IoT ready by 2020

Digitalising ahead of 2030Putting the AI into maintenanceThe new approach to SCADAMaking sense of industrial data

5

The industrial internet of things. Digitalisation. Brilliant manufacturing. Whatever you may call it, the idea is the same: with modern technologies, plant managers can create a substantial positive difference in the way industry operates. We can operate more efficiently; we can reduce production downtime; we can decrease operating costs and increase profitability. And it can all be done remotely.

There’s been so much discussion around modern industrial automation and digital technologies that countries across the world are introducing initiatives to support it and lead the way into the next industrial revolution. In Germany, the industrie 4.0 initiative has been active since 2011 and helped the concept gain wider international recognition. The UK published its Made Smarter review in 2017 and it serves a similar purpose. Meanwhile, Sweden has followed the Produktion 2030 initiative since 2013.

While all these initiatives are welcomed, they often look too much at the future rather than what is available today. That’s where this whitepaper comes in. Many automation technologies exist today that allow industrial businesses to begin the digitalisation process, allowing plant managers to see the benefits far sooner than 2030.

From incorporating machine learning into maintenance platforms to creating digital twins of plant systems, there are a number of ways that plant managers can take their first steps to digitalisation, today. In this whitepaper, we’ll explore some of the technologies available, the benefits they provide and how plant managers can use them to achieve brilliant things.

2

Tobias Antius, CEO, Novotek

Foreword

3

Tobias Antius, CEO, Novotek

Digitalising ahead

of 2030

Harry Truman once said, “Progress occurs when courageous, skilful leaders seize the opportunity to change things for the better.” For many businesses, there is seldom an opportunity like the one being presented by digital technology and connectivity.

The potential value in making systems smarter and networks stronger has not only attracted the attention of business leaders around the world, it’s also captured the interest of governments. Countries like Germany, Sweden and the UK are all trying to bolster their economies with digital transformation.

Digital infrastructure is viewed as a critical component for businesses to thrive and grow. In the UK, the Government plans to invest over one billion pounds to accelerate the development of the next digital infrastructure, including 5G.

In addition to investing into the next generation of technologies, the UK Government has outlined plans in its digital

strategy to make sure adults who lack core digital skills will have free access to basic training. The model mirrors the approach taken with adult literacy and numeracy training and will help to improve employment prospects.

While initiatives like Produktion 2030 set out long-term plans to optimise the manufacturing industry, businesses need not wait and should not be afraid to embrace the internet of things (IoT) now.

The IoT movement is growing at a significant pace and impacting virtually every industry sector. It’s predicted that the number of IoT devices used across the world, will increase 12 per cent on average per year, meaning there will be around 125 billion IoT devices in 2030.

Despite this, research has shown that many business executives want to follow the trend, rather than be seen as a leader in the journey to the industrial internet. As history shows,

4

“Digital infrastructure is viewed as a critical

component for businesses to thrive

and grow.”

faster adoption of modern technologies not only gives the advantage of seizing more market share, but also the chance to create new potential markets. By not embracing the IIoT, businesses run the risk of being left behind, so why wait?

One of the main reasons for businesses delaying digital transformation is due to concerns about lacking the experience in-house to employ the required technologies. It is because of this that GE created its cloud-based platform, Predix. Predix is a platform as a service (PaaS) that can collate industrial-scale analytics for asset performance management and operations optimisation.

As a company that also builds heavy machinery, GE understands the needs of manufacturers and has tailored its software to be specifically industrial focussed. Offering users tools like asset connectivity and industrial-grade security, Predix enables manufacturers to easily connect areas of the business that would traditionally have been siloed. This includes everything from the engineering, manufacturing, supply chain and service areas of a business.

Novotek has experience in working with businesses looking to move away from siloed operations and introducing ways of working cross-company, by integrating systems like Predix. Delivering solutions to a vast range of sectors such as food production and chemical processing, to utilities and energy.

While initiatives like Produktion 2030 are intended to drive businesses towards a more efficient way of working, the advisory timescale should not put Swedish businesses off trying to digitalise their systems now. The sooner a company’s system is digitalised, the greater chance of them retaining a competitive edge in the industrial market.

5

6

Putting the AI into

maintenance

Over the past five years, the industrial sector has begun to see the value in

digitalisation and has invested more in

adopting it. With this has come a cultural shift

from reactive equipment maintenance to proactive

maintenance that pre-empts problems.

Machine learning is streamlining predictive maintenance

7

In 2006, UK mathematician Clive Humby claimed that “data is the new oil”. Whether you’re a food processing company or an automotive manufacturer, data from production processes is the cornerstone of better efficiency, effectiveness and overall performance.

