Combining Lean manufacturing and Industry 4.0

Effective resource management requires a people-focussed approach to data, according to experts in the industry. Connor Pearce reports.

While Industry 4.0 may be heralded as the fourth industrial revolution, and seemingly involve a wholesale reformulation of manufacturing processes, the technologies and processes that it encompasses are the result of the theories and innovations that came before it.

In all the excitement that surrounds Industry 4.0, its predecessor in manufacturing organisational philosophy, Lean manufacturing, may have fallen by the wayside. But, in a manner that is appropriate for the theory’s tenets, the philosophy has quietly updated itself, and according to some industry observers, may be the key to unlocking Industry 4.0’s potential.

Professor Amrik Sohal, at the Monash University Department of Management, has followed the implementation of Lean manufacturing principles for 30 years, and has seen the practice evolve.

“What’s happened over the last half a century, across many industries, is that Lean has evolved, not only to comprise simple tools or techniques, but become a business philosophy,” said Sohal.

Lean manufacturing, which began in mid 20th century Japan came to become synonymous with the company which founded it and is often called the Toyota Production System. Initially, the philosophy focussed on removing waste, or non-value adding activities from the production line. This then shifted to the supply chain as a whole and has now made it to the management suite.

“Companies are using the philosophy that you have around Lean manufacturing as a means of operating the whole business, rather than simply using Lean on the shop- floor,” said Sohal.

With the arrival of Industry 4.0, Lean manufacturing’s prominence has receded. A focus on automation and digitalisation has made ideas around continuous improvement seem outdated, however, some proponents of Industry 4.0 technologies have seen how these technologies can be used to pursue the goals of Lean.

With the massive accumulation of data being fed through ERPs and the digitalisation of production processes visualised and managed by software, Terri Hiskey, vice president of global product marketing at software company Epicor is immersed in the new world of Industry 4.0. But, Hiskey cautions, the data that is produced via Industrial Internet of Things (IIoT) devices is only as good as what it can tell an operator.

“It’s fine if you’re getting data coming off of your machine, but what is that really telling you and how do you use that data to make that process better? To me, IIoT is the next wave of being Lean. It’s how we’re using technology to make all of those processes Leaner,” said Hiskey.

Sohal also sees how technological advances under the umbrella of Industry 4.0 can allow businesses to take steps towards reducing waste. Taking the premise of Lean and updating it for the digital era.

“The availability of data in large volumes nowadays can be used to do preplanning, so you know what’s coming, you’re much better informed of what the trends might be, and therefore you make much more informed decisions. Future capacity planning, medium-term scheduling on the shop floor – you can do that much more effectively and only invest in what’s absolutely needed,” said Sohal. “Without all of that data, you may make some decisions that may result in waste.”

In addition, Hiskey noted that without industrial software, the complex data analysis required to justify Lean manufacturing decisions was done by specialist individuals, who conducted complicated and time-consuming calculation. Today, the decreasing cost of digital software allows a much wider variety of organisation to adopt Lean manufacturing philosophies without the high initial costs and ongoing expense, as Hiskey outlined.

“Technology is now helping think through these issues, where 20 or 30 years ago it was a reliance on experts or people having to do that,” said Hiskey.

Significantly, what has changed in the past few decades is the cost of purchasing or implementing software which collates, analyses, and interprets data. Combined with the availability of sensors which have also reduced in price, a greater number of companies are able to adopt Lean Manufacturing principles.

“IIoT technologies are now more accessible and affordable for everyone. They level the playing field for small and mid-sized businesses, because smarter SMEs can use these technologies and apply them and they don’t need the number of resources that large companies might have put into this,” said Hiskey.

While this may seem to lead to a rapid adoption of data analytics technology under the banner of Industry 4.0, what makes this fourth industrial revolution so significant is that it is not an linear change. Rather, the exponential growth in the production of data requires
new methods of interpretation, ones that, as Sohal points out, were not previously available in Lean manufacturing.

“In Lean manufacturing, there was much more focus on getting employees involved or empowered to solve their own problems in the workplace,” said Sohal. “When you’re thinking about Industry 4.0, although the end results might be similar, we’re talking about a higher level of automation and hence fewer employees involved in carrying out processes on the shop-floor.”

For Hiskey, this shifting of gears when it comes to automation requires a new approach to the capturing and understanding of data, one that requires greater foresight.

“Manufacturers are struggling. They understand that if they put a sensor on a machine, they’re able to get data readings on temperature and vibration, and how much going through that machine. But you really have to have a sense of what is the optimum level of throughput with that machine, by what degree do you want to measure temperature, and at what degree is the temperature going to be too high and affect quality,” said Hiskey.

“You have to define the optimal output of that machine, and once you have those thresholds, then you can put a sensor on that machine.

