Data[ edit ] Business operations can generate a very large amount of information in the form of e-mails, memos, notes from call-centers, news, user groups, chats, reports, web-pages, presentations, image-files, video-files, and marketing material. The management of semi-structured data is an unsolved problem in the information technology industry.
This article illustrates how to use Minitab for Monte Carlo simulations using both a known engineering formula and a DOE equation. This method was initially applied back in the s, when scientists working on the atomic bomb used it to calculate the probabilities of one fissioning uranium atom causing a fission reaction in another.
With uranium in short supply, there was little room for experimental trial and error.
The scientists discovered that as long as they created enough simulated data, they could compute reliable probabilities—and reduce the amount of uranium needed for testing. Today, simulated data is routinely used in situations where resources are limited or gathering real data would be too expensive or impractical.
But at a basic level, all Monte Carlo simulations have four simple steps: Identify the Transfer Equation To do a Monte Carlo simulation, you need a quantitative model of the business activity, plan, or process you wish to explore.
Define the Input Parameters For each factor in your transfer equation, determine how its data are distributed. Some inputs may follow the normal distribution, while others follow a triangular or uniform distribution. You then need to determine distribution parameters for each input.
For instance, you would need to specify the mean and standard deviation for inputs that follow a normal distribution.
Create Random Data To do valid simulation, you must create a very large, random data set for each input—something on the order ofinstances. These random data points simulate the values that would be seen over a long period for each input.
Minitab can easily create random data that follow almost any distribution you are likely to encounter. Simulate and Analyze Process Output With the simulated data in place, you can use your transfer equation to calculate simulated outcomes. Running a large enough quantity of simulated input data through your model will give you a reliable indication of what the process will output over time, given the anticipated variation in the inputs.
Those are the steps any Monte Carlo simulation needs to follow.
Monte Carlo Using a Known Engineering Formula A manufacturing company needs to evaluate the design of a proposed product: You want to estimate the probable performance over thousands of pumps, given natural variation in piston diameter Dstroke length Land strokes per minute RPM.
Ideally, the pump flow across thousands of pumps will have a standard deviation no greater than 0. Identify the Transfer Equation The first step in doing a Monte Carlo simulation is to determine the transfer equation.
In this case, you can simply use an established engineering formula that measures pump flow: Define the Input Parameters Now you must define the distribution and parameters of each input used in the transfer equation.
Volume pumped per stroke is given by this equation: Based on the performance of other pumps your facility has manufactured, you can say that piston diameter is normally distributed with a mean of 0.
Stroke length is normally distributed with a mean of 2. Finally, strokes per minute is normally distributed with a mean of 9. With Minitab you can instantaneously createrows of simulated data.
Enter the mean and standard deviation for piston diameter in the appropriate fields. Press OK to populate the worksheet withdata points randomly sampled from the specified normal distribution.
Simulate and Analyze Process Output Now create a fourth column in the worksheet, Flow, to hold the results of your process output calculations.BOARD is an all-in-one Corporate Performance Management and Business Intelligence software solution that makes it easy to build any business analytics and planning applications without coding.
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