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Supply Chain & Inventory Control Dashboard - Power BI | 126K Units | 500 SKUs
Client Problem:
E-commerce brand with 500 products across 6 warehouses. No visibility on what to reorder, stockout risk, or how much capital needed.
What I Built - 8-Panel Command Center:
Reorder Value by Product - Tablet $55K highest risk
Stock Value by Warehouse - WH2 holds $1.38M (overstocked)
Quantity by Warehouse - WH4 11.8K units
Value by Product & Supplier - Full matrix to negotiate better rates
Stock Status: 81% In Stock, 13% Low Stock, 6% Reorder Now
Lead Time vs Stock - Visual alert for delayed suppliers
KPIs: Total SKUs 500 | To Reorder 67 | Stockout Risk 13.40% | Capital Needed $281.63K
Business Impact:
Client prevented $281K in lost sales. Reduced stockout from 13.4% to 3% in 30 days. Clear reorder list daily - no manual Excel counting.
Stack: Power BI, DAX, Supply Chain Analytics, Excel
Timeline: 3 Days | Best for: E-commerce, Retail, Warehouse Ops
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Super clean work!
Public services rarely operate in isolation.
One citizen or business journey can depend on multiple services, systems, approvals and handoffs. When one dependency slows down, understanding where the journey is blocked and what it affects downstream becomes a systems problem.
CIVARA / 007.01 Public Service Operations Command explores how service teams could see journeys, dependencies, bottlenecks and service health from one operational view.
Not another government dashboard. An exploration of the human operational layer behind joined-up public services.
Independent concept study by Jemirae, using Ghana as a research context. Not commissioned, endorsed by, or affiliated with the Government of Ghana. Demo data only.
#govtech #enterpriseux #servicedesign #digitalgovernment #jemirae
Showing where a journey gets blocked is the part that matters in a dashboard like this.
I just finished building my own open-source farm bot called Farmbt 🌱
It's a team of 13 AI agents. I asked them one question, and they came back with a ranked list of opportunities nobody has fully cracked yet.
You describe a problem area in plain words, and the agents take it from there:
🔎 Researchers search the web in parallel and pull out findings, each with its source
✅ A verifier re-opens the pages and checks every claim against the real text
🗳️ A gap hunter runs three independent passes, and only gaps that show up repeatedly survive
🥊 A critic tries to prove each gap is already solved
🛠️ Solver agents design fixes, check them against existing products and papers, and write an experiment plan
The hard part wasn't getting AI to produce answers. It was making it not make things up. So every claim is graded by evidence, confidence ratings are capped by what the source actually supports, and everything is saved, so a run that hits a rate limit picks up where it stopped.
It runs on free-tier models today and gets better with every model upgrade, with no code changes.
First test: crop-disease detection apps for smallholder farmers. 39 sources, 16 findings and 5 opportunities, ending in a concrete first experiment to run.
Open source: github.com/Samthnalyst1234/Farmbt
If you need an AI pipeline that's reliable enough to trust, for research, automation or anything in between, let's talk 👋
The step where a verifier re opens every page is a solid guard against hallucinations, and persisting state lets the run survive rate limits without missing a beat