Industry outlook

Current realisation of the fourth industrial revolution

The fourth industrial revolution has been on the agenda of the global manufacturing industry for years and many manufacturing industries are executing a strategy where Industry 4.0 technologies are key enablers for operational improvements. The revolution is also supported by Governments where many traditional manufacturing nations have established initiatives inspired by Industry 4.0 and Germany. All the Asian countries, for example, now have official national strategies addressing the fourth industrial revolution.

The current development, by country, shows that the leading countries are concentrated in:

• • Europe
  • The North Americas
  • East Asia (Chine, Japan, South Korea)

Together the top 25 countries account for more than 75% of manufacturing value add today. In this group the United States, Germany and China are leading the adoption of the fourth industrial revolution.

China stands out as the country with the highest investments and fastest development. There are many drivers behind these trends, such as:

• • A shrinking and aging workforce

  • The cost level of skilled workers is approaching that of western countries

  • The Government initiative “Made in China 2025” encourages automation and intelligent manufacturing

China’s economy has evolved too far to be carried by cheap manufacturing and the Chinese government started the campaign “Made in China 2025” to upgrade its manufacturing sector, with the overall aim of gradually replacing manual labour with robots.

China surpassed Japan as the world’s largest market for industrial robots in 2017, and estimates indicate that China will have installed at least 800 000 industrial robots by 2020 2. Between 2012 and 2017, the average annual increase in robot sales was 19% (CAGR)3. There are five countries representing 73% of total global sales in 2017: China, Japan, the Republic of Korea, the US, and Germany.

The US has the second largest manufacturing sector in the world and is in the forefront of developments surrounding the emerging technologies of the fourth industrial revolution. Robot sales in the US continue to grow. The driver in all US manufacturing industries has been the trend to automate production in order to strengthen the competitiveness of US industries.

Germany has the fourth largest manufacturing sector in the world – with over half of the output being exported. It stands out for its highly capable current workforce and a proven ability to innovate. With the launch of Industry 4.0 in 2011, Germany is a pioneer in the fourth industrial revolution. The country is the fifth largest robot market in the world, even though the market for industrial robots has stagnated in recent years.

The outlook for the workforce is positive even though there is no doubt that there will be a decline in the number of repetitive tasks. A recent report, “The Future of Jobs”, by the World Economic Forum shows that 75 million jobs will disappear, and 133 million new jobs will be created by 2022, due to the fourth industrial revolution. The new jobs are expected to be more attractive compared to the disappearing jobs, with more diverse and challenging tasks,and a higher emphasis on creativity, problem-solving and interpersonal communications skills.

Compared to previous industrial waves that required up to 80% new equipment, the fourth industrial revolution is an evolution where a significant proportion of existing production equipment can be retrofitted. After decades of automation many machines already in use have sensors or can be retrofitted with the necessary electronics. Approximately 40% renewal of the installed base of equipment is expected, and many benefits can be realised with limited need for new infrastructure.

Technology trends drive development

There are predictions that if the linking of the physical and digital worlds succeeds it could generate up to USD 11.1 trillion a year in economic value by as early. as 20255. In manufacturing alone a value of up to USD 3.7 trillion annually can be captured by improvements made to optimise operations, such as flexible manufacturing, predictive maintenance, inventory optimisation and health and safety.

To fully access the value of Industry 4.0 there are several issues to overcome from both a technical and a business perspective. To fully utilise the connection of the physical world with the digital world and achieve synergy effects, an ecosystem of connected systems and devices across the company need to interact with each other in a smart and adaptable way.

Sources of value

The fourth industrial revolution is a digital transformation of industry and is divided into four major areas, namely Smart Factories, Internet of Things (IoT), Cyber-Physical Systems and Internet of Service (IoS). Technologies can add value within manufacturing in many ways, including improving end-product quality, lower costs of production and maintenance, new service-based business models, worker safety and usage-based design of products, to mention some opportunities.

For example, sensors can be used to alert or halt equipment when they sense danger, for instance if a worker is using a tool in an incorrect or dangerous way. It is estimated that using IoT could reduce worker injuries in factory environments by 10 to 25 percent, saving as much as USD 225 billion per year in 2025.

Another source of value is the usage of data about factory equipment to make improvements in design. Usage-based design can help equipment makers improve the performance of their machines. With the help of usage data, recommendations on how to use the equipment in the most efficient way and identify cross-selling opportunities related to other, complementary equipment can also be provided.

