Manufacturers across the world would have entered 2020 expecting growing headwinds caused by economic pressures, ongoing trade disputes between America and China as well as the UK’s then-impending departure from the European Union. They wouldn’t have planned for a global pandemic that would have shut down operations, led to supply chain delays and thrown their strategic planning into chaos overnight.

Covid-19 has changed the rules of the game for the manufacturing sector. If there’s a lesson to take away from the crisis, it’s the need for manufacturers to be agile and resilient.

While technologies were transforming operations before 2020, global uncertainty and trade disruptions have proved to be an inflection point. Manufacturers need to look to automate every touch point and bridge the data gap between the back office and the factory floor and production line. By doing so, manufacturers will be better prepared to deal with sudden disruptions in the future.

This report will look at the ways the internet of things (IoT) can be deployed to improve productivity and efficiency, create business value and ensure companies are ready for the digital future of manufacturing.

The manufacturing sector accounted for around a quarter of all IoT deployments last year, according to research from GlobalData published at the end of 2020. Despite manufacturing activity being reduced worldwide, the GlobalData research indicates IoT deployments in the sector increased by 67% from 2019.

Much of the hype around the IoT is often about automating labour-intensive processes and filling the gaps left by worker attrition. While robots won’t make workers obsolete completely, IoT and machine learning – a branch of artificial intelligence (AI) focused on predicting outcomes and making complex decisions – are being relied upon to carry out routine and repetitive tasks, such as maintenance work. This is freeing up workers to focus on jobs that require intuition and involve complex decision-making.

This human element in manufacturing is going to remain crucial and the most likely scenario is workers and technology will work alongside each other efficiently and safely. There will be a drop in demand for physical and manual skills, while the demand for technological skills will increase.

To reach this point, however, will mean upskilling and reskilling existing workforces as well as creating the right working environment that can attract and nurture talent, encourage learning and development, and enable creativity to thrive.

Need for knowledge transfer

The manufacturing sector has an age problem. A report published by the National Association of Manufacturers in 2019 found that a quarter of the US manufacturing workforce were 55 years or older. Some 97% of firms surveyed admitted they were concerned about the issue of worker attrition and 49% were very concerned about it.

The loss of institutional and technical knowledge could have a profound effect on manufacturing productivity and the quality of output. The report noted that manufacturers are also worried that valuable, older workers could retire without passing on their knowledge.

“This concern is particularly acute for firms whose cultures tend to rely on passive information transfer and interpersonal connections to share knowledge,” it said.

During global lockdowns, technology has been key to companies communicating with distributed teams and ensuring they can carry out tasks and collaborate efficiently. But the disruption to the traditional way of working has presented challenges to manufacturers that usually depend on in-person training.

There are big questions about how workplaces will function in the future; a hybrid or blended model of working could become the status quo. For manufacturers, augmented reality (AR) and virtual reality (VR) are likely to become essential tools for employee learning, training and skills transfer. Research conducted by PwC found that people who train in VR are more likely to retain information: they would be four times more focused than if they were trained through e-learning tools.

Implementing AR

The promising potential of AR, which involves digital information being overlaid onto physical objects and environments, has been talked about for a number of years.

Take asset management. Engineers being trained how to carry out safety checks on equipment and machinery can be walked through the process by more experienced employees who are in the office or working remotely. Engineers can wear AR goggles and have instructions and information presented to them.

This can be an effective way to address communication challenges, especially when workers can’t be in the same location.

If AR is implemented correctly, it can be used to provide feedback. Engineers can evaluate their own performance and improve efficiency and productivity, and companies can use data that’s fed back to improve future training programmes.

The introduction of technologies such as AR into workplaces should help to attract the next generations of workers into the sector. Although there are barriers that will need to be overcome, such as addressing misconceptions among some millennials and Generation Z that manufacturing is a dirty and dangerous job.

Needless to say, the shift to digital and higher cognitive skill sets won’t happen overnight. McKinsey found that in 2016 tasks involving physical and manual skills accounted for 48% of the average worker’s time while technological skills accounted for just 12%. The consultancy has forecast that the demand for physical and manual skills will have dropped 27% by 2030 and there will be a greater need for technological skills, with demand increasing by 68%.

