In manufacturing and other industries that have complex processes, knowing which variables have the most impact on quality and at what point, or knowing which combination of variables to change in order to improve your process, can have a huge impact on the overall quality or profitability of your manufacturing process. But without making expensive and time-consuming changes in the physical processes in order to test all possible scenarios, how can you identify and predict the variables that have the most significant impact on your outputs?
If data analytics were easy, everyone would do it, right? Well, what if were easy enough for anyone to do it? Can you image what sort insights you might glean from the vast pools of data your company collects about your manufacturing processes, sales or production outputs?
If all of your data stays hidden in the depths of some process control computer or in Excel spreadsheets on the manufacturing floor manager’s desk, are they doing anyone any good?
How Multivariate Data Analysis Can Separate the Players from the Gorillas
We have more data than ever before coming at us from many sources – both in our personal lives as well as business. Data is everywhere: from the production flow of a manufacturing floor to the sales results in a grocery store to the number of shares a page gets on Facebook. How do you sort it all out in a way that makes sense? Which data should you worry about and which should you ignore?
“The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.” — Bill Gates