The UK government’s sudden interest in and commitment to the manufacturing sector may have taken companies by surprise, but the investment is timely: manufacturing processes are being transformed at a speed unseen since the last industrial revolution. But with so much excitement surrounding Industry 4.0 – including its shopping basket of technologies, including data analytics, artificial intelligence (AI), machine learning, 3D printing, blockchain, virtual reality (VR) and augmented reality (AR) – are organisations at risk of becoming bogged down in strategising and conceptualising, rather than grasping the opportunity? A lack of knowledge, combined with often low digital confidence, is challenging innovation – especially given the potential step-change in operations required to embrace robotics and AI on the production line. It is, therefore, important to take a step back: put the AI robots to one side and understand what is achievable today.
Industry 4.0 is, essentially, all about digitisation. It is about leveraging connected technologies to streamline processes and achieve not only new levels of efficiency, but also new manufacturing models that transform the ‘idea to prototype to production’ cycle, which captures the ‘light bulb moment’ of genius.
Despite this drive, manufacturers are yet to use Industry 4.0 operating principles, such as interoperability, transparency and decentralisation, to get closer to customers, suppliers and distributors, according to a study by Oracle.
The survey of 700 business leaders from manufacturing companies across the UK, France, Germany, Netherlands, Switzerland, China and the UAE found just a third have used Industry 4.0 technologies to remove all data siloes from across the value chain, while 40% have an open exchange of data with suppliers and distributors. For decision-making processes, less than half have integrated customer data, while 45% have integrated supplier and distributor data.
Although most manufacturers worldwide have invested in Industry 4.0-led programmes, only 17% have transformed their business models as a result, with only a quarter of manufacturers gaining greater visibility into how customers purchase and use their products. Just over half, meanwhile, are using customer data to inform the design and manufacture of new products.
The research did show encouraging initial results from manufacturers that have created an internal digital thread within their own organisations, with 82% of those that had integrated data internally seeing a benefit from it.
“It’s good to see manufacturers reaping some rewards from Industry 4.0, but there’s clearly a long way to go before investments begin to have the transformative impact they promise,” says John Barcus, vice-president for manufacturing industries at Oracle. “Removing siloes internally is a good start, but that digital thread has yet to extend outside the organisation and throughout the value chain. Using interoperable and interconnected cloud-based systems is the easiest way manufacturers can securely integrate supplier and distributor data, and make better use of customer and sensor data to manage the impact of disruptive forces.”
Promisingly for European manufacturers, the survey showed their Industry 4.0 gains are on a par with their Chinese counterparts, with the research showing similar results there as well. In fact, only 34% of Chinese manufacturers had integrated end user and customer data into their decision-making, which is lower than the global average (43%). However, 53% admitted it was an area needing improvement – again, against 43% globally.
In fact, when it came to next steps for Industry 4.0 implementations, manufacturers ranked customer-facing operations as the most crucial place for change in the next three years. Half said they would be focusing on removing data siloes and 47% acknowledged the need to create a more open exchange of data with suppliers distributors.
Slow adoption of AI
When it comes to AI, Nigel Thomas, head of aerospace and defence at Capgemini UK, believes that, as it matures, it’s likely to represent one the biggest technological shifts in business that has been seen.
“It is already being leveraged by organisations across various sectors to transform the way they do business, manage customer relationships and stimulate ideas. However, of all sectors including telecoms, retail, banking, utilities, insurance and automotive, the manufacturing industry is the slowest, in terms of adopting AI.”
According to Capgemini’s ‘Turning AI into Concrete Value’ report, only 20% of manufacturing companies have implemented AI at scale (telecoms was the highest at 49%).
“This isn’t too surprising, given many of these companies have large and very complex supply chains, and would need to link the data-driven technologies with sensors and IoT across the whole organisation, in order to reap the full benefits of AI,” Thomas adds. “Meanwhile, other industries have had significant drivers for adoption, including regulatory compliance requirements in financial services and consumer demand pressures in retail.”
However, the manufacturing industry is arguably a sector where there are the most use cases for the implementation of AI. “Leveraging these technologies can help a manufacturer measure asset performance, and accurately predict faults, manage risk and make forecasts that can significantly improve operations,” Thomas concludes. “Quick wins are achievable using manufacturing intelligence (MI – mainly statistics-based).In the mid-to-long term, the real challenge is ‘Deep AI’ – finding patterns in data where statistics are not enough.”
The rise and rise of Industry 4.0
While most agree that uptake has been patchy, there is no denying that momentum is building. Forbes suggests that smart factories with fully integrated IT systems that provide relevant data to both sides of the supply chain can increase production capacity by 20%. Manufacturers understand that AI can drive the automation to increase efficiency, reduce waste and accurately respond to customer demand. But, according to Tom Leeson, industry and value marketing strategist for manufacturing at OpenText, AI is only part of the equation.