A reason why this shift will be gradual is because companies are preparing employees for the future of hybrid or blended working and learning.

Recent events threw a spanner in the operations of many manufacturers with workers having to down tools either due to reduced output or for health and safety reasons. This meant manufacturers have quickly had to adapt to new ways of working that are likely to remain in place in some form or another.

As manufacturers become more connected and implement new technology, workers will be demanding a workplace that is both smart and safe.

Making safety a priority in the workplace

One thing that has become clear recently is workers want to feel safer in the workplace. The Draeger Safety at Work Report, conducted in early-2021, found that safety in the workplace has become a more important business priority since the start of the pandemic. Just over three quarters of respondents stated that safety was now higher up the corporate agenda than it was two years ago.

Wearables can be used to monitor health and wellbeing; sensors can collate environmental, location and motion data, for instance, to help manufacturers identify if and when workers are at risk. There are obvious surveillance and data privacy and security concerns, though, and any tracking scheme would need to be opt in as opposed to opt out. 

The Draeger survey also found that only a third of managers in the manufacturing sector believe their companies are advanced in accessing and acting on safety data made available through Industry 4.0. This implies there’s a knowledge disconnect.

“Data not only has a role to play in driving automation and further improving efficiency but, crucially, has the ability to make positive changes to a range of strategic workplace issues,” says Andrew Bligh, system services and training manager at Draeger Safety UK.

“The manufacturing sector has seen a broadly higher take-up of technology to manage safety in recent times. But to take the next step, companies need to consider how they can better exploit existing and available information – to utilise it more strategically to support effective solutions for their health and safety requirements.”

Ironically, it’s the tech and digital-savvy new talent that need to be attracted into the sector and can eventually help manufacturers to bridge this knowledge gap and get more value out of data.

Next we look at how manufacturers have faced critical supply chain management issues, regardless of how prepared and well skilled workforces are. Events of the last 18 months have led to shortages of key components and this has adversely impacted lead times and production plans.

A survey of 200 senior-level supply chain executives carried out in late-2020 by EY found the pandemic had a negative impact on 72% of supply chain operations. All respondents in the automotive industry and 97% of those manufacturing industrial products agreed business had been adversely affected.

The EY research identified the top priorities for supply chains. More than half of respondents said retraining and reskilling workforces will be their top priority over the next 12 to 36 months. Increasing efficiency will be the focus for 58%, while 60% will look to increase visibility.

The companies that have been in a better position to navigate the uncertainty of the last 18 months or so have been those with the tools and knowledge to prepare for change in demand. By having a business continuity plan in place, the likes of Toyota have been able to focus on production while competitors scramble to place orders for critical components in short supply.

Role of ERP software

The global semiconductor shortage that has crippled the automotive industry has underlined the pitfalls of just-in-time (JIT) manufacturing. The majority of automakers have struggled to source the materials and parts needed, and the industry on the whole has never been as unpredictable.

Though the so-called chip crunch was instigated by the pandemic, the crisis has been exacerbated by an over-reliance on sourcing chips from Taiwan and ongoing trade disputes between the United States and China.

One automaker that has managed to weather the chip crunch better than the rest of the industry is Toyota. Having had its supply chain devastated by the Fukushima earthquake and tsunami a decade ago, the Japanese automaker set about building a stockpile of chips as part of a risk management strategy. This allowed it to carry on making and selling cars at a normal level for months after the shortage started to bite.

Toyota slashed production by 40% in September and October and the industry is expecting the chip crunch to drag on into 2022. But Toyota’s decision to prepare for future worst-case scenarios by identifying high-risk parts in its supply chain highlights the importance of leveraging technology to gain greater visibility into supply chains.

The ability to be lean, while stockpiling certain components, is only possible through enterprise resource planning (ERP) software, data capture and smart monitoring of components or raw materials as they move along the supply chain.

Though no manufacturer could have avoided a situation like the chip crunch, given how the pandemic has hamstrung factories, implementing ERP software can ease the pain of similar situations in the future.

