Use case
Supply chain & demand
See every link in the chain—before the weakest one breaks
End-to-end supply chain visibility with AI-powered demand forecasting, route optimization, and automated replenishment.
When a single delayed shipment can shut down a production line, spreadsheet-based planning is a liability.
The Challenge
A consumer goods manufacturer operating 12 distribution centers and a mixed fleet of 200+ vehicles was losing $8M annually to forecast errors, excess inventory, and inefficient logistics. Demand planning lived in spreadsheets disconnected from the ERP and warehouse systems. Route planning was manual—dispatchers assigned vehicles based on habit, not data. When EV fleet vehicles were added to meet sustainability targets, range anxiety and charging logistics added a new layer of complexity. Stockouts hit 14% of SKUs monthly, while excess inventory tied up $12M in working capital.
The Innovoco Solution
We built a connected supply chain platform that unifies demand signals, inventory positions, supplier commitments, and fleet logistics into a single operational view. AI models forecast demand at the store/SKU level, trigger automated replenishment within policy bands, and optimize delivery routes—including energy-aware planning for the EV fleet that accounts for battery state, charging station availability, and delivery time windows.
Phase 1 — Demand visibility and forecast accuracy
Connect POS, promotions calendar, weather, and economic signals into a unified forecasting model. Backtest against the past 18 months of disruptions—not just smooth periods. Establish automated safety stock policies by SKU class with human approval gates for large or sole-source purchase orders.
Phase 2 — Fleet logistics and replenishment automation
Optimize delivery routes across the mixed fleet—conventional and EV vehicles—accounting for load capacity, energy constraints, charging stop requirements, and delivery time windows. Automate replenishment orders within approved bands and surface exceptions with recommended actions and supplier alternatives.
Phase 3 — Closed-loop optimization
Feed delivery performance, actual vs. forecast accuracy, and supplier OTIF back into the models. Run scenario planning for demand spikes, port delays, or fleet disruptions. As segment-level customer data matures, stores can personalize their SKU mix to match local demand patterns — stocking what resonates with their actual customer base, not regional averages. Structured post-mortems for every stockout and expedite to continuously improve.
Key implementations
Store/SKU-level demand forecasting
AI models combine historical sales, promotions, seasonality, weather, and economic indicators—producing probabilistic forecasts that planners trust because they've been backtested against real disruptions.
Energy-aware fleet route optimization
For the EV fleet: routes account for battery state, energy consumption per segment, charging station locations, and charging time—ensuring deliveries arrive on time without range anxiety or stranded vehicles.
Automated replenishment with human gates
Reorders trigger automatically within approved policy bands. Orders above threshold, sole-source changes, or blackout period overrides require planner approval with full context on why the system recommends the action.
Supplier performance tracking
On-time-in-full rates, lead time variability, and quality scores tracked per supplier—with automatic escalation when SLAs slip and pre-qualified alternates surfaced before shortages become emergencies.
Scenario planning and post-mortems
What-if simulations for demand spikes, port closures, or fleet reductions. Every stockout and expedite gets a structured post-mortem that feeds model and process improvements.
Technical Innovation
The platform connects demand planning, warehouse management, and fleet logistics through a single orchestration layer. For the EV fleet, the system models each vehicle's energy state across route segments—pruning infeasible routes before dispatchers see them and inserting optimal charging stops when needed. The result is a logistics plan that respects both business constraints (delivery windows, cost targets) and physical constraints (battery capacity, charging infrastructure) simultaneously.
Impact
- Forecast error reduced 22% in the first quarter—demand models that account for promotions, weather, and disruptions instead of just historical averages.
- $4.5M in inventory cost savings from right-sizing safety stock by SKU class—fewer overstocks, fewer stockouts, less tied-up capital.
- Stockout rate dropped from 14% to under 5%—automated replenishment catches shortages days before they reach store shelves.
- Fleet logistics costs reduced 15%—energy-aware routing for the EV fleet eliminated range-related delays and optimized charging stops across the network.
Planners spend their time on exceptions and supplier strategy instead of spreadsheet wrangling—and the fleet runs on data, not dispatcher intuition.
Forecast and replenishment finally speak the same language. Planners spend time on exceptions and suppliers, not reconciling spreadsheets.
— VP Supply Chain (anonymized)
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