science

Simulation in manufacturing systems

Simulation in manufacturing systems is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Simulation in manufacturing systems rather than just read about it. In short: 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.

Key takeaways

Simulation in manufacturing systems belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Simulation in manufacturing systems to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Simulation in manufacturing systems from memory before moving on to harder problems.

Reference excerpt

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:

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:

References

Illustrations

Worked examples

Example 1 — a first encounter with Simulation in manufacturing systems

Start with the simplest possible case. Write down what Simulation in manufacturing systems claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Simulation in manufacturing systems before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Simulation in manufacturing systems ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Simulation in manufacturing systems

In research: Simulation in manufacturing systems appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Simulation in manufacturing systems in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Simulation in manufacturing systems is common in secondary-school and first-year university syllabi. It links to neighbouring topics Manufacturing, Simulation, so understanding it makes those chapters shorter.
In everyday life: Look for Simulation in manufacturing systems outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Simulation in manufacturing systems in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Simulation in manufacturing systems means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Simulation in manufacturing systems out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Simulation in manufacturing systems in simple terms?

Why does Simulation in manufacturing systems matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Simulation in manufacturing systems?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Simulation in manufacturing systems.