- Email: [email protected]
Simulation in production system life cycle
Computers in Industry 38 Ž1999. 159–172
Simulation in production system life cycle ) Jan , Milan Gregor ´ Kosturiak ˇ Institute of Industrial Engineering, Zilina, SloÕak Republic Department of Industrial Engineering, UniÕersity of Zilina, MoyzesoÕa 20, SK-010 26 Zilina, SloÕak Republic
Abstract People managing production process need a new kind of decision support in the business environment which is being changed 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. q 1999 Elsevier Science B.V. All rights reserved. Keywords: Simulation; Production system life cycle; Dynamic modelling
1. Introduction There are several variables which affect manufacturing enterprises today—rising competition and market globalisation, stringent for high quality, low costs and short throughput times, available new technology, changes in the living standard and the value system, increased environmental problems, etc. Various 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, q421-903500054; fax: q42-89-53541; e-mail: [email protected], http:rrfstroj.utc.skr ; kpi, http:rrwww.produktivita.sk
sions in all phases of a production system’s life cycle. The new requirements for enterprise flexibility, quality improvement, costs and throughput times reduction - cannot be achieved by using the traditional approaches. While the U.S. and European industry developed the grand CIM, FMS, CADrCAM and MRP II projects, Japan introduced Just In Time and Lean Production—not to demonstrate the possibilities of the new technology but to expose operational inefficiencies and waste in the manufacturing process. The main CIM effort was in the flexibility and productivity improvement, but its implementation stressed above all the technical aspects of the factory integration and the most flexible production factor—people—remained in the background. 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. PII: S 0 1 6 6 - 3 6 1 5 Ž 9 8 . 0 0 1 1 6 - X
160
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
velops the skills, knowledge and creativity of the human resources. People in the production need a new kind of decision support in the business environment 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 simulation can be used in design, operation and continuous improvement of complex manufacturing and logistical systems.
2. Business decisions and fast-changing manufacturing environment In order to establish an effective manufacturing strategy in this turbulent environment, companies must optimise fundamental decisions concerning organisational structure, production programme Žproduct variety versus production complexity., manufacturing facilities and the entire logistical chain Žsuppliers, 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 boundaries between the departments in an enterprise ŽFig. 1.. There are also many changes in a shop floor organisation—focused factory, segmentation, fractals, manufacturing cells with self-directed manufacturing teams, etc. These concepts are the answer on many occurring problems in production systems today—e.g., various forms of waste in the production
Žoverproducing, waiting, transporting, unnecessary processing, unnecessary motion, defective parts, unnecessary inventory., isolated MRP from the operational level, wrong production schedules, overloaded production, permanent missed due dates, etc. The traditional systems for production planning and control ŽPPC. work often statically, i.e., they are not able to show the change of the actual situation in the production process in real time Žunexpected machine breakdowns, material shortage, etc... The production order schedule is, for example, planned by using the constant throughput times. But the throughput times are in fact the dynamic quantities, dependent 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 often degraded to operators for data preparing, execution of commands and plans from the computer programme 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 algorithm which 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 engineering methodology Systems engineering ŽSE. is defined w1x as the art of designing and optimising complex systems, starting 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
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
161
Fig. 1. Changes of the enterprise organisation structures.
solving cycle is reiterated in each stage of the project. Systems engineering integrates two methodologies: system design and project management. In the foreground of the system design there are the technical aspects of the project. The project management is
responsible for all the aspects of a project organisation—project planning and control, resource allocation and co-ordination, project organisation, project progress monitoring, documentation, etc. An example of the application of systems engineering
162
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
Fig. 2. Systems engineering in manufacturing system design and simulation application fields in the whole life cycle of production system.
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
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 modernisation and re-design. The new generation of simulation tools should support not only the traditional tasks Žstatistical 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
163
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 facility—various specialists in the team prepare the components and sub-assemblies, 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.
