background

AI Revolution in Manufacturing

SummaryFeb, 2019

The onset of Artificial Intelligence (AI) has been a game-changer for many industries, with manufacturing proving no different. The misconception for some however is that AI’s benefits are still in the distant future, causing many within the manufacturing space to rest on their laurels. The AI Revolution in Manufacturing report explores what immediate advantages can be gained from leaders being proactive in the opportunities that AI offers, as well as key challenges around implementation. Also featured is crucially how AI can help translate into strategic business value, as well as the five types of AI which hold the biggest potential to revolutionise the manufacturing space.

In this report

AI is a key strategic differentiator

Artificial intelligence is a potential game-changer that can have an immediate and wide-ranging impact on business

Picture this scenario. You’re an electronic goods manufacturer and you’ve had to put out a product recall following complaints from customers that their devices are faulty. Even though taking action has meant safeguarding customers, it’s come at a price as you’ve had to refund them or replace the faulty products.

Such a scenario can be avoided if the defect in question is spotted on the assembly line. “Humans tire easily and have the potential to be distracted. They’re prone to making mistakes. Automated assembly systems, combined with end-of-line buy-off equipment, such as intelligent vision systems, don’t suffer from this [problem],” says Chris Unwin, managing director of LAC Conveyors and Automation, which designs, manufactures and installs conveyor belt systems for blue-chip companies.

Vision-based systems are not new and have been used frequently by the manufacturing industry for inspecting and quality checking since the 1990s. But systems integrated with artificial intelligence (AI) take it a giant step further.

For example, US startup Instrumental, founded by two former Apple engineers, has developed a technology which can detect differences and distinguish between defects and insignificant design flaws that aren’t a cause for concern. The more images the system captures and analyses, the more it learns, and the better it becomes at spotting anomalies that could otherwise lead to product recalls.

Instrumental’s founders say the technology will help manufacturers “prevent delays and enter mass production with confidence”.

This is an example of the potential of AI, a broad term that is often misinterpreted. One common misconception is that machines and devices that learn are beginning to think for themselves, rendering human workers redundant. But this couldn’t be further from the truth. AI-driven technology only does what it’s programmed to do, but for this to happen, manufacturers need to know what they want AI to do and be aware of the tangible benefits it presents.

AI’s value lies in it being a strategic differentiator to the wider business, beyond simply an IT responsibility

According to consultancy firm PwC’s Industry 4.0: Global Digital Operations Study 2018, only 1 per cent of UK manufacturing companies are considered digital champions, compared with 10 per cent globally. Furthermore, of the 72 UK firms (1,155 were surveyed in total), 1 per cent have implemented AI, with only 24 per cent aware of its potential.

Although AI technology is still in its infancy, rapid advances have been made; uptake, however, at least in the UK, isn’t accelerating at the same pace. The reason being that many small and medium-sized manufacturers often lack an understanding of the deep technical workings of AI, says Jens Roehrich, professor of supply chain innovation at University of Bath School of Management.

“Stakeholders want to know exactly how a system arrives at a diagnosis or recommendation,” says Professor Roehrich. “And manufacturers also need to be convinced that AI can save costs and increase productivity. The return on investment needs to be mapped out clearly for them.”

The reality is that the tangible benefits AI presents can have an impact on all aspects of manufacturing operations.

“Ultimately, AI is going to help [companies] make more informed decisions at each stage of the production process,” says Professor Roehrich. “It will even be used to improve material flows and optimise supply chains, helping companies anticipate [and react to] market changes.”

The IDC FutureScape: Worldwide Manufacturing Predictions 2018 report estimated that, by 2021, 20 per cent of the top 2,000 manufacturers globally, as ranked by Forbes, will be relying on the internet of things (IoT) to scale their processes.

What will this look like? As well as vision systems, there will be sensors monitoring the health of machinery, chatbots to deal with customer purchasing requests and also software to crunch the data, to name a few.

This smart ecosystem will enable manufacturers to run constant self-diagnoses, so preventative maintenance can be carried out without having to shut down production. For example, sensors monitoring machinery may detect a sudden change in vibration levels. Using AI capabilities, the sensors might read this to mean a part is faulty, alerting operators and enabling engineers to fix the fault before it affects output or leads to quality issues.

Another of the IDC predictions is that the IoT could speed up larger manufacturers’ execution of operational tasks by 25 per cent. For manufacturers of all sizes though, it’s also a window of opportunity to improve their robustness and responsiveness.

“Making use of IoT data is not just an operational necessity, but a commercial opportunity,” says Eric Topham, chief executive and data science director at The Data Analysis Bureau, a consultancy that works with clients, including manufacturers, to understand their data science needs and implement tailored solutions.

