Within the context of a company`s energy management, the aim of benchmarking is to assess the quality level and productivity of one’s own machinery and technical facilities as well as to detect savings potential. The overall goal is to become better by learning from others on the basis of comparable indicators and figures. The main task – and difficulty – of energy management is to develop adequate and appropriate indicators.

It is especially important for any selected indicator and also for the process of benchmarking as a whole that specific targets are named. It must be always be clear what is to be achieved with the selected figure. Changed boundary conditions are often not sufficiently considered in the usual comparison of consumption figures of the previous years as well as unknown savings potentials remaining hidden.

„To be better than in the previous year does not automatically mean to be good“

A comparison with consumption indicators of other sites or processes is usually also difficult, because site characteristics often distort things or comparative figures are missing. The use of calculated indicators can help here. For a number of individual processes physical boundaries can be determined. Through the comparison of the consumption indicator with the ideal value a benchmarking can then be performed.

For complex boundary conditions or nested processes, theoretical ideal values can rarely be determined. Computational methods such as simulations are an alternative. Reference scenarios or targets can be set, which are reached during proper operation.

The comparison with specific indicators from simulation models offers the following advantages for the user:

  • high location-specific accuracy,
  • benchmarking for any time interval as well as
  • early detection of deviations from the ideal state.

We offer the following services:

  • definition of the goals of EnPI (energy performance indicators) and of the benchmarking
  • development of location-specific indicators (EnPI)
  • building up of an internal or external benchmarking
  • derivation of ideal values
  • development of simulation models and computational approaches


Graphic to benchmarking


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