Introduction
Joirson Viana is an operations, finance, and data analytics consultant who works with warehouse and e-commerce operations on reporting, ERP and WMS data integrity, and capacity planning. He holds an MBA in Data Analytics and runs his practice through jvconsultor.com.
In this interview, Viana walks through three practical questions that come up repeatedly in operational analytics work: where to look first when the numbers do not reconcile, what reporting tends to leave out, and what leaders misread when a labor report or P&L feels off.
Start at the Point of Data Entry
Asked where he looks first when a warehouse operator says the numbers do not add up, Viana points to the physical handoff between the floor and the system of record.
When the numbers do not correlate, the first place I look is the exact point of data entry—specifically, the physical handoff between the concrete floor and the ERP or WMS. Discrepancies almost always trace back to unvalidated manual inputs at intake, receiving, or bin-level transfers.
— Joirson Viana, Consultant & Data Analyst
His first step is to resolve the immediate discrepancy by auditing transaction logs back to their physical source—a receiving dock clipboard, a misconfigured scanner, or an unrecorded pallet move. That initial look, he says, distinguishes an isolated human error from a systemic workflow flaw.
Once the immediate data is reconciled, the priority shifts to engineering accountability into the system itself: automated validation rules, standardized scan points, and clear ownership so the same leakage cannot happen twice. For readers working with ERP and WMS integration, the takeaway is that reconciliation is only half the job—the other half is preventing recurrence.
What Dashboards Leave Out
Viana argues that most dashboards are built to look backward at sales and revenue, leaving out operational capacity forecasting and the compounding friction of scale.
Most dashboards are built to look backward at sales and revenue, completely leaving out operational capacity forecasting and the compounding friction of scale.
— Joirson Viana, Consultant & Data Analyst
He describes a common pattern: operators expand volume first and plan to upgrade software, bin logic, and processes later, once new personnel are onboarded. In his view, the sequence should be reversed. Just as companies model revenue forecasts, they need predictive modeling for operational load—how headcount scaling, order velocity, and SKU proliferation will stress current workflows.
Adding more people to an unoptimized floor does not increase throughput; it multiplies bottlenecks.
— Joirson Viana, Consultant & Data Analyst
To bring this into the picture, Viana integrates labor efficiency, pick-path velocity, and capacity thresholds directly into BI reporting, combining SQL, Python, and Power BI with ERP data. The goal is to let leadership upgrade technological and physical architecture before volume breaks the floor. Readers exploring capacity planning will recognize the tension between growth and infrastructure readiness.
When a P&L Feels Off, Check the Measurement First
On the third question—what people conclude too quickly when a P&L or labor report looks wrong—Viana is direct: leadership almost always jumps to workforce underperformance.
Leadership almost always jumps to the conclusion that the issue is workforce underperformance or a lack of effort on the floor. While labor execution and morale are always factors, my experience shows the root cause is usually flawed measurement architecture and inefficient tooling.
— Joirson Viana, Consultant & Data Analyst
He frames the problem as a measurement issue rather than a motivation issue. If baseline KPIs are fed by inaccurate ERP data, or if pickers lose hours to phantom inventory, poor slotting of high-turnover SKUs, and manual workarounds, even a hard-working floor team will look unprofitable on a P&L.
You cannot hold a team accountable to a broken ruler.
— Joirson Viana, Consultant & Data Analyst
Instead of blaming workers, executives should audit the integrity of their data pipelines and the physical ergonomics of the workflow. Viana cites one major cold-chain restructuring he led, where fixing underlying data visibility and unit-cost attribution—rather than pushing the floor harder—swung net margin per box from -$0.82 to +$0.09.
When you fix the measurement and the tools, performance naturally follows.
— Joirson Viana, Consultant & Data Analyst
Practical Takeaways
- When numbers do not reconcile, trace the discrepancy to the physical point of data entry before assuming a systemic problem.
- After reconciling, build validation rules and scan points so the same leakage cannot recur.
- Add operational capacity forecasting to reporting, not just backward-looking sales and revenue views.
- Treat labor efficiency, pick-path velocity, and capacity thresholds as first-class BI metrics.
- Before concluding a team is underperforming, audit data pipeline integrity and workflow ergonomics.
More about Viana's work is available at jvconsultor.com.