ERP software enables users to be better prepared for future bottlenecks by improving forecasting, predicting shortages, minimising wastage, all while optimising production lines and delivering efficiency and quality gains.

It supports both push and pull modes of manufacturing. But while JIT manufacturing has its benefits – it can help to reduce inventory carrying costs and waste as well as optimise warehouse space – demand-driven procuring, producing and distributing has its limitations.

JIT manufacturing works well when things are business as usual. But, as the chip crunch has shown, manufacturers with the leanest supply chains can fall short in a crisis.

Prior to the pandemic, the average semiconductor lead time was around two months, but in August 2021 the average lead time was a little longer than 20 weeks, according to research from Susquehanna Financial Group.

Sophie Webster, product, design and engineering manager at Cambridge-based medtech device manufacturer CMR Surgical, explains that electronics components often have long lead times, so it’s critical to “make sure you have the right parts you need on time in the right quantity”.

Having the right parts at the right time means manufacturers can adapt to sudden changes in supply and demand, and ensure production carries on as usual, says Webster. 

By using ERP software to monitor inventory as it moves through a factory and warehouse, manufacturers can maximise stock levels and avoid stock-outs.

Data-sharing in the supply chain

Even if a manufacturer has complete visibility of its inventory, they’re still at the mercy of varying supply chain lead times. The more upstream stakeholders there are, the higher the risk of delays and bottlenecks.

“Even if all other components are delivered on time, one chink in the supply chain can cause tremendous delays,” says Joseph Kruczkowski, engineering and design coordinator at Coborn Engineering, one of the world’s leading diamond tooling manufacturers. 

To maximise the true potential of ERP software and keep delays to an absolute minimum, all stakeholders in a supply chain would ideally be capturing and sharing their data, which would be updated in real time and give the whole supply chain the same level of visibility.

Let’s take a look at how technologies befitting Industry 4.0 can optimise maintenance and design processes to further minimise delays and downtime, while boosting productivity and output.

But the flip side to not embracing IoT and gaining a greater level of insight into operations is that it could lead to inferior production.

Quality is a vital cog in manufacturing. A superior component or product can result in happy customers and repeat business. An inferior component or product, however, can lead to a loss in recurring revenue. On top of this, safety issues and flaws, such as an electrical component short circuiting, can result in reputational damage for a manufacturer at best and, at worst, injury and legal action.

“Quality is a big issue,” says CMR Surgical’s Webster. “It’s a tricky process to maintain quality, both with the goods you receive and the assembly line.

“There’s also a big human element here. The quality of a product is often to the deficit of job satisfaction. Mass manufacturing can be really boring, but if workers chat, for example, they make mistakes.”

Preventable mistakes can be frustrating and even more so since recent events have sharpened the focus on quality and manufacturing to higher safety standards. Many factories and plants that have been choked by supply chain bottlenecks have also seen their production capacity reduced due to carrying out regular maintenance checks on equipment and machinery. And this is where IoT and machine learning can come into play.

Digital twins

If a critical asset fails unexpectedly, a manufacturer must shut down operations for an unspecified period until maintenance is carried out by highly skilled and trained employees. The problem is this approach is reactive and can be costly.

Digital twin technology enables manufacturers to capture a digital representation of an asset to virtualise maintenance tasks. Powered by machine learning and data from a network of sensors, a simulated model of an asset can update in real time, enabling manufacturers to keep track of performance and health.

Data captured by digital twin technology can help manufacturers to carry out preventive maintenance, fixing an asset and addressing a potential problem before the fault occurs. This can optimise the life cycle of an asset as well as minimise downtime.

Digital twin tech is being deployed in various industries, including offshore wind management and the oil and gas industry. Maintenance can be complicated when assets are typically in remote and difficult-to-reach areas. For example, a digital twin of a turbine or blade can be used to inform manufacturers of design efficiency and quality.

Data generated from the simulated model can also be used to improve research and development as well as future production.