The fourth industrial revolution will change and create new business models. For example, with the ability to monitor machines in use at customer sites, makers of industrial equipment can shift from selling capital goods to selling their products as services. Sensor data will tell the manufacturer how much the machinery is used, enabling the manufacturer to charge by usage. Thus, service and maintenance could be bundled into the hourly rate, or all services could be provided under an annual contract. The service might also include periodic upgrades such as software downloads.

Interoperability

To fully utilise the value of linking the physical and digital worlds together, it is strongly recommended to avoid choosing just one area and disregarding the synergy of creating interoperability between the systems. When IoT systems communicate with each other their value is multiplied, which makes interoperability essential for maximising benefits. It is estimated that 40% of the total value is only enabled if using interoperability.

If described as a metaphor, IoT is the nerve system of Industry 4.0. The Cloud is the brain and Edge computing and Artificial Intelligence are the small brain where repetitive things need to happen in a very distributed way. Arms and legs are automation. All these great technologies work together. And all must be put together to create a synergy for each technique.

The connected ecosystem

The ecosystem of future manufacturing is still evolving and a general target architecture will take years to settle. The scene is attracting Enterprise Resource Planning (ERP) vendors, Manufacturing Execution System (MES) vendors, equipment manufacturers, general IT and Cloud providers, etc., all bringing different solutions and values to the table.

The original vision of the German Industry 4.0 initiative advocates that the shop floor will become a marketplace of capacity (supply), represented by the Cyber-Physical Production System, and production needs (demand), represented by the Cyber-Physical System. Hence, the manufacturing environment will organise itself based on a multi-agent like system. This decentralised system with competing targets and contradicting constraints will generate a holistically optimised system, ensuring only efficient operations will be conducted. The combination of Cyber-Physical Systems and Cyber-Physical Production Systems is likely to trigger significant changes in manufacturing production and control, towards completely decentralised systems.

However, many claim Industry 4.0 defines a target model which will most likely take years or decades to reach. In the meantime, they say, companies should still try to continue investing in centralised MES systems and keep improving the performance of their operations.

The challenge most companies have is a high investment in legacy systems where the benefits from Industry 4.0 do not justify a replacement. The solution applied by many is to evolve step-by-step as software and equipment develops. We already see that the convergence of information technology and industrial automation is creating huge opportunities for production, without a complete replacement of systems.

Standards and models for Industry 4.0 technologies and software have been developed during recent years – the Industry 4.0 component model, for example. It is intended to help producers and system integrators create hardware and software components for Industry 4.0. It enables better description of cyber-physical features and enables description of communication among virtual and cyber-physical objects and processes. The hardware and software components of future production will be able to fulfil requested tasks by means of implemented features specified in the Industry 4.0 components model.

A typical system can be integrated into customer-specific IT infrastructures via open IT standard interfaces such as REST or OPC-UA, and provides additional machine, operating and process data. Intelligent machines with a modern architecture make production fit for Industry 4.0. We see leading companies investing in multiple technologies and solutions to support their fourth industrial revolution: A global Cloud solution for data and computer calculations; Industry 4.0 specific solutions to connect equipment and business systems with the functionality to run the production, complemented byniche systems to support specific areas of the process.

High level Opportunities

The table list examples of opportunities with data driven services for R&D, Production, and Service.

Data driven opportunities

In the last two years, 90% of all information in the world has been created and IoT is a big contributor to this. It is estimated that in 2006 around 2 billion IoT devices created data, whereas in 2020 it is projected to be around 200 billion devices. Today, factories’ analytic capabilities have not caught up with all provided data. According to a machine learning company, manufacturing companies discard 98% of all the data they can collect because they do not have the operational analytics capabilities to integrate that data into their operations.

Data driven opportunities

“Use cases” for data driven opportunities:

• • Reduce cost of maintenance

  • Improve flexibility of the assembly line

  • Ensure quality of end-product

Cost of maintenance

A major opportunity that comes with a connected industry is the reduction of maintenance equipment costs. With the help of IoT, the parameters of a device’s data, such as temperature, vibration and sound are collected using sensors. Once data is collected and normalised, predictive analytics can be employed by applying machine learning techniques. With predictive maintenance, manufacturers can estimate the remaining value of an asset and accurately determine when a manufacturing plant, machine, component or part is likely to fail, and thus need to be replaced. Also, with input from condition-based data, timely intermediate treatment of a machine can take place which can prolong its lifespan.

