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Manufacturing Simulation with Plant Simulation
and SimTalk
Steffen Bangsow
Manufacturing Simulation
with Plant Simulation
and SimTalk
Usage and Programming with
Examples and Solutions
ABC
Steffen Bangsow
Freiligrathstraße 23
08058 Zwickau
Germany
E-mail: steffen@bangsow.de
ISBN 978-3-642-05073-2 e-ISBN 978-3-642-05074-9
DOI 10.1007/978-3-642-05074-9
Library of Congress Control Number: 2010923701
c
2010 Springer-Verlag Berlin Heidelberg
Originally published in German “Fertigungssimulationen mit Plant Simulation und SimTalk” with
Carl Hanser Verlag, Munich.
c
2008
This work is subject to copyright. All rights are reserved, whether the whole or part of the mate-
rial is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,
broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Dupli-
cation of this publication or parts thereof is permitted only under the provisions of the German
Copyright Law of September 9, 1965, in its current version, and permission for use must always
be obtained from Springer. Violations are liable to prosecution under the German Copyright Law.
The use of general descriptive names, registered names, trademarks, etc. in this publication does
not imply, even in the absence of a specific statement, that such names are exempt from the relevant
protective laws and regulations and therefore free for general use.
Typesetting: Camera-ready by author, data conversion by Markus Richter, Heidelberg
Cover Design: Scientific Publishing Services Pvt. Ltd., Chennai, India
Printed on acid-free paper
987654321
springer.com
Preface
Based on the competition of international production networks, the pressure to in-
crease the efficiency of production systems has increased significantly. In addi-
tion, the number of technical components in many products and as a consequence
also the requirements for corresponding assembly processes and logistics proc-
esses increases. International logistics networks require corresponding logistics
concepts.
These requirements can be managed only by using appropriate Digital Factory
tools in the context of a product lifecycle management environment, which allows
reusing data, supports an effective cooperation between different departments, and
provides up-to-date and relevant data to every user who needs it.
Simulating the complete material flow including all relevant production, stor-
age, and transport activities is recognized as a key component of the Digital Fac-
tory in the industry and as of today widely used and accepted. Cutting inventory
and throughput time by 20–60% and enhancing the productivity of existing pro-
duction facilities by 15–20% can be achieved in real-life projects.
The purpose of running simulations varies from strategic to tactical up to opera-
tional goals. From a strategic point of view, users answer questions like which fac-
tory in which country suits best to produce the next generation product taking into
account factors like consequences for logistics, worker efficiency, downtimes, flexi-
bility, storage costs, etc., looking at production strategies for the next years. In this
context, users also evaluate the flexibility of the production system, e.g., for signifi-
cant changes of production numbers –– a topic which becomes more and more im-
portant. On a tactical level, simulation is executed for a time frame of 1–3 months in
average to analyze required resources, optimize the sequence of orders, and lot sizes.
For simulation on an operational level, data are imported about the current status of
production equipment and the status of work in progress to execute a forward simu-
lation till the end of the current shift. In this case, the purpose is to check if the target
output for the shift will be reached and to evaluate emergency strategies in case of
disruptions or capacities being not available unexpectedly.
In any case, users run simulation to take a decision about a new production sys-
tem or evaluate an existing production system. Usually, the value of those systems
is a significant factor for the company, so the users have to be sure that they take
the right decision based on accurate numbers. There are several random processes
in real production systems like technical availabilities, arrival times of assembly
parts, process times of human activities, etc., so stochastic processes play an im-
portant role for throughput simulation. Therefore, Plant Simulation provides a
whole range of easy-to-use tools to analyze models with stochastic processes, to
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