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A simulator-based approach to cellular manufacturing system design
Computers ind. Engng Vol. 29, No. 1-4, pp. 32%331, 1995
Pergamon 0360-8352(95)00093-3
Copyright © 1995 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0360-8352/95 $9.50 + 0.00
A Simulator-based Approach to Cellular Manufacturing System Design Lorace L. Massay, Ph.D., Silvanus J. Udoka, Ph.D. Department of Industrial Engineering,North Carolina A & T State University 419 McNair Hall, Cavensboro,North Carolina 2741 l and Colin O. Benjamin, Ph.D. Department of Engineering Management, University of Missouri-Rolla Rolla, MO 65401 Abstract Cellular Manufacturing (CM) is a product-oriented organizational strategy that is reco~i:,ed as one of the criticaldements in the rejuvenationof outdated and unproductive manufacturing plants. Sucxessful planning, design, and implementation of Cellular Manufacturing Systems (CMS) require the development of sound, replicable system design approaches. In this paper, a simulator-based approach for the design of manufacturing cells and systems is described. The approach utilizes holistic systems design concepts and addresses Hitomi's three fundamental aspects of manufacturing systems: transformational, procedural, and ~ . A distinction is made betweensystemlogic and system structure and the concepts of logical design and physical design are utilized. The approach described can assist the designer by providing a systematic holistic methodology to the CMS design process.
Introduction In order to compete against global challenges, companies need to accord top priority to streamlining their product realizationefforts and sho~-ning developmentcycles of market-competitive products. Cellular Manufacturing (CM) is a product-oriented organizational strategy that is recognized as one of the critical elementsin the rejuvenationof outdated and unproductive manufacturing plants. The aim of CM is to reduce setup times, throughput times and material handling cost, and therefore, to reduce inventories and market response times [Kinney and McGinnis 1987]. The allocation of equipment to a subset of parts will cut setup Coyusing part-family tooling and sequencing), reduce run times and make materials handling more efficient (by reducing setup and move times, wait times, and using smaller transfer batches). In CM, the production facility is arranged along product layout lines and the mmsformation of raw materials into finished products occurs within a single organizationalunit or cell. The machinesare laid out to allow for a continuous work flow through a series of operations and distances between machines are minimized to allow for ease of material transfer within each cell. A Cellular Manufacturing System (CMS) thus represents a mixture of a job shop producing a large variety of parts and a flow shop dedicated to mass production of one product. A CMS in which the individualcells arc integra~t~by an automated material handling system, and a central computer has control over real time routing, load balancing, and work scheduling is referred to as a Flexible Manufacturing System (FMS). CM is receiving considerable attention both flom industry and academia. This level of interest is related both to manufacturing induswy's fight for survival and to the emergence of new manufacturing technologies such as Flexible Manufacturing Cells and Systems, and Computer-Integrated Manufacturing. Because of the complexity of these systems, their efficient design presents continuing challenges for engineeringsystem designers. Theirs,F_c~sfulplanning, design, and implementation require the development of sound, replicablesystem design approaches. Wu (1992) points out that the potential benefits to industrial efficiency offered by the new computer-based technologies will not be fully realized uniess there is a ccmeapcmdinginvestment in research into the design, evaluation, organization, and management of advanced manufacttaing systems. In this paper, a simulator-based approach for the design of manufacturing cells and systems is ~ b e d . The approachutilizes a holisticsystems design concepts and proven design axioms. The approach can be readily adopted by manufacturing system designers and engineers. 327
17th International Conference on Computers and Industrial Engineering
328
CMS Design Concepts The proposed approach addresses the three fundamental aspects of manufacturing systems: transformational, procedural, and structural (Hitomi 1979). Muther and Hales (1979) identify three fundamentalsin layoutplanning (relationships, space, and adjustments) and developed the Systematic Layout Planning(SLP) methodology. This manuallayoutprooedureintegrates capacity decisions, departmentalization and decisionsconcerningthe locationof departments within the layout. Montreuil and Nof (1988) distinguish betweenfacility structure and facility logic, and present the concepts of logical design and physical design of production facilities. Logical design seeks to satisfy the requirements in terms of the facility logic. Physical design involves the layout of machines on a factory floor so that the total time required to transfer material between each pair of machinesis minimized. In order to obtain full benefits, manufacturing cells and systems must be designedand evalu_a_tedas whole systems [Black 1988]. Because of the complexity of these systems, major performancepredictionproblems are ~ t e r e d during their design. Nevertheless, the high investment costs of these systems and the associated high investment risk necessitate the a priori evaluation of cell performance before implementation. Computer simulation can assist in modeling the dynamic