In many industries, maintenance usually follows protocols provided by the equipment manufacturer, where recommendations for maintenance are based on hours of use or some other fixed time interval rather than on predictive analysis. This rules-based approach can be costly for manufacturers in two ways – performing maintenance when it is not needed and missing developments between intervals which could lead to breakdowns or damage of equipment. When using IoT sensors to monitor equipment in real time, maintenance can be provided more accurately. Additional possibilities offered by driving maintenance based on IoT and data analytics are, for example, that operators can identify fault patterns by comparing the performance of one piece of equipment against a global database of similar equipment. With that information maintenance staff can decide to change the usage of the equipment that is driving faults. Also, by using meta data about equipment (e.g., models, production data, etc.) even more accurate analyses and predictions can be achieved. For example, it is more likely that an older model of an engine produces more heat and noise than a newer version.

Performing maintenance as accurately as possible, eliminates unnecessary downtime for scheduled maintenance and can prevent serious equipment trouble by detecting changes in performance that indicate a potential failure. Estimates indicate that a manufacturer can reduce maintenance costs by 5 to 10 percent and increase output by 3 to 5 percent by avoiding unplanned outages.

IoT regulators and suppliers of equipment could have visibility into equipment records and could provide timely inspections and certifications to ensure equipment reliability. Third-party repair partners could monitor the data for preventive maintenance and record their work on the system.

Flexibility of the assembly line

The digitalization we are experiencing as part of Industry 4.0 will, thanks to its intelligent fusion of mechanics, electronics and software, profoundly change the reality of production. Through cyber- physical systems, where the devices interact with each other, it allows much higher adaptability and flexibility as the sensors in the cyber-physical system provide process transparency, self-optimisation and self-adaptation to external influences. For example, a production company that receives purchases or instructions through Cloud services, and immediately makes changes in the production line, will be much more flexible in adapting to the needs and quality requirements of different customers.

Flexible manufacturing systems are becoming more popular and can be divided into two types of areas: machine flexibility and routing flexibility. Machine flexibility refers to the ability of using several machines for the same operations and the system’s ability to customise changes such as volume and capabilities. Routing flexibility refers to a system’s ability to adapt to new products and in what order the assembly puts the products together.

In the most advanced implementations, dashboards optimized for smartphones are used to present output from sophisticated algorithms that perform complex, real-time optimisations. In one case study from the Canadian tar sands, advanced analytics raised daily production by 5 to 8 percent by allowing managers to schedule and allocate staff and equipment more effectively after predictive analysis.

A flexible assembly line’s ability to change and adapt to both planned and unplanned changes is an important aspect of advanced assembly and smart manufacturing. This is due to increased machine efficiency, increased labour productivity and increased production rate, to mention a few examples.

Cobots

A Cobot, or a collaborative robot, is a robot made to operate and perform its tasks together with, and in close proximity to, humans. This is in great contrast to many industrial robots that usually have to be separated from physical contact with humans to prevent accidents.

In the context of intelligent manufacturing, the cognitive robots can perceive information uncertainty, change scheduling management and adjust manufacturing behaviour to cope independently with a complex manufacturing problem. Using distributed algorithms for reconfiguration of self-reconfigurable robots will drastically simplify the complexity of configuring robots. The Cobots can work independently and deal with changeable scheduling of a smart factory with connected assembly lines.

Machine intelligence plays an important role in supporting human-machine collaboration. This because machines will be providing assistance with every job, every role, and anything that is done on manufacturing sites where dynamic situations are present, as on advanced assembly lines. Therefore, intelligent human-machine interactions can be implemented within a complex manufacturing environment in order to increase the efficiency of flexible production.

A flexible assembly line is becoming more important in manufacturing where products today are personalised to consumer demands to a much higher degree. Cobots can assist when rebalancing an assembly line, with their ability to adjust their behaviour to new situations.

Quality of end-product

Today, with high levels of competitiveness in most markets, manufacturing industries are required to produce products and services of the highest quality to remain competitive. With the concepts of Cyber- Physical Systems as well as IoT and IoS there are many opportunities to stay competitive. Industry 4.0 can support increased product quality in several areas, for example: enhanced customisation, increased customer interaction, data based value-design, and changes from product offerings to service offerings. Offering services rather than just products can create more opportunities to interact with customers and a partnership is more likely to arise.

