Are you ready to embrace the future of manufacturing and take your business to the next level?
In this paper, we’ll explore the vital role of connectivity, compatibility, and sustainability in shaping the manufacturing industry’s future.
At the heart of this transformation is Industry 4.0 – the Fourth Industrial Revolution. It’s a game-changer that seamlessly merges the digital and physical worlds of factories. But as we march forward, we must also consider the impact of production on our planet. This is where Industry 5.0 comes in, championing values that prioritize maximizing output while minimizing waste, cost, time, resources, energy, carbon emissions, and pollution.
To make this ambitious goal a reality, we need to leverage emerging technologies, government policies, and evolving business models. Our Business Insight Note will zero in on the technological trends driving this shift, including the Internet of Things, digital twins, and automation.
We want to offer valuable insights into the manufacturing industry’s evolution and show you how embracing these emerging technologies can help you stay ahead of the curve.
Join us on this journey towards a smarter, more connected, and sustainable future for manufacturing.
Ayming Institute : the think tank of the Ayming Group.
The Ayming Institute (AI) aims to help leaders in the private and public sector gain a deeper understanding of the evolving global economy by focusing on three areas.
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Executive Summary
The future of industry is about connectivity – within the factory and between the digital and physical worlds. However, it also hinges on the compatibility of production and the planet. The ultimate aim must be to make more with less – waste, cost, time, human and material resources, energy, carbon emissions and pollution.
Industry 4.0– the concept of the Fourth Industrial Revolution, born in 2011 of Germany’s high-tech strategy – is driven by technologies. Conceived a decade later by the European Commission, Industry 5.0 is driven by values.1
The progress of this techno-social transformation depends on a convergence of new and emerging technologies, evolving government policies, and changing business models. In this – the first of two Business Insight Notes on this topic – we examine some of the significant technological trends enabling the transition to a smarter, connected manufacturing industry – notably, the Internet of Things, digital twins and automation.
In our second Note, we go on to outline three of the daunting challenges – involving cybersecurity, skills and sustainability – that manufacturing companies and connected industries face en route to a circular economy.
From hype to reality
The hype about the Internet of Things has become reality, not only in many smart homes and buildings, but also – more significantly – in an increasing number of factories as well as a diverse range of manufactured goods. This is a fast- expanding market. In 2021, global expenditure on the Industrial IoT (IIoT) was estimated to be $263 billion. It is projected to be worth $1.11 trillion by 2028.2 This IoT democratisation has been made possible by advances over recent years in the functionality, miniaturisation and cost-effectiveness of sensors.
In terms of the economic value generated by the IoT as a whole, manufacturing is set to out-do other most important applications by a factor of two/three to one. Under the upper-end growth scenario for the overall market, that would translate to a $3.32 trillion IIoT by 2030 – double the value predicted for the human health, work sites and smart city categories, each clustered around the $1.7 trillion mark.
Advanced manufacturing has enabled this progress, allowing sensors to be embedded in a single silicon substrate small enough to sit in the palm of your hand. Micro-electro mechanical systems (MEMS) is the technology used to create these microscale sensors and other devices, such as transducers, actuators, gears, pumps and switches. A leap forward from conventional integrated circuit production, MEMS allows fabrication of both mechanical and electrical components.3
Modern cars contain from 10 up to 70 MEMS sensors, depending on their sophistication, for everything from anti-lock breaking to monitoring tyre pressures or the car’s ‘heartbeat’.4 Within their protective seal, these sensors are well suited to industrial as well as automotive environments, withstanding high temperatures and vibration.5
The real-time intelligence on plant condition they can provide is crucial to predictive maintenance. Engineers can continuously monitor production plant, as the IIoT enables constant measuring and reporting on the temperature and vibration of components, chemicals, oil purity or other parameters for calibrating the risk of equipment failure.
Valued at $4.45 billion in 2020, the global predictive maintenance market is projected to reach $64.25 billion by 2030, on compound annual growth of 31%.6
Factories can be retrofitted with industrial sensors which, increasingly, are incorporated in the design and manufacture of equipment and components. Even relatively small, low-cost parts can cause prolonged and expensive stoppages in large-scale, continuous production facilities, according to FAE Technology, an Italian specialist in electronic innovation for industrial products. It cites the example of a manufacturer of hydraulic valves: by adding FAE’s smart sensors, it was able to offer its clients a predictive maintenance service at their premises.
Consumer products – other than cars and domestic appliances – can also be made smarter, giving manufacturers valuable intelligence for marketing as well as production purposes. FAE, for example, has shown a leading manufacturer of skis and boots how to track the popularity of its products at rental stations using RFID (radio frequency identification) tags as sensors.
