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. 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.
Objectives 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.
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.
Methods In the past, manufacturing simulation tools were classified as languages or simulators. 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.
One of the most used techniques by manufacturing system designers is the discrete event simulation. 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. 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, which is needed in many industries, for example, the food industry.
A framework to evaluate different manufacturing simulation tools was developed by Benedettini & Tjahjono (2009) 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 criterionUsability attributesEffectivenessAccuracy: Extend to which the quality of the output corresponds to the goalEfficiencyTime: How long users take to complete tasks with the productMental effort: Mental resources users need to spend on interaction with the productUser SatisfactionEase of use: General attitudes towards the productSpecific attitudes: Specific attitudes towards or perception of the interaction with the tool The following is a list of popular simulation techniques: 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
The following is a list of common applications of simulation in manufacturing: Number in figureApplicationSimulation Type usually usedDescription1Assembly line balancingDESDesign and balancing of assembly lines2Capacity planningDES, SD, Monte Carlo, Petri-netUncertainty due to changing capacity levels, increasing the current resources, improving current operations to increase capacity3Cellular manufacturingVirtual simulationComparing planning and scheduling in CM, comparing alternative cell formation4Transportation managementDES, ABS, Petri-netFinished products delivery from distribution centers or plants, vehicle routing, logistics, traffic management, congestion pricing5Facility locationHybrid TechniquesLocating facilities to minimize costs6ForecastingSDComparing different forecasting models7Inventory managementDES, Monte carloCost of holding, inventory levels, replenishment, determining batch sizes8Just-in-timeDESDesign of Kanban systems9Process engineering-manufacturingDES, SD, ABS, Monte Carlo, Petri-net, HybridProcess improvement, start-up problems, equipment problems, design of new facility, performance measurement10Process engineering-serviceDES, SD, Distributed simulationNew technologies, scheduling
rules, capacity, layout, analysis of bottlenecks, performance measurement11Production planning and
inventory controlDES, ABS, Distributed, HybridSafety stock, batch size, bottlenecks, forecasting, and scheduling rules12Resource allocationDESAllocating equipment to improve process flows, raw materials to plants, resource selection13SchedulingDESThroughput, reliability of delivery, job sequencing, production scheduling, minimize idle time, demand, order release14Supply chain managementDES, SD, ABS, Simulation gaming, Petri-net, DistributedInstability in supply chain, inventory/distribution systems15Quality managementDES, SDQuality assurance and quality control, supplier quality, continuous improvement, total quality management, lean approach
References