Recap: Digital Transformation for Manufacturing
Digital Transformation, like so many other terms in the Industry 4.0 buzz word bingo wheel, can easily get lost in translation and be dismissed as another “sounds neat, but not realistic for me” technology topic. The panel discussion held on March 24th at the Pittsburgh Technology Council offices at Nova Place, showed that it was anything but. The session was sold out with over sixty manufacturers and technology and services providers in attendance. On the panel were Steve Parker and Zack Pu from Kennametal and Francois Estellon from Matthews International – SGK Brand Solutions.
The discussion covered a range of topics from how their journey started, considerations for cybersecurity, and pitfalls they encountered.
Zack and Steve shared how their efforts started as a top down initiative – management wanted to implement technology. This approach can go awry, however in this case it provided the critical element of management support. The technology team started with small projects benefiting the shop floor to gain buy-in. Francois added that while it may be tempting to convince everyone that digital transformation is the right step, but it is not necessary. In fact, do not try to convince everyone; focus on finding an “in” with a key decision makers and stakeholders.
One astute audience member had a great question, even quoting a great philosopher. (In this case, Spiderman) With great power (data) comes great responsibility – how do you balance cybersecurity requirements with your digital transformation? Francois recommended to focus on “time to recover” – early warning (and quick reaction) is just as important as a good defense.
The key pitfall had a simple premise – do not be enamored by technology. There are no silver bullets, and they all take a team effort and change management across your organization for successful implementation. Remember to focus on training – you may even need to start with tablet or PC training if those who will be using the technology in their daily work are not accustomed to interacting with these systems. Finally, start simple. Get your arms around gathering data before you reach for Artificial Intelligence, Machine Learning, or predictive maintenance.