AI for Parts & BOM Management
How AI component sourcing finds standard and vendor parts by requirement, and checks your approved parts first so you stop creating duplicate part numbers.
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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
Component sourcing stopped being an availability problem and became a search problem, and most engineering workflows have not caught up. The purchasable universe now runs to hundreds of millions of catalog entries and effectively unbounded configurator space, while the set of parts your own company has already approved stays small, cheap to use, and almost impossible to search by what the parts physically are. The cost of that gap shows up as new part numbers, each one carrying real qualification and carrying costs for the life of the product. An AI layer that reads a requirement in engineering language and searches your own vault alongside outside catalogs closes the gap at the point of decision. The process change is simpler still: ask whether the part already exists before allowing a new one.
You need a linear guide. Not a novel one, a linear guide: 300 mm of travel, a 40 kg moving load, and an environment that occasionally sees coolant mist. Somewhere in the world that part exists, is in stock, and has a CAD model you could drop into your assembly in under a minute. Somewhere in your own company, an engineer specified almost exactly the same guide fourteen months ago, got it approved, and has been buying it ever since.
Neither of those facts is visible from inside your design tool at the moment you need them. So you open a browser, start filtering a supplier catalog, and by the time you have a part number you have lost most of an afternoon and quietly added another line to the approved parts list. This is the sourcing step, and it is one of the least examined time sinks in mechanical design.
Why Component Sourcing Became a Search Problem
Thirty years ago the hard part of sourcing a standard component was finding out what existed. Catalogs were paper, distributor coverage was regional, and the binding constraint was availability. That constraint is gone, and the numbers the catalog platforms publish about themselves make the scale plain.
TraceParts, one of the larger CAD content platforms, describes its library as more than 100 million CAD models and product datasheets drawn from hundreds of supplier catalogs. MISUMI, which sells configurable mechanical components, describes its own offering as over 20 million products and, because most of those products are parametric rather than fixed, roughly 80 sextillion possible part configurations, with some dimensions selectable in increments of 0.001 mm.
Read those figures as an engineer rather than as marketing and the implication is uncomfortable. A configurable catalog is not a list. It is a parameter space, and a parameter space cannot be browsed. It can only be queried. The moment a supplier moves from a fixed range to a configurator, the burden shifts from the supplier's catalog structure onto your ability to state your constraints precisely enough to land on a single part number.
That is a search problem, not an availability problem, and it explains why sourcing a standard component still costs a working engineer hours despite everything being nominally online. It is also why the tooling conversation has moved toward assisted selection rather than better filters, a shift covered in more depth in our look at platforms for automatic mechanical component selection.
IN PRACTICE
The geometry search has been invaluable, helping me find standard parts instead of designing new ones, saving a huge amount of time and effort. The search system is smart and CAD-aware.
- eytan s., R&D Engineer
The Three Places the Right Part Already Lives
When an engineer says they cannot find a part, they usually mean they cannot find it in one specific place. In practice the candidate set is spread across three stores, and only one of them is easy to search.
Parts your company has already approved. These live in a PDM vault, a PLM item master, or an ERP material record, often with a supplier, a price, a lead time, and a qualification history attached. This is the highest value store and usually the worst indexed, because it is searchable by part number and description rather than by what the part physically is.
Supplier and distributor catalogs. Vast, well structured, and organised around the supplier's product families rather than your requirement. Finding the right entry means already knowing the vocabulary a given supplier uses for the thing you want.
Parts sitting inside released assemblies that nobody has indexed. A bearing block chosen for a machine three programmes ago is a fully qualified, already purchased solution. It is also effectively invisible unless someone remembers it.
The order matters because introducing a new part number is not free. Published estimates for the fully loaded cost of adding one new part number to a manufacturing business generally land somewhere between 5,000 and 25,000 dollars once qualification, documentation, tooling, inventory setup, and supplier onboarding are counted. A United States Department of Defense logistics study put the cost of adding a single new stock item at roughly 27,500 dollars. Recurring carrying cost for a duplicate part is often estimated at 4,500 to 7,500 dollars per year, every year, for a part that adds no function. The arithmetic behind those figures, and what to do about them, is the subject of our guide to part consolidation and cutting part count.
A sourcing workflow that checks store two first and never checks stores one and three is, in cost terms, the expensive way round.
What Actually Slows You Down at the Sourcing Moment
The friction is specific and it is worth naming, because each item has a different fix.