The IIoT also enables smarter logistics management – from incoming components to finished products. Tags on pallets or individual items provide location and other data to help improve materials handling, inventory management and overall efficiency and control.
As of 2021, the top three uses for IoT technology across all industries involved process automation and the two forms of remote monitoring of assets – ‘read only’ and ‘read/write’ (the latter allowing remote control). Not far behind were location tracking, asset/plant performance, quality control and predictive maintenance.7
ShapeHowever, while a wealth of data can now be collected in real time, it is only valuable if analysed so it can inform decisions that improve factory performance.
Managers, engineers and machine minders can make productive use of some of this information. But having multiple machines and interconnected systems in a modern manufacturing plant means that data too is being mass-produced. To process it, big data analytics involving artificial intelligence (AI) and cloud computing are being harnessed along with the IIoT.
The analytical power of AI and machine learning (ML) extends the technology’s capabilities for many applications, and enhances the business case. FAE Technology is involved in two European projects designed to expand IIoT applications in this way.
Under the EU’s Horizon 2020 Programme, the Bergamo-based company is helping digitalize foundry operations through the SNIPE project – the Sensor Network for Intelligent Predictive Services. This is a demonstrator funded by Trinity, which is aiming to develop an intelligent monitoring infrastructure to collect and analyse data from non-digitized foundry processes, so as to manage their maintenance through predictive algorithms.
Using FAE’s tailor-made wireless network and IoT gateway, sensors register and transmit readings of parameters such as engine vibrations, furnace temperatures and sand humidity levels. Sent to the cloud, this data is processed by an AI predictive maintenance algorithm. Conditions in the various sections of the production cycle can be monitored in real time by the maintenance manager via a dashboard showing the probability of failure. FAE has also built a wearable device displaying simple ‘traffic-light’ indicators for line operators.
Purification of industrial air is the focus of the Green Factory project, part of the EU’s Life Programme. Emissions from machinery and chemicals, including particulates and volatile organic compounds (VOCs), are hazardous to human health and the environment. The project partners are LOSMA, a manufacturer of air filter systems, and MA.EL, which designs and makes dies and equipment for cold-forming metals. FAE sensors in its plant monitor various pollutants (and even bacteria and the COVID coronavirus). Again, AI in the cloud operates the filters to manage air quality, allowing the air to be recirculated while achieving 35% energy savings.
Better management of energy consumption was already an increasing priority for many businesses on cost and sustainability grounds. The spike in the prices of oil, gas and electricity caused by war in Europe only intensifies that need.
Whatever the type of building, this means measuring energy usage for heating, ventilation and air conditioning (HVAC). IoT sensors can also monitor the HVAC system’s performance in terms of temperatures and air quality throughout the building. This real-time data, which may include recommendations for adjustments to improve efficiency, can guide decision-making by the production or building manager.
AI takes this a stage further by automatically optimising consumption. Evogy – a green tech company providing energy-as-a-service (EaaS) solutions for energy efficiency and sustainability – models the environmental system at the algorithmic level. By learning consumption trends and factoring in weather forecasts inside buildings and production environments, the service promises the best process control strategy on a predictive basis, by balancing energy, economic, environmental and comfort factors without human intervention.
On a large scale, an intelligent control system can manage an entire manufacturing process or smart factory. Such automated manufacturing set-ups are usually described as cyber-physical systems (CPS). Definitions vary, but CPS are intelligent engineering systems that seamlessly integrate computation, networking and physical processes.8
Computer-based algorithms monitor (through IIoT connectivity) and control the various mechanisms of production. With the help of AI, the systems can evaluate operational conditions and make decisions autonomously, with human managers only supervising operations.
Another trend is taking the intelligence in smart manufacturing closer to the factory floor – from the cloud to the component. ‘Tiny machine learning’ (TinyML) involves running AI algorithms directly on board the sensor, again thanks to advances being made in integration at the silicon level. Data is analysed locally in real time and decisions are made directly on the edge, with associated benefits not only in computational capacity but also power consumption and data storage costs. These devices are now in an exploratory stage of their life cycle but are expected come onto the market soon.
Twin worlds merge
Just as cyber-physical systems allow the cyber and physical worlds to converge, digital twins aim to integrate them seamlessly.9
A digital twin can be defined as a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning (ML) and reasoning to help decision-making.10 As with CPS, the technology – which can involve significant investment – has become more affordable with the help of the IIoT (Industrial IoT).
