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.Computers in Industry 38 1999 159172
Simulation in production system life cycle
Jan Kosturiak ), Milan Gregor Institute of Industrial Engineering, Zilina, Sloak Republic
Department of Industrial Engineering, Uniersity of Zilina, Moyzesoa 20, SK-010 26 Zilina, Sloak Republic
Abstract
People managing production process need a new kind of decision support in the business environment which is beingchanged rapidly. They need new tools for dynamic modelling of enterprise processes to search for answers to the following
basic questions: What is to be changed? To be changed into what? How to change it? This paper presents some new trends
in the area of simulation of manufacturing systems and gives some recommendations, derived from experience, for effective
simulation application in the whole production system life cycle. The paper summarises how discrete-event simulation can
be used in the design, operation and continuous improvement of complex manufacturing and logistical systems. A
combination of simulation with systems engineering methodology and the horizontal and vertical extension of simulation
models in an enterprise are described. Last part of the paper briefly presents the main results of above-mentioned approach
in logistics, flexible manufacturing, electrical engineering industry, furniture assembly and tyre manufacturing. q1999
Elsevier Science B.V. All rights reserved.
Keywords: Simulation; Production system life cycle; Dynamic modelling
1. Introduction
There are several variables which affect manufac-
turing enterprises todayrising competition and
market globalisation, stringent for high quality, low
costs and short throughput times, available new tech-
nology, changes in the living standard and the value
system, increased environmental problems, etc. Vari-
ous modelling techniques have experienced a great
boom, due to their ability for functional testing and
optimisation of dynamic processes in an enterprise.These tools are able to analyse complex and dynamic
relationships in production and they support the deci-
)
Corresponding author. Tel.: q421-89-6462703, q421-903-
500054; fax: q42-89-53541; e-mail: [email protected],
http:rrfstroj.utc.skr;kpi, http:rrwww.produktivita.sk
sions in all phases of a production systems life
cycle.
The new requirements for enterprise flexibility,
quality improvement, costs and throughput times
reduction - cannot be achieved by using the tradi-
tional approaches. While the U.S. and European
in du str y d ev elo pe d th e g ra nd C IM, FMS,
CADrCAM and MRP II projects, Japan introduced
Just In Time and Lean Productionnot to demon-
strate the possibilities of the new technology but to
expose operational inefficiencies and waste in themanufacturing process. The main CIM effort was in
the flexibility and productivity improvement, but its
implementation stressed above all the technical as-
pects of the factory integration and the most flexible
production factorpeopleremained in the back-
ground. The new technology must be implemented
into the organisational framework that uses and de-
0166-3615r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved. .P I I : S 0 1 6 6 - 3 6 1 5 9 8 0 0 1 1 6 - X
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velops the skills, knowledge and creativity of the
human resources. People in the production need a
new kind of decision support in the business envi-
ronment which is being changed rapidly. They need
the new tools for dynamic modelling of enterprise
processes in search for answers to the following
basic questions: What is to be changed? To be
changed into what? How to change it?
An enterprise have to be considered as an entire
system in the solving of this questions. The strategic,
tactical and operational decisions in an enterprise
must be co-ordinated. Also the supply, distribution,
and the whole logistical chain of an enterprise must
be optimised as an integrated system. The local focus
on the enterprise processes often leads only to local
improvement. This causes a shift of the problem e.g.,
the movement of the bottlenecks, inventories and
various forms of waste in the factory instead of their
elimination.This paper summarises how discrete-event simula-
tion can be used in design, operation and continuous
improvement of complex manufacturing and logisti-
cal systems.
2. Business decisions and fast-changing manufac-
turing environment
In order to establish an effective manufacturingstrategy in this turbulent environment, companies
must optimise fundamental decisions concerning or-ganisational structure, production programme prod-
.uct variety versus production complexity , manufac-turing facilities and the entire logistical chain sup-
.pliers, production process, distribution and servicing .
Enterprise organisational structures are dramatically
changed today. The hierarchical, centralised and
static structures are transformed into dynamic, agile
structures with the removing the traditional bound-aries between the departments in an enterprise Fig.
.1 .
