1 1999 kosturiak j. comput ind simulation in production system life cycle

7/28/2019 1 1999 Kosturiak J. Comput Ind Simulation in Production System Life Cycle

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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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( )J. Kosturiak, M. Gregorr Computers in Industry 38 1999 159172160

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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( )J. Kosturiak, M. Gregorr Computers in Industry 38 1999 159172 161

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 .

1 1999 kosturiak j. comput ind simulation in production system life cycle

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