Chibuike Dialaekwe

Port congestion and transit delays cause reactive, costly disruptions with no early warning system. Companies only discover delays after goods are already stuck.

AI-powered dashboard scoring each shipment lane by risk level, flagging emerging risks 48–72 hours before they escalate.

Retailers face costly stockouts and overstock due to volatile demand and manual replenishment. No ABC/XYZ segmentation — all SKUs treated equally.

Power BI dashboard with automated replenishment alerts driven by ABC/XYZ analysis and safety stock calculation across 500+ SKUs.

Seasonal demand spikes are consistently underplanned, leading to lost sales in peak months and overstock in off-peak months. Finance and supply chain use different numbers.

Python SARIMA model delivering 12-month demand estimates with confidence intervals and a scenario toggle.

Procurement teams lack visibility into supplier pricing trends, leaving cost-saving opportunities unidentified. Spend data is fragmented across multiple systems.

Excel model with SQL-aggregated PO data, supplier comparison tables, and automated alerts when prices exceed category average by >10%.

Last-mile delivery accounts for 53% of logistics costs, yet route planning is done manually. Drivers take suboptimal routes, causing excess fuel consumption and missed windows.

Python routing optimizer using Berlin district geodata. Calculates optimal delivery sequences for 5 vans across 50 daily stops.

Companies discover supplier issues only after delays occur. No standardised scoring framework exists for proactive risk management.

Power BI scorecard rating 30 suppliers across 5 dimensions with composite risk scores and automatic alerts below threshold.

Warehouse managers lack a unified view of pick rates, accuracy, and throughput across shifts. Data requires manual Excel manipulation to report.

Live Power BI dashboard tracking 12 warehouse KPIs with shift-level drill-down and automated daily PDF reporting.

S&OP is done in disconnected spreadsheets, causing misalignment between forecasts and production capacity. No shared data model exists.

Interactive Excel S&OP simulator reconciling demand forecasts with production capacity constraints and inventory positions.

Freight costs are fragmented across multiple carriers and invoice systems, making total cost of shipment nearly impossible to benchmark.

SQL pipeline aggregating invoices from 8 carriers. Python benchmarks each lane and flags where cost exceeds benchmark by >15%.

Manual reorder point calculation leads to delayed purchase orders and frequent stockouts. Static reorder points don't adjust to changing demand or lead time.

Excel model with dynamic reorder point formulas, colour-coded alert list, and draft PO generation using Z-score safety stock logic.

Purchasing teams have no historical visibility into supplier lead time accuracy. Promised lead times in contracts often differ from actual performance.

SQL tracker comparing promised vs actual lead time per supplier, calculating reliability scores and flagging >20% variance for review.

EU CSRD regulations require Scope 3 emissions reporting, but emissions data is fragmented across carriers and not calculated consistently.

Python tool calculating per-shipment CO₂e using GLEC Framework. Includes modal shift scenario comparison (road vs rail vs air).

Companies manage inventory metrics in isolation with no visibility into whether turnover ratios are competitive versus industry benchmarks.

Power BI model comparing inventory turnover, DIO, dead stock %, and GMROI against sector benchmarks with RAG status.

Traditional monthly forecasting misses sudden demand signals from market events, competitor promotions, or weather patterns.

AI agent monitoring daily POS data with Python anomaly detection identifying deviations >2σ from rolling baseline and triggering alerts.

Procurement teams deliver savings but struggle to quantify and present them in a consistent, finance-approved format. Without a tracker, claims lack credibility.

Excel tracker categorising savings by type with finance sign-off columns. Outputs executive-ready waterfall chart and monthly pipeline report.

Multi-depot distribution networks are rarely re-evaluated. Depot locations established years ago may now be generating excess transport cost.

Python optimization using weighted centre-of-gravity algorithm comparing current network vs 3 scenarios: 1, 2, or 3 depots.

Food and pharma companies lose millions to write-offs due to poor batch visibility. FEFO picking is not consistently enforced across locations.

SQL system pulling batch expiry data from WMS, generating risk tiers and recommending redistribution or markdown actions.

Companies invest in demand planning but never measure forecast accuracy systematically. Planners repeat the same systematic biases each cycle.

Power BI scorecard tracking MAPE, forecast bias, and tracking signal by product family, planner, and customer with drill-down capability.

Purchase orders are tracked in ERP but data is never surfaced into actionable analytics. PO cycle time, compliance, and spend concentration are invisible.

SQL pipeline extracting PO data into analytics hub. Power BI tracks cycle time, spend by supplier, compliance rate, and open PO aging.

Demand planners spend 70% of their time on routine replenishment decisions — reviewing stock levels and creating POs — instead of strategic planning.

AI automation layer monitoring live inventory levels, running short-term forecasts, and generating ranked purchase recommendations every morning. Reduces processing from 4 hours to 45 minutes.