Design of Experiments (DOE): Fine Tune Your Production Processes

Did you know that Design of Experiments (DOE) can help fine-tune your production processes? It’s no secret, and could bring about a range of beneficial results, including increased revenue and smoother production. Overall, Design of Experiments is an invaluable addition to your toolkit. With powerful analysis techniques to help create the perfect production arena, it’s a great Six Sigma tool. But how can it help your business? How do you use it to fine tune production processes? In this article, we talk about how to use DOE to optimize your production results and achieve success.

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.