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On the Cusp of the next Industrial Revolution

Last year a student approached me to take part in her research entitled ‘The adaption of lean implementation techniques for an industry 4.0 revolution’. I admit my immediate reaction was: ‘This topic is out of my comfort zone’. Nevertheless, I mulled it over. It’s an area I am interested in, but not one that I’d properly applied my mind to. I started to wonder:

  • How will Lean need to evolve in the future?
  • What does Future Work mean in terms of Respect for People?
  • What is the impact of Industry 4.0 on us Lean thinkers?

Keeping an open mind, I answered the student’s questions and it helped me realise the importance of this hot topic. The Lean community (that’s you and us) should start a dialogue about it, rather than brush it under the carpet. I am a strong believer in the first principles, but there’s no doubt we have to adapt to changing environments, and our thinking will need to move with the times.

What is AI / Industry 4.0 / Digital Era?

When you Google it, you get a multitude of answers, but no single, accepted definition. The internet and papers I read sprouted dozens of buzz words and offered up colourful explanations including, but not limited to:

…“smart factory”…“systems are developed into flexible, agile, integrated, responsive networks, that can self-optimise and self-adapt in real time”… “systems can autonomously run an entire production process”…”they can take data from a myriad of sources, and clean it up into something you can visualise and solve problems with…. or it can solve the problems for you” …”systems have the ability to learn and adjust from data, in real time”…”they make decisions without human intervention or concentrate the decision-making to a smaller, highly skilled group”…”digital journeys start by thinking big, starting small in manageable chunks and then scaling quickly”…

These explanations make sense, more or less. The information is a little intimidating, however, as it’s hard to know at what point you have a handle on it: the more you read the wider the learning gap emerges. But, I found this next diagram helpful for those who want to understand the evolution over time. In Figure 1 below, Prof. Roser paints a picture of how industry has evolved over the centuries and puts the shift to fourth gear in context for us. It looks to me that the 3rd revolution is getting a turbo-boost into the clouds: taking computer and automation to the next level of integration and connectivity in the 4th revolution. It’s no longer just plain automation.

Figure 1: Source:

Back to Basics – what problem are we talking about?

As always, I try to go back to basics to give my mind a solid foundation to launch from and to help digest the challenge. Some of you may have seen Figure 2 either on the Lean Enterprise Institute website or perhaps by attending one of our workshops . It’s a wonderful overview of the thinking, principles and practices to consider when taking the leap towards organisational transformation. (check out this video link for the animation).

Figure 2: Lean Transformation Framework

As Lean Thinkers we begin with a fundamental question that interrogates the reason for the organisation’s existence. Why are we here? What value do we bring to our customers? Essentially, what is our Value-Driven Purpose? And each person in the organisation should be clear as to “what problem am I here to solve” in support of that Value-Driven Purpose.

When we have this clarity, it becomes easier to take a hard look at the actual work being done, and to see which parts contribute to value and which parts are blatant waste.

You can watch the video to hear the whole explanation, but I want to harp on the “what problem are we trying to solve?” part. Over the years I’ve witnessed great companies throw good money after sub-par automation and tech. They often feel perplexed after the spend, real buyer’s remorse, as the problems weren’t actually solved by the new system, new robot, new piece of tech. In some cases, the “improved” technology only succeeded in baking in the waste and the problems that were already present. It’s sad to see a slick new tool produce lousy information at the speed of light (Roser, 2017). We caution leaders to really think about what the problem is first, understanding the causes well, before designing a tech solution that seems wonderful on the surface but where more expensive inefficiency lurks just below the surface. Don’t get me wrong. I love progress and technology! I only wonder if we’ve understood the problem before assigning the solution. When we have followed this thought-process, the sky is not the limit (giving kudos to Elon Musk here)!

So, what problem are you trying to solve?

Do you need to compete with better speed, quality and cost? Do you need to bring products to market faster? Do you need better flexibility in your processes to respond to a variety of requirements? Are quality issues crushing your profit levels? Invest in the tech that will support these goals. Understand the gap between where you are and where you need to be before you spend time and money on the answers. You will be happier for it.

African AI in Action

The developed world is leading the way, but we have some promising examples right here. A fascinating example is how a local company that specializes in Machine Learning is helping revolutionalise the manufacturing industry. DataProphet have developed a solution to predict and detect defects and scrap, improving manufacturing yield, and boosting profits to double! Ka-ching! Now that’s fantastic return on investment. Not just in profitable returns, but better quality for the customers and smoother processes. They take Big Data and transform it into real-time information that creates visibility of the problems and helps optimise the processes. Both supervised and unsupervised. Check out this video to learn more.

