AI AgentDemand Forecasting
ML forecasting across sales, inventory and supply signals
The challenge
Forecasts are built in spreadsheets from last year plus a feeling, so stockouts and excess sit side by side in the same category. Promotions and supply constraints are handled from memory, and when a number turns out wrong nobody can say which driver actually moved.
The outcome
Azure Data Factory brings sales, inventory, price and supply signals into Azure Synapse, Azure Machine Learning fits and back-tests models per item and location, and Azure OpenAI explains the drivers in plain language. Forecasts inside tolerance publish to replenishment; the rest reach a planner with the confidence interval.
01 — Architecture
End-to-end architecture
Sales, inventory, price and supply signals land through Data Factory, are curated into features, and fitted by Azure Machine Learning per item and location. The Foundry agent turns the numbers into drivers a planner can argue with, and a forecast outside tolerance is never pushed to replenishment unreviewed.
- Signals: Dynamics 365 / ERP, External signals
- Ingest: Azure Data Factory, Azure Blob Storage
- Prepare: Azure Functions, Azure Synapse
- AI & models: Azure Machine Learning, Microsoft Foundry Agent, Azure OpenAI models, Azure AI Search
- Data & records: Azure SQL, Azure Cosmos DB, Azure Key Vault
- Plan & insight: Planner override, Replenishment feed, Power BI / Fabric
02 — Workflow
Process & decision workflow
How raw signals become a plan the business can act on — ingest, prepare, train, forecast and explain, then branch. Forecasts inside the accuracy tolerance publish and trigger replenishment, while drift or an unusual signal goes to a planner with the drivers and the confidence interval.
- Ingest: Sales, inventory, price and supply signals collected
- Prepare: Cleaned, aligned and turned into model features
- Train: Models fitted and back-tested per item and location
- Forecast: Demand projected with a confidence interval
- Explain: Drivers and changes described in plain language
- Publish: Plan released to replenishment and reporting
- Path 1 · within tolerance — Publish the forecast: Plan released and replenishment triggered
- Path 2 · drift or unusual signal — Planner reviews: Driver, history and confidence presented
03 — Components
Key Microsoft components
A forecast is only useful if the planner trusts it — pipeline, modelling, explanation and accuracy tracking all stay on the Microsoft stack.
Azure Data FactoryScheduled ingestion from ERP, point of sale and supplier feeds.
Azure Synapse AnalyticsModelled warehouse for curated demand and supply data.
Azure FunctionsCleaning, alignment and feature engineering.
Azure Machine LearningTraining, back-testing, registry and drift monitoring.
Microsoft Foundry Agent ServiceScenario runs, tolerance checks and review orchestration.
Azure OpenAI modelsDriver attribution and plain-language plan narrative.
Azure AI SearchRetrieval over prior plans, overrides and planner notes.
Azure Blob StorageRaw, curated and archived forecast datasets.
Azure SQLForecasts, actuals, accuracy history and overrides.
Azure Cosmos DBRun state, model versions and scenario results.
Azure Logic AppsReplenishment proposals and exception notifications.
Azure Key VaultSource system credentials and integration secrets.
Azure API ManagementSecure gateway for ERP, supplier and planning APIs.
Power BI / Microsoft FabricAccuracy, bias, service level and inventory dashboards.
04 — AI
What the agent consumes
The capabilities applied to every forecast run, and the line the models do not cross.
AI capabilities embedded in the agent
- Signal ingestion
- Feature engineering
- Time-series modelling
- Hierarchical reconciliation
- Promotion and event uplift
- Intermittent demand handling
- Confidence intervals
- Drift and bias detection
- Driver attribution
- Scenario simulation
- Narrative generation
- Workflow orchestration
AI responsibility boundaries
The model forecasts; a planner owns the plan. Every forecast publishes with its confidence interval, the drivers behind it and its back-tested accuracy, so an override is an informed decision rather than a guess. Models are monitored for drift and bias, and a forecast outside tolerance is never pushed to replenishment unreviewed.
05 — Personalization
Personalization & evolving process
The same methodology applies to every agent in the catalog. Tune the portfolio, the forecast template, the tolerances and the value model — the page structure stays identical.
Portfolio & horizon profile
Define the product hierarchy, locations, planning horizon, seasonality and the promotion calendar in scope. The personas are the demand planner, the supply planner, the category manager and finance.
Forecast template
One consistent flow for every forecasting agent: ingest, prepare, train, back-test, forecast, explain the drivers, publish and measure the outcome against the plan.
Tolerance & override rules
Declare the accuracy tolerance bands, drift thresholds, override authority and the treatment of new products and promotions. Every override is stored with its reason and later scored.
Value model
Capture baseline metrics first, then map the expected benefits: forecast error, stockouts, excess and obsolete inventory, service level, expedite cost and planning hours.
06 — Impact
Key outcomes & business impact
Starting targets for the value case — validate each one against the customer baseline during discovery.
- Forecast accuracyImprovedMeasured against the current baseline, not a vendor claim.
- Stockouts−30%Shortfall risk surfaces before the shelf is empty.
- Excess inventory−25%Less capital tied up in slow and obsolete stock.
- Planning effort−50%Planners review exceptions instead of rebuilding the plan.
Illustrative improvement index
Manual baseline = 100. Illustrative targets, not a commitment — confirm against the customer baseline.
- Forecast error: manual baseline 100, AI-assisted target 70
- Planning hours: manual baseline 100, AI-assisted target 50
- Excess stock: manual baseline 100, AI-assisted target 75
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