behavior in manufacturing cells thus providing a method for predicting performance and for evaluating alternative cell design strategies. The study of manufacturingsystemsby computer simulation can be used as a risk reduction strategy [Laughery 1990]. These general system design concepts can be combined with cell formation techniques to provide a methodology for CMS design. Simulator-based CMS Design The proposed methodology addresses the transformational, procedural, and structural aspects of manufacturingsystems, distinguishes between system logic and system structure, and utilizes the concepts of logical design and physicaldesign. The proposed systemdesignmethodologyemphasizes the axioms described in Table 1 [Massay et al 1994]. These guide the decisions required in designing manufacturing cells and systems and involve virtually every aspect of manufacturing from part design through process planning, staffing, methods, and production planning and scheduling. The design process is divided into four phases; analysis, camceptualdesign,embodimentdesign, and detailed designs. The main focus was on the embodiment design phase in which abstract concepts are developed and refined into more concrete proposals. Figure 1 is an illustration of the embodiment phase. Table 1
AXIOMS FOR CMS DESIGN
DESIGN AXIOM
PROCEDURAL GUIDANCE DERIVED
Top-down system conceptualization and analysis followed by bottom-up system synthesis.
Overall system is conceptualized and then decomposed into subsystems.This is followed by subsystems design.
Simplification is followed by integration.
Subsystems are simplified and are then integrated in a later desi~n-inte~ratin~step to form the whole system.
Alternative generation.
More than one solution is sought for each subsystem and by combining sub-solutions, a number of alternative solutions can be rapidly produced.
Iterative refinement and progressive elaboration.
The design of each subsystem is evaluated and improved progressively.
Holistic evaluation.
The integrated system design is evaluated as a whole.
Concurrent design documentation
The design as well as the design process is documented at the time the design process is being conducted and not at a later time.
In the analysisphase, part-feature/process data are analyzed to identify part families or combinations of families that represent opportunities for exploiting CM. A number of different techniques have been developed for identifying part families; Group Technology code analysis/process flow analysis, Production
17th International Conference on Computers and Industrial Engineering
329
Flow Analysis,mathematicalprogrmmnmgmethods, Cluster Analysis, and visual examination. The identified part families, their features and process plans, production quantities, and planned scheduling methods define the required capabilities and capacities for the cell. These requirements, rather than specific equipment types, provide the input to the preliminary design phase. In the logical cell design stage of the preliminary design phase, production machines along with matmal handling and other support equipment, capable of producing the part families in the required quantity and quality, are identifiecL Then, alternativelogical designs for each cell are developed from the process plans and the identifiedequipment,with appropriateconsiderationof production and inventory control, maintenance, personnel, and computer integration strategies. Simulation models are constructed, evaluated and the best logical design for each cell selected. The part families, production quantities, part-machine routings, in-cell buffer capacities, material handling plan, and personnel requirements are passed as input to the physical cell design stage. In the physical cell design stage, alternative machine and equipment layouts are developed. Personnel work space, part staging, and in-cell buffer space, are determined. Alternative layouts for each logical cell design are developedand evaluated,and the best physicalcell layout chosen. Part families, inter-cell quantities, inter-cellroutings, required machine services, the list of cells and space requirements for each cell, are provided as input to the physical system design stage. In the physical system design stage, the individual cell space requirements are aggregated and compared with the total space available. Inter-cell material handling plans are developed and alternative physical system layouts are developed. For this stage, facility layout methodologies (SPIF) are used. Material flow plans are developed for each alternative physical system design. These plans are used to in the Logical SystemIntegrationStageto determinethe best flow logicfor the system and to obtain estimates of total system performance. The alternative physical system designs together with their respective logical flow system constitute the alternative preliminary designs. These designs are evaluated against corporate performance criteria and the best is selected for detailed design and implementation. Role of simulator-based modeling Simulation models using SIMFACTORY, a discrete event simulator package are used fast at the logical cell design stage. In this stage, efficient material flow is the dominant factor, and simulator is an excellent tool for the analysis and design of the in-ceU material flow. The procedural or management considerationscan be included in the model and the cell performance evaluated. The results of the simulation model can then be used as input information to the physical cell design process. In the Physical Cell Design stage, a CAD system was used for drawing and arranging the machines and equipment within the cells. Workplace layout design principles