“Its power comes from the combination with analytics,” he explains. “Big data analysis is challenging for manufacturers faced with the exponentially increasing volume, velocity and variety of product, operational and customer data. AI and analytics will become just a normal business operation. The companies that will succeed most will be those that build the machine and deep learning capabilities into their existing business intelligence and predictive analytics systems. We’ll also see more manufacturers move toward the cloud to manage and process their data and content, making it easier to conduct in-depth analytics at an enterprise level.”
He believes that AI and analytics are two of the technologies that are underpinning the rapid acceptance of Industry 4.0. The manufacturing industry is at the forefront of the IoT usage and IoT is growing quickly. Research shows that there were 8.4 billion ‘things’ connected to the network in 2017 – a 30% rise on the year before. Industry 4.0 uses IoT and analytics to create ‘smart factories’ that can radically change how products are made, shipped and sold.
“This year, more manufacturing companies will begin to implement their version of Industry 4.0 to achieve improvement in production and operational areas, such as engineering, maintenance, asset performance, product lifecycle management and the supply chain,” Leeson says. “A few forward-thinking companies will look to the potential of Industry 4.0 to develop and introduce new business models, such as mass customisation and the development of data-driven products and services.”
A catalyst for change
Without doubt, the inexorable march of Industry 4.0 has accelerated the use of technology and processes, such as robotics, IoT and AI. The adoption of these technologies has created a digital supply chain that interprets the big data to enable ‘smart manufacturing’ to take place. “Data science and machine learning are helping manufacturers make better data-driven decisions, with prescribed solutions for unplanned supply chain events and disruptions,” comments Hans-Georg Kaltenbrunner, VP of industry strategy for manufacturing, EMEA at JDA. “AI, meanwhile, is being used to redefine labour standards and performance within the warehouse, streamlining productivity of workers, robotic workers and mobile robots.
“It is in the future, however, that AI and machine learning will reach its potential and move the manufacturing sector forward. With AI embedded in the self-learning supply chain, machines will be able to examine supply chain strategies to determine where supply chain failures have occurred and why, along with what combination of external events – such as transaction data, loyalty data, inventory data, IoT, weather data, competitor events, market performance, traffic data or socio-economic events – contributed to the supply chain failure. This is where machine learning comes in to play, where the algorithms will sift through this data to learn how these factors interact to result in a high probability of a supply chain failure.”
Overcoming IT challenges
The rise of Industry 4.0 does not come without challenges, especially to IT departments that are increasingly involved in the operation of the manufacturing process. The desire of manufacturers to use new tech like machine learning and cognition is bigger than ever, leaving IT teams in a struggle to drive innovation, while retaining control over existing systems. The manufacturing sector is no doubt racing rivals in the path to digital transformation, yet legacy systems still hold the sector back from experimenting with innovative technologies, such as AI.
“Perhaps part of the problem lies in the reliance on technology that was not developed specifically for their needs – with only a few building businesses-specific applications themselves,” says Johan den Haan, chief technology officer at Mendix. “Manufacturers have a great opportunity to use readily available building blocks of consumer-based technology to streamline innovation
from the ground up. New development approaches, like ‘low-code’, enable manufacturers to more easily experiment with tech, combining speed with the ability to scale up and modify applications, as and when required.
“Low-code brings business and IT together to test, build and deliver fresh ideas to market in just weeks, fast-tracking product innovation. AI is certainly a key driver of Industry 4.0, but the speed and success of its uptake requires a fresh look at current systems and practices to ensure they’re fit for purpose and able to facilitate changes in technologies.”
While there has already been a lot of effort put into building proof of concepts for new technologies in manufacturing processes, according to Prasad Satyavolu, chief digital officer – manufacturing and logistics, at Cognizant, later this year we will start to see integration, such as voice-based analysis systems and robotics, really ramp up. He believes that, once this happens, there will be an evolution of intelligent factories built on layers of technologies working in unison and handled by a workforce with new skills.
“Building on the core manufacturing systems, such as ERP and PLM, more sensor technology needs to be adopted to measure aspects, such as temperature, which could impact productivity and processes, whilst voice-based integrated analysis systems will make processes increasingly seamless and curb downtime, by drilling down into collected data and spotting potential issues,” he explains. “Technologies such as cobots, 3D printing, blockchain, VR and AR will create a truly digital workforce and environment. Use of an overarching platform will also be essential to help make sense of the data being collected by technologies, systems and connected product, to improve the end-products delivered.”
In the future, it will become important for manufacturing organisations to incentivise and reward continuous learning to encourage humans to know how to interact and work alongside intelligent machines. Setting up a Centre of Excellence can help manufacturers pilot, manage, report on and champion the role of automation across the business. However, such is the complexity of automation, robotics programmes are intertwined with far too many systems for any single company to build or supply a complete solution.
To solve this, we will see more manufacturers participating in a cross-industry ecosystem of partners to pool expertise, which could include designers, strategic advisors, systems integrators and academic researchers.