An area where machine learning can have a big impact is in improving design processes. The standard approach to designing has long depended on clear communication between design and production teams. Although the pandemic has forced organisations to improve communication and collaboration between workers in different locations, reactive project management, where there are no contingency plans, can often get in the way.

“Where careful planning reduces wastage, mistakes or under or overstocking, the inverse causes chaos, and it’s extremely difficult to keep output up under these conditions,” says Kruczkowski at Coborn Engineering.

“Things have gotten better since we started using ERP software to manage the process, but it’s still chaotic when people interfere.”

A symptom of reactive management is it can lead to designs not being communicated properly and this can result in poor design decisions and an inferior finished product.

Benefits of generative design

A solution to eliminating weak design is AI-powered generative design software. It can be used to generate several iterations of whatever’s being manufactured until it meets certain metrics and constraints, streamlining the production process.

If used in conjunction with digital twin technology, generative design enables designers to understand how a product will perform in the physical world and then make any necessary tweaks to the design. Generative design is particularly useful when a design involves a complex structure, such as a honeycomb lattice, for example.

By automating the computer-aided design process, manufacturers can reduce the time it takes to get a product to market or move it along the supply chain to the next stakeholder downstream.

Machinery on the factory floor holds vast quantities of data, but the problem is that it’s often siloed. Connecting the disparate dots is critical if manufacturers are to leverage the value from the data their operations generate.

There is a broad awareness of the benefits of smart manufacturing. A recent PwC report found that 87% of industry leaders believe that smart factory technologies will accelerate innovation and design development, and 89% believe these technologies will improve their supply chain relationships. However, Douglas Bellin, Global Lead of Business Development for Smart Factories and Industrie 4.0 at Amazon Web Services, points out many companies in the manufacturing sector hold the same concern: that smart manufacturing is too expensive, too difficult or too complex to implement.

“From a technology aspect, there are so many moving parts that have to come together, from the Internet of Things, artificial intelligence, machine learning and automation, and so many more,” says Bellin. “It’s hard for companies to know where to start and what to start with.”

While some manufacturers may have IT departments with valuable experience, they may not have the necessary skills and operational knowledge to be able to implement technologies from an IT perspective.

“As a manufacturer, the core focus should be manufacturing; not technology or technology implementation,” Bellin adds.

This is where AWS can be of value, by bridging the knowledge gap that exists and helping manufacturers on their cloud implementation journey. AWS works backwards from a customer’s desired goal to understand what they are trying to achieve to identify the technologies that will help them to more easily improve productivity, performance and output quality. “The technology will work, but putting it together with a use-case in mind is key,” says Bellin.

When data comes from multiple technologies, it typically isn’t visible until it’s all in one system. The cloud offers a way to help bring together the data sources and the computing power needed to ensure a cloud transformation project is successful.

One avenue for leveraging data along the cloud transformation journey are plug-and-play systems. They enable quick insights into areas for improvement that can be integrated into existing systems, software and processes with minimal disruption to operations.

AWS also has a network of partners that offer software-as-service solutions to accelerate the transformation and achieve business goals. “While there’s a cost associated with a new partner for operations to help with these [smart manufacturing] needs, the cost is offset by the savings and improvements afforded by the collaboration,” says Bellin.

By utilizing a broad and deep set of services and partners available to them, manufacturers can advance their operational and technological capabilities at a low cost. And if manufacturers are seeing savings, it then makes it easier for them to scale down. They can decrease computing power or databases when needed, based on workload demands and without being held back by additional capital expenditure or start-up costs.

With investment in the cloud, the smart manufacturing journey has only just begun. As Bellin concludes, the journey never ends; it’s a constant evolution.

The key to manufacturers getting the most out of IoT and machine learning, though, will be to reskill and upskill employees, and attract new talent into the sector.

If implemented with operations correctly, IoT in manufacturing can very much be a virtuous circle of efficiency, productivity and safety.

There’s no guarantee where the path to Industry 4.0 will lead over the next five to 10 years. Starting the journey now will ensure manufacturers give themselves a competitive edge. Staying on top of technology trends will be critical to ensure manufacturers don’t get left behind in the digital revolution.