11 minute read

Manufacturing Analytics

from Industry 4.0 Magazine

by GB Media & Events Ltd.

from Industry 4.0 Magazine, page 14

Blockchain

Manufacturing Analytics

at the Core of Industry 4.0

As we embark on the fourth industrial revolution, manufacturers across the globe – and across all industry sectors – are captivated by the promise of various cyber-physical systems that enable the computerization of manufacturing. In particular, decentralized intelligence, which helps to create intelligent object networking and independent process management with the interaction of the real and virtual worlds, represents an exciting new aspect of the manufacturing and production process.

Peter Guilfoyle, Northwest Analytics

The basic principle is that by connecting machines and systems, we can create intelligent networks along the value chain that control each other. For example, machines would be able to predict failures and trigger maintenance processes autonomously, or self-organize logistics that react to changes in production. Industry 4.0 technologies include many of today’s buzz-words, like Big Data, artificial intelligence, machine learning, virtual reality, the cloud, internet of things (IoT) and M2M (machine-to-machine communication).

These technologies offer the vision of a future with efficient, self-automated manufacturing processes that monitor themselves, so they never go wrong.

However, while many manufacturers are eager to embrace these new technologies, it isn’t uncommon for their progress to falter as they meet a number of stumbling blocks along the way. Typically, these companies stumble because they failed to establish the basic foundation on which to build their digital platforms. That foundation lies in manufacturing analytics – the core of everything that is Industry 4.0.

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What is ‘manufacturing analytics’?

Manufacturing creates and stores more data in a day than many companies do in a month. Plant engineers and operators need knowledge and information quickly, but data interpretation is a constant struggle. Which signals should they listen to? What do those signals mean?

Manufacturing analytics is the science of drawing insights from raw process information sources. The majority of process data offers little value in its raw, unprocessed state, but applying the right tools to those data can reveal trends and metrics that are useful in optimizing the overall efficiency of a business or system, but which would otherwise be lost in a mass of data points and digits.

Manufacturing analytics can be descriptive, predictive and/or prescriptive. Software that captures and analyzes big data is already widely available, offering the ability to detect trends, spot anomalies and predict patterns that provide useful insights and considerable value. In other words, manufacturing analytics makes sense of data, and converts it into value.

Lloyd Colegrove, PhD, Data Services Director, Fundamental Problem Solving Director at The Dow Chemical Company, began driving Dow’s digital transformation in the early 2000s. As a leading adopter of manufacturing analytics technology within the chemicals sector, Dow is now benefiting greatly from the investments made. However even leaders like Dow needed to start somewhere.

“The question I asked one of our lab technicians about 15 years ago was – what do you do with all the data?” explains Colegrove.

“He looked at me and said we quality assess it to make sure the product is in specification. So my next question was – wouldn’t it be better to use the data to avoid problems before they occur? Why wait until the lab tells us there’s a problem after it has happened?”

Colegrove is widely viewed within the industry as a visionary for manufacturing analytics. His vision is based on the observation that when plants found themselves in trouble, they could understand how the trouble arose by examining the data leading up to the incident, but that it would be far better to know in advance that these issues were about to happen so they could be prevented altogether. Manufacturing analytics provides a means to realizing this promise of the digital age, by unlocking the full potential of vast quantities of manufacturing data – in real time.

It is important to understand that it is only with a core foundation of manufacturing analytics that all that data can be interpreted and implemented for value-added outcomes. Until this foundation is in place, it is simply not feasible to construct more advanced platforms for systems such as automation or artificial intelligence, as the data management required for those more advanced systems can only be provided – and must be underlaid – by core foundational analytics software.

industry 4.0 Issue no 10 - April 2019Manufacturing analytics

Video:

Resource requirement for Industry 4.0 Analytics.

Where Industry 4.0 should start

As any company embarks on its journey to Industry 4.0, it should begin with manufacturing analytics that is simple to use and understand – for example real-time Statistical Process Control (SPC) or a univariate analytics system – yet provides an extensible platform that will then accommodate and enable additional analytics approaches (e.g. multivariate, machine learning).

That software needs to be compatible with existing equipment so it can maximize on existing technologies and data sources, and it should be scalable for future Industry 4.0 technologies. Also key to success is a userfriendly interface, such as a simple dashboard that provides real-time signals for operators and engineers to quickly see and understand, so they can take any actions that might be necessary to run the plant efficiently and safely.

The case study below provides an example of a major chemical company on its journey to Industry 4.0. Crucially, that journey started with an in-depth review of processes and unmet needs, and looked at how best to maximize on existing strengths, expertise and technology in moving forward to the next stage of the company’s evolution. In looking for a solution to processing issues that were impacting the

bottom line, the company set a goal to achieve a better, data-driven understanding of parameters changing during the manufacturing process, and the real-time effects of those changes, with the goal of achieving more consistent, more efficient product manufacturing. Manufacturing analytics helped the team to realize what their data meant, which parameters were important, and reduced the 8 hours previously spent collecting and analyzing these data to zero. A simple green/red dashboard enabled operators to quickly and easily see which parameters required attention, so any issues could be dealt with before they affected product quality or caused shut-down. The success of this project reflected careful planning regarding the implementation and applicability of the new technology, setting the company on its path to Industry 4.0 in a useful and productive way.