Like what you see?

Why not join Michael Grant (CTO DataProphet) at our Lean Summit Africa where he will present case studies such as this.

Not to be missed.


At Halfway Toyota, in an effort to make administration processes quicker and easier, they’ve developed a custom app to streamline the booking process. This is after years of development on their processes and people capability in an effort to truly transform how they do business. They’ve accomplished the implementation of tech that delivers real value for their customers as well as simplifying the lives of their employees. Check out this article on Planet Lean for the full story.

But what about the People?

It’s true. With the evolution of processes and the automation of repetitive tasks, there has certainly been an impact on people, their skills and what jobs they perform. Smart Factory self-sufficiency could replace roles that require repetitive and fatiguing activities (Deloitte, 2017). With advances in technology new jobs have emerged and are opening up exciting opportunities for skills development. This means even existing roles and responsibilities may need to re-align to suit the changed environment.

As parents, many of us have become overly-conscious and curriculum-sensitive as we try to decipher the prospects for future generations. How will our skills and that of our children need to adjust and grow as technology plays a bigger role in our lives? According to a post from the World Economic Forum, there are three skills we need to develop to find jobs in the future, as AI and automation transform the workplace:

  • Social and emotional skills such as empathy, adaptability, the ability to communicate and negotiate effectively. Empathy will be key in a job market dominated by automation and AI. By 2030 Mckinsey thinks 25% more time will need to be spent on social skills than today (I wonder what their baseline for this is).
  • Technological skills such as data analysis, engineering and research. People will need to work alongside automated systems. Mckinsey predicts a 55% increase in the number of hours spent using technological skills by 2030.
  • Higher cognitive skills which includes strong writing, mathematical skills, critical thinking and complex information processing. These are skills doctors, accountants and writers have. Mckinsey predicts by 2030 the hours spent using higher cognitive skills will increase by 8%.

This shines a light on the Capability Development portion on the framework in Figure 2. If our strategy and understanding of the work takes us on a digital journey, how will we need to develop the capability to become Future Workers? And how will this impact job security and families?

I enjoyed watching this video of AI in action at Amazon. Their set-up for order fulfillment is cutting edge, but I also enjoyed seeing 4000 employees who are skilled to work alongside the robots. The Senior Vice President was quoted saying that the robots are:

“helping people do their jobs, not replacing people”.

(I was astounded by the amount of inventory! But I guess if you really have to keep it, at least make it easier to store and retrieve!).

What does this mean for Lean?

As improvement enthusiasts, we are taught to follow cycles of simplify, simplify, simplify, stabilise, stabilise…before we automate. But a very real risk is that when we automate we inadvertently curtail the learning: we teach the machine or computer all we know in the same way we would train a new employee in current best practice. But we ‘forget’ how to do the actual work because we no longer do the actual work. In that way, automation unintentionally de-skills us (Norman Faull, 2018). Should we worry about that with AI? We think so. Should we fight automation and AI? Definitely not.

No doubt the progress we’re talking about here will catapult performance, protect companies from extinction, and help them thrive in what looks like a pretty tough, competitive future. But what problem are you trying to solve? What is the actual work? How will you develop the capability? Lean Thinking can help you through the thought-process you need for this advanced learning journey. We recommend thinking Lean before thinking AI, digital and tech.

Last points to leave you with:

  • Figure out what constitutes waste before cementing it with new technology. Understand the work.
  • Mapping, 5S Workplace Organisation, Visual Management, Jidoka, Poke Yoke, PDCA…Lean tools, principles, techniques remain valuable and still have a place to understand the work, visualise problems, perform experiments – but could automation help and can it take on some of the processing?
  • Data, converted to information is still a way to visualise and expose problems.
  • Garbage in, still generates garbage out.
  • Problem-solving can and will be done by machines, but some problems will still sit with humans albeit where they use different skills to address them.
  • Leadership capability will evolve with their teams.
  • Humans will need to develop their skills to work alongside AI.
  • Think deeply about coupling productivity gains with innovation and skills development to capitalise on liberated capacity.

We’d love to hear your views on this hot topic. Feel free to drop us a comment.

Warm Regards,

Rose Heathcote
CEO, Lean Institute Africa

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Sources and Further Reading
Deloitte, The Smart Factory, 2017, Burke et al
World Economic Forum
Industry Week
The Future of Manufacturing Employment