Manufacturers today are under pressure from customers to deliver ever-increasing product customization. This requires discrete manufacturers to manage multiple configurations. Each new feature, option or market variant multiplies the number of possible configurations. That can quickly reach millions or even trillions of combinations for even a simple customizable product.
Discrete manufacturers use different systems for working with customizable products. Engineers use product lifecycle management (PLM) to design products, while sales teams employ configure, price, quote (CPQ) solutions to price and order products. Manufacturing, on the other hand, uses enterprise resource planning (ERP) to produce and deliver products. Because these systems often evolve independently, misalignments inevitably creep in. Sales may promise a product combination that manufacturing can't deliver, or engineering may introduce options that aren't visible to sales.
Organisations need a reliable way to verify these configurations, or they risk selling products that can't be built or building products that don't meet specifications or what was promised to the customer. Verification – the process of ensuring that configuration data and rules are correct, complete and consistent across systems – has emerged as the quality gate of the digital thread. It ensures that what's engineered, sold and produced are always aligned. Verification involves ensuring that configuration data and rules are accurate, complete and consistent across systems.
The importance of verification amid increasing complexity
Consider this: 10 configurable options with five choices each result in almost 10 million combinations. With 27 options, you get 7.5 quintillion possible combinations. It's the same as the total number of sand grains (or insects) on Earth. It would take decades to check all these combinations on the world's most powerful computer. Even if you used ALL computers in the world (including all cell phones, servers, embedded devices, etc.), it would still take several hours. This problem is known as a combinatorial explosion.
Manual testing of configurations is impossible at this scale, and even testing alone is insufficient. Testing samples just a few configurations manually. Traditional testing can reveal the presence of errors but never prove their absence.
Verification takes a different approach. It uses logic-based algorithms to mathematically analyze all possible configurations. It also provides proof that rules are logically consistent, that valid combinations aren't accidentally blocked, and that invalid ones can't slip through. Verification is akin to proofreading an entire document with software, rather than skimming a few pages. Automated verification gives manufacturers mathematical proof that their models behave as intended, i.e., that rules don't block valid combinations or allow invalid ones.
It operates across systems, verifying alignment between product lifecycle management (PLM), enterprise resource planning (ERP) and configure, price, quote (CPQ) models. Each has a different view of the product:
- PLM defines engineering intent.
- ERP governs how products are built.
- CPQ/CRM determines what can be sold.
Misalignment across these systems leads to costly errors. Sales teams promise configurations that manufacturing can't produce. Engineering models omit variants that the sales department expects to offer. Verification acts as the bridge, ensuring consistency between these models. For example, a wind turbine manufacturer used automated configuration validation to ensure sales and engineering models matched, preventing invalid orders before they reached production.
Consequences of skipping verification
There are four major impacts of unverified configuration models. One is operational disruptions; that is, discovering late that an ordered configuration can't be built. The second is increased costs. When configuration models go unverified, errors surface late and expensively, resulting in added costs due to rework, wasted materials or re-engineering cycles. For instance, one mistake could cost a manufacturer of complex machinery several hundred thousand dollars.
The third is customer dissatisfaction due to delayed deliveries or altered specs. And the fourth major impact is compliance and safety risks, especially in automotive, aerospace and energy sectors, where precision is non-negotiable.
Misalignments rarely stem from a single bad rule; they arise from interactions across multiple systems. Common hidden issues include:
- Blocked values, where legitimate options disappear due to contradicting constraints.
- Gaps in rules that allow combinations that engineering never intended.
- Model drift as updates in one system aren't reflected in another.
Each system misalignment compounds over time, eroding trust in configuration data and creating "invisible" errors that only surface downstream. Verification prevents these by catching inconsistencies early, before they hit the shop floor or the customer.
Making verification a core part of the configuration lifecycle
Verification shouldn't be a one-time step, or an after-the-fact check; it should be embedded in every phase:
- Model creation – Engineers verify rules as they build them, ensuring immediate feedback.
- Cross-system alignment – Automated validation confirms that PLM, ERP and CPQ models remain synchronized.
- Change management – Every rule or feature update triggers re-verification to prevent regression errors.
Verification should also be embedded in governance: product managers, solution architects, and data stewards share responsibility for ensuring model integrity. The goal is not just technical correctness but organizational confidence in configuration data.
Verification tools can automatically detect contradictions, unreachable configurations or version drift between systems, providing confidence before deployment. Verification is part of a broader quality assurance mindset, not just a technical exercise.
Verification as the Foundation of the Digital Thread
As model-based engineering and AI-driven automation reshape manufacturing, verification becomes indispensable. It transforms configuration management from a manual, error-prone activity into a provable, scalable discipline.
By verifying the alignment between PLM, ERP, and CPQ models, manufacturers can prevent costly rework and invalid orders; maintain trust in their product data; and strengthen the digital thread that connects design, sales and production.
Using verification, even products with more legal combinations than grains of sand on earth can be ensured for correctness in a few seconds, avoiding the combinatorial explosion, drastically reducing the risk of costly rework and recalls, and maintaining a good customer relationship.