164
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
ponents can be developed in the different locations and their exchange and integration can be realised in the computer network Že.g., Internet.. Project management techniques should also be implemented in this model design phase w2x. The integrated application of a simulation model in the whole life cycle of a logistic or manufacturing system can improve considerable the economic results 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 Žshop 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 process ŽCIP, Kaizen.. This, recent very popular concept, is based on finding and eliminating waste in machinery, labour or production methods w3,4x. The Japanese approach to the improvement process emphasises above all the incremental improvements in the shop floor level in the small teams. Eliyahu Goldratt’s w5,6x view ŽTheory of Constraints, Fig. 3.
Fig. 4. Simulation in continuous improvement process ŽIPI Zilina..
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
is focused above all on the system constraints Žbottlenecks. in the enterprise logistic system and on the integration of the operational measurements Žthroughput, inventory, operating expense. with the overall management measurements Žreturn on investment, 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
165
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 Institute of Industrial Engineering ŽIPI., Zilina and implemented in a number of Slovak companies is shown in Fig. 4 w7–9x. 4. Integrated approach—horizontal 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.
166
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
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.
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
Fig. 7. Logistical system - furniture production.
Ø Horizontal integration of the manufacturing and assembly processes with the entire enterprise logistics chain and with the external processes in the manufacturing environment Žsuppliers and various supply strategies, distribution network, economic changes on the market, demand forecasting, etc... Ø Vertical integration of the decision making processes at strategic, tactical and operational level in production planning and control system. ŽFig. 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: Ø Over capacity Žincreased overhead costs—light, power, heat, insurance, increased building costs, additional capital costs for unused equipment.. Ø Under-Capacity Žovertime costs and possible lost business due to longer throughput times and inefficient inventory floating..
167
Detailed simulation analyses that enable to finetune 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 applications of simulation. On-line simulation integrated with the enterprise information system and shop floor data collection system offers the following main advantages: Ø Direct bi-directional data exchange between simulation model and its environment during simulation run. Ø Pro-active management support which optimally integrates the advantages of the computer technology and human resources. Ø Flexible and event-driven analyses to provide visibility of what impact of unanticipated changes that occur will have on the shop floor. Ø Graphical user interface and animation. Ø Testing of the ‘what if’ or ‘what now’ scenarios Že.g., re-routing orders, re-prioritising a specific
Fig. 8. Simulation results - production output and throughput times.
168
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
Fig. 9. Flexible manufacturing system.
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
Fig. 10. Chair production.
169
170
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
Fig. 11. Electric socket manufacturing.
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
171
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 enterprise levels and their integration.
Ø
Ø Ø
5. Industrial applications The simulation specialists of the Institute of Industrial 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: Ø Logistical Chain in Furniture Production and Distribution - the simplified structure of the logistical
Ø
system is presented at Fig. 7 and the main results at Fig. 8. In a Flexible Manufacturing System ŽFig. 9. the production throughput was increased of 100%, and the throughput times were decreased of 30%. Also the testing of various control strategies brought considerable improvement of the production indicators. Fig. 10 presents the results of a simulation projects 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 production 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 involvement and motivation of people. This crucially affect the methods and tools for designing and managing these complex systems which have shorter life
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J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
cycles due to changing requirements and new technologies. The wide availability of simulation tools and powerful computers create the appropriate conditions 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 company?’ However, the right formulation of this question 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 ‘‘simulationist’’. He must manage this method, the simulation tool, the required theoretical basis and he must objectively estimate the requirements and costs for the simulation project and the expected profit from this technique.
References w1x F. Daenzer, F. Huber, Systems Engineering ŽVerlag Industrielle Organisation 1985.. w2x E. Slamkova, Industrial ´ M. Gregor, H. Turekova, ´ J. Kosturiak, ˇ Engineering. University of Zilina, 1997 Žin Slovak.. w3x M. Imai, Kaizen, ŽRandom House, 1986.. w4x I. Masın, ˇ´ M. Vytla, Ways to the Higher Productivity. IPI Liberec 1996 Žin Czech.. w5x E.M. Goldratt, The Haystack Syndrome. ŽNorth River Press 1991.. w6x J. Kosturiak, M. Gregor, E. Slamkova, ˇ ´ F. Chromjakova, ´ J. Matuszek, Methods and Tools of the Enterprise Logistics. TU Bielsko Biala 1996. w7x R. Debnar, ´ I. Kuric, Simulation - Tool for Productivity and Profit Increasing. INFORWARE 4r1998. w8x J. Basl, Integration of the Key Software Areas in an Enterprise. 4th International Conference System Integration 96, Prague 1996.
