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Plan with a clearer view of what comes next.

Forecasting models learn from history and the factors that move it - seasons, festivals, promotions, weather, prices - to give planners a better estimate of demand, output or yield, with an honest range around it.
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Forecasting

What It Is

Spreadsheet forecasts usually repeat last year with a percentage on top. Machine-learning models can use many more signals and update every day, by product, location or asset.

A good forecast is one people trust and use. That means showing why the number changed, measuring accuracy openly against what actually happened, and fitting the forecast into the planning meeting rather than adding another screen.

How It Works

The system from the sensor to the people who act on it. Every project is sized to the site, but the layers stay the same.

  1. Data

    History & drivers

    Sales, orders, production, weather, calendars and prices gathered and cleaned.

  2. Features

    Feature pipeline

    Seasonality, holidays, promotions and lags built automatically.

  3. Model

    Forecast models

    Statistical and machine-learning models compared and combined.

  4. Check

    Backtesting

    Accuracy measured against real outcomes before and after go-live.

  5. Use

    Planning tools

    Forecasts in dashboards, the ERP or the S&OP meeting, with explanations.

Key Technologies

The protocols, standards and components we work with on this use case.

Models

  • Gradient boosting
  • ARIMA / ETS
  • Hierarchical forecasting
  • Probabilistic forecasts

Inputs

  • POS & order data
  • Weather & irradiance
  • Promotion calendars
  • Festival calendars

Operations

  • MLOps
  • Backtesting
  • Forecast accuracy tracking
  • S&OP
  • ERP integration

Typical Deliverables

What a project usually hands over. We agree the exact scope with you before work starts.

  • Data audit and clean history
  • Baseline and improved models, compared openly
  • Forecasts at the level planners need
  • Dashboards with explanations and ranges
  • Automated daily or weekly runs
  • Accuracy reports and a re-training plan
The practice behind it: AI & Machine Learning

Where It Applies

Industries where this use case answers a problem we see often. Each links back to the sector page.

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