Bringing Together IT and Operational Excellence Teams for Successful Digital Transformation

Minitab Guest Blogger | 12/21/2021

Topics: Predictive Analytics, data literacy, data science, digital transformation



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.

We had the opportunity to get Greg Kinsey's thoughts on several topics for a series of articles. In this blog, Greg shares his vision for digital transformation.

What's your definition of digital transformation?

For many people, digital transformation is about looking at new technologies and asking “what can I do with that technology?” But in practice, this thinking is backwards. To me, digital transformation should focus more on the transforming and less on the technologies. It should be purpose-driven, driving towards a clear vision of future competitiveness.

The question is – what do you want to transform? Your operations? Processes? Culture? Business model? Supply chain? Your product offering? And how will that look and perform? Digital transformation is about applying technologies to re-invent operational capabilities and competencies, to achieve a targeted future state.

When I meet with clients, I often ask: “Do you have a vision for your factory of the future? What do you really want to achieve? What will your operations be like in 2030?” Surprisingly, leaders often struggle to find a clear answer. It is an important question since it determines your operational excellence and digital transformation strategies.

What is operational excellence?

Traditionally, operational excellence teams have a strong “Kaizen” mission, focused on those pesky problems that hold back daily performance – bottlenecks, defects, downtime, etc. This is absolutely necessary. However, the scope of operational excellence should be expanded to cover the creation of new capabilities, and the resulting higher levels of performance. It’s about re-inventing the system of operations.

In essence, operational excellence defines the “what” of digital transformation, while the technologies define “how” you will get there. The operational excellence function should fully explore how digitalization can act as this enabler. Most IT teams are not very close to the operational aspects of a manufacturing or supply chain site, so IT alone cannot drive the definition and execution of digital transformation.

The challenge is: how to bring together the two teams, so that the digital technologies can drive operational excellence to reach the defined targets and the future vision.

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Why is it necessary to reinvent your operating model to create new ways of working when it comes to digital transformation?

Every industrial organization has an operating model. It was set up to operate in a certain way, and some organizations have a vision on how they want to improve that model. When you transform the model, you can create radically different performance.

One example is the lean production journey of the last 30 years. Lean is a fundamentally different production model, which uses half the physical space and requires half the inputs, whether that's energy or materials or human activity, or other things going into the process. Inspired by the famous Toyota Production System, the lean model is about radical new ways of working and of developing your workforce.

In another example, you might be facing a rapid change in customer requirements. Maybe the market demand is shifting to more customized products. Making the move towards mass customization requires re-inventing your operating model. This is not easy, as you are likely under constraints related to suppliers. At the same time, you may be transforming to a more local and sustainable supply base. And your production model might be optimized for high volume runs. So the complexity of changing your operating model can be enormous. Usually there is a complex web of economic, political, social, and market constraints which impact your operations.

These examples demonstrate why you can't do digital transformation in isolation. You must combine digital strategy with an overall strategy to re-invent your operating model.

How do you think organizations can best bring IT and operational excellence teams together?

Alignment must start at the top. In my experience, when companies don't take the time to align the leadership team, it will come back to bite them later. The first step is to get the top executives to agree on the vision for transforming the end-to-end value stream.

The value stream includes how value is created, people work, material moves, processes work, IT systems work, and management manages. The leadership team should develop an aligned approach, with healthy debate amongst each other.

What are our priorities? Where do we start? What are the key use cases? Which ones are short term? Medium term? Long term? And what does success look like? What are the outcomes we want to achieve? What does that mean in terms of operational KPIs.

Getting that right at the beginning is critical. There needs to be a clear direction.

The second important aspect is bringing together cross-functional work groups, with wide representation from operations. Real innovation occurs by engaging people who work in the processes, or in Japanese, the Genba – the place where value is created. The shop floor workforce is usually rich with ideas and insights that can help you shape digital use cases and solutions. And since they will become the users of the digital solutions, it is critical to address their needs in the design of the user experience.

And gaining acceptance from the users is critical. If technology is forced on people, whether it's better or not, they might reject, sabotage, criticize, or generally prevent it from being successful because no one asked for their inputs. Good communications are key throughout the process.

What were the critical factors that contributed to the success of digital transformations you've seen?

