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An ERP dashboard for warehouse portfolio management should go beyond static reporting — it needs to act as a real-time operational command centre. By unifying inventory, multi-location stock visibility, order flows, and logistics performance into a single intelligent layer, teams can monitor throughput, predict demand fluctuations, and optimise space utilisation dynamically. With AI-driven insights, the dashboard can flag anomalies, recommend restocking strategies, and streamline allocation across warehouses. The goal isn’t just visibility — it’s decision acceleration, where managers move from reactive tracking to proactive control of the entire warehouse network.
#ERP #WarehouseManagement #SupplyChain #Logistics #AI #DataDriven #Operations #SaaS #BusinessIntelligence #Automation
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The Q4 peak and seasonal trend identification is one of the most valuable things a sales dashboard can show — once you know when your strongest period is, you can plan inventory and marketing spend around it instead of reacting after the fact.
The top customer contribution view...
Optivolt AI — Energy Management SaaS Landing Page
Enterprise energy platforms are notoriously hard to communicate with. Decision-makers land on a product page and immediately face a wall of technical jargon, kWh metrics, carbon calculations, and infrastructure specs with no clear sense of what the platform actually does or whether it's built for them.
The Solution:
We designed Optivolt AI, a full B2B SaaS landing page that makes a technically complex energy platform feel immediately clear and trustworthy. A blue-dominant palette with real sky photography anchors the sustainability narrative, while a stats-row dashboard card in the hero surfaces $3,240 saved, 2.4 tons of CO₂ reduced, and 87% efficiency before the user scrolls once.
What we focused on:
⌙ Hero Clarity: Dual CTA "Get a Demo" and "View Dashboard" serve two distinct user intents without friction
⌙ Dashboard-in-Hero: Real operational metrics embedded in the hero make the product tangible instantly
⌙ Solution Card Grid: Four capabilities Analytics, AI Optimization, Infrastructure Mapping, and Forecasting separated cleanly without requiring deep reading
⌙ Use-Case Differentiation: Commercial Buildings vs. Retail & Campus Networks shown as distinct cards with photography
⌙ Performance Trust: 32% average cost reduction, ±5% forecast accuracy, 24/7 AI monitoring displayed as bold stat metrics
Tools: Figma, Jitter
Medicine Supply Chain Disruption Predictor
Built a production-ready ML system that predicts drug shortage risk using real FDA data. Collected and cleaned 1703 real drug shortage records, trained an XGBoost model, and deployed it as a live REST API — accessible to anyone worldwide.
The biggest challenge was identifying and removing 6 sources of data leakage that were causing fake 100% accuracy. After fixing this, the model delivers honest, generalisable predictions.
The entire system is containerised with Docker, automatically rebuilt and redeployed via a Jenkins CI/CD pipeline on every code push, and visualised through an interactive Power BI dashboard.
Result: A complete ML + DevOps project — from raw data to live deployed API — built independently in under 2 weeks.
great job👌
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