From demand signal to optimised supply
The Supply Chain Planning process connects demand sensing, inventory optimisation, production scheduling, and supplier collaboration into a single intelligent loop. MyWave agents translate real-time demand signals into optimised supply plans, reduce excess inventory, and ensure production lines are never starved of material.
Each step shows the AI agent responsible, what it does, which systems it touches, and what it produces.
Aggregates demand signals from POS data, customer order history, market trends, and promotions calendars to generate a short-term demand forecast at SKU/location level.
Runs ensemble forecasting models (ARIMA, ML-based, causal) and selects the best-fit model per SKU, incorporating seasonality, promotions, and lifecycle adjustments.
Calculates optimal safety stock levels per SKU and location based on demand variability, supplier lead time variability, and target service levels.
Generates a feasible supply plan that balances demand requirements against production capacity, material availability, and supplier constraints.
Converts the supply plan into a detailed production schedule, sequencing work orders to minimise changeover time, maximise throughput, and meet delivery commitments.
Processes MRP results and intelligently filters, prioritises, and resolves MRP exceptions — converting actionable exceptions into purchase orders or production orders automatically.
Shares rolling forecasts with key suppliers via the supplier portal and collects capacity confirmations, flagging gaps against planned requirements.
Continuously identifies slow-moving and excess inventory, calculates E&O provisions, and recommends disposition actions — redistribution, markdown, or write-off.
Coordinates inbound shipments from suppliers, manages dock scheduling, and triggers early warning alerts for delayed deliveries that could impact production.
Tracks end-to-end supply chain KPIs — forecast accuracy, fill rate, inventory turns, OTIF — and generates automated performance reports with root cause analysis.
Typical results from full deployment
Process Phases