Case Study
| CS4I | Carbon Footprint Detection & Simulation

The (r)evolution for a climate-neutral future

x positioning for the future
x reducing CO2
x managing efficiently

Tags digital intelligent processes | Industry 4.0 | SAP
Reading time

Germany is to become climate neutral by 2045. The focus is therefore not only on the production facilities of the industrial companies, but on the entire value chain - following one principle: efficiency first. Every non-generated kilowatt hour contributes to the goal of reducing emissions. But how can companies track their entire CO2 footprint - and set the screws for their climate goals as economically as possible? The "Climate Solutions for Industries (CS4I)" project, funded by the Federal Ministry of Education and Research, finally offers a comprehensive and realistic picture of all relevant factors.

Evolution in a nutshell

01 The challenge

Create transparency - from start to finish

To determine CO2 emissions, companies have largely used historical data in the past, which does not represent the entire scope of all emission-driving factors. In cooperation with the Fraunhofer Institute, INTENSE AG and future user adoption companies, the vision of a platform creating comprehensive transparency and offering companies the unique opportunity to simulate their future emission scenarios became reality.

»The project not only combines our core competencies of innovative technologies and intelligent processes, but also reflects the direction in which we see the entrepreneurial evolution heading:
towards sustainability, in every respect.«

objective partner

02 The solution

A platform that visualizes climate protection

Looking beyond company boundaries

The basis for determining a real carbon footprint is the collection of numerous physical data at every point in the process: from production to transport, from energy supply to packaging. Factors that influence the carbon footprint beyond the company, can also be captured, normalized, and uploaded to the platform - such as upstream and downstream impacts from suppliers and customers - for a truly comprehensive, realistic model of the carbon footprint.


Real data and realistic simulation
From the obtained data, CS4I determines a product-specific CO2 footprint, based on actual measurement data instead of estimated data. Companies can now use this data to plan their next moves: Not only do they immediately recognize climate-friendly options, but they can also use a digital twin to simulate scenarios for better climate protection. Decision-makers can change parameters in the individual processes, assess their impact and ask any "what if" question, until the potential climate protection measures match the company's framework conditions.

03 The result

Efficient for climate and economy: A powerful decision-making tool for industrial companies

CS41 extracts from data what is already considered as crucial success factor for companies: more sustainability. Where there once were blind spots on the map, CS41 offers a clear picture and shows possible routes.


  • Comprehensive evaluation and forecast of climate efficiency
    based on data - and before the actual production starts
  • Matching of economic and climate protection goals
    by simulating realistic scenarios
  • Identification of previously unseen saving opportunities during operations and beyond the company boundaries

As a basis for intelligent decisions, CS4I puts German industrial companies on the right track to remain internationally competitive and at the same time to be at the forefront of sustainable management.

More article


It’s time for evolution

x Markenrelaunch
x Design & Experience
x Evolution

Customer Experience


Road to Transformation –
Wofür benötigen Sie die Business Technologie Plattform?

18. October 2022

10:00 – 10:45 Uhr



Visions of Industry 4.0 – Sustainability

15. September 2022

10:00 – 10:45 Uhr

Industry 4.0

Case Study

Forging the future with IoT

x Reduce downtime
x Simplify complexity
x Establish efficient processes

Industry 4.0 | Process optimization | SAP