For decades, dimensional engineering has focused on predicting variation before production begins. Advanced tolerance analysis tools have enabled engineers to evaluate design robustness, optimize tolerances, and reduce manufacturing risk long before the first part is produced. While this predictive capability remains essential, one critical question has remained largely unanswered:
What happens when production reality deviates from the prediction?
No manufacturing process operates perfectly at nominal. Supplier variation, material inconsistencies, tooling wear, fixture movement, environmental conditions, and normal process drift continuously influence assembly quality. When these variations accumulate, manufacturers are often forced into a reactive cycle of troubleshooting, fixture adjustments, repeated builds, and engineering investigations. The result is lost production time, increased cost, and unnecessary quality risk.
The Manufacturing Challenge
Every manufacturing engineer has experienced the same situation.
An assembly that performed well during launch gradually begins to drift. Gap and flush conditions change. Critical functional dimensions move out of tolerance. Measurements no longer match engineering intent.
The traditional response is familiar: review measurement reports, analyze multiple dimensions independently, adjust one fixture, rebuild the assembly, measure again; and repeat until acceptable. This process relies heavily on experience and trial-and-error because assembly dimensions are highly interconnected. A small movement in one locator can influence numerous measurements throughout the assembly. Improving one dimension may unintentionally worsen another.
Without understanding these interactions, determining the correct adjustment becomes both time-consuming and uncertain.
Moving Beyond Prediction
Traditional dimensional analysis software has long provided engineers with the ability to answer questions. Will this design meet requirements? Which tolerances contribute most to variation? Is the assembly robust enough for production?
These are predictive questions. But once production begins, manufacturers face an entirely different challenge: Which fixture should be adjusted? How much should it move? What is the minimum number of changes required? And what will the dimensional result be before making physical changes?
These are corrective questions. And we need a new approach to answer them.
A New Approach to Root Cause Resolution
Traditional troubleshooting often focuses on identifying the source of variation. And we now need to focus on something even more valuable: Determining the fastest path back to nominal performance.
Rather than asking “What went wrong?”, manufacturers need now to be able to ask, “What is the minimum action required to restore performance?” This distinction dramatically reduces engineering effort while improving production responsiveness.
A simple answer: Drive to Nominal
Drive to Nominal is an intelligent optimization capability within 3DCS that uses actual production measurement data together with existing dimensional variation models to calculate the optimal fixture and locator adjustments required to restore an assembly toward its target nominal condition.
Instead of relying on engineering intuition or repeated experimentation, Drive to Nominal mathematically determines which locators have the greatest influence on assembly performance, the minimum number of fixture adjustments required, the precise amount each locator should move and the predicted dimensional improvement before changes are implemented
Unlike traditional optimization methods that require new analysis models, Drive to Nominal works directly from an organization’s existing 3DCS dimensional variation model and transforms dimensional engineering from a predictive discipline into a continuous operational capability, enabling manufacturers to respond faster, optimize smarter, and maintain production excellence throughout the life of a program.
Key benefits for your organization
- Faster Production Recovery: Reduce troubleshooting time by providing immediate, data-driven corrective recommendations.
- Reduced Trial-and-Error: Eliminate multiple fixture adjustment iterations by predicting optimal corrections before implementation.
- Improved Assembly Quality: Maintain dimensional targets despite changing manufacturing conditions.
- Reduced Downtime: Restore production stability more quickly, minimizing disruptions to manufacturing schedules.
- Lower Manufacturing Cost: Reduce engineering labor, scrap, rework, and repeated build validation.
- Continuous Process Stability: Proactively compensate for gradual process drift before dimensional issues become customer concerns.
Instead of ending when production begins, the dimensional variation model continues to provide value throughout the manufacturing lifecycle—guiding corrective actions, reducing process variation, and maintaining product quality.