I structure messy operational data, find where the margin leaks, and replace manual spreadsheets with live reporting on labor, customers, pricing and the weekly P&L. My background is distribution, warehousing and e-commerce; the methods work in any business that runs on people, inventory and margin.
The interactive demos run on synthetic data generated for this site and mirror systems I built and run in production. The figures under Track record come from real operations, shared without company names.
Across distribution, repack and e-commerce operations, the approach stays the same: structure the data, find where the money leaks, fix the workflow, and keep it measured. These results come from my own work in real operations.
Repack department restructuring
Rebuilt the department's information validation workflows and financial records. A swing of $0.91 on every box, while departmental income doubled.
Repack department restructuring
departmental income
AI and automation
shipping overtime after digitalizing manual workflows and automating data validation
AI and automation
employee hourly output in the same rollout
E-commerce operational audit
of unbudgeted costs traced to departmental mismanagement, which led to restructured workflows
Reconciled raw, multi-source financial and supply chain datasets, ensuring 100% data consistency across enterprise systems.
Built interactive Power BI dashboards tracking departmental P&L variances and overall output metrics for senior leadership.
Delivered actionable insights on historical sales and purchase trends to optimize inventory levels and procurement and to drive executive decisions.
Evaluated complex datasets with time-series analysis to pinpoint seasonal waste trends and outdated labor charges hitting the bottom line.
Restructured the repack department's validation workflows and financial records, doubling departmental income and moving from a $0.82 loss to a $0.09 net profit per box, all costs included.
Deployed AI tools (Claude, ChatGPT, Gemini) to digitalize manual workflows and automate data validation, cutting shipping overtime by 70% and doubling hourly output.
Restructured e-commerce workflows after audits showed departmental mismanagement accounted for 80% of unbudgeted costs.
Monitored warehouse performance end to end, auditing daily and monthly costs, productivity and output by department. Managed payroll personally to find and remove cost inefficiencies early.
Identified and reported ongoing warehouse payroll overcosts, enabling the move to optimized, cost-efficient staffing models.
Supported financial health through data-driven auditing and managed accounts payable and receivable records across systems, accelerating customer payments.
Documented complex data sources and reporting steps to establish rigorous audit controls and remove key-person dependency across financial operations.
Each report answers a question someone in the operation asks every day or every week. The demos below use a wholesale distributor as the example and run on synthetic data in your browser, so click around. The same four questions exist in almost any business; see how they translate.
| Report | The question it answers | Used by | Refresh |
|---|---|---|---|
| Daily labor intelligence | Who worked yesterday, what did it cost, and who is about to cross into overtime? | Floor supervisors, general manager | Every morning |
| Customer risk radar | Which accounts are quietly slipping away, and how much weekly revenue do they carry? | Sales team, general manager | Weekly |
| Price list automation | Can the customer price list go out accurate, twice a week, without anyone editing cells by hand? | Sales and pricing | Twice a week |
| Weekly operations review | Did the operation make money this week, and is every unit getting cheaper to move? | General manager, ownership | Weekly |
Hourly labor is one of the largest costs any operation can actually control, yet overtime usually shows up as a surprise on an invoice two weeks later. This dashboard moves that moment to the next morning.
A time clock export reconciled by hand in Excel, and overtime discovered only when the staffing agency's bill arrived.
On Thursday, supervisors can see who will pass 40 hours by Saturday and rebalance shifts before the overtime is paid. Punch errors are caught the same week, so billing questions are settled with evidence.
Fix changes paid hours. Review is worth a second look. Info explains an adjustment the rules made automatically.
In production this tab also exports the list as a CSV for HR.
Customers rarely announce they are leaving. They order a little less, skip a week, then stop. The radar reads sixteen weeks of order history and flags the pattern while there is still time to pick up the phone.
Select a customer to see their order history and the suggested next step. Column headers sort the table.
Scrolling through order history one customer at a time, and noticing a lost account only after it had already gone.
Keeping an account costs far less than winning a new one. The radar turns sales calls from reactive to planned, and puts a weekly dollar figure on every conversation.
Twice a week the supplier price list becomes the customer price list, with an adjustment on every price. By hand, one missed cell is either a margin leak or an awkward call with a customer.
Editing every price cell manually, hunting for pallet prices written inside free-text notes, and clearing working colors one by one.
A repetitive, error-prone edit becomes a step that takes seconds and reports exactly what it changed, so the list can be checked before it goes out.
# the whole transformation, run on every upload ADJUSTMENT = 2.00 PALLET = re.compile(r"\*\*\s*\$(\d+(?:\.\d{1,2})?)\s*Pallet Qty\s*\*\*") for row in price_list: for col in ("case_price", "five_plus_price"): if is_number(row[col]): row[col] = round(row[col] + ADJUSTMENT, 2) clear_fill(row, col) # fonts, borders and widths untouched row["notes"] = PALLET.sub( lambda m: f"** ${float(m[1]) + ADJUSTMENT:.2f} Pallet Qty **", row["notes"])
Leadership needs one page that answers two questions: did we make money, and are we getting more efficient? This review joins volume, labor and financial results into unit economics, so a busy week is never mistaken for an efficient one.
A static weekly summary rebuilt by hand, read once, and hard to compare with the week before.
Labor cost per box strips out volume swings, so real efficiency gains stay visible even when volume drops. Days that lose money are flagged automatically instead of being averaged away.
Select any bar to review that week.
What did labor cost, which customers are slipping, are prices going out right, and did each unit make money? The demos use a wholesale distributor, but the systems are not tied to it. Here is how each one reads in other operations.
All four reports read from the same shared database. A new file is validated, the rules run, and every stakeholder's link shows the new numbers. Nobody works from last week's attachment, and nobody has to ask which version is right.
Weekly records, roster and rate tables. Admins write, everyone else reads the same numbers.
The same five steps took each of these reports from a spreadsheet someone dreaded to a link people open without being asked.
Who acts on this number, and when? A supervisor needs yesterday's hours before the shift starts; ownership needs the trend once a week. The view follows the decision.
Overtime thresholds, lunch deductions, agency markups. I document them in plain language and confirm them with HR and finance, so the dashboard and the invoice agree.
Every hour assigned to a person, every person matched to a rate, totals tied back to the source file. Leadership only sees numbers that already passed an audit.
One live link, with the default view set for whoever opens it most. Supervisors land on today; leadership lands on the weekly trend.
Rollout emails open with the numbers that matter to the reader, in English or Spanish, and close with the next decision to make.
What I bring to an operations, finance, supply chain or workforce engagement, in any industry.
I'm open to consulting and analytics work in operations, finance, supply chain and workforce planning, in any industry. The quickest way to reach me is a message on LinkedIn.
contact@jvconsultor.com