Manufacturing analytics with energy measurements

MICA® manufacturing analytics from HARTING and IBM uses power consumption to reveal information about the state of machines and their actual wear and tear.


• Create transparency in the production process
• Detect anomalies and analyse errors
• Predictive maintenance
• Optimise energy consumption

Scope of Services

• MICA Energy
• Current sensors. Up to 99 current sensors can be connected, as needed, to a MICA.
• IBM Bluemix. The IBM cloud platform serves as the basis for MICA manufacturing analytics. It provides versatile modules (Software as a Service) that are used within the framework of the solution package.
• IBM's Watson IoT platform. The interface between MICA and IBM Bluemix is the IBM Watson IoT platform. This service provides an infrastructure for a secure connection as well as software components for information and risk management components, and real-time and edge data analysis.


• Transparency. MICA manufacturing analytics makes it clear precisely when any single machine is operating.
• Efficiency. Utilisation and standby times can be clearly recognised; this can be used to identify optimisation potentials in your resource usage and productivity.
• Retrofitting. MICA manufacturing analytics can also be used for older systems which do not themselves provide data about their processes.
• Predictive maintenance. With IBM's predictive maintenance, anomalies can be detected and proactive maintenance can be carried out.

MICA® manufacturing analytics, from HARTING and IBM, creates more transparency in production and encourages efficient actions. The approach is simple: sensors on the power lines are used to measure the power consumption of individual consumer loads. This data is transferred to MICA and saved there on-site. MICA makes the initial analysis and sends only relevant data to IBM’s Bluemix (IBM’s cloud platform).  There, the data is evaluated using Watson technology and converted into valuable information, which can be queried from any web browser. In this way, users can compare over weeks and months to see exactly when and how each individual machine works.

Status monitoring based on power consumption: as a KPI for predictive maintenance and energy use optimisation

MICA® manufacturing analytics uses power consumption to reveal information about the state of machines and their actual wear and tear. The workflow and usage of energy changes when parts are worn. This indication of wear makes predictive maintenance possible. Power-hungry components, which may be operating unnecessarily, can be found and their use can be sensibly limited.

About Us

Author Lars Hohmuth
Telephone +49 5772 47-7093