Simulation Modeling Overview

Simulation Modeling is defined as a customized model that simulates a real-life process. This process may be something such as an assembly line or an automated construction service. The model integrates input variables and operating parameters from the actual business process. Likewise, this allows for the user to manipulate and number of factors and see how they will affect the over simulation. Where Six Sigma comes into play is the desire to use the simulation model. The philosophy behind Six Sigma is to constantly improve a business process, increasing efficiency, raising the standard of quality, and deterring defects. Simulation Modeling provides an excellent insight to what changes you could and should not make within your corporation.

Benefits

Furthermore, Simulation Modeling is best when visualizing a complex, dynamic process. A working model of a complex process allows you to visualize parts and variables you would otherwise not notice. Additionally, this is ideal when discussing real-time reactions to minor (or major) changes to a process. Typically, Simulation Models display results quickly, allowing you to demonstrate a process change as it happens. Finally, it allows you to follow your project goal closely. For some, the aim of implementing Six Sigma is to achieve one specific goal, i.e. increasing production line speed by 20%. With Simulation Modeling, your project goal is the center of your variable manipulations. As you alter certain variables within a process, you’re provided with immediate results. When meeting with management, investors, or other project leaders, Simulation Modeling offers an organized, easy to understand visualization of your process, your goal, and how it can be achieved.

Risks

However, Simulation Modeling does have risks. For example, if you become overly depended on the model, you may lose your focus on your project goal. Suddenly, you begin to alter other variables that are independent of your project, which can impact your overall results. Additionally, it is easy to overuse a Simulation Model, which can form unrealistic expectations for your process. While something happens in a model, it is not a guarantee to happen in real-life. This expectation for a process to follow a simulation eye to eye can impact your overall analysis of the project and deter you from achieving your set goal.

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Measurement System Analysis  (MSA) is a tool or a set of procedures employed to judge the quality of measurement systems. A manufacturing company may have to deal with the following two scenarios if it has a poor measurement system in place. The first case can be that the quality control department reports zero defects, and so the product is declared ‘defect-free’. Later, with a sudden increase in the number of defectives and products returned, the company realizes that the products may have been ‘out of tolerance level’ or wrongly labeled as ‘OK”. The second case can be when due to very stringent quality control mechanisms exercised, good products are rejected as bad/defective ones (look at figure below showing both types of errors).

The role of Measurement System Analysis helps to scientifically answer the following set of questions:
• Is the current measurement system up to the mark? Are the techniques employed reliable, accurate and precise?
• Is the measurement system able to distinguish between good and bad sample data?
• Is the sample itself representative of the population and has the sample been collected on the technique of random sampling (without anyone’s judgment)?
• Is the measurement system stable and reliable over a period of time or are there any inconsistencies?

A faulty or unscientific measurement system leads to poor or inconsistent conclusions. This can jeopardize the entire manufacturing procedure and people may play the ‘blame game’ in order to justify the results. A lot of books and online references suggest that there are some parameters to judge any measurement system: bias, linearity, stability, repeatability and reproducibility. Let us assume that a quality engineer is responsible for examining clevis pins, which have the same dimension, same weight and are produced by the same machine. Now if there is a variation in the average values of the sample, it can be attributed to only a few possible set of causes: either there is something wrong with the sampling system or there is a human error involved (the operator who is testing the pins may be committing an error).

While the measurement system has to be stable (give consistent values over a period of time), accurate (acceptable difference between the average values and the actual values of the sample measured), it must not ignore the human role towards precision and accuracy. An operator is expected to perform the same task repeatedly and yet, there are big chances of error. Any measurement system can be judged on this account to test if the operator is able to test the same part with the same measurement device and get the same result or that different operators test the same part and get the same results. These are called the features of repeatability and reproducibility of measurement systems.

Conclusion: The above discussion gives an overview to the purpose of measurement system analysis and explains how such an effort can prevent a bad/defective product from reaching the consumer’s workplace or home. Statisticians and Quality Assurance engineers often couple the use of MSA with Statistical Control Techniques.

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