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This is what almost 7 years of warehouse experience looks like in a single Excel file.
Most businesses think they need expensive software to run a clean warehouse operation.
They don't.
They need the right system.
Over almost 7 years managing large-scale warehouse operations, I built a warehouse operations tool in Excel that significantly reduces manual work and brings everything into one place.
Here is what it does:
Production planning
Pulls SAP data and tracks planned vs delivered quantities. Calculates remaining balance automatically. Reduces the time your team spends chasing numbers.
Pack material ordering
Every label, bottle, and outer case is BOM-linked. When stock drops below the required quantity — the system flags it instantly so you can act before it's too late.
Raw material tracking
Tracks available stock against safety stock and MOQ for every material. Tells you exactly what needs attention and when.
Yes — some manual input is still required.
But that's the point.
This system is built around how real warehouses actually work — not how they look in a software demo.
It gives your team the structure, visibility, and control they need — without replacing their judgment.
If your warehouse is still running on scattered spreadsheets with no structure at all — this is your next step.
📩 DM me and let's build one for your business.
What part of your warehouse operation takes the most manual time right now? Comment below 👇
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Does someone on your team lose a day or two building the sales report every week or month?
That was the starting point at Mokobara, the premium travel brand. Pulling sales across all their stores, checking returns, reconciling the numbers and emailing each manager took 1 to 2 days every cycle, with 8+ hours of that spent just reconciling.
I built them an automation on Make.com a few months ago. It has run on its own ever since, weekly and monthly:
Make pulls every sales record from BigQuery, 100K to 250K per cycle. That is too many for one request, so it reads them page by page and stitches them back together.
It calculates the numbers the team actually uses: net sales per store after returns and discounts, return rates and the change against the last period.
It builds a CSV with the full breakdown and a short email summary you can read in 60 seconds.
It reads a Google Sheet of store representatives and emails every one of them the report for their own store.
The result: the reporting problem is gone. The report went from 1 to 2 days of manual work to fully automatic, and the 8+ hours of reconciliation dropped to zero.
Two things I would do the same way again:
Calculate "net sales" in the automation, not in the warehouse. The business rule for what counts as a net sale is not what the raw data stores.
Send people their slice, not the whole report. One report for everyone gets skimmed by everyone.
The figures in the image are placeholders, the real ones stay with the client.
What report is your team still building by hand? Tell me where the data lives and I will tell you how I would automate it.
Hi, I'm Rohan — a data analyst who turns messy data into clean dashboards and clear business insights 📊
Recently completed a 20-day end-to-end analytics pipeline (Excel → SQL → Python → Power BI) for a fictional retail chain — 4 interactive dashboards tracking sales, operations, HR & customer intelligence, with ₹1.79M in revenue analyzed across 200 orders.
I also work on HR analytics, real estate dashboards, and retail sales analysis — check out my full portfolio below 👇
Open to data cleaning, dashboard building, and SQL/analytics projects of any size. Let's turn your data into decisions!
#DataAnalytics #PowerBI #SQL #Excel #Python
