# Ali Oztan

Ali Oztan works on Procurable AI, a product for helping procurement teams model costs, assess supplier price increases and negotiate. Oztan’s posts also discuss how cost decisions made during product design can affect manufacturing costs later.

- Profile: https://b2binfluencer.marketing/creators/ali-oztan/
- LinkedIn: https://www.linkedin.com/in/alicanoztan
- LinkedIn followers: 9,986
- Based on 16 dated posts, latest 2026-10-07

## Summary

Ali Oztan explains manufacturing procurement cost problems and practical responses for buyers and engineers.

- Best for: Manufacturing procurement software campaigns about cost intelligence and supplier negotiation.
- Not for: General business or AI campaigns seeking broad reach and workplace trends.
- Check first: Confirm audience analytics, engagement, rates, sponsor policy and evidence for product claims.

### Takeaways

- Cost decisions during design can become harder to change later.
- Supplier price increases can be checked with cost drivers and price indices.
- They tried a negotiation sequence but models took two weeks or more.

## Main ideas

- **Cost is decided during design, so cost modelling has to move there**: Oztan argues that most design engineers are incentivised to move fast: get a first version out that meets form, fit and function, and keep the program moving. Cost and lead-time optimisation typically come later, once constraints appear. By then, RFQs are out, purchase orders are issued and tooling may already be in progress, making changes expensive. Oztan proposes modelling cost during design, so it informs decisions rather than serving as after-the-fact analysis. Engineers can then see what a choice saves or costs before parts go to suppliers. The approach works only if modelling is fast and effortless. Oztan describes that visibility as what they wished they had had as a design engineer.
- **Speed and supplier credibility decide whether cost analysis happens at all**: Across the posts, Oztan identifies time and credibility—not analytical method—as the barriers. In the design post, a slow or manual process simply does not happen. The margin post says procurement teams rarely have time to market test supplier pricing or build cost models, so parts sourced for years quietly get more expensive. The negotiation post recounts a personal failure: Oztan tried the method while leading procurement, but each model took two weeks or more and the supplier did not take them seriously.
- **A supplier price increase is an information problem before it is a bargaining problem**: Oztan frames a familiar request—a supplier cites higher raw material costs and asks for 15% more—as an information asymmetry problem. Suppliers know exactly which cost drivers make up their price and which have changed, while buyers often lack time to reconstruct that for every part they buy or judge whether a change justifies the ask. The capability Oztan describes identifies a part's cost drivers and compares the requested increase with relevant producer price indices over time, alongside a should-cost model, giving the buyer objective data. An earlier launch post makes a larger claim for the same technology: it is patent-pending, can predict true cost drivers in seconds, and is more accurate than every large language model and procurement tool available. That accuracy comparison is Oztan's own product claim. Ask for independent testing to assess it.
- **Price drift is a quiet leak in manufacturing margin**: Oztan argues that procurement teams rarely have time to market test pricing or rebuild cost models, so parts and materials sourced years ago gradually get more expensive. With manufacturing profit margins around 5%, that drift directly erodes profitability. Oztan calls it one of manufacturing's most common and least visible sources of margin leakage. This is the clearest statement in the set of why the other problems matter commercially.
- **Procurement software should work for AI agents, not only for people clicking screens**: Oztan describes having AI agents attempt real sourcing workflows end to end to stress-test Procurable's interface. They call it the most useful product feedback loop the team built that year and note that agents hit friction before users do. The argument is that most procurement software was designed for humans clicking through screens, and the next generation needs to work for both humans and agents. This is the set's only position on software design rather than buying outcomes.
- **The answer to a problem is a working product, demonstrated on the reader's own parts**: A recurring priority in the posts is to pair each problem with something built and put it directly in front of readers. The cost-intelligence launch announces the capability and says customers are already validating quotes. The negotiation post closes by asking readers to message for a demonstration. The event post reports demoing Procurable AI at a First Resonance and Anthropic event and argues that manufacturing is being redefined, with a large opportunity ahead for the supply chain. These posts are shorter and more product-forward than the analytical ones.

