than traditional manual methods, which can help to minimize the impact of any negative events on your business.
Start by creating a unified third-party intelligence repository including the events, observations, and measurements that can be used to train a machine learning model. The quality and quantity of data available for training and testing play a significant role in determining the performance and accuracy of a machine learning model. Include internal and external data such as:
Understanding the digital footprint and related cyber exposures of critical third parties can predict risks that could have a direct impact on your company’s operational resilience.
The geographic location of a supplier or manufacturing facility is a critical factor to consider. Regions prone to natural disasters such as earthquakes, hurricanes or floods are more likely to experience supply chain disruptions. Similarly, areas with political instability, civil unrest or trade restrictions can also disrupt supply chains.
Relying on a single supplier or manufacturing facility can increase the risk of supply chain disruptions due to natural disasters or political instability. Diversifying suppliers across different geographic regions can help mitigate these concentration risks.
Natural disasters or political instability can also affect transportation infrastructure, such as roads, bridges and ports. This can cause delays or even prevent the movement of goods.
M&A activity, business news, negative news, regulatory and legal filings, financial performance and operational updates can signal changes in a supplier’s strategy, which can impact your company’s ability to deliver to its customers.
Suppliers appearing on sanctions and enforcement lists (e.g., OFAC, SEC, etc.) or those that employ politically exposed persons (PEPs) can signal business or legal problems that can disrupt their ability to meet commitments.
Analyzing large volumes of data to try and spot trends is impossible with manual data collection and analysis methods. Third-party risk management platforms include built-in ML analytics capabilities to greatly simplify the process and expose potential risks before they impact the business.
To better understand how to take advantage of ML analytics in your third-party risk management program, request a demo today.
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