You have to know the vocabulary before you can search. A requirement stated in engineering terms, 40 kg moving load with coolant exposure, does not map onto a catalog tree organised by product family. You translate manually, and if you translate wrongly you never see the right part.
The decisive data is trapped in datasheet PDFs. Dynamic load rating, permissible moment, corrosion class, and duty assumptions sit in tables and footnotes across dozens of documents. Comparing four candidates means opening four PDFs and building a table by hand.
Catalog results carry no internal status. A supplier page cannot tell you that your own organisation already approved that exact part, or rejected it after a field failure, or has it on a restricted list. That link between the outside catalog and the inside record almost never exists.
Lifecycle risk is invisible at design time. Nothing on a product page warns you that a series is approaching end of life, which is how an obsolescence problem gets designed in years before anyone notices. We covered the downstream cost of that in component obsolescence management.
Downloaded geometry arrives without engineering context. A neutral STEP body drops into the assembly with no parametric history, no material record, and no tie back to the part number that produced it, so the decision has to be re-documented by hand. Keeping that thread intact is much of the work described in AI BOM generation from concept to components.
None of these are search-box problems. They are all context problems, which is why better filters have never fixed them.
Where an AI Layer Changes the Sourcing Step
The useful move is not another catalog. It is a layer that sits above the systems you already run and answers a requirement rather than matching a keyword. Leo is built for that position: an AI assistant for mechanical engineers, trained on more than a million pages of engineering standards, textbooks, and technical literature, connected to an organisation's own knowledge base rather than replacing it.
Three capabilities matter at the sourcing moment. The first is describing a requirement in ordinary engineering language, or pointing at existing geometry, and getting candidate parts back. That turns the vocabulary problem into the machine's problem instead of yours. The second is searching your own approved parts, released assemblies, and item records in the same action as searching outside options, so an already qualified part surfaces before a new one is created. The third is a cited answer. When a recommendation rests on a load rating or a standard, the underlying source is attached, so the selection can be checked rather than trusted.
Leo offers integrations with leading PDM and PLM platforms, including SolidWorks PDM, Autodesk Vault, PTC Windchill, Siemens Teamcenter, and Arena PLM, alongside local and network directories and ERP systems. The point of that breadth is coverage of the three stores described above, so the reuse-first check is one query instead of three separate hunts. The same context is what makes downstream steps such as the engineering RFQ and supplier quoting process less repetitive, because the specification and its rationale already exist in a structured form.
On the questions engineering leaders ask before any of this is allowed near a vault: Leo is SOC-2 certified and GDPR compliant, no AI is trained on customer data, and intellectual property stays protected.
The geometry search has been invaluable, helping me find standard parts instead of designing new ones, saving a huge amount of time and effort.
That is the whole economic case in one sentence from a working engineer, and it is worth noting what it does not claim. It does not say the AI chose the part. It says the engineer found the part that already existed, which is the decision that was being lost.
A Sourcing Workflow That Holds Up Under Audit
Tooling only helps if the process around it changes. Five steps make the difference, and none of them require a new department.
Make reuse the first gate, not a review comment. Before a new part number can be requested, require evidence that approved and previously used parts were searched. A screenshot of the query is enough. The failure mode is not dishonesty, it is that nobody was ever asked.
Keep the approved vendor list machine readable. An approved list in a spreadsheet that is emailed quarterly cannot participate in a search. The same list as a queryable record can rank results by whether the part is already qualified.
Capture the reason, not just the part number. Record the constraint that drove the selection, the load, the environment, the standard. Three years later that sentence is the difference between a five-minute substitution and a full requalification.
Put a lifecycle review on a calendar. Check the status of purchased components on a fixed cadence rather than when procurement reports a shortage. Obsolescence found at design review is a design choice; found in production it is a line stoppage.
Measure two numbers. New part numbers created per project, and the share of components on each new bill of materials that already existed in an approved state. If the second number is not moving, the workflow has not changed regardless of what tooling was bought.
These are unglamorous. They are also the only reliable way to convert faster search into lower cost, because the saving is not in the minutes spent looking. It is in the part numbers never created.
FAQ
Find the part before you design it
Search your approved parts and supplier options in one query.
Bring your own vault and a real sourcing question. Leo returns candidates with the standard or datasheet figure behind each one, so the selection can be checked.
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