Beyond Application Integration: Common Data Models Evolve into Inference Data Models

Manufacturers must balance customer expectations, resource constraints, and profitability, while at the same time reacting to disruptions across global supply chains. This isn’t easy to accomplish without a single version of the truth or SVOT.

System architectures have evolved beyond data integration to solve this challenge using a Common Data Model (CDM). While this solved the Single-Version-of-Truth (SVOT) challenge, it didn’t deliver on the potential value, which is improved decision-making and business outcomes. Recent advancements have delivered on the evolution of the common data model into an inference data model, which delivers on the potential business value of an SVOT across both manufacturing functions as well as their supply chain networks.

What is a Common Data Model

Common Data Models (CDM) unify data from different systems into a single source of truth. But while Common Data Models help align data across departments, they can only go so far. True agility and predictive accuracy require more than shared data; they require intelligence.

Its primary focus is consistency, quality, and exchange data/share functionality. Basically, ensuring that different business applications, such as sales, manufacturing, and supply chain planning, all speak the same data language.

For example, when there is a new sales order, a manufacturer using a Common Data Model ensures that the same order details are visible to planning, manufacturing, and scheduling teams without reformatting or translation.

Manufacturers that use a Common Data Model across their Industry 4.0 systems can have their demand planning generate forecast orders, which become sales or firm orders, and subsequently planned or scheduled orders. The Manufacturing Execution System (MES) then creates manufacturing orders, and the scheduling system optimizes the dependencies between sales and production.

This integrated approach means that execution issues no longer derail plans. Instead, the system dynamically aligns planning with real-time execution, minimizing delays and maximizing profitability.

With a strong Common Data Model, manufacturers can achieve:

• Shorter order lead times
• The ability to scale mass customization efficiently
• More accurate forecasting and demand responsiveness
• Higher margins and improved customer satisfaction

But as powerful as a Common Data Model is, it’s still limited to organizing data, not interpreting it. This is where a manufacturer might look beyond CDM and look at an Inference Data Model (IDM).

What is the Inference Data Model

An Inference Data Model (IDM) is built for the next generation of intelligent manufacturing.

It provides the ability to leverage real-time data across all systems and deliver a mathematically optimized set of decisions across both demand and supply. These decisions can be either autonomous or interactive with users, depending on the decision-making guardrails that have been established. The mathematics includes both traditional techniques like Mixed-Integer Linear Programming (MILP) and Multi-Entity Input-Output modeling (MEIO), along with newer advancements across the AI spectrum. From an AI perspective, this means that systems can now deliver on decision support as well as decision execution in real time.

An inference data model approach is the only viable way to deliver on adaptive manufacturing.

Ultimately, an Inference Data Model combines the structure of a Common Data Model with the intelligence of AI-driven agent-based reasoning, ultimately allowing manufacturers’ predictions, optimizations, and responses to be significantly more accurate and actionable than those relying solely on Common Data Models.

Let’s look at it this way: a global medical device manufacturer receiving an order for a highly customized surgical tool. Now, the sales system records the order attributes such as model type, precision level, and material specification, while the production system manages the Manufacturing Bill of Materials (MBOM) required to make it.

Now, during production, a supplier delay pushes delivery beyond the promised deadline. If the manufacturer had a traditional Common Data Model set up, this issue would be caught too late, requiring manual intervention, rescheduling, and costly overtime.

But, with an inference data model, the system instantly correlates supplier delays, available resources, and production line capacity. It then infers the most efficient solution, automatically reallocating materials, adjusting schedules, and updating delivery forecasts, before the issue is even noticed by a manufacturer.

The outcome is simple: on-time delivery, reduced costs, and improved customer trust.

The Difference Between the Two

A Common Data Model puts all data in one place for consistency, whereas an Inference Data Model pulls relevant data from many sources, identifies hidden patterns, and draws new conclusions that didn’t explicitly exist before. Basically, enabling smarter, faster decisions. Therefore, a Common Data Model focuses more on data structure and quality in comparison, whereas the Inference Model focuses on speed, performance, and contextual relevance for real-time AI predictions. Essentially, the Inference Data Model understands the data in an MES system, which is important to a Sales Inventory and Operations Planning (SIOP) or Advanced Planning and Scheduling (APS) system, and vice versa. The receiving systems have been programmed with mathematical optimization that then leverages that enhanced inference data representation to deliver on improved decision making and execution in production, as well as across the supply chain network.

How Eyelit Technologies’ Inference Data Model Works

Eyelit’s AI infrastructure integrates both a Common Data Model and an Inference Data Model, giving manufacturers the best of both worlds: structure and intelligence.

Here’s how it works:

• It synchronizes data across demand and supply, ensuring a shared, unified data foundation.
• It translates attribute-based demand data (like customer preferences or product configurations) into manufacturing MBOM equivalents and vice versa in real time.
• It identifies high-value use cases that close the gap between planning and execution
• It models the data representations across both planning and execution applications
• It applies Agentic AI to govern the workflow across systems in real time, leveraging the enhanced data representation required for improved decision-making
• It adapts plans, schedules, and execution based on mathematical optimization
• It consistently updates plans and schedules using decision-making algorithms that improve operational efficiency and responsiveness. These updates are continuous and incremental, understanding the balance of improvements vs. manufacturing disruption
• It allows manufacturers to gain AI-driven insights without replacing their existing infrastructure.

The result is a system that doesn’t just connect data. It infers meaning, adapts instantly, and acts intelligently, essentially delivering on Adaptive Manufacturing

While a Common Data Model brings order to the chaos, an Inference Data Model transforms that order into actionable intelligence. By connecting data, interpreting it, and inferring new insights, we help manufacturers not just keep up with Industry 4.0, but lead it.

It’s not about collecting more data. It’s about connecting, learning, and smarter manufacturing than the rest.