Why the current Industry 4.0 approach is all wrong
Updated: Aug 20
“Intelligence is what you use when you don't know what to do”. This famous Jean Piaget statement was quoted to me by one of my clients. He got it from one of his staff who worked on the shop-floor. At the time they were meeting with a broader team to talk about various issues at the production facility they worked at. I was rather impressed at this as I had never heard the saying before. Furthermore, it came from someone who actually experienced the practicalities of standing on a shop-floor and could appreciate the relevance of this quote. It captures the essential predicament of problem solving in dynamic settings such as complex manufacturing; there is no one fixed formula. There are often many variables at play. In addition, these aren’t merely technical. Many are in fact people and behaviour related. Hence, when you mix the technical with people, things can really get tricky.
And that gets at the heart of why the current Industry 4.0 approach is problematic. It is too technology-centric, without appreciating the complexity of people and change. We have recently visited several businesses who have spent considerable money trying to implement Industry 4.0 technology. In all cases the projects themselves have delivered little value. Tools have been built, budgets have been spent (and often exceeded) but adoption has failed. The initial excitement of the client has waned, and cynicism has replaced it. Why? Because whoever sold it to them failed to give them a pathway to progress to an Industry 4.0 capability - preferring instead to install their latest and greatest technology. Everything else was meant to be simple and straightforward. Of course, it wasn’t.
What pathway are we talking about? First and foremost, it should be building intelligence as defined by the aforementioned quote. It is not just about data or information. It is about learning through improved connectivity. That process of learning has to have a strong rationale (the “why”) and a momentum (the how). Plus, it requires the technology to be designed in a way to support this learning process.
In pursuing mastery in any field, notions of learning, practice and improvement are inextricably linked. The progression from unskilled to competence and understanding has a beginning point that is rife with ambiguity and “not knowing”. Therefore, the process requires focus, patience and discipline. This is equally true in large organisations where one must coordinate multiple people to achieve complex goals. We therefore require a combination of structure, systems and leadership to drive this organisational learning. And of course, we need a process to turn data into information and ultimately knowledge and wisdom. But this is where it is crucial to provide technology that can cater for and respond to the dynamics of organisational learning. It is no use to provide static, pre-defined “solutions” to problems that have not yet been properly understood. We therefore advise our clients to start simple and build from there. In addition, it is important to underpin any Industry 4.0 journey with the right habits and practices for getting staff to engage in critical analysis and thought. Once this is established the organisation has earned the right to explore further technology development.
An important hallmark of any successful change programme is the sense of urgency an organisation possesses - sustaining momentum despite setbacks and early failures. Any skilled change agent will, however, be acutely aware that the “window of urgency” is not open forever. There are many factors that seek to undermine it. And once it is lost, people’s attention and priorities will move to other things, leading many promising initiatives – including Industry 4.0 – to fizzle out and ultimately fail. Conversely, it is possible, even vital, to generate momentum within the workplace that is based on early wins and a collective belief that these tools can be transformational.
Industry 4.0 has promised a new industrial revolution through the emergence of more connectivity of data and machines. Add to this a vast amount of data storage, machine learning and other tools of Artificial Intelligence. There is a dangerous misconception that the need for critical thinking is no longer important as machines will do that job for us. On the contrary, intelligence is more important than ever.