In a competitive landscape, customising a product is a way to create a unique selling point for a company. In Industry 4.0 where mass production gives way to mass customisation, products have, to a greater extent, unique characteristics defined by the end-customer. The physical flows will be continuously mapped on digital platforms. All data gathered from a Cyber-Physical System will provide an opportunity to analyse, refine and generate recommendations on how to improve the production and increase the quality of the end-product or service.

The gathered data, connectivity and mobile devices combined give the possibility to build intuitive mobile applications. Downloading and later using an app specifically built to operate one specific device will become a reality.

The same combination of mobile devices, increased reliability and inexpensive positioning systems will allow representation of real time positioning in 3D maps. This, in turn, opens the door to augmented/mixed reality scenarios. These are expected to bring tangible gains in areas such as identification and localisation of materials or in maintenance-related activities.

Augmented reality

Augmented reality devices for employees, such as tablets, helmets or glasses, help to increase both communication and visualisation of contextualised data. The technique allows repair personnel to see inside the machine that needs repair with the help of digital overlays, or see through walls to the cables and pipes behind in order to know where to drill or cut. Oftentimes, machine-learning algorithms are used to analyse IoT data and then flag any anomalies or make recommendations to decision-makers. Staff walking through a plant will be able to access unique sets of metadata associated with each machine and the staff can consult an expert if they run into something they are unfamiliar with or if they require a second opinion.

Cyber security

IT security is essential to Industry 4.0 and Smart Connected Assembly, and to enable the digitalisation that will give the advantages and opportunities future technologies have to offer.

Cyber security risks

• • Plant security

  • Network security

  • System integrity

General benefits description of data driven opportunities

The process to derive value from data is generic on a high level and includes four steps:

• • Data– Capture all possible data related to the object and processes from multiple sources

• • Trend shift

    – Analysis to identify correlations

    – Analysis to detect deviations from a normal pattern

    – Identify deviations that can be observed a time prior to a breakdown or malfunction

    – Machine learning to automate the detection

  • Recommendations

    – Draw insight from the analysis

    – Business domain expertise to review analysis and determine recommendations

  • Implementation– Implement changes to control systems, equipment, or other applications

The key to successful data driven services is to combine analytical capabilities and domain expertise. This will guarantee development of adequate insight and intelligenceto determine the correct recommendation.

Atlas Copco’s portfolio of systems and services

Atlas Copco’s market and positioning

Atlas Copco is the market leader for assembly solutions in many industry segments. In the automotive industry with its semi-manual assembly processes Atlas Copco is the clear leader. In other assembly-intensive general industries, such as Electronics, Offroad, and Aerospace, Atlas Copco is one of the leading suppliers of assembly solutions.

Atlas Copco develops market leading solutions for multiple joining technologies, including tightening, riveting, and gluing. Atlas Copco Innovation Centres in the US, China, and Germany work closely with customers to develop assembly solutions and processes using these joining technologies and supporting control and analysis applications.

Industry benefits

Atlas Copco – outlook

The Automotive industry is currently leading the development of manufacturing and assembly concepts and processes. The development is driven by environmental requirements, with the introduction of alternative drivelines, and fierce market competition, with an explosion of the number of models and variants, as a means of meeting the competition. These two trends call for increased flexibility in production with leading automotive manufacturers rapidly approaching the vision of “mass production one”. To produce multiple car models or even a mix of cars and motorcycles in one assembly factory is the new norm.

For assembly production the vision of “mass production one” is getting closer through new concepts where traditional moving assembly lines are replaced with new solutions based on AGV´s (Automated Guided Vehicles) that enable multiple and flexible flows on the shop floor. The assembly is set up with a main flow and multiple paths or extra loops to handle the differentiation in models and variants.

This also requires new concepts for managing material flows and the set-up of stations with tools and control software. One approach is kitting of material and tools that will follow the car in the flow throughout the assembly process.

This is enabled by wireless electric tools that know:

• • Where they are physically located and which step in the process to perform at this location

  • What model and variant is to be assembled

  • What material or component to mount

  • What screw or bolt to use

  • Tool fixture

  • What tool program to use for each specific tightening

All these needs to be dynamic in the most advanced assembly processes of today.

The future of the Automotive and other industries is changing, and no one can tell how far the development will go. New solutions and standards will emerge. Atlas Copco is actively contributing to the development together with its customers, and is a trusted partner for support on the journey.

