4.1 Selection and Evaluation of Relevant Research Approaches
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the visualization of the energy demand in form of diagrams, key performance
indicators, and statistic tools.
As part of the above mentioned Ecomation research team, Haag developed a
model-based planning and evaluation methodology to permanently keep the production processes in energetically favorable area [Ha2013]. Using the methods of
systems technology to merge the main processes and the production periphery,
Haag extends the status-based approach of Dietmair and Verl by considering the time-based dimension and thus converting it from a static to a dynamic
model. He integrates the areas of production planning and control and allows the
evaluation of planning alternatives already in the early planning stages of production process planning. Haag implements a performance measurement system
in the form of a computing system for assessing planning scenarios. He considers production targets (quality key figures, overall equipment effectiveness, and
throughput times) to be able to use them as weighted factors for the evaluation
of planning scenarios. Haag validates his approach on the example of a cutting production. The modeling and simulation are conducted in Plant Simulation
version 10, the parametrization is done in an external database. Energy data, as
well as setup times and process data are taken over into the simulation model
using an external interface. He proves that his approach is suitable for evaluating the influence of technological and organizational parameters on the overall
energy consumption of production as well as for determining the optimal set of
parameters.
Schlegel, Stoldt, and Putz present an approach integrating energy efficiency analysis in material flow simulations [SSP2013]. Assuming status-based
energy consumption patterns in the production, they develop a component model
that is enlarged by a software package (eniBRIC). This allows for the consideration of energetically relevant inputs and outputs depending on the operating state
of a machine. An evaluation module is used for data aggregation and data analysis. The approach is validated in the automotive industry. Despite the additional
efforts for creating the simulation model as well as the increase of simulation time
due to parameter variation, the methodology allows a comprehensive analysis of
resource consumption, the comparison of process alternatives and the dimensioning of infrastructure facilities. The simple parametrization of eniBRIC allows for
the use in different industry sectors.
Using the discrete event simulator SIMIO, Cataldo, Taisch, and Stahl present an approach for evaluating the energy consumption of an automotive engine
assembly line [CTS2013]. To model the behavior of each production machine,
they divide the mechanical functional behavior of a machine into small single
77
the visualization of the energy demand in form of diagrams, key performance
indicators, and statistic tools.
As part of the above mentioned Ecomation research team, Haag developed a
model-based planning and evaluation methodology to permanently keep the production processes in energetically favorable area [Ha2013]. Using the methods of
systems technology to merge the main processes and the production periphery,
Haag extends the status-based approach of Dietmair and Verl by considering the time-based dimension and thus converting it from a static to a dynamic
model. He integrates the areas of production planning and control and allows the
evaluation of planning alternatives already in the early planning stages of production process planning. Haag implements a performance measurement system
in the form of a computing system for assessing planning scenarios. He considers production targets (quality key figures, overall equipment effectiveness, and
throughput times) to be able to use them as weighted factors for the evaluation
of planning scenarios. Haag validates his approach on the example of a cutting production. The modeling and simulation are conducted in Plant Simulation
version 10, the parametrization is done in an external database. Energy data, as
well as setup times and process data are taken over into the simulation model
using an external interface. He proves that his approach is suitable for evaluating the influence of technological and organizational parameters on the overall
energy consumption of production as well as for determining the optimal set of
parameters.
Schlegel, Stoldt, and Putz present an approach integrating energy efficiency analysis in material flow simulations [SSP2013]. Assuming status-based
energy consumption patterns in the production, they develop a component model
that is enlarged by a software package (eniBRIC). This allows for the consideration of energetically relevant inputs and outputs depending on the operating state
of a machine. An evaluation module is used for data aggregation and data analysis. The approach is validated in the automotive industry. Despite the additional
efforts for creating the simulation model as well as the increase of simulation time
due to parameter variation, the methodology allows a comprehensive analysis of
resource consumption, the comparison of process alternatives and the dimensioning of infrastructure facilities. The simple parametrization of eniBRIC allows for
the use in different industry sectors.
Using the discrete event simulator SIMIO, Cataldo, Taisch, and Stahl present an approach for evaluating the energy consumption of an automotive engine
assembly line [CTS2013]. To model the behavior of each production machine,
they divide the mechanical functional behavior of a machine into small single