Without data, you’re just making your way in the dark. Isn’t it time to learn more?
As W. Edwards Deming said, “In God we trust; all others must bring data.” This becomes more and more important in manufacturing today, along with a host of other industries. Consider the Oakland A’s of Moneyball fame, not to mention robot umpires now taking to the field. Data is gaining ground in unexpected places.
Whether in professional baseball or manufacturing, data-driven quality is the next step for many organizations. If you’re not already pushing for data-driven processes, you could be left behind.
According to a Deloitte article by Tom Davenport, Ashwin Patil, and Derek Snaidauf, the old way of doing things is changing: “At one time, using data to measure and improve quality meant inspecting products after manufacturing, or counting the number and types of warranty reports. While these are still valid activities, the world of product quality and safety is increasingly being revolutionized, like many other areas of business, by new forms of data and analytics.”
What do these new forms of data look like? According to the authors, in an automotive manufacturing application, it comes from a variety of data sources, including real-time data from vehicles and GPS, warranty and safety data, call center conversations, social media, and the supply chain. But this is just the first prerequisite. The second requirement calls for technologies that can make sense of this data. Perhaps this would involve machine learning or voice recognition technologies.
Implementing the right programs can be difficult, but having the right tools can help. Software continues to play a role in finding a solution to manufacturing problems.
"Quality management is like The First 48 detective show,” John Fowler, quality director of Versabar, told Intelex. “You must gather data as fast as possible, speed is of the essence and no detail can be lost.”
He found that adopting a new software system (in this case, Intelex’s Quality Management System) helped the organization improve. Versabar now was able to streamline processes, become more efficient, and better track their data. Many companies have found that upgrading their software has paved the way to upgrading their quality.
In addition to the right tools, having an example to benchmark is also a good idea. The upside of adopting a trend is that you’re not the only organization working on implementing these ideas.
In an article for the Harvard Business Review, Thomas C. Redman writes, “It was immediately clear that the biggest successes stemmed not simply from technical excellence but from softer factors such as a deep understanding of business problems; building the trust of decision makers; explaining results in simple, powerful ways; and working patiently to address dozens of concerns among those impacted. Conversely, otherwise excellent technical work died on the vine when we failed to connect with the right people, at the right times, or in the right ways.”
So there’s some ideas of what not to do. In addition, too much data or the wrong data is not helpful, but this doesn’t mean that data collection should be ignored. The key part of the process is to then act upon that information you’ve so diligently collected.
“From the technology point of view Big Data is commonly defined by 4 Vs: Volume, Velocity, Variability and Veracity,” according to the authors of a paper in IFAC. “However from the viewpoint of manufacturing industries it is more important how to combine data with production knowledge instead of processing huge amounts of data in real-time. In this context the focus must be set on the usability of Big Data and not only on the technological limits of data processing.”
In other words, data that is not useful—and used—by the organization, is not that valuable at all. Using and understanding the data is paramount.
“The recurring element that underpins much of this revolution is the collection, utilization and understanding of data, or the study of ‘Informatics'; almost all of the areas linked with the intelligent manufacturing research area rely on the capture and analysis of data in some way,” write the authors in a paper presented by Procedia. “To this end the use of advanced data analytics and machine learning is a key technology to develop to further these other technologies; and the next step in this chain lies in utilizing the vast reserves of data through data mining and knowledge discovery, to better understand these manufacturing processes.”
The authors discuss how the rate of technological advancement is outpacing adoption levels, but that there is more to be done even for smaller manufacturing enterprises.
The future is here, and it’s available for anyone to adopt.