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.

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The Problem

Forecasting Built on Gut Feel and Stale Data

Sales teams submitting forecasts based on optimism, not order signals
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
What We Deliver

Service Scope

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.

How We Work

Engagement Process

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.

Common Questions

Frequently Asked Questions

Work with Metrotechs

Every engagement starts with an assessment.

Not a proposal. Not a sales call. We tell you what we find, not what you want to hear. The Launchpad assessment maps your operation before any software work begins.

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