Operationsand systems.And the softwareunderneath.
I work on warehouse and consignment distribution, and I build the tools that keep its numbers honest: a WMS on Google Sheets and Apps Script, ledger patterns, parsers and dashboards. I own the business rules and the testing; AI writes most of the code.
Featured
case study
The system I run at work, end to end, with real screens from the live board.
Five files.
Now one WMS.
A plush toy brand's stock lived in five files that never talked to each other. I connected inbound, warehouse slots, shipments, a field app for store visits and partner sell-out into one ledger.
- Stock count: three different totals for one warehouse
- First time it could be measured: 16% of stock on store shelves
- Field app live, 8 stores on one board
- 27.7% on store shelves
Projects,
by rack
How I
work
Built by ops, powered by AI. Three steps, the same on every tool.
Write the rule
When a process breaks, I write down the rule it must never break again, in business terms.
Build the smallest tool
I describe the behaviour, AI writes the code, and the tool lives where the team already works: Sheets, Excel, a phone.
Prove it breaks
Every rule gets a test, and the test is first shown to fail with the rule removed. Then it ships.
Writing
Notes on how I got here, on Medium.
Dari Bisnis LPG Keluarga ke Python: Cerita Gue Belajar Data Analyst di DQLab
From a family LPG business to Python: how I started learning data analysis.
About
Photo IDIskandar
- Base
- Jakarta
- Access
- Rack · Store · Data
I come from the business side, not software engineering. My work sits where the warehouse, the stores and the numbers meet: where stock really is, whether a delivery can be proven, and whether a report can be trusted.
I would rather ship a small tool that enforces one rule than a big dashboard nobody trusts.
Five files.
Now one system.
Working on warehouse, distribution or retail operations data? I am happy to compare notes.