Atlas Copco solutions

Atlas Copco has provided connected products for more than a decade. The company has, over many years, built up knowledge in connected assembly processes and has extensive experience of how to architect a connected ecosystem. Today, Atlas Copco is a market leader in connected tools and has the largest service organisation of all tool suppliers. This gives the company a unique position where it can not only gather the data but also analyse it. Then, with its expertise in assembly and tightening, the company can give specific recommendations on how to improve a connected assembly process, or recommend maintenance based on prescriptive analysis.

Atlas Copco’s proven solutions are now further integrated horizontally and share data seamlessly between all applications supporting the life cycle of assembly, from applications supporting R&D, through production to applications used in service and analysis.

The integrated loop includes a portfolio of tools, systems and services for smart connected assembly.

• • Programming the tool

  • Operating the tool

  • Process control system

  • Operator support systems, operator guidance systems

  • Quality control

  • Service and maintenance systems

  • Data collection and data analysis

  • Data driven services

The integrated and modular architecture of applications gives our customers two important benefits:

1. 1. Scalable solutions where customers start with selected modules, and expand functionality or scale and grow by adding modules and capabilities as the needs emerge, or enable scaling to expand scope

   2. Solutions easily integrate with other systems a customer may use and smoothly align with a company’s ecosystems. For example, a common platform for data analysis

To support flexibility of the assembly line Atlas Copco tools can be positioned with high accuracy on the shop floor. The wireless tool includes full processing power and all control logic to operate advanced tightening processes. The tool can be reprogrammed automatically based on location, product model at the station, and material or component to mount.

In addition, all tool functionality can be managed remotely from the virtual control centre using a tablet or smartphone.

The development of data driven services for maintenance, quality and process optimisation has reached a level where failures can be predicted well before a problem arises, and potential quality issues detected even though the process has been performing within specification.

This is achieved by data analysis and machine learning. Large sets of data from tightening are analysed and any anomaly is detected. That is, any deviation from normal data, such as torque profile, is detected. A deviation is an indication that something could be wrong and should be inspected. Atlas Copco has solutions based on this technique for early warning indicators, predictive maintenance, and anomaly detection to improve the quality of the end-product.

In addition, production uptime is improved by increased “first time right” passes of the processes. This is achieved by continuous analysis of all deviations and “not OK” results. The insight from the analysis is then used to gradually eliminate root causes of different disturbances and problems.

All components, even on the shop floor, can now be connected. This is a key requirement for the Connected Assembly. Once a design has been created in engineering, its data can be pushed to production, so that the production and assembly steps can be executed accordingly. Once the manufacturing step has been completed, confirmation can be provided automatically and in real time via MES or a station control system. It also allows machine tool manufacturers to perform remote maintenance on products being used by their customers thanks to the availability of cheap, high-volume data links.

Roadmap to implement the future

The importance of scaling up

Many manufacturing companies are actively rolling out fourth industrial revolution technologies at scale. However, a common obstacle appears when companies are launching pilots and never get past that stage. This results in companies never achieving the benefits of scaling up and interoperability. The importance of not only scaling up one “use case” and technique but succeeding in scaling up several different “use cases” and technologies, to meet synergy effects, should be highlighted. Reasons for this are:

• • Companies see new technologies and drive the change as a technology forward approach instead of a business value back approach. It is important to understand the competitive advantage of your business model and to enhance it by using advanced technology.

  • Usually slow decision processes and slow in acquiring new technologies and creating a partnership.

  • The backwards approach of only looking at return on capital investments. Even small amounts of capital.

  • Not only using the bottom up approach for these things where people are looking for small improvement areas and then start pilot programs. It is very important to pull all things together where top management lean in and decide which direction to take, otherwise it’s not possible to scale up.

  • Data security and confidentiality issues also affect IoT adoption. To gather the data for usage-based design improvements, manufacturers need access to data about how their customers are using products. This may raise questions about confidentiality since a manufacturer is likely to consider specific details about factory performance to be confidential. This concern needs to be overcome if IoT impact is to be maximised.

The above-mentioned reasons originate from a business perspective. There are also digital challenges for a broad adoption of IoT and interoperability. To mention a few:

• • Reliable data networks with enough capacity. Data networks in factory settings must often operate in environments with large amounts of electromagnetic interference. The continuous flow of data between machines and to remote computer systems in IoT setups also requires high bandwidth and long-distance communications.

