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:
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]
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]
The following is a list of common applications of simulation in manufacturing: [9]
Number in figure | Application | Simulation Type usually used | Description |
1 | Assembly line balancing | DES | Design and balancing of assembly lines |
2 | Capacity planning | DES, SD, Monte Carlo, Petri-net | Uncertainty due to changing capacity levels, increasing the current resources, improving current operations to increase capacity |
3 | Cellular manufacturing | Virtual simulation | Comparing planning and scheduling in CM, comparing alternative cell formation |
4 | Transportation management | DES, ABS, Petri-net | Finished products delivery from distribution centers or plants, vehicle routing, logistics, traffic management, congestion pricing |
5 | Facility location | Hybrid Techniques | Locating facilities to minimize costs |
6 | Forecasting | SD | Comparing different forecasting models |
7 | Inventory management | DES, Monte carlo | Cost of holding, inventory levels, replenishment, determining batch sizes |
8 | Just-in-time | DES | Design of Kanban systems |
9 | Process engineering-manufacturing | DES, SD, ABS, Monte Carlo, Petri-net, Hybrid | Process improvement, start-up problems, equipment problems, design of new facility, performance measurement |
10 | Process engineering-service | DES, SD, Distributed simulation | New technologies, scheduling rules, capacity, layout, analysis of bottlenecks, performance measurement |
11 | Production planning and inventory control | DES, ABS, Distributed, Hybrid | Safety stock, batch size, bottlenecks, forecasting, and scheduling rules |
12 | Resource allocation | DES | Allocating equipment to improve process flows, raw materials to plants, resource selection |
13 | Scheduling | DES | Throughput, reliability of delivery, job sequencing, production scheduling, minimize idle time, demand, order release |
14 | Supply chain management | DES, SD, ABS, Simulation gaming, Petri-net, Distributed | 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 |
A simulation is an imitative representation of a process or system that could exist in the real world. In this broad sense, simulation can often be used interchangeably with model. Sometimes a clear distinction between the two terms is made, in which simulations require the use of models; the model represents the key characteristics or behaviors of the selected system or process, whereas the simulation represents the evolution of the model over time. Another way to distinguish between the terms is to define simulation as experimentation with the help of a model. This definition includes time-independent simulations. Often, computers are used to execute the simulation.
Computer-aided manufacturing (CAM) also known as computer-aided modeling or computer-aided machining is the use of software to control machine tools in the manufacturing of work pieces. This is not the only definition for CAM, but it is the most common. It may also refer to the use of a computer to assist in all operations of a manufacturing plant, including planning, management, transportation and storage. Its primary purpose is to create a faster production process and components and tooling with more precise dimensions and material consistency, which in some cases, uses only the required amount of raw material, while simultaneously reducing energy consumption. CAM is now a system used in schools and lower educational purposes. CAM is a subsequent computer-aided process after computer-aided design (CAD) and sometimes computer-aided engineering (CAE), as the model generated in CAD and verified in CAE can be input into CAM software, which then controls the machine tool. CAM is used in many schools alongside CAD to create objects.
Computer simulation is the process of mathematical modelling, performed on a computer, which is designed to predict the behaviour of, or the outcome of, a real-world or physical system. The reliability of some mathematical models can be determined by comparing their results to the real-world outcomes they aim to predict. Computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics, astrophysics, climatology, chemistry, biology and manufacturing, as well as human systems in economics, psychology, social science, health care and engineering. Simulation of a system is represented as the running of the system's model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions.
A hybrid system is a dynamical system that exhibits both continuous and discrete dynamic behavior – a system that can both flow and jump. Often, the term "hybrid dynamical system" is used, to distinguish over hybrid systems such as those that combine neural nets and fuzzy logic, or electrical and mechanical drivelines. A hybrid system has the benefit of encompassing a larger class of systems within its structure, allowing for more flexibility in modeling dynamic phenomena.
Operations management is concerned with designing and controlling the production of goods and services, ensuring that businesses are efficient in using resources to meet customer requirements.
In control theory, advanced process control (APC) refers to a broad range of techniques and technologies implemented within industrial process control systems. Advanced process controls are usually deployed optionally and in addition to basic process controls. Basic process controls are designed and built with the process itself, to facilitate basic operation, control and automation requirements. Advanced process controls are typically added subsequently, often over the course of many years, to address particular performance or economic improvement opportunities in the process.
A discrete-event simulation (DES) models the operation of a system as a (discrete) sequence of events in time. Each event occurs at a particular instant in time and marks a change of state in the system. Between consecutive events, no change in the system is assumed to occur; thus the simulation time can directly jump to the occurrence time of the next event, which is called next-event time progression.
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Model-based design (MBD) is a mathematical and visual method of addressing problems associated with designing complex control, signal processing and communication systems. It is used in many motion control, industrial equipment, aerospace, and automotive applications. Model-based design is a methodology applied in designing embedded software.
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Continuous Simulation refers to simulation approaches where a system is modeled with the help of variables that change continuously according to a set of differential equations.
FlexSim is a discrete-event simulation software package developed by FlexSim Software Products, Inc. The FlexSim product family currently includes the general purpose FlexSim product and healthcare systems modeling environment.
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Micro Saint Sharp is a general purpose discrete-event simulation and human performance modeling software tool developed by Alion Science and Technology. It is developed using C# and the .NET Framework. Micro Saint Sharp allows users to create discrete-event simulations as visual task networks with logic defined using the C# programming language.
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