Digital twins have had an interesting gestation since Michael Grieves – now chief scientist at the Digital Twin Institute in the US – proposed this conceptual model for product lifecycle management at a manufacturing engineers’ conference in Michigan in 2002.11 The first practical application is attributed to NASA’s attempt to improve physical-model simulation of spacecraft in 2010. The oil and gas industry, which pioneered dynamic digital modelling in design, was an early adopter. Most of the sector’s digital twins are used in the design of platforms and similar installations. Data on both planned and existing installations is fed into the models to help ensure designs are up-to-date and robustly tested. But in operations too, virtual models can be used to evaluate procedures to optimise production by simulating extreme circumstances as well as modelling existing conditions.12
Manufacturing, arguably, is set to be the most comprehensively disrupted by digital twins – from product and process design through production to maintenance and optimisation. Thousands of sensors placed throughout the physical process collect data on different parameters for environmental conditions, machine behaviour and task performance. All this data is continuously communicated and collected by the digital twin. According to Grieves, the next phase now underway is to use AI to organise and analyse all this data, which is otherwise overwhelming. This will augment decision-making, widening humans’ narrower perspectives to identify highly efficient engineering scenarios with the highest probability of success.13
Significant gains are already being achieved in manufacturing with digital twins. Home appliance specialist Electrolux invested in Industry 4.0 digital manufacturing technology to simulate and streamline its global production lines. Using a Siemens production lifecycle management system, it built and tested 100%-digitized modular factory models before committing to the real factory. This way, Electrolux got its products to market 20-30% sooner, and saved 15-20% in production costs.14
Pharmaceutical giant GSK is digitizing its entire vaccine development and production process so vaccines can reach people faster at optimum quality. Working with Siemens and ATOS, the aim is to create a virtual version of the entire value chain of processes that progress in silos. Their first digital twin proof- of-concept is for the development and production of adjuvants, additives that boost immune response. Using mechanical models and AI, they developed a hybrid model to simulate and monitor the ‘black box’ of adjuvants particles. The digital twin makes it possible to collect data to understand what is happening in real time during vaccine production and optimize operations. The next step is to develop digital twins for the entire development process.15
France’s Dassault Systémes uses the term ‘virtual twin’ to distinguish between a digital twin prototype of a specific object as opposed to visualising, modelling and simulating the entire environment and experience for sustainable business innovation across the full product lifecycle.16 Applications for its model-based systems engineering approach range from Croc shoes to new surgical techniques. Its systems are also being used in the development of small-scale nuclear reactors, automotives, aerospace, shipbuilding, and energy grids. A model-based approach allows most problems to be solved up front, avoiding the delays and huge costs of modifications made later in the product development process.17
The market for digital twins is set for exponential growth over the next five years. Users were estimating to be investing $2.1 billion in the technology in 2021. By 2027, that is expected to reach $43.6 billion – a compound annual growth rate of 68.9%.18
It is worth noting also the developing relationships of digital twins and their cousins – virtual and augmented reality (VR/AR). Together, they are the precursors of metaverse solutions in industry. The industrial metaverse is a concept that merges advances in the IIoT with the latest developments in 3D representation and extended reality. While IIoT has been the driver for Industry 4.0, the metaverse will be another enabler of Industry 5.0.19
The convergence of metaverse and further advances in AI, microchips, networking and computing power will ensure that digital twins not only interact in real time but look like and physically behave like real machines in every way.
Automation marches on
Having proven their value in the automotive industry, robots are driving automation in other sectors of manufacturing. That onward march faltered amid the pandemic, but quickened in 2021 with a 31% increase in worldwide sales of new industrial units. This exceeds the previous year-on-year record of 22% in 2018, according to the International Federation of Robotics.20 Within six years, annual installations of robots have more than doubled. In all, there are around 3.5 million operational robots around the world.
Given the importance of robotics to national competitiveness, governments are promoting the technology and its developers.21 Not surprisingly, the countries with the strongest R&D programmes – China, Japan, South Korea, Germany and the USA – are also leading deployment.
Asia is the biggest market, putting almost three quarters (74%) of new robots to work in 2021. China is the largest adopter, followed by Japan and South Korea. Europe’s robotics stock grew 24%, led by Germany with 28% of installations; Italy deployed 17% and France 7%.
ShapeUK industries invested in 7% fewer robots in 2021, driven by a sharp fall in automotive
investment. This sector, which is the most invested in robotics, saw a fall in the US
too. But installations in the Americas overall rose in line with the global trend (31%). Industries where adoption is growing strongly include metal and machinery, plastic and chemical products, and food and beverage manufacturing.
Resurgent inflation in materials and labour costs (and shortages) are increasing the onus on efficiency and productivity. After the disruption caused by the pandemic, revamping of supply chains and re-shoring of manufacturing also spur investment in robotics.
