Faunhofer Project Center for Production Management and Informatics PMI

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  • Publication
    Manufacturing lead time estimation with the combination of simulation and statistical learning methods
    ( 2016)
    Pfeiffer, A.
    ;
    Gyulai, D.
    ;
    Kádár, B.
    ;
    Monostori, L.
    In the paper, a novel method is introduced for selecting tuning parameters improving accuracy and robustness for multi-model based prediction of manufacturing lead times. Prediction is made by setting up models using statistical learning methods (multivariate regression); trained, validated and tested on log data gathered by manufacturing execution systems (MES). Relevant features, i.e.; the predictors most contributing to the response, are selected from a wider range of system parameters. The proposed method is tested on data provided by a discrete event simulation model (as a part of a simulation-based prediction framework) of a small-sized flow-shop system. Accordingly, log data are generated by simulation experiments, substituting the function of a MES system, while considering several different system settings (e.g.; job arrival rate, test rejection rate).
  • Publication
    Simulation-based production planning and execution control for reconfigurable assembly cells
    ( 2016)
    Gyulai, D.
    ;
    Pfeiffer, A.
    ;
    Kádár, B.
    ;
    Monostori, L.
    In order to meet the continuously changing market conditions and achieve economy of scale, a current trend in the automotive industry is the application of modular reconfigurable assembly systems. Although they offer efficient solution to meet the customers needs, the management of these systems is often a challenging issue, as the continuous advance in the assembly technology introduces new requirements in production planning and control activities. In the paper, a novel approach is introduced that enables the faster introduction of modular assembly cells in the daily production by offering a flexible platform for evaluating the system performance considering dynamic logistics and production environment. The method is aimed at evaluating different modular cell configurations with discrete-event simulation, applying automated model building and centralized simulation model control. Besides, the simulation is linked with the production and capacity planning model of the system in order to implement a cyclic workflow to plan the production and evaluate the system performance in a proactive way, before releasing the plan to the production. The method and the implemented workflow are evaluated within a real case study from the automotive industry.
  • Publication
    Performance measurement in flow lines â Key to performance improvement
    ( 2016)
    Stricker, N.
    ;
    Pfeiffer, A.
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    Moser, E.
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    Kádár, B.
    ;
    Lanza, G.
    Key Performance Indicators (KPIs) are frequently used for measuring a production systemsâ performance. The selection of KPIs should lead to a set being as small as possible but taking into account all relevant aspects of the system. This paper provides an analytical approach to determine the set of relevant KPIs for specific production lines, allowing for a transparent and complete performance measurement. An LP was formulated for the proposed KPI model and a significant reduction of the number of KPIs used could be realized. The analytical model was tested in a real industrial application.
  • Publication
    Simulation support in construction uncertainty management: A production modelling approach
    ( 2016)
    Pfeiffer, A.
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    Kádár, B.
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    Bohács, G.
    ;
    Gáspár, D.
    The execution of construction projects such as a highway construction or the elevation of a new bridge is a complex, highly equipment-intensive process and are subject to many different uncertainties. This is very similar to the manufacturing execution level in production systems where predefined productions plans and schedules cannot be completely implemented due to unexpected internal and external changes and disturbances. Following this analogy, the paper proposes the application of a discrete-event simulation based method which was already applied in the decision-support for manufacturing control to develop the decision-support in the execution of a construction project where the effects of the deviation from the short-term schedule can be easily and quickly analyzed.
  • Publication
    Supporting multi-level and robust production planning and execution
    ( 2015)
    Stricker, N.
    ;
    Pfeiffer, A.
    ;
    Moser, E.
    ;
    Kádár, B.
    ;
    Lanza, G.
    ;
    Monostori, L.
    Operating current production systems influenced by the factors of increasing dynamics and volatility poses a need for robustness. Among different enablers for robustness the appropriate ones for specific production systems have to be identified and evaluated. In this cooperative paper multi-objective decision support models will be presented evaluating the best enablers for the levels of production network, plant and shop-floor. The suggested models for the stabilization of the production system's performance under volatile environment use analytical and simulation based approaches on the regarded levels.
  • Publication
    Automatic simulation model generation supported by data stored in low level controllers
    ( 2012)
    Popovics, G.
    ;
    Kardos, C.
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    Pfeiffer, A.
    ;
    Kádár, B.
    ;
    Vén, Z.
    ;
    Monostori, L.
    One of the most widespread techniques to evaluate various aspects of a manufacturing system is discrete-event simulation (DES). However, building a simulation model of a manufacturing system is a difficult task and needs great resource expenditures. Automated data collection and model buildup can drastically reduce the time of the design phase as well as support model reusability. Since most of the manufacturing systems are controlled by low level controllers (e.g., PLCs, CNCs) they store structure and control logic of the system to be modeled by a DES system. The paper introduces an ongoing research of PLC program processing method for automatic simulation model generation of a conveyor system of a leading automotive factory. Results of the validation process and simulation experiments are also described through a case study.
  • Publication
    Execution data connected simulation for production operation planning and control
    ( 2012)
    Kádár, B.
    ;
    Pfeiffer, A.
    ;
    Monostori, L.
    Due to the large number of the resources, the frequent reengineering of manufacturing processes and the often changing product types, the maintenance of the simulation model and the provision of up-to-date input data are almost always difficult. The paper introduces new quantitative analysis method including processing time and batch calculation algorithms applied in the automatic building of discrete-event simulation systems. The simulation model and the mentioned algorithms are both directly interfaced with real execution databases and the overall solution supports the shop-floor managers in making control decisions. The applicability of the elaborated approach is illustrated by the results of experiments relying on real historical data.
  • Publication
    Novel it solutions for increasing transparency in production and in supply chains
    ( 2011)
    Monostori, L.
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    Ilie-Zudor, E.
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    Kádár, B.
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    Karnok, D.
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    Pfeiffer, A.
    ;
    Kemény, Zs.
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    Szathmári, M.
    The paper summarises the main challenges and problems related to transparency in production firms and networks. The transparency problem of production are treated from three aspects, i.e., factory level data gathering tight-coupled with productions simulation; extracting knowledge from large, complex, time-dependent noisy and anomalous process logs; and identity-based tracking and tracing services within and beyond organizational borders. For all these fields of industrial relevance novel approaches and solutions are presented.