were incorporated to produce effective workplace designs. Not only was space allocated to machines and equipment, but space was also provided for operators, the material to be worked on, and the work completed. However,at this stage, the cell designs did not make efficient use of total available space. In system design,the objectiveis to minimize inter-cell material movement. System layout is now the dominant factor and therefore layout design precedes the Logical System Integration Stage. In the Physical System Design stage, the cells were arranged and integrated to form complete systems within the total space available. This design-integratingstep improvedthe effectivenessof space utilization and established inter-cell flow patterns. Someof the available layout planning methodologieswere effectively used in this step. In the Logical System Integration stage, individual cells are linked by an inter-cell material transport systems and detailed simulationmodels are developed. Simulation models, in which the models of the individual cells are aggregated, are developed by chaining the cell models. This aggregate model of the total system is used to determine the flow logic for the total system and to obtain performance estimates of the total system. Thus, the complete system design is evaluated as a whole integrated system. IHustrative Case Studies When used in an academicenvironment,simulationanalysiscan provide invaluable insights to systems analysts and decision makers and can serve as a catalyst for stimulating alternative cell/system design improvements. The ease study from the University of Missouri-Rolla described in the following sections of this paper illustrates these concepts and focuses on the logical design of a machining work-cell. The methodologywas appliedto three cases that represent the range of problems and levels of complexity typically encounteredby designers of manufacturing systems. Three catseswere classified according to (1) the area of application (academia, quasi-industry, and industry), and (2) level of system complexity (low, medium, and high). Table2 shows the classificationof the cases included in the study, The key benefit of the methodology ~ I E 29: I/4-g
330
17th International Conference on Computers and Industrial Engineering
lay in its systematic structuring of the activities that need to b¢ considered at certain stages of the cell design process. This ensured that all design issues were attended to at the right time, and no important issues wore ignored.
Table 2.
CLASSIFICATION OF CASES INCLUDED IN STUDY Application Area Academia
quasi-Indust~
High System Complexity
Low
Industry Marathon Electric Plant
UMR FMS DemMaTec FCIM
One case, the MarathonElectricproject,involveda fairly complex inter-cell material transport system. The Logical System Integration Stage was applied and the results obtained showed that what appeared to be a sound PhysicalSystem Design was in effect below the performance targets set for system throughput. The University of Missouri-Rolla Flexible Manufacturing System 03MR FMS) and the DcmMaTec Flexible Computer-Integrated Manufacturing (FCIM) projects were of lower complexity (two and four cells respectively)and the methodology was quite easily applied. Its application to the complex Marathon Electric reorganization project (60 cells) was much more difficult and time consuming. However, in this case the benefits of using the methodologywere most clearly evident. A I~rformance deficiency was discovered in the Physical System Design developed. These results were used to stimulate the search for setup reduction by process and methods engineers in a genuine Concurrent Engineering effort. Conclusion CeUularManufacturing Systems are quite complex and their successful implementation requires the development of a sound, replicable design methodology for cells and systems. Simulation modeling, and in particular manufacturing simulators, can play a pivotal role in developing and evaluating logical cell designs. The methodology illustratedin this paper provides a frameworkfor cell design and overall system performance evaluation. Simulators enhanced by animation play a pivotal role in the logical phases of cell design and system integration. The design methodology, intended for use by system designers and en"gmeers,is equally applicable to the design of new cells and systems, as well as improvement in the operation of existing ones. Consistentlygood results were obtainedwhen it was applied to three case scenarios representing varying levels of system complexity. This provided preliminary confirmation of its effectiveness and shows that it is applicable to the range of situations typically encountered by manufacturing systems designers. References Black, J.T. (1988), "The Design of Manufacturing Cells (Step one to Integrated Manufacturing Systems)", Proceedings of Manufacturing International '88, pp. 143-157. Hitomi, K. (1979), Manufacturing systems engineering, Bristol, PA: Taylor and Francis. Kinney, H.D and McGinnis, L.F. (1987), "Manufacturing Cells Solve Material Handling Problems", Industtqal Engineering, vol. 19, no.8, pp.54-60. Laughery, R. (1'990), "Simulation Changes The Way Industry Thinks About Planning," Industrml Engmeermg,vol.22, no.6, pp. 50,85. Massay, L.L., C.O. Benjamin, and Y. Omurtag (1994), "Design Axioms for Cellular Manufacturing System Design,". Proceedings of the lstt World Automation Congress (WAC '94), August 14-17, 1994, Maui, Hawaii, U.S.A., p.151-156. Montreuil, B. and Nor, S.Y. (1988), "Approaches for Logical vs. Physical Design of Intelligent Production Facilities, Manufacturing Research and Technology 6: Recent Developments in Production Research, pp. 352-360.