Ongoing advances in sensors, software and vision systems are making the technology more accessible for smaller manufacturers, as does robotics-as-a-service, increasingly charged on a unit of production basis.22
Robots are becoming smarter too and easier to work with. There is a clear trend towards user interfaces that allow simple icon-driven programming and manual guidance.23 Robotics companies and software suppliers are bundling their wares to ease setup and installation. Pre-configured applications also support lower-cost deployment.
Learning robots are moving mainstream as AI matures. Supported by AI and IIoT (Industrial IoT) systems, robots can perform complex tasks more precisely and quickly. They can also add value in more functions, such as logistics and quality control – robots can now inspect parts as they build them to detect issues in real time and improve overall product or process quality and yield.24
Most industrial robots work on assembly lines and in production bays. Pick-and-place robots (used both in modern manufacturing and logistics) are equipped with advanced machine vision systems and can identify, grasp, and move objects quickly and efficiently to increase production speed.
Cobots (collaborative robots) with integrated safety functions share spaces with human workers to help them accomplish more, such as by lifting and positioning large components.25
According to ABI Research, the global cobot market – valued at $600 million in 2021 – is expected to grow by almost a third each year, reaching $8 billion by 2030.
Once goods have been manufactured, a robot palletizer can place them onto pallets more accurately and cost-effectively, again freeing human workers from performing repetitive and onerous tasks. Used in loading and unloading processes, they may take various forms such as cranes, mechanical arms or conveyor belts. Robotic systems in warehouses also move shelves, or place and extract items. Automating these processes helps prevent workplace accidents while improving efficiency and speed.
Autonomy on the move
While cobots have been the fastest-growing segment in industrial robotics, mobile robots are now not far behind.26
Used mainly to transport parts on manufacturing sites or feed machines, mobile robots are surging in popularity in warehouses as well as factories and other environments. More than 100,000 units were shipped globally in 2021 – a record that will be short-lived as the annual total is expected to surpass 600,000 by 2025.27
They come in many shapes and sizes, from automated platforms, trains and tractor units to forklifts, as shown by Spanish robotics specialists Moontech and Atlas.
Spain has hosted a series of pilot projects to trial 5G infrastructures in industrial and transport applications, some part-funded by the EU. Collaborative innovation by clusters of logistics operators and technology companies is also being encouraged at regional level.
An industrial park in Valencia is the test site for the 5GLOGIC project. The aim is for the Juan Carlos I Industrial Park in Almussafes to become the first industrial estate in Europe connected and managed using 5G communications technology. The partners include Mobility Innovation Valencia (MiV) – which grew out of an automotive cluster and is developing a programme for startups in smart and sustainable mobility28 – the city council and park operator.
5GLOGIC is piloting logistics solutions that will increase capacity and efficiency through digitalisation and automation. This involves transporting components to and from production lines and warehouses using automated guided vehicles (AGVs). Connected via 5G, they have autonomous and remote-controlled modes, and are designed to minimise reaction times for navigation and security tasks.29
Part of the EU’s Horizon 2020 5G Infrastructure Public-Private Partnership (5G-PPP), the project involves Ford’s engine manufacturing plant. It will evaluate how the technologies deployed – including smart AGV operation based on human gesture recognition and virtual reality (VR), edge computing, advanced robotics and AI – contribute to production line efficiency as well as the optimisation of logistics and warehouse distribution chains.30
AGVs have been around for decades, but most mobile robots now boast enhanced levels of autonomy and intelligence. These autonomous mobile robots (AMRs) are sometimes also equipped with robot arms, capable of highly dexterous manipulation. AMRs are attracting huge interest from investors who pumped US$5.7 billion into the global robotics sector in 2021 – a 38% increase, according to ABI Research. The IFR also expects sales of AMRs to continue growing, predicting a 31% year-to-year increase in the logistics sector in 2023.31
Whereas AGVs required guided paths such as magnetic tape, or operator oversight, to navigate, AMRs understand their operating environment. Data from cameras, laser scanners and other sensors feeds simultaneous localisation and mapping (SLAM) algorithms as they move around obstacles and operate safely alongside people.32
Vision- and AI-enabled, AMRs can often operate with better accuracy and consistency than human operators, when picking, placing, and sorting objects, which is critical to order fulfilment in e-commerce.33
Upwardly mobile
This online purchasing boom has also raised hopes among developers of drones, who see opportunities in manual warehouses. Unmanned aerial vehicles (UAVs) are flying high in other industries – such as construction and agriculture, despite strict regulation of airspace. But their application in manufacturing is more confined. Less than 2% of drones in this burgeoning $15 billion global market are said to have been deployed in the manufacturing sector.34
Inspection of high-level storage racks for inventory accuracy – a key business metric – is the most immediately promising use case. In manually operated warehouses, the recording of the highly repetitive loading and unloading process is prone to human error. Stock checks are time-consuming, labour-intensive and involve safety risks.