The number one factor is focusing on the innovation rather than the technology. The innovation is about exploring the problems that you want to solve and conducting a deep analysis of those problems. Sometimes it involves questioning the question - “am I even asking the right questions?” It's about doing things like conducting ethnography to understand how people do things today and how could they do them differently, maybe in a better way. It's about ideation, generating sometimes crazy ideas which might give you a breakthrough. It's about ideation and experimentation.

Why can digital transformation fail?

Probably the biggest cause of failure is being too solution-centric. People sometimes fall in love with a solution too early, and just want to install the technology without deeply analyzing the problems to be solved, in the context of operational excellence. The technology can become like a hammer looking for nails. This distracts people from focusing on potential use cases and discovering innovative approaches to solving the real problems. I guess the IT industry is partly to blame for this, with all the product-led go-to-market approaches of the past 20 years

A second cause of failure is the lack of internal alignment across the management team that I mentioned earlier.

And the third cause is getting back to what we talked about earlier. Is your objective to install a lot of technology or to transform the way the work is done? There is a serious amount of process re-engineering and organizational change that goes along with the digitalization. And usually the entire management model needs to be modernized.

Minitab's Chief Marketing and Strategy Officer Joshua Zable discusses how engineering and tech come together to achieve digital transformation. 

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What’s the 4th Industrial Revolution going to be?

Since 2015 people have been talking about a 4th industrial revolution. The concept has always been somewhat vague, but for many it is simply about massive deployment of technology, namely IT (information technology) and OT (operations technology). I disagree with this simplistic view. And by the way, the growing use of IT and OT in industry is not new. It was the 3rd industrial revolution which brought us industrial robots, IT, and enterprise software -- like MES, which was born in the 1980s.

If we take a deeper look at these industrial revolutions, we realize it is mainly about a fundamental change in how work is done. This means a change in production model, which requires a transformation in the labor competencies, organizational models, and management methods. The social and economic models of industry are revolutionized.

At a high level, you could say that:

  • The 1st Industrial Revolution was about specialized work. Craftsmen.

  • The 2nd Industrial Revolution was about standardized work, mass production, and Taylorism. The concepts of Henry Ford.

  • The 3rd Industrial Revolution was about pull systems with autonomous workers, often with single-piece flow and U-shape cells. The concepts of Toyota.

So we moved from specialized work to standardized work to autonomous work over a period of about 250 years.

Moving forward, I believe the 4th Industrial Revolution is a shift to knowledge work. Industrial workers will no longer be paid to do physical work, but instead paid to do knowledge work. 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. Most of the physical work will be done by autonomous or semi-autonomous machines, as we already see today in an advanced electronics or automotive factory.

This 4th revolution should create a step-change in the economic and social models of industry, as did the previous revolutions. Any organization thinking about how to transform in this way, cannot just think about technology. You should think about your factory of the future, the operational capabilities, and the required human competencies.

And it is also important to develop a vision of how your knowledge-based industrial workforce will be better—better for your bottom line, better for the planet, better for your customers, and above all – better for the workers themselves.

How does predictive analytics fit in?

Great question. Being able to anticipate problems before they happen with a high degree of probability - that's cool. And that is what we call predictive analytics. I can't imagine anyone in any kind of job that wouldn't want to be able to accurately predict problems before they happen. My team has started doing projects in this area – using data science to perform advance cause-and-effect modelling. The three main use cases in manufacturing are: predicting quality problems, predicting unscheduled downtime, and predicting production bottlenecks.

Once the predictive data is in front of operators, sometimes it can guide them on how to prevent that problem from happening—that is getting into prescriptive analytics.

“Predictive analytics is powerful, and fundamentally it's going to change the nature of the workforce and what it's like to work there.” – Greg Kinsey

Manufacturing has a reputation of being reactionary, people always “fire-fighting” 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 fully harness the power of real-time data, 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.

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Two other interviews with Greg Kinsey have been published:

- Data Science for Everyone: Understanding the Importance of Predictive Analytics. Data analytics are vital to understanding how your business works, and predictive analytics can tell you where your business needs to go. According to Industry Executive Greg Kinsey, with the right tools at your fingertips, anyone can make better decisions that will improve performance and reduce mistakes. Read this blog to learn more >

- How to Foster a Culture of Innovation: A Q&A with Greg Kinsey. Innovation can fail when sticking to a set plan, avoiding risks, delivering short-term KPIs, and managing shareholders take priority. What can organizations do to foster a culture of innovation, then? Get a possible answer in our latest Blog >