331
17th International Conference on Computers and Industrial Engineering
Muther, R and Hales, L. (1979), “Systematic Planning of Industrial Facilities: Vol. l”, Management & Industrial Research Publications, Kansas City, Missouri. SIMFACTORY: Reference Manual and User’s Guide, CACZZ’roductsCompany, La Jolla, CA, 1987. Wu, B (1992). Manufacturing systems design and analysis. New York, NY: Van Nostrand Reinhold.
I
PART FAMILIES QUANTITIES PRoc~ssBs l EQUIPMENT l ROUTING (IN-CEU) l BUfFER CAmCmES l YAlfRIAL HANDIJMG PLAN l SlAmNG
l l
INTER-CELl ROUTIMGS SPACE AVAIlABLE
Figure 1 The Five Stages of the Embodiment Design Phase
PART FAMILIES I @UANTlTiES PROCESSES BQUJPWNT ROUTING (IN-CELL) BUFFER CAPACITIES MATERIAL NANDUNG PLA SZAFFINC . CELL SPACE REGUMEMENTS . CELL SERVICES REQUIRU) 1
Pergamon 0360-8352(95)00093-3
Copyright © 1995 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0360-8352/95 $9.50 + 0.00
A Simulator-based Approach to Cellular Manufacturing System Design Lorace L. Massay, Ph.D., Silvanus J. Udoka, Ph.D. Department of Industrial Engineering,North Carolina A & T State University 419 McNair Hall, Cavensboro,North Carolina 2741 l and Colin O. Benjamin, Ph.D. Department of Engineering Management, University of Missouri-Rolla Rolla, MO 65401 Abstract Cellular Manufacturing (CM) is a product-oriented organizational strategy that is reco~i:,ed as one of the criticaldements in the rejuvenationof outdated and unproductive manufacturing plants. Sucxessful planning, design, and implementation of Cellular Manufacturing Systems (CMS) require the development of sound, replicable system design approaches. In this paper, a simulator-based approach for the design of manufacturing cells and systems is described. The approach utilizes holistic systems design concepts and addresses Hitomi's three fundamental aspects of manufacturing systems: transformational, procedural, and ~ . A distinction is made betweensystemlogic and system structure and the concepts of logical design and physical design are utilized. The approach described can assist the designer by providing a systematic holistic methodology to the CMS design process.
