AI for Parts & BOM Management
Value engineering cuts product cost by targeting function, not corners. Learn the method, where it stalls, and how AI makes it fast to run in 2026.
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8 min read
Michelle Ben-David
Michelle Ben-David is a mechanical engineer and Technion graduate. She served in an IDF elite technology and intelligence unit, where she developed multidisciplinary systems integrating mechanics, electronics, and advanced algorithms. Her engineering background spans robotics, medical devices, and automotive systems.
BOTTOM LINE
Value engineering is one of the few disciplines that attacks product cost on purpose, at the point where cost is actually decided. The method has not changed much since 1947, and it does not need to. What held it back was access to information: what parts already exist, what a feature really costs, what a past decision was based on, and which standards apply. AI removes that bottleneck by connecting to the data a team already owns and surfacing it in seconds. The result is that reuse before redesign and validation before proposal become routine instead of aspirational. Teams that make value engineering continuous, rather than an annual workshop, are the ones that turn cost reduction into a habit instead of a fire drill.
Most engineering teams do not have a cost problem so much as a visibility problem that turns into a cost problem. By the time a design reaches production, a large share of a product's total cost is already committed, locked in by the material choices, part counts, and tolerances that were set months earlier. Value engineering is the discipline built to attack that committed cost on purpose, rather than discovering it by accident during a late scramble to hit a target price. It asks one disciplined question about every part and feature: what function does this deliver, and is there a cheaper way to deliver the same function without hurting quality. The idea is old, the math behind it is simple, and yet most teams still run it too late and too rarely to capture the savings it can find. This guide covers what value engineering is, where it stalls in real teams, and how AI is making it faster to run in 2026.
What Value Engineering Actually Is
Value engineering is a structured method for improving the value of a product, where value is defined as function divided by cost. The goal is not to make a product cheaper by making it worse. The goal is to deliver the same function, or better function, at a lower cost. Lawrence Miles developed the approach at General Electric in the late 1940s during postwar material shortages, and the United States Department of Defense adopted it in the decades that followed. Today it is standard practice across automotive, aerospace, medical device, and industrial equipment programs.
Engineers often blur two related terms. Value analysis studies a product that already exists and looks for cost that can be removed without hurting performance. Value engineering applies the same thinking earlier, during design, before tooling and supplier commitments freeze the cost in place. The earlier the work happens, the cheaper the change. A material substitution proposed during concept design costs almost nothing to make. The same substitution after production tooling is cut can cost tens of thousands of dollars and weeks of schedule.
The most common levers are consistent across industries: material substitution, design simplification, part consolidation, tolerance relaxation where it is safe, and supplier or process changes. None of these is exotic, and most engineers already know them. The hard part is knowing where to apply them without introducing risk, which is a knowledge problem more than a creativity problem.
Teams that treat value as a ratio, and not just a cost target, tend to make better decisions, because the ratio forces an honest conversation about what the customer is actually paying for. A part can be expensive and still be good value if it carries a function nothing else can. A cheap part can be poor value if it exists only to prop up a feature no one uses. Holding both numbers in view at once is what keeps a study from sliding into blunt cost cutting.
IN PRACTICE
Leo found a nature-inspired solution that let us use standard, off-the-shelf parts. No custom manufacturing. No dedicated engineer. We saved around $400 per system.
"Leo found a nature-inspired solution that let us use standard, off-the-shelf parts. No custom manufacturing. No dedicated engineer. We saved around $400 per system."
- Chen, Team Lead, ZutaCore
The Function Analysis Step Most Teams Skip
The heart of value engineering is function analysis, and it is the step most teams skip when they are under deadline pressure. Instead of asking how to make a bracket cheaper, function analysis asks what the bracket is actually for. A single bracket might exist to locate a sensor, carry a load, and resist vibration. Once each function is named, the team can ask whether every function is truly necessary, whether one part could deliver several functions, and whether a function is being over delivered relative to what the customer actually needs.
