AI & BI use case · Business Intelligence

Consumption forecasting

Forecast energy and media consumption precisely.

What it's about

Consumption forecasting produces reliable predictions for electricity, gas, water and steam from historical consumption, production plans and weather data.

  • Cost reduction
  • Better sustainability balance
  • Better planning
Business case & ROI
−15 %

energy costs

−20 %

forecast variance

4–6 Mon.

payback

+10 %

planning accuracy

Calculated from cheaper energy procurement, reduced peak loads and better budgeting.

How we do it
  1. 01

    Data integration

    Consolidate data from ERP, MES and further sources for Consumption forecasting.

  2. 02

    Data quality

    Clean, harmonise and validate data for plausibility.

  3. 03

    Model development

    Train and validate the Consumption forecasting model on historical data.

  4. 04

    Pilot

    Pilot Consumption forecasting in one area and collect feedback.

  5. 05

    Rollout

    Scale the solution and integrate it into operational processes.

5

Steps

6

Data sources

4

Stakeholders

From first data access to production – every step delivers a tangible interim result.

Data typically needed

Energy consumption

Electricity, gas, water and steam per area.

Production data

Orders, quantities and feedback.

Weather data

Outside temperature, humidity and weather alerts.

ERP data

Master and transaction data from ERP.

Historian data

Time series from the process control level.

Sales data

Orders, revenue, channels and customer feedback.

Stakeholders
  • Sustainability lead

    Captures and communicates sustainability metrics.

  • Production manager

    Uses insights directly in daily operations.

  • Controlling

    Quantifies effects and supports budgeting.

  • IT

    Builds on a scalable and secure data infrastructure.

Typical business value
01

Cost reduction

02

Better sustainability balance

03

Better planning

04

More transparency

05

Higher efficiency

The data platform advantage

With a solid data foundation this use case gets faster, cheaper and far more stable.

Central data platform

Real-time data integration

Scalable analytics pipelines

Reusable data products

Build a data platform
Synergies & positive side effects

Energy optimisation

Process data enables demand-based energy control.

Demand forecasting

Data flow enables more precise forecasts.

Automated reporting

Metrics are provided without manual effort.