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Dashboard, reports, and KPIs delivered in silos can only tell one side of the story and miss on integrating other areas of the process. In managing third-party risks, simplifying complex and significant volumes of information is critical to support nimble and resilient teams and companies. Solutions embedded with AI and machine learning capabilities (which are predictive in nature) not only automate and optimize processes, but also deliver more accurate models for predicting disruptive events that can affect the company’s supplier base.
There are many companies specialized in providing digital platforms and portals for risk management. They deliver huge volumes of data using methods such as web crawling, web scraping, and machine learning, all of which enable up-to-date, curated, and relevant information across different risk factors, such as those in fraud, cybersecurity, environmental, social, and governance (ESG), and food safety and quality assurance (FSQA).
Risk can be defined in different ways and levels, depending on the industry, so vendors don’t necessarily offer a one-size-fits-all solution. Instead, there’s strong focus on the kind of data that the vendor provides and how it can be easily integrated. In many cases, these solutions are built using APIs that interface data across tools and through repositories.
There are other considerations when defining the data strategy for risk management. A holistic approach includes removing costly system maintenance and ensuring there is no duplication in effort or data sources, unnecessary tools, and expensive licensing. Change management, which covers ongoing training, maintenance, and support, is also a key component, as this approach relies on adaptable and dynamic digital processes to support business users.
Best-of-breed technology for third-party risk management
An integrated, best-of-breed model in procurement analytics solutions for managing third-party risks can provide these advantages:
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An end-to-end integrated data strategy designed to maximize value, where systems and databases are integrated and where data flows efficiently through a multisource architecture, which enables more accurate and valuable insights
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Scalability through a pilot-testing approach — before scaling up a system, it can be debugged and improved to cater to unique customer needs
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Fuller transparency and integration in data flow, which reduce challenges in governance and increase confidence in both the data and the decisions made
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Compounded benefits of integrated AI-powered solutions, which use APIs for data integration
While this best-of-breed approach provides many advantages, companies need to evaluate their environment and consider the caveats and drawbacks depending on the business’s unique dynamics and situation:
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Procurement Analytics Solution |
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Cons |
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Single Suite |
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Best of Breed |
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A comparison of the pros and cons of using a single suite or best-of-breed solution for procurement analytics
Complementing best-of-breed solutions with reliable data
Data accuracy cannot be achieved if there is no holistic view and approach on data flow. Likewise, the benefits of best-of-breed solutions would be difficult to fully realize if there is no intentional design to integrate data that will effectively support the procurement function.
As shown in the earlier table, best-of-breed solutions offer modularity, flexibility, agility and innovation, faster ROI, and specialization. However, they can pose challenges if the company has no clear plan for data governance, data connectivity, standardization of processes, and a central and efficient repository of data that can be analyzed and accessed.
When there is no connectivity and integration, companies might fail to see the potential impact that risks pose even if they have the accurate information to evaluate the supplier’s risk. This considerably slows down the company’s ability to respond to alerts and prescribe actions downstream to stop any potential repercussions once a risk or issue is identified.
This is seen in many cases where quality issues are identified but the appropriate alerts don’t go to the responsible managers in a timely manner. Another example is when a supplier might default on important contract terms, and as such, delivery targets and penalties need to be applied where appropriate, including delaying payments. These highlight how risks can be avoided if data is effectively related across the appropriate processes upstream and downstream.
Companies can capitalize on partners with the expertise to implement a strategy of deriving insights from an integrated best-of-breed model. Best-of-breed technologies bring depth and sophistication in specific areas in procurement where volumes of data are generated and are specific in area and scope. However, they can pose challenges when an end-to-end approach is required, like in risk management. The ultimate goal is to use the capabilities of these tools to their full potential while creating a seamless analytics hub that offers a single, centralized place that is easy to manage, maintain, and update.
Managing third-party risks with procurement analytics
Organizations that embed supplier or third-party risk management in their sourcing and procurement gain fuller visibility into risks that can affect their business. Combined with analytics solutions, they can better analyze their performance as well as anticipate and mitigate risks faster and smarter. The abundance of data and today’s technologies provide the capabilities to automate these, but procurement leaders and decision-makers need to look at the unique dynamics of their organization so they can better gauge which solutions best address their needs. They can start by:
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Defining the business's requirements by outlining the use cases for risk management, such as risk profiles relevant to the organization and the problems that need to be solved.
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Narrowing down the business and technical capabilities or technologies needed for the use cases, which includes listing down their pros and cons.
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Developing a road map and implementing measurable success factors for integrating the solution.
Lingaro Group’s supply chain analytics practice provides data engineering and advanced analytics services for logistics, warehousing, and transportation that enable enterprises to drive cost efficiencies and enhance service levels and operational efficiency. Lingaro helps organizations use data to gain end-to-end visibility in their supply chain that, in turn, enables them to accurately forecast demand, enhance resilience and responsiveness, maximize inventories, optimize resource allocation, streamline routes, minimize waste, and generate savings.
Download our white paper, “Procurement Analytics: Forging Business Resilience Through Supplier Risk Management and Revenue Protection,” to learn more about a holistic, end-to-end approach to third-party risk management.
