Most people who support supply-chain software have never worked a gate at 6 a.m. I do—and I turn operational pain points into reliable reporting and decision tools.
A representative yard flow — an illustrative view, not site data
At a high-volume logistics site, vehicles move through gate, yard and dock processes while warehouse teams, ground operations and external carriers depend on the same accurate operational picture.
My role spans coordination and clerk responsibilities. As the operational point of contact between warehouse teams, yard teams and drivers, I continuously prioritise movements, maintain accurate status information and resolve exceptions.
The core discipline is that the yard management system has to be the truth. Every decision downstream depends on it, so keeping it accurate under time pressure is the job.
The end-of-shift report depended on a manual count. I built a read-only browser tool that turns the live yard table into a repeatable report in seconds, then validated its output against manual checks before using it to support shift handovers.
A userscript that reads the yard table in place and summarises the operational categories used during a shift. Each result can be traced back to the source rows, allowing the report to be checked against the operational view.
Paste an approved export and generate a structured handover report covering yard status, vehicle flow, operational categories and available capacity. The spreadsheet reconciles its results with the live panel on the same source data.
Designed as a read-only workflow with no external data transmission. Source records remain unchanged, and validation checks make each reported result traceable to the underlying operational data.
JavaScriptDOM extraction Excel modellingCSV pipelines PythonCloudflare Workers TableauSQL
When a yard or transport platform ships a feature, someone in my seat decides in about ten seconds whether it survives contact with a shift. I have been on that side of every rollout: the field nobody fills in, the report that doesn't reconcile, the workaround that becomes policy.
That is unusual in an implementation or support team. The domain knowledge normally has to be taught. I arrive with that domain knowledge and the technical fluency to read an export, reproduce a customer’s problem and identify which assumption in the data model failed.
I was a performance analyst at Hyderabad FC before I moved to Germany, and I have not stopped since. Football Talkies is a blog I have written and maintained for years, migrated across hosting stacks myself when the old one stopped making sense.
The Kerala Super League pipeline is a Python project that pulls, cleans and publishes match data for an under-documented regional league — the same extract, reconcile, publish loop as the yard tooling, on a dataset I chose because I wanted it to exist.
FLAMES is a small web game deployed on Cloudflare Workers. It is a compact example of taking an idea through development, deployment and ongoing maintenance.