“Turning data into hard-to-replicate services can give those that adopt AI applications a competitive edge. Manufacturers are increasingly facing competition from economies including China, and need to add differentiating and hard-to-copy revenue streams to their business models,” says Mr Topham.

If manufacturers want to reap the rewards that the advent of Industry 4.0 and the trend of automation offers, AI shouldn’t be seen as an ancillary or auxiliary part of their company. AI’s value lies in it being a strategic differentiator to the wider business, beyond simply an IT responsibility.

Overcoming internal AI challenges

What are the internal barriers slowing adoption of artificial intelligence and how can these be overcome?

So you’ve heard about how artificial intelligence (AI) can bring efficiency and other benefits to different aspects of your manufacturing processes. You’ve identified areas of your business that need automating. What’s the next step you need to take?

It’s not enough to simply invest in intelligent vision systems and sensors, install smart software to help monitor the health and performance of machinery, and then sit back and let the magic happen. It’s much more complicated and failing to take the right action could pose barriers to being able to implement AI successfully. Here are some of the main challenges.

By 2020, the discrete manufacturing industry, which is related to products that can be counted, seen or touched, will be spending £35 billion on the internet of things (IoT). This will account for 16 per cent of the total £220-billion spend globally, according to 2017 research by Boston Consulting Group.

As manufacturers invest more in IoT, there will be an explosion of connected devices and sensors on assembly lines and in production plants, all constantly communicating with each other and generating large volumes of data. However, the more connected factories become and the more devices and sensors in communication, the more robust manufacturers’ IT infrastructure will need to be to cope with the wealth of data that needs to be processed.

“Simply put, more data means more opportunity for insight and therefore more value. But more data also means more modelling and that will require access to smarter, more powerful computing,” says Tate Cantrell, chief technology officer at Verne Global, which provides datacentre solutions on an industrial scale, supporting companies to deliver intensive, machine-learning applications.

By upskilling employees and developing a long-term strategy for digital transformation, manufacturers can create more efficient, productive factories that will reshape the industry

Mr Cantrell argues it’s imperative that manufacturers consider high-performance computing (HPC), particularly in the cloud, as it enables AI application to return results more quickly.

To explain the way it works, it isn’t too dissimilar to connecting the dots on a piece of paper. The more dots there are, the longer it will take to draw a picture. Likewise, the more data points, the longer it will take to connect them up, process the data and then return the results, although we’re talking a matter of milliseconds.

“By choosing a cloud-enabled approach to HPC, users can scale up their overall computing capabilities. And by incorporating AI into HPC applications, companies can ensure even smarter and more efficient computing,” says Mr Cantrell. “Applications that are built with the cloud in mind enable companies to stay ahead of the innovation curve.”

Those manufacturing businesses that fail to invest adequately in their IT infrastructure, Mr Cantrell concludes, end up limiting their ability to push boundaries and will fail to realise their ambitions.

Once you have the data, you need to be able to make sense of it. While software can do the crunching, present unstructured data in digestible formats, offer insights and even make recommendations, it can’t tell you what specific actions you need to take.

If manufacturers are to act on the AI-driven insight that their IoT ecosystem is generating, then they need to rethink their workforce and the type of people they are hiring.

A 2017 report by Engineering UK found there will be demand for an estimated 186,000 engineers every year until 2024. Many of these jobs will be in roles such as automated system engineers and manufacturing software engineers. As such, a new breed of manufacturing employee will be needed, who is tech savvy with an interest in data-driven decision-making.

“Job roles will inevitably change and some will be made obsolete, but humans will remain fundamental. Retraining and upskilling staff to work alongside machines will be key to ensuring [manufacturers] have the necessary skills to work efficiently,” says John Kirven, senior proposition consultant at Canon. His role is to support manufacturing businesses by understanding their processes, challenges and goals, subsequently linking them with the technology and services that meet their needs.

The type of skills likely to be in demand are an ability to operate automated manufacturing systems, ability to work with computerised systems, and ability to read and write code. A broad understanding of machine-learning, predictive analytics and algorithms is also likely to be required.

To close the skills gap, Mr Kirven believes manufacturers will need to engage with universities and colleges. This will help in demystifying any misconceptions young people may have about manufacturing that jobs are low paid, or there’s little room for professional growth or progression. The truth is it’s a high-value industry.

Another benefit of engaging with educational institutions is reaching young people, who may not have previously considered a career in manufacturing, with transferrable skills.

