Solving the Procurement Paradox With AI-Driven Vendor Risk Analysis

There’s no way to escape the fact that deliveries get delayed and sometimes never arrive at all. For consumers, that can be a headache. For directors of supply chain operations and supply chain managers in consumer packaged goods (CPG) and discrete manufacturing, who juggle thousands of components for hundreds of products, those inbound delays are a daily struggle. Worse, they come with substantial revenue consequences and negatively impact their service performance KPIs.

The sheer volume of delivery delays will always outstrip your ability to find replacements for even specific components. The key is understanding which delays are the most critical so you can act accordingly given limited resources. Yet even with so much on the line, many supply chain managers still rely on Excel, legacy ERP systems, and subjective judgements for managing risk. These outdated and manual processes cost them valuable time and ultimately result in actions misaligned to actual priorities.

You can make better risk mitigation decisions faster with the right set of technologies. Using an AI algorithm trained on data automatically ingested from your ERP systems, you can process multiple risk factors, determine your priorities, and support data-backed decision-making.

The need for speed in vendor risk mitigation

Guaranteeing on-time, in-full (OTIF) delivery is the most difficult when orders need to be rapidly filled. While particularly true for CPG and discrete manufacturers, any producer with tight turnarounds and highly variable production plans feels the pressure. With facilities running around the clock to serve shifting demand, scheduling production runs requires constant adjustments — a challenge even when all components are always available.

The imperative to mitigate supply chain risks has always been there. But the advent of supply chain disruption-as-a-rule and the heightened scheduling pressure of just-in-time production strategies has heightened the intensity. As the scale of complexity and variability facing supply chain managers has increased, so has the frequency of delivery delays impeding OTIF contract fulfillment — resulting in serious revenue losses.

The reality is that supply chain managers can never mitigate every inbound supply risk. They must quickly triage the risks by concentrating on the components that align with their goals — whether that’s maximizing OTIF delivery, minimizing revenue losses, prioritizing key clients, or supporting promotional activity.

Contending with variability and complexity in risk assessments

Assuming there will always be more disruptions than you can combat, vendor risk management requires proper prioritization and smarter resource allocation decisions. That’s why it’s critical to understand the factors that influence the degree of risk associated with a given inbound delay:

• Importance for production plan: Not all components are equally critical — some only affect one product, others multiple product lines.
• Impact on contracts: The revenue impact of failing to provide OTIF will vary from contract to contract.
• Availability of alternatives: Certain components can only be provided by one vendor, while others are multiply sourceable.
• Vendor reliability: Risk increases exponentially when sourcing components from vendors with historically poor fulfillment records.

Accounting for intuition and client relations

Quickly understanding these multiple factors is not the only challenge facing procurement officers. Fulfillment timeframes have become so abbreviated that producers must rely on advanced demand forecasting to anticipate their next purchase orders. That introduces even more uncertainty into the risk equation and increases their margin of error.

Material supply managers attempt to counteract this uncertainty by relying on their own intuition, experience, and subjective judgement to evaluate and mitigate vendor risk. Within that calculation, their main consideration will always be service. However, any calculations must include future revenue, and that factor is affected by the impact a delay has on a strategic partnership and the importance of a given market.

While it is entirely correct to consider strategic partnerships and market impacts when evaluating risk, it also lacks transparency. These factors are often siloed in other divisions or only exist in the minds of experienced operators, making them subjective and difficult to quantify. Without formalizing these considerations as part of the risk assessment, supply chain managers lack clarity about their own priorities.

Driving superior risk prioritization with AI

So, supply chain managers must quickly make risk mitigation decisions to minimize losses. At the same time, they lack the technology to quickly calculate the myriad factors necessary for their risk evaluations. Moreover, they must rely on their own experience and subjective judgements to factor in client relationships and market impact. All these elements make the accuracy of their final decision impossible to verify.

How can you support your supply chain managers to quickly evaluate and prioritize risk in a transparent manner and make better decisions on mitigation tactics? By using AI algorithms trained on data ingested from your ERP systems and external data platforms, you can predict where delays will occur and create a scoring system to understand their production impact.

This platform allows you to formalize and automate the entire risk evaluation process in a fraction of the time. Additional benefits include:

• Minimizing revenue losses by understanding a component’s revenue impact based on its use in multiple product lines
• Formalizing subjective evaluations by quantifying client relationships and market importance and weighting its revenue impact
• Accounting for future outcomes by accurately estimating additional delays based on vendor reliability and alternative sources

Boost vendor risk mitigation agility and revenue

How do we know that AI improves risk mitigation decision-making? SoftServe built just such a platform for a discrete manufacturing client, who then saw a 25% increase in service performance for specific product lines. With the increased reliability of their supply chain, our client was able to reduce their inventory costs by 15%.

The degree of improvement that you will see depends on the current state of your operations. Our experts estimate that most CPG and discrete manufacturing companies can expect service performance improvements between 7% to 25% depending on their product line and production plan.

Supply chain managers need swift and transparent risk evaluation methods. AI algorithms, powered by data from ERP systems and external platforms provide a solution. Predicting delays and scoring their impact empowers directors of supply chain operations to make informed decisions while enhancing transparency and boosting agility.

No matter the size of your operation, this could have a major impact on your top-line revenue figures. When you are ready to boost your OTIF fulfillment with robust risk mitigation.