There are also many changes in a shop floor
organisationfocused factory, segmentation, frac-
tals, manufacturing cells with self-directed manufac-
turing teams, etc. These concepts are the answer on
many occurring problems in production systems to-
daye.g., various forms of waste in the production
overproducing, waiting, transporting, unnecessary
processing, unnecessary motion, defective parts, un-.necessary inventory , isolated MRP from the opera-
tional level, wrong production schedules, overloaded
production, permanent missed due dates, etc.
The traditional systems for production planning .and control PPC work often statically, i.e., they are
not able to show the change of the actual situation inthe production process in real time unexpected ma-
.chine breakdowns, material shortage, etc. . The pro-
duction order schedule is, for example, planned by
using the constant throughput times. But the through-
put times are in fact the dynamic quantities, depen-
dent on the efficiency of the production resources
and on the product mix. Insufficient attention is
given to the order release control in the production
system and to the utilisation of the bottlenecks at the
shop floor. The operation of many PPC systems is
expensive, they are inflexible and people are oftendegraded to operators for data preparing, execution
of commands and plans from the computer pro-
gramme and the level of freedom of decision making
is very restricted. The mentioned problems of the
current PPC systems leads to the fact that the skills
and intellect of people being insufficiently used in
the production. The production supervisor usually
knows very well where the main problems in the
production system are and he has enough experience
for flexible reactions to various situations. Instead of
the difficult control systems with fixed algorithmwhich is often not fully understood by the user, the
production managers need above all the decision
support tools, which enable rapid modelling of the
various control scenarios and testing of possible
consequences of decisions.
3. Combination of simulation with systems engi-
neering methodology
. w xSystems engineering SE is defined 1 as the art
of designing and optimising complex systems, start-
ing with an expressed need and ending up with the
complete set of specifications for all the system
elements. The main phases of systems engineering
are: problem analysis and setting of goals, synthesis
and analysis, evaluation and decision. This problem
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Fig. 1. Changes of the enterprise organisation structures.
solving cycle is reiterated in each stage of the pro-
ject. Systems engineering integrates two methodolo-
gies: system design and project management. In the
foreground of the system design there are the techni-
cal aspects of the project. The project management is
responsible for all the aspects of a project organisa-
tionproject planning and control, resource alloca-
tion and co-ordination, project organisation, project
progress monitoring, documentation, etc. An exam-
ple of the application of systems engineering
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Fig. 2. Systems engineering in manufacturing system design and simulation application fields in the whole life cycle of production system.
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methodology in design and operation of production
systems is presented in Fig. 2.
Systems engineering deals with a system in its
whole life cycle, i.e., from analysis and design,
through implementation and operation to its mod-
ernisation and re-design. The new generation of sim-
ulation tools should support not only the traditionaltasks statistical data analysis, model building and
.verification, etc. but also the decisions concerning
situation analysis and the defining of the project
objectives, the generation of solution variants and
their evaluation, etc. Large simulation models of
logistic systems are designed and built on a project
basis. The features of the object oriented simulation
make team based co-operation in the development of
the model possible. It is similar, for example, in the
assembly of a production facilityvarious special-
ists in the team prepare the components and sub-as-
semblies, which are then assembled into the system.
In the similar way the specific modelling objects and
submodels are designed, tested and finally integrated
into a common hierarchical model. The model com-
Fig. 3. Theory of constraints, simulation and continuous improvement of production system.
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ponents can be developed in the different locations
and their exchange and integration can be realised in .the computer network e.g., Internet . Project man-
agement techniques should also be implemented inw xthis model design phase 2 .
The integrated application of a simulation model
in the whole life cycle of a logistic or manufacturing
system can improve considerable the economic re-
sults of simulation. The rough simulation model,
developed for the purpose of system analysis and
conceptual design, can be refined and used for the
stage of system re-design. The same model, extended
with control functions and interfaces with the envi-
ronment shop floor data collection and production.planning and control database , can support dynamic
scheduling of the production orders, capacity plans,
labour allocation, etc.
A relatively new application area of simulation is
its incorporation into continuous improvement pro- .cess CIP, Kaizen . This, recent very popular con-
cept, is based on finding and eliminating waste inw xmachinery, labour or production methods 3,4 . The
Japanese approach to the improvement process em-
phasises above all the incremental improvements in
the shop floor level in the small teams. Eliyahuw x .Goldratts 5,6 view Theory of Constraints, Fig. 3
.Fig. 4. Simulation in continuous improvement process IPI Zilina .