The speed and flexibility of drones may offer advantages, especially if they perform automatic inventory checks throughout the facility. Home furniture giant IKEA teamed up with VERITY – the autonomous drone specialist known for its drone light shows – for its successful trials of an automated drone solution.35
Drones take off from a charging station, go from pallet to pallet collecting images, videos and 3D depth scan data. Downloaded back at base, this data is processed by an algorithm that identifies missing and misplaced pallets. The drones operate overnight or between shifts so there is no danger to staff working below.
The trials – at a distribution centre and a warehouse, both in Switzerland – convinced IKEA of the drones’ potential to greatly reduce the many working hours required to manually check inventory at its 440 stores and 80 high-bay warehouses worldwide. Drone checking was launched at five other Swiss locations, while 30 more were due to follow group-wide in the near future.
A different drone system was also implemented at two warehouses in Malmo and Padua, following a successful trial with France’s Hardis Group. An independent five-year research study covering both pilot projects noted the difficulty in quantifying the significant value of stock accuracy when making a business case for drones. But the expectation is that further technological evolution will carry drones into the mainstream in warehousing.36
UAVs have as yet unproven potential to deliver benefits in other manufacturing areas. Applications include monitoring assets – such as silos for corrosion, and production equipment for heat loss / distribution to anticipate faults – and intralogistics, transporting parts or materials between warehouse and assembly belt.
Last-mile drone delivery to households is higher profile (so far, mainly medicines to patients in inaccessible locations).37 But the same limiting factors apply in and around factories too – safety (in the air and on the ground), battery life and payloads.38
As further technical advances are made – probably spurred by take-up and adaptations in other industries – UAVs may become viable in manufacturing more widely, especially on larger industrial sites.
Conclusion
Ongoing advances in the enabling technologies outlined here will continue to shape and power the industrial revolution now happening worldwide. As will others – this review is by no means exhaustive, and bound to be overtaken by developments such is the pace of innovation.
Competition is the other great driver. Whatever markets they serve, manufacturing companies competing fiercely on the price of their output are engaged in a relentless race to optimise efficiency while continuously redesigning processes and products, and minimising time to market and costs.
However, while technology developers also compete to provide them with the best solutions, openness in innovation and collaboration is essential to integrate multiple converging technologies. Users will also demand interoperability. While some major manufacturers will be able to fashion customised solutions, many more operating at various scales will utilise ‘plug and play’ packages that bolt onto their existing systems and smart manufacturing as-a-service offerings.
Whether styled as an Industrial 4.0, 5.0 or metaverse, the degree of change, human ingenuity and investment this revolution demands are formidable. The scale of the challenge is of a similar order, and inter-liked with the existential problem of sustainability, which we address – along with human resources and cybersecurity issues – in Part II of this Business Insight Note.
Contributors
Ilaria Cairo
Innovation Senior Consultant at Ayming Italy
Ilaria has worked for 7 years as a Strategy & Operations Consultant using continuous improvement and Lean Approaches, focusing on new enabling technologies, using assessment, organisational diagnostics and technological check-ups for SMEs and Large Companies to ensure
skills and investment capabilities in Industry 4.0 technologies. Ilaria now works with clients to find the best optimisation strategy for industrial investments and search for new funding opportunities for R&D projects at European and local levels, focusing on new product development and technologies.
Donia Razazi
Senior Industry Expert at Ayming Spain
Donia has more than 20 years of experience in the industrial sector, specialising in the automotive sector. He worked in companies such as Valeo and TRW as project manager.
He also collaborated with the Spanish Ministry of Industry and Trade in several project as the Spanish Electric Vehicle Strategy Plan or the Automotive Industry Agenda.
Today as an Industry Expert at Ayming Spain, he promotes collaborative projects in the industrial ecosystem, and helps companies to implement innovative solutions that improve their competitiveness.
Robert Miles
CEng MIChemE, Senior Manager R&D Tax Incentives at Ayming UK
For over six years Robert has managed the innovation funding for a range of large businesses within the manufacturing industry as one of Ayming’s senior innovation managers. The sectors in which his clients are include Building Materials, Chemicals, Automotive, Aeronautical, Packaging and Composites. Robert is a charted Chemical & Process Engineer, with a background in Oil and Gas processing and design.
