Data-Driven Manufacturing Is The First Order of Business
Companies need to do the right things to prepare for the future. Having the right names for that future state is less important.
What should manufacturers be doing right now? One urgent exhortation is to connect machine tools and other production systems to share data, then use this data to improve performance. Computer networks based on internet technology make this a compelling (and practical) possibility. Getting connected, we can say, is a prescription, a directive, a recommended course of action.
What will being connected look like for manufacturers? What should we call this emerging state of connectedness? Here we have a wide, and sometimes confusing, choice of terms we can apply. Digital manufacturing, intelligent production and the smart factory are some appropriate descriptions that can be used to characterize this new way of organizing and managing the production of parts and finished goods. Other names for this visionary concept are Industry 4.0, the fourth Industrial Revolution and the Industrial Internet of Things (IIoT).
Noting the distinction between prescriptive and descriptive usage is important because it brings greater clarity to the barrage of pronouncements about these developments. When these concepts are discussed, it helps to think about the speaker’s viewpoint, although the intent of their statements is often a muddle of the prescriptive and descriptive. My impression is that descriptions of Industry 4.0 and IIoT predominate, whereas practical examples of how to implement these concepts are underrepresented. Pundits aplenty are telling manufacturers that they better get on the digital bandwagon, or else they will be overtaken by competitors. Yet detailed advice and step-by-step instructions are rare, and these often amount to admonitions to address “soft” management concerns such as transforming company culture, the need to communicate, staying in touch with customers and so on.
However, life on the shop floor is not experienced on an ideal or theoretical plane. It’s about getting things done—making good parts and delivering them on time. On the shop floor, thinking and acting must be eminently purposeful. This is why I’ve been harping on the theme of using data from connected systems to make better decisions about manufacturing processes. This formula explains and justifies steps shops are taking in this direction. The industry panelists cited in my article “3 Perspectives on Machine Monitoring,” for example, boldly recommend the merits of installing machine monitoring to this end.
As companies adopt such innovations and work through the minor (or major) disruptions that result, their experiences and insights can impart lessons to their peers. We are working hard to bring these success stories to light, taking pains to explain the critical details of new technology along the way. In the context of what these leading shops are accomplishing, it is accurate to say: Here is manufacturing that is data-driven and digitally integrated; here is part production that is intelligent because knowledge and information are the controlling elements; or here is a factory that is smart in the sense that people, machines and computers are reasoning, analyzing and learning together.
Introduced at IMTS 2008, this communications protocol for CNC machines and other manufacturing equipment is already helping shops and plants implement effective machine monitoring systems. Although these "early adopters" are motivated by the long-term promise of enterprise-wide efficiency gains, their experience with pilot projects shows that benefits derived in the short term are substantial and worthwhile.
While OPC UA and MTConnect are both http-based protocols, there are differences between them, and each is best used in differing scenarios.
A panel discussion at the recent Top Shops Conference focused on various points of view regarding the value of connecting machine tools to a network for monitoring performance and recording results. Because machine monitoring helps a shop make better decisions about manufacturing processes, it is a good example of data-driven manufacturing in action.