Simulation in manufacturing systems is the use of software to make computer models of manufacturing systems, so to analyze them and thereby obtain important information. It has been syndicated as the second most popular management science among manufacturing managers.[1][2] However, its use has been limited due to the complexity of some software packages, and to the lack of preparation some users have in the fields of probability and statistics.
This technique represents a valuable tool used by engineers when evaluating the effect of capital investment in equipment and physical facilities like factory plants, warehouses, and distribution centers. Simulation can be used to predict the performance of an existing or planned system and to compare alternative solutions for a particular design problem.[3]
The most important objective of simulation in manufacturing is the understanding of the change to the whole system because of some local changes. It is easy to understand the difference made by changes in the local system but it is very difficult or impossible to assess the impact of this change in the overall system. Simulation gives us some measure of this impact. Measures which can be obtained by a simulation analysis are:
Parts produced per unit time
Time spent in system by parts
Time spent by parts in queue
Time spent during transportation from one place to another
In time deliveries made
Build up of the inventory
Inventory in process
Percent utilization of machines and workers.
Use of simulation in manufacturing
Some other benefits include Just-in-time manufacturing, calculation of optimal resources required, validation of the proposed operation logic for controlling the system, and data collected during modelling that may be used elsewhere.
The following is an example: In a manufacturing plant one machine processes 100 parts in 10 hours but the parts coming to the machine in 10 hours is 150. So there is a buildup of inventory. This inventory can be reduced by employing another machine occasionally. Thus we understand the reduction in local inventory buildup. But now this machine produces 150 parts in 10 hours which might not be processed by the next machine and thus we have just shifted the in-process inventory from one machine to another without having any impact on overall production
Simulation is used to address some issues in manufacturing as follows: In workshop to see the ability of system to meet the requirement, To have optimal inventory to cover for machine failures.[4]
MethodsEdit
In the past, manufacturing simulation tools were classified as languages or simulators.[4] Languages were very flexible tools, but rather complicated to use by managers and too time consuming. Simulators were more user friendly but they came with rather rigid templates that didn’t adapt well enough to the rapidly changing manufacturing techniques. Nowadays, there is software available that combines the flexibility and user friendliness of both, but still some authors have reported that the use of this simulation to design and optimize manufacturing processes is relatively low.[3][5]
One of the most used techniques by manufacturing system designers is the discrete event simulation.[6] This type of simulation allows to assess the system’s performance by statistically and probabilistically reproducing the interactions of all its components during a determined period of time. In some cases, manufacturing systems modelling needs a continuous simulation approach.[7] These are the cases where the states of the system change continuously, like, for example, in the movement of liquids in oil refineries or chemical plants. As continuous simulation cannot be modeled by digital computers, it is done by taking small discrete steps. This is a useful feature, since there are many cases where both, continuous and discrete simulation, have to be combined. This is called hybrid simulation,[8] which is needed in many industries, for example, the food industry.[3]
A framework to evaluate different manufacturing simulation tools was developed by Benedettini & Tjahjono (2009)[3] using the ISO 9241 definition of usability: “the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency, and satisfaction in a specified context of use.” This framework considered effectiveness, efficiency and user satisfaction as the three main performance criterion as follow:
Performance criterion
Usability attributes
Effectiveness
Accuracy: Extend to which the quality of the output corresponds to the goal
Efficiency
Time: How long users take to complete tasks with the product
Mental effort: Mental resources users need to spend on interaction with the product
User Satisfaction
Ease of use: General attitudes towards the product
Specific attitudes: Specific attitudes towards or perception of the interaction with the tool
The following is a list of popular simulation techniques:[9]
Instability in supply chain, inventory/distribution systems
15
Quality management
DES, SD
Quality assurance and quality control, supplier quality, continuous improvement, total quality management, lean approach
ReferencesEdit
^Rasmussen, J.J.; George, T. (1978). "After 25 years: A survey of operations research alumni, Case Western Reserve University". Interfaces. 8 (3): 48–52. doi:10.1287/inte.8.3.48.
^Lane, Michael S.; Mansour, Ali H.; Harpell, John L. (1993-04-01). "Operations Research Techniques: A Longitudinal Update 1973–1988". Interfaces. 23 (2): 63–68. doi:10.1287/inte.23.2.63. ISSN 0092-2102.
^ abcdBenedettini, Ornella; Tjahjono, Benny (2008-08-13). "Towards an improved tool to facilitate simulation modelling of complex manufacturing systems". The International Journal of Advanced Manufacturing Technology. 43 (1–2): 191–199. doi:10.1007/s00170-008-1686-z. ISSN 0268-3768. S2CID 110079763.