Introduction In order to compete against global challenges, companies need to accord top priority to streamlining their product realizationefforts and sho~-ning developmentcycles of market-competitive products. Cellular Manufacturing (CM) is a product-oriented organizational strategy that is recognized as one of the critical elementsin the rejuvenationof outdated and unproductive manufacturing plants. The aim of CM is to reduce setup times, throughput times and material handling cost, and therefore, to reduce inventories and market response times [Kinney and McGinnis 1987]. The allocation of equipment to a subset of parts will cut setup Coyusing part-family tooling and sequencing), reduce run times and make materials handling more efficient (by reducing setup and move times, wait times, and using smaller transfer batches). In CM, the production facility is arranged along product layout lines and the mmsformation of raw materials into finished products occurs within a single organizationalunit or cell. The machinesare laid out to allow for a continuous work flow through a series of operations and distances between machines are minimized to allow for ease of material transfer within each cell. A Cellular Manufacturing System (CMS) thus represents a mixture of a job shop producing a large variety of parts and a flow shop dedicated to mass production of one product. A CMS in which the individualcells arc integra~t~by an automated material handling system, and a central computer has control over real time routing, load balancing, and work scheduling is referred to as a Flexible Manufacturing System (FMS). CM is receiving considerable attention both flom industry and academia. This level of interest is related both to manufacturing induswy's fight for survival and to the emergence of new manufacturing technologies such as Flexible Manufacturing Cells and Systems, and Computer-Integrated Manufacturing. Because of the complexity of these systems, their efficient design presents continuing challenges for engineeringsystem designers. Theirs,F_c~sfulplanning, design, and implementation require the development of sound, replicablesystem design approaches. Wu (1992) points out that the potential benefits to industrial efficiency offered by the new computer-based technologies will not be fully realized uniess there is a ccmeapcmdinginvestment in research into the design, evaluation, organization, and management of advanced manufacttaing systems. In this paper, a simulator-based approach for the design of manufacturing cells and systems is ~ b e d . The approachutilizes a holisticsystems design concepts and proven design axioms. The approach can be readily adopted by manufacturing system designers and engineers. 327
17th International Conference on Computers and Industrial Engineering
328
CMS Design Concepts The proposed approach addresses the three fundamental aspects of manufacturing systems: transformational, procedural, and structural (Hitomi 1979). Muther and Hales (1979) identify three fundamentalsin layoutplanning (relationships, space, and adjustments) and developed the Systematic Layout Planning(SLP) methodology. This manuallayoutprooedureintegrates capacity decisions, departmentalization and decisionsconcerningthe locationof departments within the layout. Montreuil and Nof (1988) distinguish betweenfacility structure and facility logic, and present the concepts of logical design and physical design of production facilities. Logical design seeks to satisfy the requirements in terms of the facility logic. Physical design involves the layout of machines on a factory floor so that the total time required to transfer material between each pair of machinesis minimized. In order to obtain full benefits, manufacturing cells and systems must be designedand evalu_a_tedas whole systems [Black 1988]. Because of the complexity of these systems, major performancepredictionproblems are ~ t e r e d during their design. Nevertheless, the high investment costs of these systems and the associated high investment risk necessitate the a priori evaluation of cell performance before implementation. Computer simulation can assist in modeling the dynamic behavior in manufacturing cells thus providing a method for predicting performance and for evaluating alternative cell design strategies. The study of manufacturingsystemsby computer simulation can be used as a risk reduction strategy [Laughery 1990]. These general system design concepts can be combined with cell formation techniques to provide a methodology for CMS design. Simulator-based CMS Design The proposed methodology addresses the transformational, procedural, and structural aspects of manufacturingsystems, distinguishes between system logic and system structure, and utilizes the concepts of logical design and physicaldesign. The proposed systemdesignmethodologyemphasizes the axioms described in Table 1 [Massay et al 1994]. These guide the decisions required in designing manufacturing cells and systems and involve virtually every aspect of manufacturing from part design through process planning, staffing, methods, and production planning and scheduling. The design process is divided into four phases; analysis, camceptualdesign,embodimentdesign, and detailed designs. The main focus was on the embodiment design phase in which abstract concepts are developed and refined into more concrete proposals. Figure 1 is an illustration of the embodiment phase. Table 1
AXIOMS FOR CMS DESIGN
DESIGN AXIOM
PROCEDURAL GUIDANCE DERIVED
Top-down system conceptualization and analysis followed by bottom-up system synthesis.
Overall system is conceptualized and then decomposed into subsystems.This is followed by subsystems design.
Simplification is followed by integration.
Subsystems are simplified and are then integrated in a later desi~n-inte~ratin~step to form the whole system.
Alternative generation.
More than one solution is sought for each subsystem and by combining sub-solutions, a number of alternative solutions can be rapidly produced.
Iterative refinement and progressive elaboration.
The design of each subsystem is evaluated and improved progressively.
Holistic evaluation.
The integrated system design is evaluated as a whole.