“AI has the ability to streamline processes and boost productivity, but this can only be achieved if manufacturers utilise machines to their full potential and, in doing so, invest in their staff as well,” says Mr Kirven.

“Ultimately, by upskilling employees, bringing in the necessary expertise [and talent], and developing a long-term strategy for digital transformation, manufacturers can create more efficient, productive factories that will reshape the industry.”

How AI can help translate real business value

With the promise that artificial intelligence (AI) brings, it has never been more urgent for leaders to understand the potential applications of the revolutionary technology. A Gartner survey found that only 4 per cent of chief information officers have already introduced elements of AI into their firm, yet 46 per cent are planning to implement AI solutions in the future

As a nascent and rapidly evolving technology, many executives are unsure of how to start the transition away from legacy systems and begin to embrace the exciting, but complex, range of AI-based tools.

Antony Bourne, president of IFS Industries at enterprise software provider IFS, believes that firms should start small on their AI journey. “It’s important to prove that a piece of AI technology can solve a business pain point and create real value, before implementing it throughout the company,” he says.

The set of AI-backed solutions available to businesses is expanding, presenting enterprises with an array of entry points into the technology. From implementing chatbots to improve the customer journey to introducing business process automation to streamline laborious tasks, few firms are unable to benefit from the next generation of AI technologies.

The growing number of case studies illustrating how AI has been able to transform inefficient processes and bring about tangible business improvements is helping C-suite executives visualise the power of AI.

Cubic Transportation Systems, a provider of integrated systems and services for transportation, have made use of machine-learning to give ticket machines the ability to self-diagnose. For example, if a ticket machine at Paddington Underground Station stops working, the machine will attempt to figure out the exact reason for the breakdown and then, if possible, fix it.

If it is not able to fix the issue itself, the machine will automatically raise a works order for an engineer to carry out a repair. The ticketing machine can even select the most suitable engineer who has the right skills to resolve the problem and send them a text message to arrange a service visit.

“Process automation and machine-learning have helped Cubic to be more efficient in how they manage their assets out in the field,” explains Mr Bourne. “The result has been a decrease in the IT overhead with a reduction of their field resource controllers by 75 per cent and with machine availability seeing an increase of 20 per cent.”

By using our AI technologies, firms can differentiate themselves in a crowded and competitive market

Advances in AI technology are also driving corporate interest in a diverse range of AI-based tools, including the internet of things (IoT), chatbots, robotics and natural language processing (NLP). For example, developments in NLP are set to alter drastically how customer service is performed with machines being increasingly able to understand and analyse human speech and text.

“If the software understands what the consumer wants, it can automatically do it for them. Automating interactions in this way goes beyond just reducing the volume of emails,” says Mr Bourne. “Customers receive more relevant responses and customer service staff are happier as they don’t have to perform as many mundane tasks.”

The advent of cloud solutions has radically changed the type of AI tools that businesses can make use of. IoT devices can now collect huge amounts of data and send it directly to the cloud, where it can undergo analysis to uncover actionable insights that can help identify new business opportunities and improve efficiency.

Before introducing AI technologies at a firm, these insights may have gone unnoticed and present a clear example of how immediate results can be gained when dealing with AI. “Businesses need a lot of data to make sense of it in a meaningful way. The cloud gives you that ability to hold data and then be able to analyse it instantly,” says Mr Bourne.

For companies at the top of their industry, adopting AI tools is no longer an optional add-on to current processes. “Companies that don’t embed AI into their product, service or processes will lose their competitive edge in the market; they simply won’t be able to keep up with the technology deployed by rivals,” he says.

“Internally, you won’t be as efficient. You may have three staff members performing data entry tasks to process invoices, as opposed to a competitor who uses an AI robotics process automation solution that is working 24 hours a day.”

Due to the massive demand for a tech-savvy workforce, even larger companies will find it difficult to acquire the right mix of employees who are experienced in implementing AI-based solutions. To extract the most value from AI and ensure these technologies are being used in the right business areas, choosing a knowledgeable partner that is well versed across the AI spectrum is crucial to this transition.

“There is a risk that companies going it alone on AI can go down dead ends and make costly mistakes. This is why firms considering starting their AI journey need to know there are companies such as IFS out there that can help guide them to meet their goals more quickly,” says Mr Bourne.

As an industry leader in enterprise software, IFS offers both readily available off-the-shelf AI tools and bespoke industry-specific solutions suitable for businesses of all sizes. “We have the knowledge and experience of a wide range of AI technologies in relation to different needs. By using our AI technologies, firms can differentiate themselves in a crowded and competitive market,” Mr Bourne concludes.

