AI for Parts & BOM Management
Make or buy decisions shape a product's cost, lead time, and risk. Learn how mechanical engineers use AI to weigh part reuse, should-cost, and manufacturability.
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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
Make or buy decisions are among the highest-value choices a mechanical engineer makes, yet they are usually settled in seconds with incomplete information. The teams that get them right do not have better instincts. They have faster access to the four inputs that matter: whether the part already exists, what it truly costs to make, whether it can be manufactured cleanly, and what the market already offers. AI that connects to a company's own PDM, PLM, and ERP data, and that reads CAD directly, turns hours of hunting into a quick, cited answer. That does not replace engineering judgment. It gives judgment something solid to stand on, and it keeps the reasoning on record so the same part is never designed, priced, or sourced twice.
Every mechanical assembly hides a series of quiet decisions. Should this bracket be machined in-house, or is there a catalog part that does the same job for a fraction of the price? Should the team design a custom actuator, or buy a standard unit and shape the surrounding geometry to fit it? These are make or buy decisions, and they set a large share of a product's cost, lead time, and risk long before the first prototype reaches the bench.
Most engineers make these calls dozens of times a week, usually on instinct and under deadline pressure. The trouble is that instinct rarely accounts for what the company already owns, what a part truly costs to produce, or whether a design can even be manufactured cleanly. This guide breaks down what a make or buy decision really involves, where teams go wrong, and how AI is starting to give engineers the context they need to decide quickly and defend the choice later.
What a Make or Buy Decision Actually Involves
A make or buy decision is the choice between producing a component internally and sourcing it from an outside supplier or a standard catalog. In mechanical engineering, the make path usually means designing a custom part and manufacturing it through machining, casting, molding, or fabrication. The buy path means selecting an off-the-shelf component, a commercial standard part, or a supplier's custom build.
The decision looks simple on the surface, but it touches nearly every function in a product organization. Design engineering owns the geometry and the requirements. Manufacturing owns feasibility and capacity. Procurement owns supplier relationships and unit price. Quality owns inspection and compliance. When these groups evaluate the same part in isolation, they often reach different conclusions, which is how a component ends up designed twice or sourced from three vendors at three prices.
A sound make or buy call weighs at least five factors together: total landed cost, lead time, required volume, in-house capability and capacity, and the strategic value of controlling that part. A one-off bracket for a low-volume machine rarely justifies custom tooling. A high-volume housing that defines product performance might justify keeping production close, even at a higher unit cost. The skill is in seeing all five factors at once rather than optimizing for the one that happens to be top of mind.
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.
Chen, Team Lead, ZutaCore
Why Engineers Get Make or Buy Decisions Wrong
The most expensive mistakes in this area are rarely dramatic. They are small, repeated defaults that compound across a bill of materials. A few patterns show up again and again:
Designing what already exists. Engineers reach for a blank sketch because searching the existing library is slower than starting fresh. The result is a new part number for a component the company already makes or stocks, when part reuse would have been faster and cheaper.
Guessing at cost. Without a should-cost estimate, the make option gets judged on gut feel. Machining time, setups, material waste, and finishing are routinely underestimated.
Ignoring manufacturability. A custom part looks cheaper on paper until the shop flags a thin wall, an unreachable feature, or a tolerance that drives inspection cost. By then the design is already committed.
Underrating the hidden work in buying. Buying is not free of engineering effort. Qualifying a supplier, managing a second source, and handling obsolescence all carry ongoing cost that never appears on the first quote.
Deciding in a silo. When design, procurement, and manufacturing never compare notes, the same part gets evaluated three different ways, and the company loses the volume pricing that comes from standardizing on one choice.
None of these mistakes come from a lack of skill. They come from a lack of accessible context at the moment the decision is made. The information exists somewhere in the PDM vault, the ERP system, or a senior engineer's memory, but it is not in front of the person drawing the part.
The Data Behind a Good Make or Buy Call
A defensible make or buy decision rests on data that is usually scattered across systems. Pulling it together by hand is the reason these calls get rushed. Four inputs matter most.
The first is part reuse. Before anything gets designed or sourced, the team needs to know whether a functionally equivalent part already lives in the CAD library or the PDM vault. Reusing a qualified part skips design, prototyping, and supplier qualification in one step.
The second is should-cost. A grounded should-cost estimate, based on material, process, and volume, turns a subjective argument into a number that both engineering and procurement can trust.
The third is manufacturability. Early design for manufacturing feedback tells the team whether the make path is clean or riddled with features that will raise cost and scrap. Catching this at the concept stage is far cheaper than catching it on the shop floor.
The fourth is supplier and standard part availability. Knowing that a compliant, standardized commercial part exists, with a real lead time and a known price, often settles the decision before custom work ever begins. When these four inputs sit side by side, the choice usually makes itself.
How AI Changes the Make or Buy Workflow
The bottleneck in make or buy analysis has never been engineering judgment. It has been the time it takes to gather context. This is where an AI intelligence layer built for mechanical engineering changes the workflow. Leo connects to an organization's full knowledge base, including PDM, PLM, local and network directories, and ERP, and reads CAD geometry directly rather than relying on file names or metadata.
In practice, that means an engineer can ask whether a similar part already exists and get a geometry-aware answer drawn from the company's own vault, not a keyword match. Leo offers integrations with leading PDM and PLM platforms, including SolidWorks PDM, Autodesk Vault, PTC Windchill, Siemens Teamcenter, and Arena PLM, so the search reflects what the business actually owns. When no internal match exists, Leo can surface standard and vendor parts before an engineer starts generating new geometry, which mirrors how an experienced specialist would approach the same problem.
Because Leo is trained on more than one million pages of engineering standards, books, and articles, its answers come with citations an engineer can click and verify. That matters for a make or buy decision, where a wrong assumption about a material property or a compliance requirement can push a team down the expensive path. Leo runs as an intelligence layer on top of existing PDM and PLM systems, not a replacement for them, so the decision and its rationale stay where the rest of the product data lives.
A Practical Framework for Faster Make or Buy Decisions
Teams that decide well tend to follow a repeatable sequence rather than debating each part from scratch. A workable framework has five steps:
Search before you sketch. Check the existing library for a functionally equivalent part. Reuse is almost always the cheapest outcome available.
Check the catalog. If nothing internal fits, look for a standard or commercial part that meets the requirement before committing to custom geometry.
Estimate should-cost early. Put a defensible number on the make option so the comparison is grounded rather than emotional.
Run a manufacturability check. Confirm the custom design can be produced without hidden cost drivers before it moves downstream.
Document the rationale. Record why the team chose make or buy so the next engineer does not reopen the same question a year later.
The goal is not to remove judgment from the process. It is to make sure that judgment is applied to complete information, and that the reasoning survives after the decision is made. When the context is instant, engineers spend their time deciding rather than hunting for the inputs.
FAQ
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