# Ana Luiza Paes Araújo das Neves

Ana Luiza Paes Araújo das Neves writes about supply chain work, with a focus on procurement, supplier quality, inventory accuracy and warehouse management. Araújo das Neves's posts connect day-to-day process decisions—such as qualifying suppliers or investigating stock discrepancies—to operational reliability, cost and working capital.

- Profile: https://b2binfluencer.marketing/creators/ana-luiza-paes-ara-jo-das-neves/
- LinkedIn: https://www.linkedin.com/in/anaaraujopn
- LinkedIn followers: 4,212
- Topics: supply-chain, operational-excellence, procurement, supply-chain-management, inventory-management
- Based on 13 dated posts, latest 2026-09-24

## Summary

Ana Luiza Paes Araújo das Neves is most useful for practical supply chain commentary on supplier quality, inventory accuracy and warehouse management.

- Best for: A Portuguese-speaking manufacturer seeking supplier quality and inventory accuracy workshop fits.
- Not for: A consumer brand or software vendor needing English video launch and reach proof does not fit.
- Check first: Confirm audience details, recent comparable results, and whether they offer sponsorship or contact routes.

### Takeaways

- Supplier quality starts before the purchase order: qualify, specify, measure, treat deviations, develop suppliers.
- Inventory accuracy is a reliability problem; system stock can differ from physical stock.
- Cutting stock is not automatically efficient; value alone can miss cheap critical items.

## Main ideas

- **Supplier quality belongs before the purchase order, not in pre-audit season**: Their argument is that a supplier can hit the delivery date and agreed price and still be a bad buy if the material arrives outside specification, so quality work has to start earlier in the buying process. They lay out five moves: qualify whether the supplier's technical capacity, processes, resources and controls match how critical the supply is; specify technical requirements, inspections, certificates, acceptance criteria and traceability before contracting; measure more than on-time-in-full by adding rejections, non-conformities, repeat failures, rework, technical performance and whether corrective actions actually worked; treat a deviation by finding its cause, impact and recurrence instead of stopping when the material is replaced; and develop strategic suppliers rather than discarding everyone below expectation. The conclusion is a money argument: price measures what is paid, while quality helps measure what is actually received.
- **Inventory accuracy is a reliability problem for the whole company, not a warehouse number**: A stock discrepancy is not only a wrong balance. Once the system figure cannot be trusted, they argue, purchasing rebuys material that already exists, planning counts availability that is not there, maintenance schedules work it cannot execute, and finance sees an asset that does not physically exist. The fix they describe is daily rather than annual: receiving discipline, addressing, recording movements, access control, cycle counting, analysis of discrepancies and treatment of root causes. Counting again corrects the balance; understanding why it was wrong corrects the process.
- **The warehouse is infrastructure the rest of the business depends on**: Treating the storeroom as the place where materials are kept, in their view, leaves money sitting still or leaking. They connect six functions in a working warehouse: receiving (did what was ordered actually arrive), inspection (material, quantity, documentation and technical requirements), addressing (can the item be found quickly), movement (does every entry and exit leave a trace), inventory (does physical stock match the system), and preservation (will the material still be fit for use when needed). When one fails, the effect crosses the company: purchasing rebuys, maintenance waits, operations lose availability, finance holds immobilised capital, and supply chain shifts into emergency mode that ends in urgent freight, spot buying or a stoppage. They replace the usual question—how much material is in stock?—with a more pointed one: how far can the business trust the stock in the system?
- **Cutting stock is not automatically efficient**: They push back on the reflex that lower stock means higher efficiency. Reducing stock improves working capital until the item cut is one that cannot be allowed to run out. Excess stock immobilises capital, takes up space, raises obsolescence risk and hides planning problems; insufficient stock also costs, and critical shortages can cost much more. ABC classification helps, but financial value alone is not enough. They want decision-makers to weigh value, operational criticality, replenishment lead time, origin and logistics exposure, alternative suppliers, consumption, and the impact of a stockout together. Their aim is the right stock for the risk the business has decided to carry, not the lowest possible level.
- **Industry fairs are for finding suppliers, not only for the conference stage**: One post shows a priority beyond day-to-day process work: walking exhibition halls to look for new suppliers and solutions. Writing about the relaunched Rio Oil & Gas fair, they describe large stands and a clear focus on high-level networking, rate the talks, roundtables and congress programme as the high point, and place the real value of the visit in meetings, reconnections, new contacts and possible suppliers.