  • Limited standardisation of data means that substantial systems integration work is needed to combine data from multiple sources. In addition to this challenge there is a connectivity and storage challenge. Issues of data ownership within and across organisations can complicate aggregation.

  • Once data are aggregated, then comes the biggest challenge of all: analysing the data to get actionable information. Data being generated require sophisticated and often custom-made programming and expertise in both data analysis and the machinery and processes that the sensors are monitoring.

Maturity model

Companies are adopting Industry 4.0 and the level of adaptation can be illustrated with a maturity model. In the model six dimensions are used to describeIndustry 4.0 adaptation.

Companies should strive to develop the same maturity level for each dimension, before investing in further development in one of the dimensions. That is, advanced equipment with sensors and the possibility to be controlled remotely is not going to work without secure, high-speed connectivity.

Secondly, it is important to understand the general equipment readiness for Industry 4.0. This will include to what degree current equipment could be controlled through automation and integrated into the broader operating system. Where sensors and actuators can be added to existing equipment and which equipment needs to be replaced. A fully autonomous workplace may be a distant vision, but it is good to have a view of what that vision could be and the potential benefits.

Roadmap to Smart Connected Assembly

The roadmap to Industry 4.0 and Smart Connected Assembly is different for every company. The roadmap should take a company from its individual starting point to its desired target situation. This is an exploratory journey for most companies as new technologies and opportunities need to be tested and evaluated. In parallel, new organisational capabilities need to be developed.

The technology roadmap can serve as a reference for an assessment of current capabilities and facilitate the discussion on ambition level and desired future capabilities.

Success factors when designing a roadmap to implement Industry 4.0 concepts:

• • Ensure you solve a business problem, driven by business demand or business need

  • Look at a solution for the big picture; how will multiple point solutions work together to reach interoperability?

  • Clear solution landscape

  • Implement a number of “use cases” to get greater impact

  • Learn and adapt in an agile fashion

Atlas Copco has extensive experience and know-how from supporting customers in the design and development of Industry 4.0 and Smart Connected Assembly solutions. A systematic approach to developing and implementing Industry 4.0 strategies ensures business benefits and alignment. This know-how is exemplified in two roadmaps presented below:

• • Roadmap – Explore Industry 4.0 for newcomers

  • Roadmap – Scale from concept pilots to industrialised solutions

Explore Industry 4.0 for newcomers

A scenario and roadmap for a company that is at maturity level “newcomer” is outlined in this section. The objective for companies in the newcomers’ group is typically to learn and explore the new technologies, and to identify where in the production process the Industry 4.0 concepts can be applied. That is not necessarily linked to where they will bring the best business benefits. The maturity is described using the six dimensions:

• • Technology and equipment

  • Connectivity

  • Product data

  • IT systems

  • Organisation and culture

  • Strategy

The technology and equipment are normally the core of an early development, but it is important to understand the business value behind the development. Companies should identify where process sensors can be added either through an upgrade of existing equipment or by replacing some equipment or tools.

The connectivity between sensors, IT systems and data storage needs to be established for the pilot scope. Communication standards and security solutions should be part of the scope. However, a standalone pilot is a good start and will require less sophisticated security and standardisation.

The importance of proper product data is often underestimated and insufficient detail of product and process data can be a road blocker that requires a significant effort to overcome. For example, the quality of assembly can be measured with high precision using different sensors. However, it assumes that a definition of desired quality exists – what torque and angle is required when tightening the screw.

In the early phases it is of less importance to integrate with control systems and other IT systems. Normally, companies at the newcomer level don’t even have the right systems in place to support Industry 4.0 concepts. They should limit the IT solution to a level that is sufficient to validate the Industry 4.0 concepts that are being explored and evaluated.

The implementation of Industry 4.0 concepts is a development and change journey for any company, even though the initial phase will have limited organisational impact. One key objective of the initial phase is the learning of new concepts, and companies should strive for an organisation and culture of collaboration and agility to prepare for the coming changes. From a strategic perspective, the approach of newcomers should be to explore and learn the new technology and create a vision for how Industry 4.0 concepts can benefit the company in a set of “use cases”. These “use cases” will serve as input to the strategy development and formulation of a roadmap going forward.