w9x B. Mi Ieta, J. Kral, ´ Production Planning and Control. University of Zilina 1998 Žin Slovak.. Professor Jan born 1961, is ´ Kosturiak, ˇ the Managing Director of the Institute of Industrial Engineering Zilina ŽSlovakia. and he is lecturing production systems design and computer integrated manufacturing at the Department of Industrial Engineering University of Zilina. He has international experience from the Fraunhofer Institute of Production Technology and Automation ŽIPA. in Stuttgart Ž1987–1988, 1992., AESOP GmbH Stuttgart Ž1992., FH UlmrGeislingen Ž1992–1998. and University of Technology, Institute of Flexible Automation—INFA Vienna Ž1993, 1997., TU Salerno Ž1996., Nottingham Trent University Ž1997.. Professor Milan Gregor, born 1955, is the Head of the department of Industrial Engineering at the University of Zilina and he is lecturing computer simulation, decision processes in production and marketing. He has international experience from the University of Technology Vienna Ž1988., Saarlandes University in Ž1992. and BWI ETH Saarbrucken ¨ Ž1993., TU Salerno Ž1996., NotZurich ¨ tingham Trent University, Japan Productivity Centre Ž1997.. Jan and Milan Gregor have published three books: ´ Kosturiak ˇ Factory 2001—Revolution in the Corporate Culture Ž1993, in Czech., Just in Time—Philosophy for a Good Management Ž1994, in Slovak., Simulation of Production Systems Ž1994, 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 companies involving simulation projects and implementing new production philosophies.
Jan and Milan Gregor are lectures on simulation tech´ Kosturiak ˇ nology as visiting professors at TU Lodz Bielsko Biala ŽPoland. and FH Ulm ŽGermany..
Simulation in production system life cycle ) Jan , Milan Gregor ´ Kosturiak ˇ Institute of Industrial Engineering, Zilina, SloÕak Republic Department of Industrial Engineering, UniÕersity of Zilina, MoyzesoÕa 20, SK-010 26 Zilina, SloÕak Republic
Abstract People managing production process need a new kind of decision support in the business environment which is being changed 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. q 1999 Elsevier Science B.V. All rights reserved. Keywords: Simulation; Production system life cycle; Dynamic modelling
1. Introduction There are several variables which affect manufacturing enterprises today—rising competition and market globalisation, stringent for high quality, low costs and short throughput times, available new technology, changes in the living standard and the value system, increased environmental problems, etc. Various 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, q421-903500054; fax: q42-89-53541; e-mail: [email protected], http:rrfstroj.utc.skr ; kpi, http:rrwww.produktivita.sk
sions in all phases of a production system’s life cycle. The new requirements for enterprise flexibility, quality improvement, costs and throughput times reduction - cannot be achieved by using the traditional approaches. While the U.S. and European industry developed the grand CIM, FMS, CADrCAM and MRP II projects, Japan introduced Just In Time and Lean Production—not to demonstrate the possibilities of the new technology but to expose operational inefficiencies and waste in the manufacturing process. The main CIM effort was in the flexibility and productivity improvement, but its implementation stressed above all the technical aspects of the factory integration and the most flexible production factor—people—remained in the background. 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. PII: S 0 1 6 6 - 3 6 1 5 Ž 9 8 . 0 0 1 1 6 - X
160
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
velops the skills, knowledge and creativity of the human resources. People in the production need a new kind of decision support in the business environment 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 simulation can be used in design, operation and continuous improvement of complex manufacturing and logistical systems.