Plant managers that are familiar with the industrial internet of things (IIoT) will know that one of the concept’s biggest selling points has been the insight it can provide into equipment performance and process effectiveness, which in turn creates benefits for the company’s bottom-line.

This has changed the culture of maintenance in plants that have started adopting IIoT technology. Rather than responding to a breakage or conducting planned maintenance based on expected equipment lifespan, engineers can make informed decisions about when to maintain systems based on the equipment’s condition.

Minimising unplanned downtime has obvious benefits, but it’s the reduction in scheduled downtime that adds significant value in terms of increased overall through put for no new capital outlay. However, achieving this is challenging due to the volume of data and subsequent analysis that is required to confidently change maintenance schedules.

This is where an opportunity arises for machine learning in industrial maintenance. With machine learning, algorithms can be trained to identify correlating factors in data to not only flag up a problem but also the root cause of it. It sounds straightforward in principle, but the number of potential things to consider can be too high for a human to work through effectively.

Within a single machine, there can be dozens of sensors or other health signals. To get a clear picture of all the things that affect reliability, that data should be evaluated

Putting the AI into

maintenance

“With machine learning, algorithms can be trained to identify

correlating factors in data to not only flag up a problem but also the

root cause of it.”

8

alongside things like maintenance records and a history of what the machine was running. Even ambient conditions and crew data can give clues as to what issues can crop up.

The only effective way to navigate the abundance of variables is with an IoT platform with machine learning, such as GE Digital’s Predix platform and Asset Performance Management (APM) suite. Connecting an IoT-enabled machine to the platform allows Predix’s machine learning algorithms to analyse it with the APM’s combination of standard measures and advanced analytics. This allows maintenance staff to not only spot when a machine needs maintenance, but also why.

For example, a semiconductor manufacturer might find that it rejects ten per cent of its output due to faults in the manufacturing process. Although all the machines may be IoT-connected, there is too much data for an engineer to reasonably analyse. With Predix’s machine learning algorithms, the APM could, for example, identify that a machine has elevated vibration levels, which is damaging the semiconductors.

The algorithms can then assess this against historic data to spot patterns in how often this occurs, identify the performance signs that precede it and — if integrated into a management system — send alerts to engineers as the machine requires maintenance. This makes it possible for the machine to receive maintenance only when its conditions indicate it should, changing from preventative to condition-based predictive maintenance.

In effect, machine learning allows maintenance data analysis to become a more automated process. In fact, there are certain industrial applications where the algorithms could be permitted to directly reconfigure a machine with the right settings. And as machine algorithms learn, this will become an increasingly viable way of improving efficiency.

Whether you believe data is the new oil or not, it’s indisputable that it’s a valuable resource that fuels overall operational improvement for plant managers and maintenance engineers. The key to achieving this is to use industrial analytics intelligently and effectively to strike oil in industrial maintenance.

9

“Machine learning allows maintenance

data analysis to become a more

automated process.”

10

Times have changed since the 1980s. Power suits are out, Madonna is no longer

on MTV and IBM’s first PC now looks extremely outdated. So shouldn’t SCADA have developed since the 1980s too?

The new approach to SCADA

Novotek was part of the team of companies that initiated PC-based SCADA back in the 1980s. While it was revolutionary in the automation industry, plants have changed enormously since then, so SCADA systems must reflect this. The rise of the Internet of Things (IoT) means that there are far more connected devices around the plant, all of which need monitoring by SCADA systems.

With increased connectivity comes the need for a new generation of SCADA system that is more flexible and innovative. If plants are investing in new devices to monitor the performance of their production against key performance indicators (KPIs), then they need a connected system in place to help them to measure this across the plant.

This is where the concept of a system of systems (SoS) has emerged over the last decade. One of the most widely accepted definitions of this concept comes from Popper et al in 2004, which is “a collection

of task-oriented or dedicated systems that pool their resources and capabilities together to obtain a new, more complex ‘meta-system’ which offers more functionality and performance than simply the sum of the constituent systems.”

It’s easy to see how this relates to the modernplant. Increased connected devices, more data and better monitoring across a plant will offer improvements for the plant manager. But the key is connectivity.

Current SCADA systems were designed for more closed and controlled industrial environments, but many industries now require more data points to be monitored and controlled, meaning that self-contained systems are no longer viable. Many plant managers now want to integrate their own systems with enterprise systems and real-world applications to improve their monitoring performance.

11

12

“We expect that the next generation SCADA/DCS systems will be an integral part of a large ecosystem of people, devices, and processes that need to collaborate in order to achieve goal-driven targets,” explain German academics Stamatis Karnouskos and A rmando Walter Colombo.

Previously, before the development of the cloud, connecting systems to other systems on the factory floor never had great success. However, the cloud has made this much more accessible.