“Where I’ve seen a lot of IoT projects get stuck is when people think that ‘Oh, if I put this sensor on my machine, it’s going to yield a lot of data and I’m going to understand more about my machine.’ But it’s not going to understand more about your machine unless you understand what it is you want to monitor,” said Hiskey.

To return to the value of Lean in a highly automated workplace, and to avoid generating huge quantities of unneeded data, Sohal suggests a return to the fundamentals of Lean manufacturing. As an investment in digitalisation involves an initial cost, ensuring that the investment pays dividends requires smart thinking of what the final outcome will be.

“Once that investment in digitalisation is in place, we may end up with less flexibility, compared to what you might have under Lean. Lean is very much built on the basis of keeping things simple,” said Sohal.

Hiskey also knows that too much data is not a good thing and can inhibit a company’s efforts to streamline their production systems.

“You’re just opening yourself up to a whole bunch of stuff that isn’t meaningful to you. That’s contrary to the Lean philosophy of really trying to focus in on certain areas and be more productive.”

According to findings from research and advisory firm Forresters, between 60 and 73 per cent of data collected within an enterprise goes unused.

Avoiding creating such a waste of data requires intelligence at the planning stage, prior to any implementation of technology. Making these decisions at the outset requires a workforce that is adapted to the landscape of Industry 4.0, and one that is empowered to take advantage of the benefits of Industry 4.0. Just as Lean manufacturing freed up workers to be more productive by removing waste in terms of down time, if Industry 4.0 is going to be implemented successfully, then individuals will need to be equipped with the skills to interpret and understand the data produced by Industry 4.0 technologies.

Barry McCarthy, treasurer of the Association for Manufacturing Excellence (AME), has been leading Lean learning workshops for AME, and has become concerned that the impact of Industry 4.0 on the human workforce is not being properly accounted for.

“Most of the stuff that I’ve seen on Industry 4.0 is around the technology. Well, one of the technologies that we need to put on the table is how we manage it. If we don’t start to improve that, we’re not going to improve the happiness and reduce the stress of workers through all of this change.

“It’s a big change for people and I don’t think they see or understand what’s coming. In this case, we have to give workers more autonomy rather than less, and if you look at Industry 4.0, it’s going to take a lot of base ground away from people.

“If we don’t start to develop people into better problem solvers, thinkers, and innovators in our workplace, then there’s going to be no ground for us,” said McCarthy.

Sohal also warns that in the focus on technology and systems under Industry 4.0, the people who are going to implement the innovations, from the shop-floor to the management suite, are sometimes forgotten.

“Regardless of what production system you have, a Lean production system or a highly automated Industry 4.0 system, you still need people. You can’t just think about people on the shop-floor, you need to think about people at the leadership level and those who are responsible for implementation,” said Sohal.

Retaining the skills and knowledge of those who have had their hands in the production process will be key to the effective implementation of Industry 4.0 and for the technology to go towards Lean outcomes.

However, bridging gaps in knowledge between those who have the practical knowledge on the shop floor and the data that Industry 4.0 generates, is something that software is well placed to do.

“We’re keeping that in mind that people’s roles are shifting. We don’t expect someone who has worked on this production line for 20 years to now be able to delve into all the insights of data and understand what that’s telling you, but we can give you the software package that shows you the amount of waste coming off this production line,” said Hiskey.

While on the one hand this means making software systems intelligible for all who may be required to use them, on the other hand, utilising current and innovative systems can draw in a new generation of workers to manufacturing, who may have been put off by stereotypes about the industry.

“When you’re trying to attract these workers, you have to consider the employee experience. The application of Industry 4.0 technologies are things that attract more workers because they want to work with newer technology. It’s not just the application of those technologies but it’s the ability now for them to work anytime, anywhere, for them to pick up their tablet or their phone and to be able to monitor these things from wherever they are,” said Hiskey.

Flavourtech utilised cloud computing to meet the needs of its dispersed workforce.

One example of this is Griffith- based manufacturer Flavourtech, which produces aromas for use in the food, beverage, and pharmaceutical industries.

Flavourtech realised that as much of their work was done by employees while out on the road, they needed a software system that could adapt to the nature of their workforce. With this in mind, Epicor was able to develop a solution that enabled information to be inputted from mobile devices and be worked on collaboratively.

In this case, with a clear use case and desired outcome, Industry 4.0 technologies were used successfully, and reduced waste as time did not need to be spent getting back to desktops at head office.

Making these decisions and taking the humans that the technology is designed to benefit can lead to great outcomes but keeping in mind the value of the skills of people that machines cannot imitate will be paramount, according to McCarthy.

“Data can be interpreted in any different way, and AI is not going to always interpret it properly at this point, so we have to have people that can interpret it correctly for the business. Interpretation of that data really requires good storytelling skills because they have to have a look at the data and be able to convince other people that this is what the data means,” said McCarthy.