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is focused above all on the system constraints bot-.tlenecks in the enterprise logistic system and on the
integration of the operational measurements .throughput, inventory, operating expense with the
overall management measurements return on invest-.ment, net profit, cash flow . The local decisions and
improvements must be measured according to their
impact on the global corporate goals. Simulation
technique is an ideal tool for identification of the
real constraints and for testing and evaluation of
the proposed measures and their impact on the entire
company. Integration of modelling methods with a
team based continuous improvement process is an
optimal combination of the best Japanese, American
and European techniques.
A new approach to the integration of simulation
with an improvement process, developed by the In- .stitute of Industrial Engineering IPI , Zilina and
implemented in a number of Slovak companies isw xshown in Fig. 4 79 .
4. Integrated approachhorizontal and vertical
extension of simulation models in an enterprise
The traditional simulation tools make it possible
to model the manufacturing lines, flexible manufac-
Fig. 5. Integration of simulation modelling in enterprise.
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turing systems, manufacturing cells, etc. The future
development of the new simulation systems is di-
rected to the integrated enterprise modelling in two
directions.
Fig. 6. Integration of simulation with manufacturing system design tools.
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Fig. 7. Logistical system - furniture production.
Horizontal integration of the manufacturing and
assembly processes with the entire enterprise logis-
tics chain and with the external processes in themanufacturing environment suppliers and various
supply strategies, distribution network, economic
.changes on the market, demand forecasting, etc. . Vertical integration of the decision making
processes at strategic, tactical and operational level .in production planning and control system. Fig. 5 .
At the strategic level the aggregate system is
modelled and details of the operating or control logic
are not included. The corporate long-term plans for
production requirements and production resources
are prepared on the strategic level. A goal is to
correlate, to the highest degree possible, planned and
actual requirements and resources.
The experience shows two typical mistakes in the
planning without simulation: Over capacity increased overhead costslight,
power, heat, insurance, increased building costs,.additional capital costs for unused equipment .
Under-Capacity overtime costs and possible lost
business due to longer throughput times and inef-.ficient inventory floating .
Detailed simulation analyses that enable to fine-
tune or optimise the performance of a system are
performed at the tactical level. On the tactical level
the production volumes of the individual products
are planned, the due dates for their completion and
the production order release times are scheduled,
orders for raw materials and purchased components
are determined, etc. Daily scheduling decisions are
supported at the operational level. Simulation is used
for example to decide what jobs are running on what
machine and in what order. A plant manager can test
his new schedules or control polices when machine
failure or material shortage occur, etc.
An example of an integration of simulation with
manufacturing system design tools is in Fig. 6.
The above mentioned problems, as well as the
increase of the computer performance and simulation
software capabilities, led to the broad on-line appli-
cations of simulation. On-line simulation integratedwith the enterprise information system and shop
floor data collection system offers the following
main advantages:
Direct bi-directional data exchange between simu-
lation model and its environment during simula-
tion run.
Pro-active management support which optimally
integrates the advantages of the computer tech-
nology and human resources.
Flexible and event-driven analyses to provide vis-
ibility of what impact of unanticipated changesthat occur will have on the shop floor.
Graphical user interface and animation.
Testing of the what if or what now scenariose.g., re-routing orders, re-prioritising a specific
Fig. 8. Simulation results - production output and throughput
times.
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Fig. 9. Flexible manufacturing system.
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Fig. 10. Chair production.
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Fig. 11. Electric socket manufacturing.
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Fig. 12. Tyre production - Matador.
order, re-distributing manufacturing resources,.adding overtime, etc. .
There are more possibilities on how to integrate
simulation in an enterprise structure. The traditional
approach is building of standard interfaces with the
other software packages, e.g., SQL, DDE, RPC,.Socket Interface, etc. . Another way of integration is
the building of specialised simulation toolkits for
supporting decision making processes at various en-
terprise levels and their integration.
5. Industrial applications
The simulation specialists of the Institute of In-
dustrial Engineering Zilina who developed the above
described approach implemented their solutions in
the more than 20 industrial application - above all in
automotive industry, warehousing and logistics,
Transportation and process industry.
The following projects will be briefly presented in
this section:
Logistical Chain in Furniture Production and Dis-
tribution - the simplified structure of the logistical
system is presented at Fig. 7 and the main results
at Fig. 8. . In a Flexible Manufacturing System Fig. 9 the
production throughput was increased of 100%,
and the throughput times were decreased of 30%.