At a much lower cost than an on-site solution, plant managers can access new functions and systems through applications in the cloud. However, they must be using a more modern SCADA system that can enable them to do this.

At Novotek, we recently integrated a standard GE HMI and SCADA system with Amazon Alexa voice control and Philips Hue to change the colour of the lights in the control room or the factory floor. This was to show our customers that with a modern SCADA system that can connect to the cloud, not only can they use additional industrial control systems, but also applications in the cloud that they never would have dreamed of for industrial applications.

While there are endless ways of using these applications, the integration of Philips Hue and Alexa showed one example of how increased connectivity can improve monitoring. The Philips Hue bulbs can be installed on machines or above screens, changing colour to show performance indicators at a glance.

A red bulb could be a visual indicator of a machine’s poor output and could attract the attention of someone working on the shopfloor far faster than the operator in the control room monitoring the screens. This would lead to the problem being resolved in a more timely fashion, reducing the risk of downtime if the problem had gone unnoticed.

The Alexa system can be used by the plant manager as they walk into the control room. Simply by asking the Alexa module about the status of the plant, the plant manager could receive a verbal briefing of the most important KPIs or whether the last shift left any important notes for the handover period.

As it is a verbal system, it also frees up time for the operator to continue with other tasks in the meantime, increasing productivity and giving them more regular updates on the plant’s function.

13

The integration with Alexa and Philips Hue is only one example of how SCADA systems can be brought into the modern age to fit with the needs of increasingly interconnected plants. To help more plant managers achieve this, Novotek has partnered with Kepware to ensure that all its SCADA systems are able to be connected to whatever application is required.

The systems support over 1000 devices and have over 100,000 licences in operation across a wide range of industries, meaning that the plant manager’s needs can be met. Plant managers must also consider that if they have old, outdated machinery, they should work with an experienced SCADA provider who is able to integrate their machines with the modern SCADA software. By doing this, they can remove the cost of procuring new equipment simplyso it can be connected to the monitoring system.

Using a SCADA system that can be connected to create a SoS approach opens a realm of possibilities for the plant manager. They could integrate cameras, access systems or specialised analytics, which would all be connected and controlled through the SCADA system.

This allows plant managers to extend the monitoring of their plant much more easily than before. Without the need to connect new interfaces or configure the different system to communicate with the existing SCADA, it gives the plant manager much more freedom and flexibility to try out new applications.

However, none of this is possible without the right SCADA system. Without the connectivity and interoperability of a modern SCADA system, plant managers are greatly restricted in their choices of applications that could take their plant control systems to the next level.

With Alexa and Philips Hue just a couple of examples of how non-industrial applications can improve plant monitoring, opening the SCADA system up to the cloud can offer countless new applications for plant managers. While the SCADA systems of the 1980s may now seem very basic with these possibilities in mind, it’s important that plant managers reconsider their monitoring applications and look towards the future of plant control.

14

Making sense of industrial

data

15

“Executive interest in performance metrics is high, yet companies often don’t achieve the results they want.” This is the view of the Manufacturing Enterprise Solutions Association (MESA), and it’s one that we regularly observe in European businesses.

Product and process innovation matters more today than ever before. To set themselves apart, manufacturers must always be working one step ahead of their competitors. For example, due to the large amount of different brands all competing for space on the supermarket shelves in the food industry, manufacturers must constantly innovate.

Own-brand products are becoming increasingly popular across Europe, with well-known brands carrying out large-scale promotions in 2017 to regain favour with consumers. However, own brands are fighting back by investing in innovation. Gerald Lindinger-Pesendorf of German consultants Dr. Wieselhuber & Partner said, “up until recently, private labels mainly copied big brands, now the best ones are innovating”.

While important for the business, innovation brings challenges for plant managers, who must change or add processes in the production line. Plant managers must also meet increasingly strict supply schedules to get the products to the shelf on time. They must also balance this with reducing costs, as the own-brand products must still be cheaper than the branded goods.

To be constantly adaptable and cost-efficient, manufacturers in all industries, not just the food sector, need to have an accurate representation of what is going on at every level of their plant. This will allow them to see where improvements can be made.

As the MESA International’s Metrics that Matter report says, “metrics matter in business performance. The better the company’s system for metrics, the more their operations performance improves, and the more their business and financial performance improves.”

“Metrics matter in business performance. The better the company’s system

for metrics, the more their operations performance improves, and the

more their business and financial performance improves.”

16

There is a multitude of data for plant managers to measure, especially as connected devices are becoming increasingly common in most plants. Sensors are either inbuilt or retrofitted to most machines, collecting granular data such as output from each machine or motor performance.