2. Business decisions and fast-changing manufacturing environment In order to establish an effective manufacturing strategy in this turbulent environment, companies must optimise fundamental decisions concerning organisational structure, production programme Žproduct variety versus production complexity., manufacturing facilities and the entire logistical chain Žsuppliers, 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 boundaries between the departments in an enterprise ŽFig. 1.. There are also many changes in a shop floor organisation—focused factory, segmentation, fractals, manufacturing cells with self-directed manufacturing teams, etc. These concepts are the answer on many occurring problems in production systems today—e.g., various forms of waste in the production
Žoverproducing, waiting, transporting, unnecessary processing, unnecessary motion, defective parts, unnecessary inventory., isolated MRP from the operational level, wrong production schedules, overloaded production, permanent missed due dates, etc. The traditional systems for production planning and control ŽPPC. work often statically, i.e., they are not able to show the change of the actual situation in the production process in real time Žunexpected machine breakdowns, material shortage, etc... The production order schedule is, for example, planned by using the constant throughput times. But the throughput times are in fact the dynamic quantities, dependent 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 often degraded to operators for data preparing, execution of commands and plans from the computer programme 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 algorithm which 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 engineering methodology Systems engineering ŽSE. is defined w1x as the art of designing and optimising complex systems, starting 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
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
161
Fig. 1. Changes of the enterprise organisation structures.
solving cycle is reiterated in each stage of the project. Systems engineering integrates two methodologies: system design and project management. In the foreground of the system design there are the technical aspects of the project. The project management is
responsible for all the aspects of a project organisation—project planning and control, resource allocation and co-ordination, project organisation, project progress monitoring, documentation, etc. An example of the application of systems engineering
162
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
Fig. 2. Systems engineering in manufacturing system design and simulation application fields in the whole life cycle of production system.
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
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 modernisation and re-design. The new generation of simulation tools should support not only the traditional tasks Žstatistical 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
163
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 facility—various specialists in the team prepare the components and sub-assemblies, 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.
164
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
ponents can be developed in the different locations and their exchange and integration can be realised in the computer network Že.g., Internet.. Project management techniques should also be implemented in this model design phase w2x. The integrated application of a simulation model in the whole life cycle of a logistic or manufacturing system can improve considerable the economic results 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 Žshop 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 process ŽCIP, Kaizen.. This, recent very popular concept, is based on finding and eliminating waste in machinery, labour or production methods w3,4x. The Japanese approach to the improvement process emphasises above all the incremental improvements in the shop floor level in the small teams. Eliyahu Goldratt’s w5,6x view ŽTheory of Constraints, Fig. 3.
Fig. 4. Simulation in continuous improvement process ŽIPI Zilina..
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
is focused above all on the system constraints Žbottlenecks. in the enterprise logistic system and on the integration of the operational measurements Žthroughput, inventory, operating expense. with the overall management measurements Žreturn on investment, 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
165
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 Institute of Industrial Engineering ŽIPI., Zilina and implemented in a number of Slovak companies is shown in Fig. 4 w7–9x. 4. Integrated approach—horizontal 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.
166
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
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.
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
Fig. 7. Logistical system - furniture production.
Ø Horizontal integration of the manufacturing and assembly processes with the entire enterprise logistics chain and with the external processes in the manufacturing environment Žsuppliers and various supply strategies, distribution network, economic changes on the market, demand forecasting, etc... Ø Vertical integration of the decision making processes at strategic, tactical and operational level in production planning and control system. ŽFig. 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: Ø Over capacity Žincreased overhead costs—light, power, heat, insurance, increased building costs, additional capital costs for unused equipment.. Ø Under-Capacity Žovertime costs and possible lost business due to longer throughput times and inefficient inventory floating..
167
Detailed simulation analyses that enable to finetune 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 applications of simulation. On-line simulation integrated with the enterprise information system and shop floor data collection system offers the following main advantages: Ø Direct bi-directional data exchange between simulation model and its environment during simulation run. Ø Pro-active management support which optimally integrates the advantages of the computer technology and human resources. Ø Flexible and event-driven analyses to provide visibility of what impact of unanticipated changes that occur will have on the shop floor. Ø Graphical user interface and animation. Ø Testing of the ‘what if’ or ‘what now’ scenarios Že.g., re-routing orders, re-prioritising a specific
Fig. 8. Simulation results - production output and throughput times.
