You might be thinking, six weeks and you’re still not telling us what happened? When you are solving a manufacturing or development problem you might hear the same thing from your leadership – when will we get the results?Continue Reading
Previously in our designed experiment on driving the golf ball as far as possible from the tee, we tested our four experimental factors and determined how many runs we needed to produce a complete data set.
Now let’s analyze the data and interpret the covariates and blocking variables.Continue Reading
Rafael Villa is a chemical engineer working as a Research and Development leader in the home care division of a company in Peru. His company that manufactures a vast range of consumer package goods and B2B products ranging from food items, such as cereal and candies, to cleaning supplies and home good items. Focusing on the design, formulation and optimization of consumer products, his R&D career has spanned multiple...Continue Reading
In our continuing effort to use experimental design to understand how to drive the golf ball the farthest off the tee, we have decided each golfer will perform half the possible combinations of high and low settings for each factor. But how many times should each golfer replicate their runs to produce a complete data set?
Tomorrow marks the 47th anniversary of the premiere of the great 1971 movie Willy Wonka and the Chocolate Factory, wherein the reclusive owner of the Wonka Chocolate Factory decides to place golden tickets in five of his famous chocolate bars, and allow the winners of each to visit his factory with a guest. Since restarting production after three years of silence, no one has come in or gone out of the factory....Continue Reading
Last week we began an experimental design trying to get at how to drive the golf ball the farthest off the tee by characterizing the process and defining the problem. The next step in our DOE problem-solving methodology is to design the data collection plan we’ll use to study the factors in the experiment.Continue Reading
As we broke for lunch, two participants in the training class began to discuss, debate, and finally fight over a fundamental task in golf —how to drive the ball the farthest off the tee. Both were avid golfers and had spent a great deal of time and money on professional instruction and equipment, so the argument continued through the lunch hour, with neither arguer stopping to eat. Several other class participants...Continue Reading
If you have a process that isn’t meeting specifications, using the Monte Carlo simulation and optimization tools in Companion by Minitab can help. Here’s how you, as an engineer in the medical device industry, could use Companion to improve a packaging process and help ensure patient safety. You can also check out our webinar recording, Seeing the Unknown: Identifying Risk and Quantifying Probability with Monte Carlo...Continue Reading
How do you commit to realistic forecasts and timelines when resources are limited or gathering real data is too expensive or impractical? Can simulated data be trusted for accurate predictions? That’s when Monte Carlo Simulation comes in.Continue Reading
The convergence of the widespread deployment of low-cost sensors, cloud and greater compute power has brought together a multitude of connected devices which can monitor, collect, exchange, analyze and deliver insight like never before. Industry 4.0 (or Industrial Internet of Things) is transforming industries. This is especially true within manufacturing where many organizations are making investments into ‘smart...Continue Reading