Case study: Dow Chemical

Background: The Dow Chemical Company is a multinational chemical corporation headquartered in Michigan, USA. With a presence in about 160 countries, it employs about 54,000 people, and is the world’s second-largest chemical producer by sales (around $58 billion).

Challenge: One if the company’s US plants had repeated upsets resulting in unscheduled downtime. The problems were costing money in terms of fixing the equipment, lost manufacturing time, and the need to reprocess the catalyst under manufacture, as it did not meet the required quality standards when these issues occurred. The plant’s management realized that they needed to learn how to better listen to the signals that their plant was sending to the operators, and how to better respond to those signals. To satisfy these requirements, Dow selected the NWA Focus EMI ® platform from Northwest Analytics.

Solution: Using Dow’s existing expertise, the team identified parameters of importance. This was no simple task – the company had previously been spending 8 hours a week to gather, analyze and visualize their data, but scanning the different datasets in isolation made it difficult to understand what was important.

Analytics helped the team to realize what their data meant, which parameters were important, and reduced the 8 hours previously spent collecting and analyzing these data to zero. With the help of the company’s IT team, the data analytics software connected to existing Dow data sources (e.g. historian data, laboratory and technology data) in real time to capture key data and calculate relationships across different sources. The findings were presented in a simple green/red dashboard that enabled operators to quickly and easily see which parameters required attention, and included recommended actions to help balance competing phenomena.

Result: ROI was rapid, removing the time spent analyzing data and allowing the production team to focus on other things. A better understanding of the data available, and the real-time nature of the platform meant that any issues were dealt with before they impacted the catalyst or caused shutdown, allowing the plant to have its longest catalyst run ever – in terms of time and product – and an ‘Industry Excellence Award’ from Frost and Sullivan. As the value of the pilot was appreciated by senior management, the data analytics software was marked for roll-out to other Dow plants across the globe.

Video: Smart Data Analytics: BMW Group relies on intelligent use of production data.

“A lot of the current human work can be replaced, but people will always be needed to take some decisions. You can’t replace human creativity,”

Industry 4.0 is an additive process

Rather than making a choice between the installation of manufacturing analytics software and the introduction of these advanced technologies, Industry 4.0 needs to be considered an additive process, whereby manufacturing analytics forms the grounding on which more advanced technologies can then be built. Once the analytics is in place, gleaning value from a company’s data and providing a foundation for digital transformation, it is feasible to look at – and ultimately be successful with – more narrow-cast analytics techniques like artificial intelligence, neural networks and machine learning.

For example, machine learning for predictive maintenance should be more sensitive to faint signals because of the foundational analytics in place. Experience built by human experience with the manufacturing analytics interface will provide vital insights into how to best respond to those signals under different circumstances.

Additionally, the software must have input from all possible relevant parameters. It is highly likely that during the implementation of foundational manufacturing analytics, engineers, operators and plant management will achieve a greater understanding of ongoing processes, what the data mean, and which parameters are most important. This will involve signals from unexpected sources that might not have been known or considered before the manufacturing analytics journey started.

Jasper Rutten is Advanced Analytics Manager at Huntsman Corporation, where his goal is to set the route for digitization and advanced analytics for the company’s upstream manufacturing facilities within the Polyurethanes Business. As manager of the department, he is leading a global journey to bring Industry 4.0 into practice. His experience provides a great example of building a digitized approach in an additive way.

“We started our first Proof of Concepts about 3 years ago, about the same time that Industry 4.0 in Europe was taking off,” explains Rutten. “Working in the polyurethanes division, our aim is to get maximum production out of our units. So we wanted to use all kinds of tools to mine the data and build clever models – neural networks – that would help us run our processes in a more optimal way. We’ve done a lot of conceptual work now, and we know there’s value. But now we’re looking at stage two of real implementation – the long-term view is to get close to full automation.”

Rutten says that currently most companies’ processes are not ready for full automation due to, for instance, cybersecurity issues. However, his vision is that at some point in the not too distant future, algorithms will be one of the key tools helping actively monitor chemical plants. That’s not to say that there will be no people at all involved. Even ‘full automation’ will require some human input, in Rutten’s view.

“A lot of the current human work can be replaced, but people will always be needed to take some decisions. You can’t replace human creativity,” he points out. He expects new roles will emerge, although it is difficult to predict at this moment what these will look like.

Conclusion

The digitization of manufacturing can already be seen picking up speed as more and more companies start to realize the promise that it brings: higher quality products, produced more efficiently, and more economically, with better process safety.

The initial goal of manufacturing analytics is to avoid potential problems altogether by detecting early signals and fixing any anomalies before they impact products or processes. Real-time, analytics-based monitoring offers an early warning system that protects plant assets and product quality – and the people working at those plants.

Beyond this lies the promise of artificial intelligence, neural networks and machine learning.

However, building a fully digitized company that embraces such advanced technologies needs to be seen as an additive process, in which the manufacturing analytics provides a core, or backbone, from which more vertical analytics systems can draw strength and structure. Once this foundation is in place, the potential for analytics in manufacturing would appear to be almost limitless. However, that future can only be built on a foundation of manufacturing analytics – the technology that lies at core of Industry 4.0.

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