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Methods-Energy Measurement
(2016)
Owing to a high inflexibility of the factory layout, manufacturers of masonry units are bound to organizational adjustments seizing optimization measures. Regarding such plants, having a given complexity based on a rigid concatenation of heterogeneous sub-processes with heavy goods to be transported, conventional measures such as Lean Management principles involve great efforts in execution. Therefore, an IT solution for planning and controlling the operational processes is to be developed. This solution will be implemented through simulation-supported optimization to support dealing with a higher complexity and setting up a more resource-efficient manufacturing process.
As a basis, a corresponding factory is mapped sufficiently accurate in every detail in a discrete event analysis (DEA) model. In this paper, a methodology, how to configure an arbitrary calcium silicate masonry unit (CS) plant in a simulation model, is presented for the first time. Relevant data is cataloged and modelling approaches for the controlling methods are pointed out.
Special regard is paid to optimization measures at the crucial point of the transition from bulk material to piece goods, which has not been regarded yet in discrete event simulation modelling. The major aspect is a comparison of a unit-based approach and a variable-controlled approach, regarding the runtime.
A case study follows conclusively, which aided in validating the methodology by simulating various scenarios. As a result, several strategic and operational optimization potentials were identified.
In case of concatenated, complex material flow structures such as those in calcium silicate masonry unit plants, plan deviations pose a major challenge for an efficient order processing in production. In order to overcome the separation between the planning and operational levels in traditional production planning and control systems, this paper presents a validated concept to react immediately to critical events while maintaining efficiency using a modular, rolling-reactive optimization tool. A substantial difference to conventional rescheduling models is the intensive integration of event-discrete simulation, which delivers more realistic results and reduces the threat of an obsolete model due to simple modelling.