What leaders see
Promising pilots that do not change daily work.
Teams test tools, get useful output, and still copy results into spreadsheets, tickets, emails, or ERP screens by hand.
Services
AI & Machine Learning · Demand Forecasting
Stop guessing what your customers will order next quarter.
Most operators 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.
01
The Problem
Forecasting Built on Gut Feel and Stale Data
The problem is rarely that the model cannot generate an answer. The real problem is that the data, permissions, exception rules, and action boundaries are not governed well enough for AI to affect production work.
What is actually happening
The automation cannot reach the operating record.
Source data, permissions, business rules, exception handling, and audit trails are not clean enough for the system to take action.
What gets worse
Automation scales uncertainty.
Bad inputs move faster, decisions become harder to trace, and teams lose confidence before AI becomes operationally useful.
02
What Changes
What this work should improve.
Most operators 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.
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
How It Fits Your Operations
Where this work touches your business.
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 Launchpad captures before Metrotechs scopes delivery
- 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.
Bring the problem into Launchpad
Build the Roadmap before you build the solution.
Launchpad documents what is wrong, captures what your team knows, and connects this service to the business outcome it needs to improve.
Built around the people, processes, records, and decisions that make the business work.
Measured by what becomes easier, clearer, safer, or more reliable after launch.
04
Delivery sequence
How the work moves from problem to measurable change.
Most operators 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 your ERP's MRP and purchasing modules. Forecasts flow into planning without manual intervention - the model runs in the cloud, and planning 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.
Straight answers to what operators ask before committing budget to this work.
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.