Key Principles of Design of Experiments and How to Use Them

Design of Experiments (DOE) is a sophisticated device with which to perfect your production processes. With DOE, you can optimize your processes for the better. Likewise, this is done by ensuring they run faster, smoother, and are more cost-effective in the future. One of many benefits is the data that comes with excellent experiment design. Good data will allow you to make the most informed decisions, which in turn will provide measures of predictability for future results and consequences. Having this level of awareness will only benefit you in future experiments. Likewise, it will help you to fine-tune your production processes according to your needs.

The Key Principles

• View DOE as a factor-based concept. By varying your factors together, through the use of a factorial grid, you can get a better idea of how each factor influences the other, and how they interact in production.Additionally,  varying one single factor at a time will only aggravate time constraints and will fail to give you as clear a picture of the inter-factor connectivity when viewed in suspension.

• Randomizing experiment order. Randomizing the order in which you run your experiments in the factorial grid can help prevent partiality from creeping in. It can also help recognize any unidentified extraneous variables that are holding your production back. Variables like these can cause processes to stagnate, which is why quick excision can make a world of difference.

• Blocking noise. Nuisance and/or extraneous variables that have no stake in production other than that they exist to cause problems should be blocked as soon as they are identified. These bothersome variables have no data-value and are the cause of much difficulty for those working (or trying to work) with DOE. Removing these issues may seem tricky, but it’s as easy as removing a particular category from your dataset, e.g. name or gender.

• Replicate your experiments. Persistence and repeat experiments almost always lead to the best (or at least most revealing) results, as some experiments may not always yield the same outcomes with a second run. Replication can help reduce any potential noise and/or variation masking preventing you from recognizing factors pertinent to production. This enables you to change your processes in such a way that will benefit the entire company. By making small adjustments here and there, you can transform the entirety of your production.

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The most helpful tool in the Six Sigma Analysis Toolkit is Root Cause Analysis. Root Cause Analysis is a problem-solving method that locates and corrects errors within a system. This tool is typically implemented when a manufacturing, supply chain, or other business processes continually experiences defects in production. Working retroactively, you begin at the location of the defect and works backwards towards the original cause of the problem. Six Sigma professionals utilize this tool as it is a crucial aspect of the methodology. For employees, Root Cause Analysis is the best way to improve business operations, increase efficiency, and deter defects. In this article, we will outline a few of the tools you can use for Root Cause Analysis and how they will help you.

The Tools

First

The first tool is a Scatter Diagram. Using numerical data, this diagram displays graphs that show correlations between two different variables. When conducting Root Cause Analysis, your goal is to find the initial point of the defect during a process. Scatter Diagrams provide you with finding relationships that might be leading to your errors with the system.

Second

Like the Scatter Diagram, A Fishbone Diagram is also an easy way to identify leading causes of your system errors. Also, known as the Ishikawa Diagram, this tool helps by identifying all possible causes for your defects. Then, these causes are sorted and categorized, in an organized manner. This allows you to see how many variables are related to the defect, if they originating from the same area, and if they are directly (or indirectly) causing your errors.

Third

Next, Pareto Charts are a useful tool that graphically show which factors and variables are most significant to your production error. Since the goal of Root Cause Analysis is to locate and correct errors within your system, Pareto Charts easily show where you should most likely begin your search. These charts may show you variables you have not yet considered or even speed up finding the root cause of your errors.

Forth

The last, and most useful, Root Cause Analysis tool is the 5 Whys. How the 5 Whys works is by your first defining your specific problem. Next, you ask why the problem happened in the first place and evaluate if this leads you to your root cause. If it does not, ask “Why?” again and continue to do so, isolating the cause of the problem until you have your final answer. This tool is a simple, yet effective, way to identify your root cause, show relations between different variables in your system, and requires as little or as much effort as you desire.

Why Use Them?

Conducting Root Cause Analysis is the perfect opportunity to showcase your knowledge of the Six Sigma methodology. By doing this, you can show to effectively locate and correct errors within your operations, improve business efficiency, and decrease the chances of future defects.

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How Does It Work? 

Starting off, you must first decide upon a goal you wish to achieve through Value Stream Mapping. This can include reducing waste, increasing manufacturing speed, and more. You must also designate where the start and end of your process are.

Next, you begin to list out each stage during the process value stream flow. For manufacturing, these may include building select parts, moving them along an assembly line, and constructing the final product. However, multiple industries use this analysis tool such as logistics, software development, supply chain, and others.

After labeling each stage during your value stream, the next criteria are to add in parameters to be measured. How long a process should take, the average number of employees there, and the machine power limitations are all examples of measurable metrics to assess. Additionally, each variable’s value should be considered. Each process and its variables can be categorized as ‘value adding’ or ‘value losing,’ depending on the progress of the value stream.

After this, you will collect data and analyze your findings. Furthermore, at this point, you can reassess certain process, make changes towards efficiency, and calculate how else to improve your business management.

The Pros and Cons

 Like all good models, there are certain benefits and risks Value Stream Mapping possesses. For the benefits, this tool provides you with valuable insight to the efficiency of your business processes. You can measure any number of variables, make changes where needed, and implement a more effective, sustainable procedure. Likewise, you can map display the exact flow of your value stream and locate where limitations or hindrances may exist. However, certain risks are also present. First, Value Stream Mapping works ideally for simple, linear processes. More complex, multi-level productions will greatly restrict the usability of this tool. Additionally, this tool follows a basic pen and paper technique, which can limit the accuracy of its analysis. Other tools that depend on computer simulations or engineered programs have proven to be more accurate and effective at enhancing the quality of business processes.

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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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