## Work to read

- [Quality starts before the purchase order](https://www.linkedin.com/posts/anaaraujopn_supplierquality-procurement-supplychain-activity-7506380320435466240-eNvM)
- [When the system says three units and the shelf says zero](https://www.linkedin.com/posts/anaaraujopn_inventorymanagement-supplychain-warehouse-activity-7508566087790338050-fIrb)
- [The warehouse as reliability infrastructure, not a storeroom](https://www.linkedin.com/posts/anaaraujopn_supplychain-warehousemanagement-inventorymanagement-activity-7508195593152778241-eCv6)
- [Low stock is not the same as efficiency](https://www.linkedin.com/posts/anaaraujopn_inventoryoptimization-supplychain-workingcapital-activity-7508917267045376000-oFjH)
- [Field notes from the Rio Oil & Gas fair](https://www.linkedin.com/posts/anaaraujopn_e-hoje-foi-dia-de-feira-a-feira-rio-oil-activity-7507943979749945344-kCoO)

## Partnerships

Ana Luiza Paes Araújo das Neves's posts do not include a sponsorship offer, a rate card, a media kit or a partner-inquiry route. Paes Araújo das Neves's posts are supply chain commentary on procurement, supplier quality, inventory accuracy and warehouse management, plus one write-up of the Rio Oil & Gas trade fair. None of the posts carries a sponsored label, a paid-partnership disclosure or a brand mention presented as a commercial arrangement, and none links to a booking page, contact form or partnership address. For a buyer, this means there is nothing in the posts to plan around: no published format, no stated deliverable, no price signal and no named channel for approaching a commercial collaboration. That is different from saying such work never happens — it may simply not be documented in the posts here. The practical step is direct outreach through the LinkedIn profile already listed on this page, and treating any scope, deliverable and disclosure terms as things to establish in that conversation rather than assume from the posts here. The account's size and subject focus make the topics of supplier qualification and inventory accuracy a plausible starting point for a sponsored technical explainer or a workshop-format collaboration, but that is a campaign idea to be tested with the creator, not a format they have published.

## Alternatives

- Ocaoa Amorim: Choose this creator when the manufacturer's bottleneck is the logistics system and distribution flow, not supplier qualification or stock record accuracy. The posts explain how to choose between a warehouse management system, a transport management system and a distribution management system by first identifying the operation's main pain. They set out what each system controls, who it suits and what implementation demands: reviewed processes, consistent data, trained staff and planned integration with the ERP. The other post covers cross-docking as a distribution model, including its planning, technology and integration requirements, who already uses it, and indicators to track: on-time-in-full, logistics cost per order, the returns rate and vehicle occupancy. If distribution is slow, or a system purchase is being driven by trends rather than a diagnosed problem, these posts are closer to the task than a workshop built on supplier metrics and cycle counting.
- Rafael Soares: Choose this creator when the obstacle is the data underneath the operating routine, not the routine itself. One post argues that data quality is the foundation for AI or analytics work, describes the problems caused by information scattered across folders, servers, separate clouds, print, email and social channels, and identifies practical blockers: no technology team, no time and competing emergencies pushing the work down the list. The other discusses the CIO's role in explaining AI to employees and training them, while making clear that these tools should support human decisions rather than replace them. This is the upstream version of the trust problem the client faces: if records cannot be trusted, accuracy and supplier work rest on sand.
- Paulo D'Elia: Choose this creator when the client wants AI embedded in daily operational workflows rather than a workshop on procurement and inventory methods. One post distinguishes using AI as a standalone tool from running it as an operational layer that holds context, pulls information, connects systems and turns decisions into execution. It gives concrete business examples: checking whether sales staff fill the CRM in line with the funnel stage, testing whether pipeline volume and timing are sufficient to hit targets, sorting teams by delivery risk, reading finance data for early warning, and following up on commitments made in daily meetings. The other post sets out a three-layer sequence—joined-up marketing, sales and customer success first, revenue second, engineering and AI on top—along with five foundations needed before any AI agent is useful and a continuous cycle of roadmap, sprints, deployment, monthly review and bottleneck hunting. For a manufacturer whose actual gap is that data and processes are not joined up well enough to deploy AI safely, this framing addresses the problem directly.

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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/