Digital Transformation of Manufacturing: Opportunities, Challenges and Lessons Learned
Greg Kinsey is a senior advisor in the fields of operational excellence, digital transformation, and Industry 4.0, helping industrial companies with their Industry 4.0 strategy, implementation, stakeholder buy-in and alignment, Genba engagement, and benefits realization. In January 2023, he joined the international operations consultancy and Minitab Gold Level consultant Argon & Co as a Partner, leading the Digital Manufacturing practice.
In his talk at the Minitab Exchange event in Munich on September 20th, 2022, Greg shared his experience and the key learnings of manufacturing leaders' journey to digitize daily operations to improve performance. Greg described the most common challenges companies face and his advice to address them.
Digital transformation will bring the 4th industrial revolution
If you were to go back in time to a factory in the 1800s, during the 1st industrial revolution, you would have found craftsmen - highly skilled people doing specialized work with very little standardization.
The 2nd industrial revolution ushered in standardized work. People with specific skills, repeating specific tasks, specializing on designated aspects of the assembly operation. It was a new scientific way of managing and controlling processes. Taylorism and Fordism brought new ways of working to manufacturing.
The 3rd industrial revolution introduced the concepts of continuous improvement and quality systems, along with new IT and OT systems. Workers began to work side by side with automated production and gained increased autonomy and responsibility for results. This brought in the Six Sigma approach for problem-solving and controlling process variation, and what is known as the Toyota Production System or Lean management.
The 4th industrial revolution will bring a fundamental change in that we'll work on more of a knowledge base. For the most part, workers won't be doing physical work, but they'll be managing the physical work through knowledge. The value they create will be based upon what they know, how they bring data together to optimize productivity, solve problems, monitor processes, and manage operations.
What are the digital transformation opportunities for manufacturers?
When we talk about digital transformation, it's about evolving the way work is done. It's about using digital tools to radically modernize and improve the daily life of workers, managers, and the performance of a factory. If we think about what this change looks like, it's about moving from a reactive manufacturing culture to one that's more predictive and controlled… and data-driven.
Traditionally, manufacturing is dominated by putting out fires and constantly solving problems. With the vision of a digital factory, you have a better idea of what's coming. You can better predict how your processes will perform in the future, which means you can manage your manufacturing in a more controlled way. It's also about moving from a fragmented operated model to one that's more integrated. Whether it's silos of data, silos of people, silos of processes – we need more connected teams and consistent data definitions up and down the process.
This also involves shifting from historical knowledge, operating based on what's happened in the past, to a smart factory that can predict what will happen in the future. So instead of having an environment where processes, machinery, and people are fixed, we have more flexibility thanks to the intelligence that gets built into the processes.
What are some of the key challenges to digital transformation?
Focus on Problems, Not Solutions
The first mistake I see companies make quite often is they lose sight of the problems they want to solve. Too often, the solution is driving the investment rather than solving the problem. One of the best ways to stay focused on the problems you want to solve is to connect your digital initiative with your Operational Excellence initiative.
If you have project leaders and problem solvers like Lean Six Sigma Black Belts, they already have a portfolio of issues they are trying to tackle. Rather than trying to install a new piece of software, maybe think about how digital can supplement and accelerate that problem solving that's already underway.
Gathering the Data
Another big challenge is data acquisition. Gathering the right data can be a time-consuming process, and it might be spread out in various places and in different formats. You might have a mix of old machines and new machines – some have data ports, but others don't. How do you connect all these different programmable controllers which might have different networks and protocols? You may not have sensors everywhere and your vision might require you to have more IoT devices.
Speaking of data, it always takes longer to clean than we anticipate. I recommend building a data dictionary – a way of labelling and cataloguing your data. This will give you the metadata around the data that describes what's in it, making it much easier to effectively use.
Learn How Minitab Statistical Software empowers all parts of an organization to predict better outcomes and improve processes.
No Clear Vision for the Future
Another area companies should focus on is creating a clear roadmap and identifying the future architecture to achieve their goals. What will your factory look like in five years, or ten years? Your technology choices can be difficult, and there will be some constraints with legacy in IT systems. It's very important to build architecture that's extensible, so you don't build something that becomes obsolete.
Classical Methods for Project Management
Digital transformation doesn't use the same methods as a classical IT project – in fact, it's the opposite. Classical IT is about applying a known solution to a known problem and your IT systems probably look very similar to the ones your competitors deploy. Digital innovation is about using an agile process to build up a database and develop bespoke solutions to apply machine learning, AI, or advanced analytics to solve your specific problems. By definition it's innovation, rather than off-the-shelf solutions.
Lack of Management Engagement and Alignment
One of the biggest challenges is how you engage your people. Often, the quality manager will have one agenda, but the logistics manager might have a different set of pain points. Ask the plant manager what they need, and it could be a completely different story. The key is aligning your operational excellence function with your digital function, so that everybody is aligned and engaged around the goals and how to achieve them.
Ignoring the Genba
Experience shows that the best ideas come from the people that work in the field (Genba) where the value is created. They understand how the processes work and how the machines operate. If you ask people how you could improve things by applying digital tools, they'll usually come up with lots of ideas because they want to modernize their workplace. They'll also feel a sense of ownership if they participate in the ideation process. They're proud of their contributions and enthusiastic about helping to drive the projects to completion.
No Benefits Realization
One of the biggest things I hear is, "We tried a proof of concept but it didn't produce any results." If you don't get those benefits, realizations in the quick wins, you won't be able to sustain your journey. Focus on making sure you get those results, and when you do, get those results verified with the help of finance. Communicate the project results and why they bring value.
What are some lessons that you've learned regarding digital transformation of manufacturing?
Integration is Essential
Your operational excellence program must be integrated with your digital transformation program. Digital transformation is just the next phase of operational excellence, and if you don't take your Lean Six Sigma program into the digital world, it will become obsolete.
Minimum Viable Data Set
You don't need to build a massive data lake. First start with the minimum dataset needed to solve a specific problem. Focus on a use case and the data needed for that purpose – you can always add more as you go.
Allow Trial and Error
In agile engineering, you want to innovate and allow for experimentation. Don't be afraid to fail fast and learn from it. Don't be afraid to pivot your direction based upon what you learned from those failures.
buy a smart factory off the shelf, you have to invent it for yourself.
If you want to innovate beyond your competitors, look for new ideas
especially outside your industry.
What role does predictive analytics play in the 4th industrial revolution?
Manufacturing has a reputation of being reactionary, people always "firefighting" and fixing things that break down unexpectedly. I think the promise of digital transformation is to create a more proactive, under-control environment, where you have a lot of knowledge about what's happening at your fingertips. Your phone in the palm of your hand becomes your main source of information that you need to be effective in your day-to-day work. Warning you of problems before they happen, based on data. This reduces the firefighting, reduces stress, and puts people confidently in control.
The term data-driven is probably overused. But when people can get dashboards and visualizations that help them make data-driven decisions when they need to fix an issue or make an adjustment, it changes the very nature of their daily work. The revolution comes when these people are not just executives sitting in offices—it's also the drivers, machinery operators, quality managers, and maintenance people, in the Genba. When the workforce in the Genba can benefit from a data-driven work environment, then maybe we've arrived at the 4th Industrial Revolution.
Original blog article is written by a guest blogger for Minitab