^ abVelazco, Enio E. (1994-01-01). "Simulation of manufacturing systems". International Journal of Continuing Engineering Education and Life Long Learning. 4 (1–2): 80–92. doi:10.1504/IJCEELL.1994.030292 (inactive 1 August 2023). ISSN 1560-4624.{{cite journal}}: CS1 maint: DOI inactive as of August 2023 (link)
^Holst, Lars; Bolmsjö, Gunnar (2001-10-01). "Simulation integration in manufacturing system development: a study of Japanese industry". Industrial Management & Data Systems. 101 (7): 339–356. doi:10.1108/EUM0000000005822. ISSN 0263-5577.
^Detty, Richard B.; Yingling, Jon C. (2000-01-01). "Quantifying benefits of conversion to lean manufacturing with discrete event simulation: A case study". International Journal of Production Research. 38 (2): 429–445. doi:10.1080/002075400189509. ISSN 0020-7543. S2CID 110084616.
^Robinson, Stewart (2014-09-22). Simulation: The Practice of Model Development and Use. Palgrave Macmillan. ISBN9781137328038.
^Venkateswaran, J.; Son, Y.-J. (2005-10-15). "Hybrid system dynamic—discrete event simulation-based architecture for hierarchical production planning". International Journal of Production Research. 43 (20): 4397–4429. CiteSeerX10.1.1.535.7314. doi:10.1080/00207540500142472. ISSN 0020-7543. S2CID 17204231.
^ abJahangirian, Mohsen; Eldabi, Tillal; Naseer, Aisha; Stergioulas, Lampros K.; Young, Terry (2010-05-16). "Simulation in manufacturing and business: A review". European Journal of Operational Research. 203 (1): 1–13. doi:10.1016/j.ejor.2009.06.004.
September 01, 2023
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Simulation in manufacturing systems is the use of software to make computer models of manufacturing systems so to analyze them and thereby obtain important information It has been syndicated as the second most popular management science among manufacturing managers 1 2 However its use has been limited due to the complexity of some software packages and to the lack of preparation some users have in the fields of probability and statistics This technique represents a valuable tool used by engineers when evaluating the effect of capital investment in equipment and physical facilities like factory plants warehouses and distribution centers Simulation can be used to predict the performance of an existing or planned system and to compare alternative solutions for a particular design problem 3 Contents 1 Objectives 2 Methods 3 Applications 4 ReferencesObjectives EditThe most important objective of simulation in manufacturing is the understanding of the change to the whole system because of some local changes It is easy to understand the difference made by changes in the local system but it is very difficult or impossible to assess the impact of this change in the overall system Simulation gives us some measure of this impact Measures which can be obtained by a simulation analysis are Parts produced per unit time Time spent in system by parts Time spent by parts in queue Time spent during transportation from one place to another In time deliveries made Build up of the inventory Inventory in process Percent utilization of machines and workers nbsp Use of simulation in manufacturingSome other benefits include Just in time manufacturing calculation of optimal resources required validation of the proposed operation logic for controlling the system and data collected during modelling that may be used elsewhere The following is an example In a manufacturing plant one machine processes 100 parts in 10 hours but the parts coming to the machine in 10 hours is 150 So there is a buildup of inventory This inventory can be reduced by employing another machine occasionally Thus we understand the reduction in local inventory buildup But now this machine produces 150 parts in 10 hours which might not be processed by the next machine and thus we have just shifted the in process inventory from one machine to another without having any impact on overall productionSimulation is used to address some issues in manufacturing as follows In workshop to see the ability of system to meet the requirement To have optimal inventory to cover for machine failures 4 Methods EditIn the past manufacturing simulation tools were classified as languages or simulators 4 Languages were very flexible tools but rather complicated to use by managers and too time consuming Simulators were more user friendly but they came with rather rigid templates that didn t adapt well enough to the rapidly changing manufacturing techniques Nowadays there is software available that combines the flexibility and user friendliness of both but still some authors have reported that the use of this simulation to design and optimize manufacturing processes is relatively low 3 5 One of the most used techniques by manufacturing system designers is the discrete event simulation 6 This type of simulation allows to assess the system s performance by statistically and probabilistically reproducing the interactions of all its components during a determined period of time In some cases manufacturing systems modelling needs a continuous simulation approach 7 These are the cases where the states of the system change continuously like for example in the movement of liquids in oil refineries or chemical plants As continuous simulation cannot be modeled by digital computers it is done by taking small discrete steps This is a useful feature since there are many cases where both continuous and discrete simulation have to be combined This is called hybrid simulation 8 which is needed in many industries for example the food industry 3 A framework