Teams that formalize this often use a function analysis system technique diagram, usually called a FAST diagram, to map functions in a how and why logic chain. This exposes the difference between basic functions that justify the product and secondary functions that were added out of habit. It is common to find that a costly machined feature exists only to support a secondary function that a standard, off the shelf component could handle for a fraction of the price.
The output of good function analysis is a short list of high cost, low value features. Those become the targets for the rest of the study. Without this step, cost reduction turns into arbitrary trimming, which is exactly how quality problems get introduced. For a related view on pricing a design before it is committed, see our guide on AI should cost estimation.
Where Value Engineering Breaks Down in Practice
The method is sound, so the reason value engineering underdelivers is almost never the theory. It is the information problem sitting behind it. Running a value engineering study well requires four things that are usually scattered across systems and people:
A clear view of what each part actually costs, including material, process, and assembly cost, not just the purchase price on a quote.
Knowledge of which parts the company has already designed, bought, or approved, so new geometry is not created for a function an existing part already covers.
The design intent and past decisions behind each feature, so the team knows which tolerances and materials are load bearing and which are legacy habit.
The relevant standards and compliance rules, so a cost saving change does not quietly break an ISO, ASME, or industry specific requirement.
In most teams this information lives in a mix of PDM and PLM systems, spreadsheets, old email threads, and the memory of a few senior engineers. A junior engineer running a value study cannot see most of it. So they either propose safe changes that save very little, or aggressive changes that get rejected in review because they missed a constraint no one had written down. This is the same tribal knowledge problem that slows down every other part of engineering, and it is why so many value engineering workshops produce long idea lists and short results. Our piece on the real cost of duplicate parts covers one expensive symptom of this gap.
How AI Changes Value Engineering
AI does not replace the judgment at the center of value engineering. It removes the information bottleneck that makes the method slow. An AI intelligence layer that sits on top of an organization's existing PDM and PLM data can do in minutes what used to take a team days of digging through folders and asking around.
Leo is an AI assistant built for mechanical engineers, trained on more than one million pages of engineering standards, books, and technical sources, and connected to an organization's full knowledge base across PDM, PLM, local directories, and ERP. For a value engineering study, that connection matters in three practical ways. First, before an engineer designs new geometry, Leo can surface parts the company has already designed or purchased that deliver the same function, which is the single biggest lever for cost reduction. Second, it can retrieve the design intent, past calculations, and prior decisions behind a feature, backed by a cited source the engineer can click and verify, so the team knows which requirements are real and which are habit. Third, it checks proposed changes against the standards and compliance rules that apply to the project, so a promising cost saving idea does not create a quality or regulatory problem later.
The effect is that value engineering shifts from an occasional workshop into something a team can run continuously, on every assembly, without pulling senior engineers off their own work. Because the same data feeds cost roll ups across the bill of materials, savings found on one assembly can be checked against the rest of the program before anyone commits to them. Related reading: our guides on AI part reuse and part standardization and BOM cost.
A Practical Value Engineering Workflow for 2026
A modern value engineering workflow does not need a two day offsite. It fits into the normal design review rhythm. The following sequence works for most mechanical teams:
Pick the target. Focus on the highest cost assemblies or the highest volume parts, because a small per unit saving there returns the most money.
Run function analysis. Name what each part and feature does, and separate the basic functions from secondary ones the customer never asked for.
Check for reuse first. Before proposing new parts, confirm whether an existing, approved part already delivers the function, since reuse avoids new tooling, qualification, and inventory cost.
Generate alternatives. For each high cost, low value feature, consider material substitution, part consolidation, tolerance relaxation where safe, and process changes.
Validate against constraints. Confirm every idea against load requirements, standards, and compliance before it reaches the review, so that good ideas do not die in the room.
The discipline that separates teams who save real money from teams who run pleasant workshops is step three and step five: reuse before redesign, and validation before proposal. Both depend on fast access to data the team already owns. Run this loop on every significant assembly and the savings compound quietly over a program, instead of arriving as a painful cost down demand near launch. For more on cutting part count and cost at the same time, see our guide on part consolidation.
FAQ
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