Roadmap – improve quality through data driven services (stand-alone pilot)

Assume an initial analysis in three steps:

• • Current maturity assessment (as described above)

  • Opportunities for improvements and desired future position. E.g., quality improvements through connected tools and in process controls, supplemented by error proofing

  • Roadmap with activities to close the gap between the current and future situation

Use an agile approach where the solution is developed through iterations and a set of minimal viable products to demonstrate functionality and prove each part of the solutions stepwise.

Select scope, for example one cell or station (or one assembly process) and one product.By the end of this roadmap companies should have a working solution for improving quality in an assembly process. In addition, the company has gained knowledge and demonstrated results to determine the business value of this “use case” – all important input to plan the next phase of its Industry 4.0 development.

Scale from concept pilots to industrialised solutions

The second scenario is aimed at companies that have performed a number of pilots to learn and test various Industry 4.0 concepts. In many companies these pilots have been separate and isolated solutions addressing an opportunity at one step in a process or part of the value chain for a specific product.

A key consideration to scale from pilots to a wider Industry 4.0 adaptation is the alignment across the production process or value chain and across the different product groups produced with their own production processes.

To scale from pilots a company needs to align the individual pilots and “use cases” on a higher level. The preferred scenario is to focus on one product group and the entire production process or value chain for this product group. As a result, it is possible that some of the pilots have to be redone with the new scope in focus. That is, the preferred first step in a wider Industry 4.0 adaptation is to ensure the piloted concepts are working together by applying and piloting all concepts for one product group or production process. This means that for each step of the value chain and one product group the “use cases” and technical solutions are proven.

The production equipment and tools already have a large number of sensors and actuators that can communicate. Focus should be on defining the standards and main technologies that the various solutions along the production process need to comply with to be able to share data and resources. In addition, robotisation and automation are introduced into the landscape.

Performance of connectivity to enable scaling of the solutions needs to be ensured. Cyber security solutions and policies, especially how to handle third-party access and associated risks, need to be developed. Companies open to providing suppliers and partners with access to equipment and data need protection against unauthorised access and attacks, such as ransomware, through these access points.

The product specification exists to a level where tolerances and tightening can be digitalised and understood by the production equipment and assembly tools. The quality assurance and error proofing solutions also share the same data. A possible development is to define a process for changing product data or production process specifications based on insights gained from data analytics.

To realise the benefits from the various “use cases” an integrated IT landscape is key. Modern IT architecture emphasises modularisation and decoupling to enable flexibility and agility. That is, business capabilities delivered by processes and supporting systems should have a well-defined scope implemented as a module that communicates and shares data through a service-oriented architecture. One benefit is that existing legacy systems will continue to serve as a backbone and new capabilities can be added by integrating modules as the need for additional capabilities arises.

 As a company adapting to Industry 4.0, the tasks of the workforce will change. Leading research indicates that the demand for resources will not disappear as removed tasks will be replaced by more advanced tasks. Hence, companies should continue building a culture and organisation that is willing and capable to adapt to a changing environment. The strategy for a company that aims to scale from pilots to companywide adaptation should have a long-term vision, e.g., “mass production one”, and an agile and flexible approach to solutions and how to implement opportunities. A key area when scaling is the business architecture and design of the ecosystem of IT systems, equipment and infrastructure. There is currently a convergence of systems and solutions from the equipment manufacturer, ERP software, and manufacturing systems. These previously separate systems with non-competing functionalities are now all adapting to Industry 4.0 and developing overlapping functionality – and companies should make some strategic choices.

Roadmap design

To scale a solution globally, a solution to reduce maintenance costs, for example, will require an industrialised solution and a strong governance of the solution. Assume a scope of a number of “use cases” for the entire assembly line, including multiple stations and processes performed for different product groups produced on the line. The roadmap design starts with an initial assessment to determine the development and change needs.

Start with an initial analysis including:

• • Current maturity assessment (as described above)

  • Pilot one product group and ensure each step of the production process is improved by at least two “use cases” building on Industry 4.0 concepts

  • Design end-2-end high-level solution and define where and how to realise the different new capabilities

  • Assess gaps in all six dimensions and define development needs to align on a similar maturity level for each of the six dimensions, i.e., be sure to reach maturity level 2.5 for all dimensions

  • Roadmap with activities to close the gap between the current and future situation

The roadmap is an approach designed to coordinate a set of individual pilots into a coherent solution that will improve all parts of the production process. Further, the pilot solutions are aligned on preferred technologies and industrialised to ensure stable and secure solutions possible to scale on a global basis.