Sales teams submitting forecasts based on optimism, not order signals
Services
AI & Machine Learning · Demand Forecasting
Stop guessing what your customers will order next quarter.
Most manufacturers forecast demand with spreadsheets, gut feel, and last year's numbers adjusted by 5%. ML models trained on your actual order history, seasonality patterns, and market signals replace guesswork with predictions your planning team can act on.
4fit signals
6scope modules
5delivery stages
Intelligence layeroperating layer
01
The Problem
Forecasting Built on Gut Feel and Stale Data
ML-driven demand forecasting for manufacturers — predict demand by SKU, customer, and channel using order history, seasonality, and market signals. Integrated with ERP planning and purchasing workflows.
Purchasing over-ordering safety stock because nobody trusts the numbers
Seasonal demand swings catching operations off guard every year despite being predictable
No visibility into channel-level or SKU-level demand patterns — just top-line guesses
02
Scope
What this service has to produce.
The work is organized as modules because implementation scope should be visible before the build starts.
01
SKU-Level Demand Models
ML models trained on your order history to predict demand at the SKU, customer, and channel level. Not top-line averages — granular predictions your planners can use for purchasing and production scheduling.
02
Seasonality & Trend Detection
Automatic detection of seasonal patterns, cyclical trends, and demand shifts across your product catalog. The model learns your business cycles without manual rule configuration.
03
Channel & Customer Segmentation
Separate forecast streams for dealer orders, direct sales, distributor replenishment, and OEM contracts. Each channel has different ordering behavior and the model accounts for it.
04
ERP & Planning Integration
Forecast outputs feed directly into your ERP's MRP, purchasing, and production planning modules. No manual re-entry or spreadsheet translation between the forecast and the action.
05
Accuracy Tracking & Drift Detection
Continuous monitoring of forecast accuracy against actual orders. Automatic alerts when prediction drift exceeds thresholds so models are retrained before errors compound.
06
What-If Scenario Modeling
Run scenarios for price changes, new product introductions, market shifts, or supply disruptions. Understand how demand responds before committing resources.
03
Architecture
The service has to fit the operating layer it touches.
Intelligence layerWhich decisions can be automated, which need review, and which should stay human-owned.
Governance dependencyThe agent needs governed inputs, clear action boundaries, and audit logging before it can touch production workflows.
Data the model must trust
ERP history
exception queues
pricing rules
quality records
fulfillment events
What we check before implementation
- Which system owns the record of truth.
- Where manual work or reconciliation enters the workflow.
- Which integrations, rules, or data cleanup have to come first.
04
Delivery sequence
How the work moves from diagnosis to production.
Most manufacturers forecast demand with spreadsheets, gut feel, and last year's numbers adjusted by 5%. ML models trained on your actual order history, seasonality patterns, and.
01
Data Audit & Readiness
Evaluate your order history depth, data quality, and ERP data availability. Demand forecasting needs 2+ years of clean transaction data. We identify gaps and remediation steps before model work begins.
02
Feature Engineering
Build the feature set — order history, seasonality indicators, pricing changes, promotional calendars, economic indicators, and channel-specific signals — that the model will learn from.
03
Model Training & Validation
Train models on historical data and validate against holdout periods. Benchmark AI forecast accuracy against your current forecasting method to quantify improvement.
04
ERP Integration
Connect forecast outputs to Odoo's MRP and purchasing modules. Forecasts flow into planning without manual intervention — AWS hosts the model, Odoo runs on the output.
05
Production & Continuous Learning
Deploy to production with accuracy dashboards, drift monitoring, and automatic retraining. The model improves as new order data accumulates.
05
FAQ
Questions that usually decide the scope.
These answers help separate a real implementation plan from a generic technology discussion.
Minimum 2 years of transactional order data for reliable seasonal pattern detection. 3–5 years is ideal. If your data is shorter or has gaps, we assess whether the available data supports the use case or if a phased approach is needed.
Next step
Start with the operating problem, then sequence the build.
Metrotechs maps the record, traces the workflow, identifies the leakage, and turns the scope into a practical plan for Odoo, AWS, data, automation, portals, and AI.
Built around real records, workflows, governance, and production handoffs.
Scoped to what can be implemented, owned, and operated after launch.