Also the testing of various control strategiesbrought considerable improvement of the produc-
tion indicators.
Fig. 10 presents the results of a simulation pro-
jects in office chair production.
Simulation of an assembly system for electric
sockets brought the results presented at Fig. 11.
Fig. 12 shows simulation model of tyre produc-
tion in Matador Puchov.
6. Conclusion
The new ISO 9000 proposal emphasizes a system
approach to all processes in logistics and production,
their ongoing improvement and the necessary in-
volvement and motivation of people. This crucially
affect the methods and tools for designing and man-
aging these complex systems which have shorter life
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cycles due to changing requirements and new tech-
nologies. The wide availability of simulation tools
and powerful computers create the appropriate condi-
tions for the broad application of simulation methods
in solving the above mentioned problems. Industrial
managers often ask in the following wrong way:
Can we afford the simulation technique in our com-
pany? However, the right formulation of this ques-
tion should be: How long can we still ignore this
technology and make the wrong decisions?
Simulation can lead to considerable improvements
in industrial companies. It can help to identify the
bottlenecks in the enterprise logistic chain or it can
support the decisions concerning investment in new
production technology. The crucial factor of the
efficient simulation application is the simulatio-
nist. He must manage this method, the simulation
tool, the required theoretical basis and he must objec-
tively estimate the requirements and costs for thesimulation project and the expected profit from this
technique.
References
w x 1 F. Daenzer, F. Huber, Systems Engineering Verlag Indus-.trielle Organisation 1985 .
w x2 E. Slamkova, M. Gregor, H. Turekova, J. Kosturiak, Industrial .Engineering. University of Zilina, 1997 in Slovak .
w x .3 M. Imai, Kaizen, Random House, 1986 .
w x4 I. Masn, M. Vytla, Ways to the Higher Productivity. IPI .Liberec 1996 in Czech .w x 5 E.M. Goldratt, The Haystack Syndrome. North River Press
.1991 .w x6 J. Kosturiak, M. Gregor, E. Slamkova, F. Chromjakova, J.
Matuszek, Methods and Tools of the Enterprise Logistics. TU
Bielsko Biala 1996.w x7 R. Debnar, I. Kuric, Simulation - Tool for Productivity and
Profit Increasing. INFORWARE 4r1998.w x8 J. Basl, Integration of the Key Software Areas in an Enter-
prise. 4th International Conference System Integration 96,
Prague 1996.
w x9 B. Mi Ieta, J. Kral, Production Planning and Control. Univer- .sity of Zilina 1998 in Slovak .
Professor Jan Kosturiak, born 1961, is the Managing Director of the Institute of
.Industrial Engineering Zilina Slovakia
and he is lecturing production systems
design and computer integrated manu-
facturing at the Department of IndustrialEngineering University of Zilina. He has
international experience from the Fraun-
hofer Institute of Production Technol- .ogy and Automation IPA in Stuttgart
.19871988, 1992 , AESOP GmbH .Stuttgart 1992 , FH UlmrGeislingen
.19921998 and University of Technology, Institute of Flexible . .AutomationINFA Vienna 1993, 1997 , TU Salerno 1996 , .Nottingham Trent University 1997 .
Professor Milan Gregor, born 1955, is
the Head of the department of Industrial
Engineering at the University of Zilinaand he is lecturing computer simulation,
decision processes in production and
marketing. He has international experi-
ence from the University of Technology .Vienna 1988 , Saarlandes University in
.Saarbrucken 1992 and BWI ETH . .Zurich 1993 , TU Salerno 1996 , Not-
tingham Trent University, Japan Produc- .tivity Centre 1997 .
Jan Kosturiak and Milan Gregor have published three books: Factory 2001Revolution in the Corporate Culture 1993, in
. Czech , Just in TimePhilosophy for a Good Management 1994,. .in Slovak , Simulation of Production Systems 1994, in German
and many papers in a wide variety of journals in the area of
computer simulation, production systems design and production
planning and control. They have consulted with numerous compa-
nies involving simulation projects and implementing new produc-
tion philosophies.
Jan Kosturiak and Milan Gregor are lectures on simulation tech- .nology as visiting professors at TU Lodz Bielsko Biala Poland
.and FH Ulm Germany .
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