Concurrent design documentation
The design as well as the design process is documented at the time the design process is being conducted and not at a later time.
In the analysisphase, part-feature/process data are analyzed to identify part families or combinations of families that represent opportunities for exploiting CM. A number of different techniques have been developed for identifying part families; Group Technology code analysis/process flow analysis, Production
17th International Conference on Computers and Industrial Engineering
329
Flow Analysis,mathematicalprogrmmnmgmethods, Cluster Analysis, and visual examination. The identified part families, their features and process plans, production quantities, and planned scheduling methods define the required capabilities and capacities for the cell. These requirements, rather than specific equipment types, provide the input to the preliminary design phase. In the logical cell design stage of the preliminary design phase, production machines along with matmal handling and other support equipment, capable of producing the part families in the required quantity and quality, are identifiecL Then, alternativelogical designs for each cell are developed from the process plans and the identifiedequipment,with appropriateconsiderationof production and inventory control, maintenance, personnel, and computer integration strategies. Simulation models are constructed, evaluated and the best logical design for each cell selected. The part families, production quantities, part-machine routings, in-cell buffer capacities, material handling plan, and personnel requirements are passed as input to the physical cell design stage. In the physical cell design stage, alternative machine and equipment layouts are developed. Personnel work space, part staging, and in-cell buffer space, are determined. Alternative layouts for each logical cell design are developedand evaluated,and the best physicalcell layout chosen. Part families, inter-cell quantities, inter-cellroutings, required machine services, the list of cells and space requirements for each cell, are provided as input to the physical system design stage. In the physical system design stage, the individual cell space requirements are aggregated and compared with the total space available. Inter-cell material handling plans are developed and alternative physical system layouts are developed. For this stage, facility layout methodologies (SPIF) are used. Material flow plans are developed for each alternative physical system design. These plans are used to in the Logical SystemIntegrationStageto determinethe best flow logicfor the system and to obtain estimates of total system performance. The alternative physical system designs together with their respective logical flow system constitute the alternative preliminary designs. These designs are evaluated against corporate performance criteria and the best is selected for detailed design and implementation. Role of simulator-based modeling Simulation models using SIMFACTORY, a discrete event simulator package are used fast at the logical cell design stage. In this stage, efficient material flow is the dominant factor, and simulator is an excellent tool for the analysis and design of the in-ceU material flow. The procedural or management considerationscan be included in the model and the cell performance evaluated. The results of the simulation model can then be used as input information to the physical cell design process. In the Physical Cell Design stage, a CAD system was used for drawing and arranging the machines and equipment within the cells. Workplace layout design principles were incorporated to produce effective workplace designs. Not only was space allocated to machines and equipment, but space was also provided for operators, the material to be worked on, and the work completed. However,at this stage, the cell designs did not make efficient use of total available space. In system design,the objectiveis to minimize inter-cell material movement. System layout is now the dominant factor and therefore layout design precedes the Logical System Integration Stage. In the Physical System Design stage, the cells were arranged and integrated to form complete systems within the total space available. This design-integratingstep improvedthe effectivenessof space utilization and established inter-cell flow patterns. Someof the available layout planning methodologieswere effectively used in this step. In the Logical System Integration stage, individual cells are linked by an inter-cell material transport systems and detailed simulationmodels are developed. Simulation models, in which the models of the individual cells are aggregated, are developed by chaining the cell models. This aggregate model of the total system is used to determine the flow logic for the total system and to obtain performance estimates of the total system. Thus, the complete system design is evaluated as a whole integrated system. IHustrative Case Studies When used in an academicenvironment,simulationanalysiscan provide invaluable insights to systems analysts and decision makers and can serve as a catalyst for stimulating alternative cell/system design improvements. The ease study from the University of Missouri-Rolla described in the following sections of this paper illustrates these concepts and focuses on the logical design of a machining work-cell. The methodologywas appliedto three cases that represent the range of problems and levels of complexity typically encounteredby designers of manufacturing systems. Three catseswere classified according to (1) the area of application (academia, quasi-industry, and industry), and (2) level of system complexity (low, medium, and high). Table2 shows the classificationof the cases included in the study, The key benefit of the methodology ~ I E 29: I/4-g
330
17th International Conference on Computers and Industrial Engineering
lay in its systematic structuring of the activities that need to b¢ considered at certain stages of the cell design process. This ensured that all design issues were attended to at the right time, and no important issues wore ignored.