168
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
Fig. 9. Flexible manufacturing system.
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
Fig. 10. Chair production.
169
170
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
Fig. 11. Electric socket manufacturing.
J. Kosturiak, M. Gregorr Computers in Industry 38 (1999) 159–172 ˇ
171
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 enterprise levels and their integration.
Ø
Ø Ø
5. Industrial applications The simulation specialists of the Institute of Industrial 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: Ø Logistical Chain in Furniture Production and Distribution - the simplified structure of the logistical
Ø
system is presented at Fig. 7 and the main results at Fig. 8. In a Flexible Manufacturing System ŽFig. 9. the production throughput was increased of 100%, and the throughput times were decreased of 30%. Also the testing of various control strategies brought considerable improvement of the production indicators. Fig. 10 presents the results of a simulation projects 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 production 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 involvement and motivation of people. This crucially affect the methods and tools for designing and managing these complex systems which have shorter life
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cycles due to changing requirements and new technologies. The wide availability of simulation tools and powerful computers create the appropriate conditions 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 company?’ However, the right formulation of this question 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 ‘‘simulationist’’. He must manage this method, the simulation tool, the required theoretical basis and he must objectively estimate the requirements and costs for the simulation project and the expected profit from this technique.
References w1x F. Daenzer, F. Huber, Systems Engineering ŽVerlag Industrielle Organisation 1985.. w2x E. Slamkova, Industrial ´ M. Gregor, H. Turekova, ´ J. Kosturiak, ˇ Engineering. University of Zilina, 1997 Žin Slovak.. w3x M. Imai, Kaizen, ŽRandom House, 1986.. w4x I. Masın, ˇ´ M. Vytla, Ways to the Higher Productivity. IPI Liberec 1996 Žin Czech.. w5x E.M. Goldratt, The Haystack Syndrome. ŽNorth River Press 1991.. w6x J. Kosturiak, M. Gregor, E. Slamkova, ˇ ´ F. Chromjakova, ´ J. Matuszek, Methods and Tools of the Enterprise Logistics. TU Bielsko Biala 1996. w7x R. Debnar, ´ I. Kuric, Simulation - Tool for Productivity and Profit Increasing. INFORWARE 4r1998. w8x J. Basl, Integration of the Key Software Areas in an Enterprise. 4th International Conference System Integration 96, Prague 1996.
w9x B. Mi Ieta, J. Kral, ´ Production Planning and Control. University of Zilina 1998 Žin Slovak.. Professor Jan born 1961, is ´ Kosturiak, ˇ the Managing Director of the Institute of Industrial Engineering Zilina ŽSlovakia. and he is lecturing production systems design and computer integrated manufacturing at the Department of Industrial Engineering University of Zilina. He has international experience from the Fraunhofer Institute of Production Technology and Automation ŽIPA. in Stuttgart Ž1987–1988, 1992., AESOP GmbH Stuttgart Ž1992., FH UlmrGeislingen Ž1992–1998. and University of Technology, Institute of Flexible Automation—INFA Vienna Ž1993, 1997., TU Salerno Ž1996., Nottingham Trent University Ž1997.. Professor Milan Gregor, born 1955, is the Head of the department of Industrial Engineering at the University of Zilina and he is lecturing computer simulation, decision processes in production and marketing. He has international experience from the University of Technology Vienna Ž1988., Saarlandes University in Ž1992. and BWI ETH Saarbrucken ¨ Ž1993., TU Salerno Ž1996., NotZurich ¨ tingham Trent University, Japan Productivity Centre Ž1997.. Jan and Milan Gregor have published three books: ´ Kosturiak ˇ Factory 2001—Revolution in the Corporate Culture Ž1993, in Czech., Just in Time—Philosophy for a Good Management Ž1994, in Slovak., Simulation of Production Systems Ž1994, 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 companies involving simulation projects and implementing new production philosophies.
Jan and Milan Gregor are lectures on simulation tech´ Kosturiak ˇ nology as visiting professors at TU Lodz Bielsko Biala ŽPoland. and FH Ulm ŽGermany..