This granular data should be fed into wider metrics such as Overall Equipment Effectiveness (OEE) or Right-First-Time quality, or submitted into functional systems that determine when inventory should be dispatched. However, this is where 95 per cent of manufacturers fail.

What mistakes are made?The biggest problem that we encounter when approaching customers from all manufacturing sectors is that they are unable to see the problems in their current data collection systems.

Board level staff often believe that their existing ERP systems are sufficient to monitor their plant. They may monitor production as an input and output, potentially even dividing the process into a couple of cost centres.

However, as plant managers will know, the production process can have numerous stages, all of which have their own variables, so a couple of cost centres are not sufficient for accurate reporting.

For example, in a plant producing fries, the potatoes are sorted, washed, cut, conveyed and then packaged. At each stage in the process, granular data needs to be collected to form an overall image of the plant, but an ERP system is incapable of collating such a detailed amount of data.

To monitor a plant correctly, the board will need a control system that can collect the most granular of data. GE’s production management systems collect incredibly detailed data, which can then be scaled up to the level required.

For example, the shift manager may need to understand that one worker was not working at a quick enough rate, or that there was a two-minute stoppage in production due to a technical issue. However, for the plant manager, they may need to see production output over a whole day, with only more

significant maintenance issues being flagged up.

The chief operating officer may only want to see production over the month, but each person must have enough relevant data accessible to them to help them be accountable for the output of the plant.

This is another problem that we commonly encounter in manufacturing plants. CEOs and board level employees often use metrics such as OEE, or look at adherences to schedules, but quite often, this is not actually based on sufficient granular data.

We’ve encountered a number of plants where when we question where those figures are originating from, the staff cannot be sure, as the production management system is not collecting all of the granular data from the factory floor. This means that board level executives are making decisions on figures that do not accurately represent what is going on in the factory.

What data should be used? Once all the granular data is available, it can seem overwhelming to any member of staff looking to base decisions on this data. Therefore, to use the data available in a successful way, the operations team must determine what metrics are truly valuable to the smooth running of their plant.

According to the MESA Metrics that Matter report, “what metrics actually matter to an operation depends on strategy, industry segment, process type and production and market conditions”. Identifying the correct metric could lead to a competitive advantage for a business.

For example, in the food industry, where there are strict regulations and consumer image is important, emphasising the traceability of the supply chain of ingredients could be beneficial. Alternatively, if product or process innovation is important, then if the plant can demonstrate issues in the production process before new products are

17

“To use the data available in a

successful way, the operations team must

determine what metrics are truly valuable to the smooth running

of their plant.”

18

produced at full volume, this can minimise any wasted product or undelivered orders due to technical difficulties.

Once senior staff have determined the appropriate metrics for them, the business must ensure that they are collecting the right data to assess these metrics. To do this, they must use a data platform that provides a secure path for machine generated and human generated data to land.

The right data platformThe data platform should then be able to share the data across a series of functional systems. In the past, data was siloed into different functional systems, meaning that staff had to work with different systems to assess each metric, rather than being able to see the whole collection of data across the board.

GE’s suite goes a step beyond. Not only is the data shared across the core functional systems such as reliability analysis or material tracking, the suite also offers machine learning tools that can help to determine a solution for difficult consistency, quality or reliability problems.

These advanced analysis tools can discover hidden relationships in manufacturing data, allowing plant managers to learn more about the capabilities of their plants and how they can use the existing structure of the plant to solve the problems that they are facing.

The performance management suite is also open to integration with other systems, for plant managers who need detailed investigation or analysis tools, or work process management functions to improve consistency in the factory.

From the most basic level to this advanced level of data analysis, no matter what the

metric is that is being measured, plant managers must be confident that they are collecting the right data at a granular level. This means that all machines and processes should be monitored closely, from motor efficiency to production outputs. Senior staff must ensure that this data does not go to waste by investing in a data platform that spans HMI/SCADA, manufacturing execution systems (MES) and analytics. By doing this, they will be able to turn the data into actionable information.

Many companies must present executive level staff with metrics to enable them to make decisions about business strategy. However, these metrics must be based on accurate information, generated from a granular level and the information must serve the needs of the person receiving it and the needs of the business. Otherwise, no matter how much interest there is in metrics, companies will never be able to improve their processes and achieve the results they want.

With all that in mind, are you ready to take the first step on the road to digitalisation? At Novotek, we’ve been providing industrial IT and automation systems across Europe for the past 30 years, so we have the knowledge and experience to help you improve your operations.

Novotek UK & Ireland | High Craighall Road,Port Dundas, Glasgow, G4 9UDTel: +44 (0) 141 332 1551 | Fax: +44 (0) 141 626 1492 | info_uk@novotek.com

Get in touch with us today and digitally revolutionise your business.