Table 2.
CLASSIFICATION OF CASES INCLUDED IN STUDY Application Area Academia
quasi-Indust~
High System Complexity
Low
Industry Marathon Electric Plant
UMR FMS DemMaTec FCIM
One case, the MarathonElectricproject,involveda fairly complex inter-cell material transport system. The Logical System Integration Stage was applied and the results obtained showed that what appeared to be a sound PhysicalSystem Design was in effect below the performance targets set for system throughput. The University of Missouri-Rolla Flexible Manufacturing System 03MR FMS) and the DcmMaTec Flexible Computer-Integrated Manufacturing (FCIM) projects were of lower complexity (two and four cells respectively)and the methodology was quite easily applied. Its application to the complex Marathon Electric reorganization project (60 cells) was much more difficult and time consuming. However, in this case the benefits of using the methodologywere most clearly evident. A I~rformance deficiency was discovered in the Physical System Design developed. These results were used to stimulate the search for setup reduction by process and methods engineers in a genuine Concurrent Engineering effort. Conclusion CeUularManufacturing Systems are quite complex and their successful implementation requires the development of a sound, replicable design methodology for cells and systems. Simulation modeling, and in particular manufacturing simulators, can play a pivotal role in developing and evaluating logical cell designs. The methodology illustratedin this paper provides a frameworkfor cell design and overall system performance evaluation. Simulators enhanced by animation play a pivotal role in the logical phases of cell design and system integration. The design methodology, intended for use by system designers and en"gmeers,is equally applicable to the design of new cells and systems, as well as improvement in the operation of existing ones. Consistentlygood results were obtainedwhen it was applied to three case scenarios representing varying levels of system complexity. This provided preliminary confirmation of its effectiveness and shows that it is applicable to the range of situations typically encountered by manufacturing systems designers. References Black, J.T. (1988), "The Design of Manufacturing Cells (Step one to Integrated Manufacturing Systems)", Proceedings of Manufacturing International '88, pp. 143-157. Hitomi, K. (1979), Manufacturing systems engineering, Bristol, PA: Taylor and Francis. Kinney, H.D and McGinnis, L.F. (1987), "Manufacturing Cells Solve Material Handling Problems", Industtqal Engineering, vol. 19, no.8, pp.54-60. Laughery, R. (1'990), "Simulation Changes The Way Industry Thinks About Planning," Industrml Engmeermg,vol.22, no.6, pp. 50,85. Massay, L.L., C.O. Benjamin, and Y. Omurtag (1994), "Design Axioms for Cellular Manufacturing System Design,". Proceedings of the lstt World Automation Congress (WAC '94), August 14-17, 1994, Maui, Hawaii, U.S.A., p.151-156. Montreuil, B. and Nor, S.Y. (1988), "Approaches for Logical vs. Physical Design of Intelligent Production Facilities, Manufacturing Research and Technology 6: Recent Developments in Production Research, pp. 352-360.
331
17th International Conference on Computers and Industrial Engineering
Muther, R and Hales, L. (1979), “Systematic Planning of Industrial Facilities: Vol. l”, Management & Industrial Research Publications, Kansas City, Missouri. SIMFACTORY: Reference Manual and User’s Guide, CACZZ’roductsCompany, La Jolla, CA, 1987. Wu, B (1992). Manufacturing systems design and analysis. New York, NY: Van Nostrand Reinhold.
I
PART FAMILIES QUANTITIES PRoc~ssBs l EQUIPMENT l ROUTING (IN-CEU) l BUfFER CAmCmES l YAlfRIAL HANDIJMG PLAN l SlAmNG
l l
INTER-CELl ROUTIMGS SPACE AVAIlABLE
Figure 1 The Five Stages of the Embodiment Design Phase
PART FAMILIES I @UANTlTiES PROCESSES BQUJPWNT ROUTING (IN-CELL) BUFFER CAPACITIES MATERIAL NANDUNG PLA SZAFFINC . CELL SPACE REGUMEMENTS . CELL SERVICES REQUIRU) 1