to evaluate different manufacturing simulation tools was developed by Benedettini amp Tjahjono 2009 3 using the ISO 9241 definition of usability the extent to which a product can be used by specified users to achieve specified goals with effectiveness efficiency and satisfaction in a specified context of use This framework considered effectiveness efficiency and user satisfaction as the three main performance criterion as follow Performance criterion Usability attributesEffectiveness Accuracy Extend to which the quality of the output corresponds to the goalEfficiency Time How long users take to complete tasks with the productMental effort Mental resources users need to spend on interaction with the productUser Satisfaction Ease of use General attitudes towards the productSpecific attitudes Specific attitudes towards or perception of the interaction with the toolThe following is a list of popular simulation techniques 9 Discrete event simulation DES System dynamics SD Agent based modelling ABM Intelligent simulation based on an integration of simulation and artificial intelligence AI techniques Petri net Monte Carlo simulation MCS Virtual simulation allows the user to model the system in a 3D immersive environment Hybrid techniques combination of different simulation techniques Applications Edit nbsp Number of papers reviewed by Jahangirian et al 2010 by applicationThe following is a list of common applications of simulation in manufacturing 9 Number in figure Application Simulation Type usually used Description1 Assembly line balancing DES Design and balancing of assembly lines2 Capacity planning DES SD Monte Carlo Petri net Uncertainty due to changing capacity levels increasing the current resources improving current operations to increase capacity3 Cellular manufacturing Virtual simulation Comparing planning and scheduling in CM comparing alternative cell formation4 Transportation management DES ABS Petri net Finished products delivery from distribution centers or plants vehicle routing logistics traffic management congestion pricing5 Facility location Hybrid Techniques Locating facilities to minimize costs6 Forecasting SD Comparing different forecasting models7 Inventory management DES Monte carlo Cost of holding inventory levels replenishment determining batch sizes8 Just in time DES Design of Kanban systems9 Process engineering manufacturing DES SD ABS Monte Carlo Petri net Hybrid Process improvement start up problems equipment problems design of new facility performance measurement10 Process engineering service DES SD Distributed simulation New technologies scheduling rules capacity layout analysis of bottlenecks performance measurement11 Production planning and inventory control DES ABS Distributed Hybrid Safety stock batch size bottlenecks forecasting and scheduling rules12 Resource allocation DES Allocating equipment to improve process flows raw materials to plants resource selection13 Scheduling DES Throughput reliability of delivery job sequencing production scheduling minimize idle time demand order release14 Supply chain management DES SD ABS Simulation gaming Petri net Distributed Instability in supply chain inventory distribution systems15 Quality management DES SD Quality assurance and quality control supplier quality continuous improvement total quality management lean approachReferences Edit Rasmussen J J George T 1978 After 25 years A survey of operations research alumni Case Western Reserve University Interfaces 8 3 48 52 doi 10 1287 inte 8 3 48 Lane Michael S Mansour Ali H Harpell John L 1993 04 01 Operations Research Techniques A Longitudinal Update 1973 1988 Interfaces 23 2 63 68 doi 10 1287 inte 23 2 63 ISSN 0092 2102 a b c d Benedettini Ornella Tjahjono Benny 2008 08 13 Towards an improved tool to facilitate simulation modelling of complex manufacturing systems The International Journal of Advanced Manufacturing Technology 43 1 2 191 199 doi 10 1007 s00170 008 1686 z ISSN 0268 3768 S2CID 110079763 a b Velazco Enio E 1994 01 01 Simulation of manufacturing systems International Journal of Continuing Engineering Education and Life Long Learning 4 1 2 80 92 doi 10 1504 IJCEELL 1994 030292 inactive 1 August 2023 ISSN 1560 4624 a href Template Cite journal html title Template Cite journal cite journal a CS1 maint DOI inactive as of August 2023 link Holst Lars Bolmsjo Gunnar 2001 10 01 Simulation integration in manufacturing system development a study of Japanese industry Industrial Management amp Data Systems 101 7 339 356 doi 10 1108 EUM0000000005822 ISSN 0263 5577 Detty Richard B Yingling Jon C 2000 01 01 Quantifying benefits of conversion to lean manufacturing with discrete event simulation A case study International Journal of Production Research 38 2 429 445 doi 10 1080 002075400189509 ISSN 0020 7543 S2CID 110084616 Robinson Stewart 2014 09 22 Simulation The Practice of Model Development and Use Palgrave Macmillan ISBN 9781137328038 Venkateswaran J Son Y J 2005 10 15 Hybrid system dynamic discrete event simulation based architecture for hierarchical production planning International Journal of Production Research 43 20 4397 4429 CiteSeerX 10 1 1 535 7314 doi 10 1080 00207540500142472 ISSN 0020 7543 S2CID 17204231 a b Jahangirian Mohsen Eldabi Tillal Naseer Aisha Stergioulas Lampros K Young Terry 2010 05 16 Simulation in manufacturing and business A review European Journal of Operational Research 203 1 1 13 doi 10 1016 j ejor 2009 06 004 Retrieved from https en wikipedia org w index php title Simulation in manufacturing systems amp oldid 1168169237, wikipedia, wiki, book, books, library,
