Here we'll cover the specifics of a capability analysis and several sticking points in interpreting and taking action on information from the analysis.
In their day to day work activities, both quality and manufacturing professionals are involved in conducting audits; collecting, analyzing, and interpreting data; plus preparing a variety of reports and summaries of the data to document product quality.
Quality is based on a series of facts and statistics collected and analyzed. To produce a quality product—and continue producing a quality product—you need data.
At the end of the day, nothing matters more than customer satisfaction. Fundamentally, this sounds quite simple; make the customer happy, and all is well. Keeping customers happy and loyal to your brand, however, is not as easy as it sounds.
Data collection on the factory floor can be a challenge. Even the smallest enterprise can generate massive amounts of data, and collecting this data is only a first step on the path to a successful IIoT project.
Not long ago, IT integration projects were an intimidating task due to the historic number of failed projects. This is not the case today because the technology exists and when combined with proper implementation they are very successful.
Data isn’t everything. But it’s perhaps the main thing standing between you and a successful project. Continuous improvement takes effort, but more than anything, it takes solid information and analysis. In other words, wouldn’t it be more helpful to use statistical process control to find out where your process is going wrong, rather than just a hunch?
Vision systems provide peace of mind when it comes to production quality but they can also generate valuable data that tracks process variability. Diving into the world of vision data can seem overwhelming, but with the right tips and tricks you can set up a system to work for you.