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Sustainable Manufacturing and Factory Planning Production Planning and Control III 12.04.2017 Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl © IWF TU Berlin Page 1 Table of Contents  Introduction  Modeling  Discrete Event Simulation in PPC  Operations Research Methods in PPC  Literature and References Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl Page 2 Factory Exemplary processes: Designing Producing Assembling Work schedule building Sales planning Testing festo.com Value creation module Grinding Factory Polishing Transportation  Challenge: A factory is a complex system:  Dynamic behavior (change of status over time) Controlling Storing lagerwirtschaft.info siemens.de Cutting  Elements with relations, high complexity Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl Source: [VDI-5200] Page 3 Ways to study a system  Ways for a model-based analysis of products, processes, equipment and the organization in the factory management. Systems Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl www.es3d.de Analytical solution Experiments Simulation Physical model Mathematical model Simulation Crash Test Simulation Crash Test www.auto.de Model required Experiment with a model of the system www.welt.de www.ep-photovoltaik.de www.siemens.de Experiment with the current system Finite-Element-Net Source: [Law-00] Page 4 Table of Contents  Introduction  Modeling  Discrete Event Simulation in PPC  Operations Research Methods in PPC  Literature and References Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl Page 5 Model  Simplified reproduction of a planned or existing system with its processes in a different conceptual or concrete system.  Its differences from the real system in terms of its characteristics that are relevant to the investigation are within a given range of tolerance. [VDI-3633]  Each Model is only a limited valid representation of the original.  The representations purpose determines the model type and degree of detail.  System models form the effect structures in a way that describes the system behavior.  Simulation models form the system behavior in a way so that they can be simulated. Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl Quelle: [VDI-3633] Page 6 Modeling: From the system to the model  Goal: mapping of a real or fictive system. System barrier System elements  Abstraction from the Original to the Model (mapping of relevant attributes).  Modeling by focusing on special aspects, e.g. technology, logistics of a production system with appropriate level of detail.  E2 Input E1 E.g. logistics of a factory, assembly / manufacturing processes, capacities, schedules, places. Output E5 E4 E3  Level of detail and the size of the model depends on the goal of the analysis, e.g.: Original (system, reality)  Model    Reduction of cycle times, reduction of queues, capacity balancing.  modeling / models do not have an end in itself; they can end in an aimless analysis. Input E2 E1 E3 Output E5 E4 A model is an abstracted reproduction of a system. Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl Source: [Küh-06] Page 7 Abstraction Detailed model doesn’t lead often to effectiveness Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl Bad, imprecise model leads to no or bad perceptions Source: [ASIM-09] Page 8 Modeling Modes  Modeling approaches: Planning Levels Network B o t t o m u p Factory Departments System Cell Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl  Top-down:  Initial point: the overall system which has to be modeled (factory location, factory area) with undetailed subsystems.  Bottom-up  Modeling of subsystems.  Then these subsystems Workstation Process T o p d o w n E.g. Milling will be connected to bigger subsystems, until the whole system is modeled. Quelle: [Wie-07] Page 9 Digital Model (examples)  Classification: Digital Model Static Model Dynamic Model Geometry oriented Structure- und processoriented models 2D-Models Bill of materials (BOM) Discrete-event oriented model 3D-Models Work schedules Kinematic model Further focus Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl Simulation models Finite-ElementModels Quelle: [Küh-06] Page 10 Table of Contents  Introduction  Modeling  Discrete Event Simulation in PPC  Operations Research Methods in PPC  Literature and References Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl Page 11 Simulation  Simulare [lat]: immitate, dissemble, copy  “Representation of a system with its dynamic processes in an experimentable model to reach findings which are transferable to reality; in particular, the processes are developed over time. “[VDI-3633]  Simulation supports the evaluation of e.g.  schedule alternatives,  emergency strategies,  training of new employees,  prognosis,  quantity planning,  order monitoring, Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl Quelle: [VDI-3633] Page 12 What is Simulation ?  ….look into the future, predicting what can or will happen (Gartner 2010)  ….to know what happens before it happens (Rockwell Software)  ….. if you do not know it, simulate it  ….answering the „What if..“ questions Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl Page 13 Some Benefits of Simulation  Improves the decision making with minimal costs  Compress and expands time to achieve a required level of detail  Explore possibilities with minimal expenses  Understand the complexity of a system and diagnose problems  Identify system constrains and limitations  Test and evaluate possible solutions  Examine the dynamic behaviour of a system Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl Page 14 Arguments to Apply Simulation When:  New technology / methods are entered,  The limit of analytic methods is reached,  System connections are too complex for human imagination  Experiments on the real system are not possible or are too expensive,  The dynamic behaviour of a system is object of the analysis. (combinations are possible) Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl Page 15 Simulation cycle  A general procedure of a simulation project.  A well defined goal of the simulation study has to be formulated initially, since the degree of abstraction dependents on this.  Simulation itself does not involve any optimization.  However, the systematic Simulation cycle Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl variation of parameters can be supported by mathematical optimization procedures to achieve a favorable parameter configuration in terms of the simulation goals. Source: [Küh-06], [VDI-3633] Page 16 Example Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl Page 17 Model Components for Simulation Models SIMULATION MODEL CAPACITY PRODUCTS MANUFACTURE CONTROL Machine Tools Process Plans Order Release Utilisation Fixtures Routings Dispatch Lead Time Tools Lot Sizes Conveyors Production Programme Transport control Production Output Buffers Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl EVALUATION Page 18 Discrete Event Simulation  Modelling and simulation of events that occur over the time, with variability and system interactions to provide highly accurate predictions of system performance and capabilities under a virtual set of conditions.  Discrete event simulation  In a discrete event-oriented simulation, the changes in the system occur in a defined point of time.  Once the event occurs, a change in the system condition takes place. Events controls the progress in the simulation. It is assumed that between events there is not any change in the state of the system.  Components of the system can be in any one of a number of discrete states at any point of time (machine may be idle or working).  Components change from one state to another instantaneously.  Discrete modeling considered changes occur instantaneously even though in real life may take a short period of time (loading and unloading of a job into a machine).  Two basic elements :  Rules which determine when the next event occur,  Rules for changing the state of the model when the next event occurs. Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl Source: [VDI-3633] Page 19 Continuous vs. discrete event simulation Simulation modelling • independent variable: time Discrete event simulation • dependent variables: function of time (e.g. machine status, number of parts) Combined simulation Dependent variables: change only at distinct points in simulation time (e.g. machine change status from busy to idle, broken or booked) Time Technische Universität Berlin Institute for Machine Tools and Factory Management Univ.-Prof. Dr.-Ing. Holger Kohl Dependent variables: are continuous function of time (e.g. time required to unload an oil tanker) Dependent variable Dependent variable Discrete event simulation Continuous simulation Time Continuous simulation Source: [VDI-3633] Page 20