## Work to read

- [Incentives drive everything: why cost modeling has to happen during design](https://www.linkedin.com/posts/alicanoztan_incentives-drive-everything-in-business-activity-7426682529023205376-5RKr)
- [Introducing Procurable Cost Intelligence](https://www.linkedin.com/posts/alicanoztan_procurement-loses-billions-every-year-because-activity-7402021530907369472-U3dQ)
- [Information asymmetry in supplier price increases](https://www.linkedin.com/posts/alicanoztan_supplier-our-raw-material-costs-have-increased-activity-7477815078260604928-Is8p)
- [The game plan that failed first, and the product built from it](https://www.linkedin.com/posts/alicanoztan_every-manufacturer-wants-to-save-money-few-activity-7512893578935742464-z-5_)
- [Margin leakage: the quiet cost of not re-testing supplier pricing](https://www.linkedin.com/posts/alicanoztan_procurement-teams-rarely-have-time-to-market-activity-7424245010528673792-9YK1)
- [Stress-testing procurement software with AI agents](https://www.linkedin.com/posts/alicanoztan_weve-been-stress-testing-procurables-ux-activity-7450945974069329921-CNi3)

## Brand relationships

- **Procurable AI** (employment)
- **First Resonance** (mention)
- **Anthropic** (mention)

## Partnerships

The posts show a product-led public profile, not a documented partnership programme. Oztan writes about Procurable AI itself — cost intelligence, supplier price-increase analysis and a negotiation tool — and the calls to action point to product demos, not sponsorship packages. The material includes no sponsor policy, media kit, rate information, audience specification, editorial guideline or disclosure of a paid brand collaboration. A manufacturing procurement software company should ask Oztan directly about formats, rates, audience and editorial terms; any commercial arrangement would need to be negotiated, likely around product explanation and procurement problem-solving content.

## Alternatives

- Oscar Vega: Choose Oscar Vega when the collaboration needs to explain supply-chain geography rather than negotiation mechanics: where critical components are manufactured, where tooling comes from, who controls raw material, and how difficult single-source dependencies would be to replace. The China+1 argument—that moving final assembly does not move the underlying supply chain, so a factory in Mexico may still depend on Chinese electronics and a supplier in Vietnam may still source tooling from China—gives a manufacturing audience a way to decide which China dependencies are genuinely critical and where alternatives should be developed. The Mexico post extends the same logic to nearshoring: when the peso strengthens, a strategy built on labor arbitrage looks different, while a business case built on proximity, lead times, inventory, USMCA access, and responsiveness survives currency and freight swings. That suits a campaign on supplier risk, sourcing footprint, or bill-of-materials visibility.
- Sarah Toomey: Choose Sarah Toomey when the campaign should feature a buyer-side procurement operator: someone who has built and run procurement functions across tech and now oversees procurement inside a finance team. They speak to how AI-native source-to-pay changes the operating model—routing requests, pulling supplier and contract data, checking policy, and moving work toward decisions autonomously. The second post adds a counterpoint that lands with a manufacturing procurement audience: buying AI is often the slowest, most expensive enterprise purchase because of security reviews and extra stakeholders, and the teams pulling ahead build visibility rather than skipping steps. That fits a collaboration on procurement process, autonomous sourcing workflows, or the friction of enterprise AI purchasing.
- Justin Etkin: Choose Justin Etkin when the need centers on negotiation intelligence and savings proof rather than cost-driver modeling. The Carta story—an incremental 21% saving found after the vendor said there was no more room—turns on distinguishing a supplier that has genuinely hit its limit from one that is merely digging in, and why those situations call for completely different responses: pushing a maxed-out vendor burns goodwill, while not pushing one with room leaves money on the table. The AI-buying argument makes the same move at portfolio level: token spend moves daily, pricing changes every few months, and a signed model can be deprecated before contract end, so procurement should jointly own what is consumed, what will be consumed next quarter, and what should be paid. That suits a spend-management, negotiation-data, or AI cost-governance collaboration and reaches finance and engineering as well as procurement.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/