Using LLMs to Surface Supply Chain Risks Hidden in ERP and Operations Data

3 min read ● Silk Team

While many supply chain disruptions are visible through the use of dashboards and red flags, other disruptions lie hidden in the data found in ERP systems, operational logs, supplier notes, exception reports, and internal e-mail communication.

One of the biggest advantages that large language models have when identifying supply chain risks is their ability to read, connect and reason across both structured and unstructured data at scale. Instead of having analysts search out potential issues, large language models will present the potential risks in advance of the issue occurring.

The Main Problem: ERP Systems Track Data But Don’t Provide Contextual Information About The Data

Enterprise resource planning (ERP) and operations platforms are some of the best platforms available for tracking data. These platforms allow companies to track:

• Purchase orders and the lead times associated with those purchases
• Inventory balances and inventory movement
• Performance metrics for suppliers
• Production schedules and production exceptions

These platforms do a great job of collecting and storing data. However, they do not provide information about what the data means collectively.

For example:

• A supplier’s lead time has increased by a small amount each month for the last few months.
• Expedite fees associated with purchasing products from the supplier are increasing.
• Comments related to backorders have indicated that there are “capacity constraints”.

As a single piece of data, none of these pieces of data indicate a problem. However, as a collective body of data, these pieces of data may be indicative of a potential supply chain disruption. Traditional reporting methods may miss these signals.

Large language models are able to make sense of all of these individual pieces of data to find connections between them.

How LLMs Find Risk In Operational Data

Large language models operate at a different level than traditional analytical methods. Instead of relying on pre-defined rules or thresholds, large language models are able to:

• Interpret free-form natural language fields (e.g., notes, comments, emails).
• Identify patterns in data across time, entities and transactions.
• Connect structured ERP data with unstructured operational context.

This ability to identify relationships between disparate data points is key to large language models being able to identify weak signals — the first indication of a potential issue that would rarely be identified as such on its own.

Some Examples Of Types Of Supply Chain Risks That LLMs May Be Able To Identify

1. Supplier Reliability RisksLarge language models may be able to identify suppliers whose reliability is decreasing even though the supplier has historically been reliable and meets SLA requirements.
2. Inventory FragilityLarge language models may be able to combine data on demand variability, replenishment timing and exception logs to identify items that are likely to experience stockouts due to a single disruption event.
3. Operational BottlenecksLarge language models may be able to identify recurring patterns in data collected from production notes, maintenance records and scheduling overrides to identify system-wide capacity constraints.
4. Policy And Compliance GapsLarge language models may be able to compare actual operational behavior with internal policy or contract provisions to identify silent compliance gaps that create either financial or regulatory risk.

Turning Risk Detection Into Decision Ready Intelligence

The true value of large language models lies in the way they present the detected risks to users.

Instead of simply presenting raw alert notifications, large language model powered systems are capable of generating:

• Risk summaries written in plain English
• Ranked lists of the most at-risk suppliers, SKUs or lanes
• Explanatory narratives that describe why a particular risk exists
• Suggested mitigation strategies based upon historical experiences

For example:

Supplier X experienced a 23% increase in late deliveries over the last 90 days, with additional evidence of capacity constraints and increasing expedited costs. Recommend identifying and qualifying a secondary supplier within 30 days.

In doing so, buried data is transformed into actionable intelligence.

Why Large Language Models Are Particularly Well Suited For Analyzing ERP Data

Data stored in enterprise resource planning (ERP) systems is notoriously difficult to work with — dense schema structures, cryptic code names, and decades of accumulated history. Large language models are particularly well-suited for working with this type of data because they:

• Understand business languages, not just field names and table names.
• Reduce reliance on rigid data models.
• Allow organizations to ask questions regarding risk using natural language.

Increasingly, companies operating platforms such as SAP and Oracle are implementing large language model based intelligence atop their existing ERP systems to unlock new value without modifying their underlying workflows.

Implementation Considerations

Large language models are not intended to replace existing ERP systems; instead, they are designed to augment the capabilities of those systems. Organizations that wish to effectively implement large language model technologies are generally advised to:

• Obtain read-only access to their ERP and operations data.
• Focus on the highest priority areas of risk (typically, suppliers and critical SKUs).
• Conduct human-in-the-loop validation on the earliest recommendations provided by the large language model.

Governance is generally considered to be the primary challenge to implementation. This includes ensuring:

• Access to the required data.
• Traceability of the AI generated insights.
• Confidence in the accuracy of the insights generated by the large language model.

Conclusion

Supply chain disruptions rarely declare themselves loudly. Instead, they tend to develop incrementally, hidden across the operational and ERP data that cannot be reasonably analyzed at scale by human beings.

However, by providing the ability to discover risk early, to explain the risk clearly and to provide context for taking action, large language models enable companies to turn their ERP data into a pro-active risk detection engine.

Therefore, in today’s rapidly evolving supply chain environment, the competitive advantage does not belong to the company that possesses the greatest amount of data; rather, it belongs to the company that is able to recognize the risk that is embedded in the data.