AI’s biggest potential in manufacturing

Smart factories, incorporating artificial intelligence, are forecast to add between $500 billion and $1.5 trillion to the global economy by 2022, according to research by Capgemini in 2017. Here are five tangible forms of AI predicted to have the biggest impact – soon

01 Predictive maintenance sensors

Ensuring equipment and machinery are running efficiently and optimally is critical to manufacturing output. The problem is most maintenance events are planned months in advance and, though they can prevent faults from occurring, it gives manufacturers little time to react if an asset or equipment breaks down unexpectedly. Schedules can’t foresee the future.

Artificial intelligence (AI) offers the ability to predict when events are likely to occur, so manufacturers can be prepared, react quickly and minimise any loss in productivity.

Sensors embedded in equipment and machinery will monitor operating conditions and tooling performance. These sensors will collate a wealth of information about an asset, both structured data – model number, year it was built, warranty details – and unstructured data – maintenance history and repair logs.

Once gathered, this data can be used by AI models to spot any anomalies, empowering manufacturers to take the preventative action before any fault occurs and minimise downtime.

02 Inventory management drones

Manually capturing stock counts and managing inventory can be a laborious process. Some manufacturers will even halt operations to carry out a full inventory check.

Drones with AI capabilities are fast becoming an effective solution to this logistical challenge. Unlike their warehouse delivery cousins, these drones for auditing and inventory management can fit around production schedules because the only person who really needs to be present is a licensed drone pilot. This minimises downtime and also allows manufacturing teams to focus their efforts elsewhere.

Using an optical system combined with computer vision, drones can guide themselves to an assigned location, and use variable image and object recognition tools to identify and collect asset and inventory information, including from barcodes, QR codes and radio-frequency identification, or RFID, tags. This information can then be relayed to an integrated software platform.

Financial services giant EY is currently exploring the use of drones with AI capabilities to improve the quality of the auditing process.

03 Chatbots for smoother customer service

Away from manufacturing operations, AI will play a role in improving customer service and experience.

There are some queries that are better dealt with by machines, such as when customers want quick answers regarding their orders. The beauty of a chatbot utilising AI is it will learn from each conversation it has and then get better at predicting questions that will be asked, scripting answers, and providing more accurate and real-time information. Because the bot will know who it is conversing with, it can use this knowledge instantly to fetch information specific to the customer.

What’s more, machine-learning algorithms can be developed so the bot improves its understanding of how manufacturers operate. When integrated with other tangible forms of AI, it could inform customers of potential future problems with their orders, information that customer service personnel might not have to hand.

For example, if sensors detect that a piece of equipment is likely to break down, this could mean a temporary halt in production, which in turn could mean there’s a slight delay in orders being completed and sent out. By making customers aware of this, it will give them peace of mind and enable them to prepare for any delays that might occur their end as a result.

04 Smarter ERP software

All connected devices, sensors, chatbots and automated systems, from factory floor to finance office, can arguably only be effective if the data leveraged from the internet of things ecosystem is used in conjunction with enterprise resource planning (ERP) software.

Gartner predicts that by 2020 most new pieces of software released on to the market will leverage AI technology. Many experts also agree that smarter, AI-based ERP solutions are just around the corner. According to a report published in December by Evans Data, 58 per cent of developers working with AI are planning to integrate it into their ERP solutions or have already done so.

ERP is key to making strategic decisions and executing routine operational tasks. AI-based solutions will offer deeper insights and identify patterns that human workers would either miss or take far longer to spot. It also allows manufacturers to keep on top of trends and deliver real-time forecasting.

A caveat is that manufacturers will have to work alongside data scientists and consider upskilling their workforces. AI may make some low-level manufacturing roles obsolete, but at the same time it will increase the need for smarter employees, with knowledge of algorithms and machine-learning, and who can help cut through the clutter.

05 Sensors embedded in products

While AI can help make the manufacturing process more efficient, embedding sensors into the products being manufactured also has its benefits.

If a company provides an end-to-end service, then it’s likely to offer off-site support and even a repair service too. As with factory floor maintenance typically scheduled months in advance, any routine checks are usually arranged ahead of time. Engineers are sent to customers’ locations even if no problems have been reported. This will often mean wasted fuel and man hours, resulting in poor productivity and decreased efficiency.

Integrating AI capabilities into products and monitoring their performance, and therefore their maintenance requirements, has a further advantage as manufacturers can see what they’re doing right, what’s going wrong and what can be improved. Data and insight gleaned can then inform future research and development decisions. This is a value-added service that can give manufacturers a competitive edge.