What is Industry 4.0?

Industry 4.0 is a set of technological changes to create a coherent framework to be introduced in the manufacturing process. Of course, the backbone of Industry 4.0 relates to how products are made, the phenomenon will most likely affect every part of our world and has implications for all types of business. A simplistic definition of Industry 4.0 is the “application of the IoT, cloud computing, cyber-physical systems (CPS), and cognitive computing into the manufacturing and service environment. Automation and connectivity within the manufacturing world is not new. Physical to digital (taking physical actions and converting that to digital records) and digital to digital (sharing insights using AI) have also been a part of manufacturing for years now. However, with the introduction of the above enablers, along with robotics, manufacturing can now proceed to a fully connected and flexible level to drive greater value both within the factory itself and across the entire supply chain. It is essentially the move from digital to physical (applying algorithms to translate digital world decisions to effect change in the physical world) is the most important part of Industry 4.0.

It is classified as Industry 4.0 because it follows the third industrial revolution of the age of computers and takes that a step further and refers to self-running computers fueled by data and machine learning. As factories become smarter, learning from an influx of data from all its systems, they will become more productive and less wasteful. “Industrie 4.0” was initially coined in 2013 by the German government and is part of its High Tech strategy with the intention to maintain and avoid losing industrial advantages against other countries.

Industry 4.0 is much more than just sensing and learning - it’s the delivery of interconnected automated global workflows that automate processes to improve quality and increase machinery availability. It is the combination of physical and digital worlds that enable collaboration between departments, partners, and people. It is the evolution of factories to self-healing, self-running ecosystems using automation, data virtualization, and wireless connectivity and IoT sensors. With Industry 4.0, the manufacturing process is faster and more efficient than in the past, due to analysing a huge amount of production data and applying machine learning and artificial intelligence.

The availability of low-cost sensors that can be retrofitted to older machinery, data storage, big data analytics, intelligent devices, and cloud technology is now allowing manufacturers to have real-time fine grained visibility into manufacturing operations across equipment, people, suppliers, processing lines, and manufacturing sites. Without analysing a huge amount of data and acting in real time when critical conditions encountered, there’s little room to improve the product.

Many industry experts agree that only around 5% of all available data sources in a factory are currently being tapped. Most companies just collect process data and typically use it just for logging purposes rather than a basis for improving operations. There are many challenges facing manufacturers today that are demanding a change. First, there is a proliferation of products. There are more options for consumers which make it difficult for producers to differentiate. Shorter product life cycles require manufacturing processes to be constantly changing and updating. Products going to the market are becoming more complicated and require more aspects of the business to be working together to succeed. And, it’s harder to get a competitive product to the market these days. Products are coming to the market much quicker now, the market has more competition and there are more options than ever.

Another trend is the demand of customers for highly personalized products. There’s a shift from mass productions towards mass customization. Eyewear, shoe manufacturers and many others allow their customers to fully personalize their product and select between multiple choices directly on their websites. Once the customization is done, a new production order is generated only for that single item.

Industry 4.0 Technology Enablers

The main features of the suggested guidelines of the new high tech strategies are:

Interoperability: cyber-physical systems (software embedded in hardware such as sensors, processors and communication technologies) allowing humans and factories to connect and communicate with each other.

Virtualisation: the creation of a virtual copy of the factory by linking sensor data with virtual plant models and simulation models; known as a Digital Twin of the factory.

Decentralisation: ability of cyber-physical systems to make decisions on their own and to produce locally thanks to technologies such as 3d printing.

Real-Time Capability: the capability to collect and analyse data and provide the derived insights immediately.

Service Orientation: the services are available over the Internet of Services (IoS) and can be utilized by other participants. We refer to IoS as APIs to exchange information between B2C and B2B.

Modularization: flexible adaptation of the factories to changing requirements by replacing or expanding individual modules.

To be successful, siloed information created in the past needs to be broken down. Connecting to multiple different data sources, unifying the underlying data and avoiding redundant information allows manufacturers to interconnect multiple departments and businesses to operate on factory wide data.

IT is becoming more integral to the manufacturing process. In the past, IT had the role to support the manufacturing process, now that has changed. IT is not only supporting but also has a primary and pervasive role in the entire manufacturing process.

The main use cases of Industry 4.0

Predictive Analytics

McKinsey study corroborates the promise: “A big data/advanced analytics approach can result in a 20 to 25 percent increase in production volume and up to a 45 percent reduction in downtime.” Downtime is expensive and lowers your OEE KPI. Moving from a reactive to a proactive approach will be key for strongly competing.

Machine Learning

Advances in machine learning have led to the increasing adoption of lean manufacturing and Six Sigma practices. Machine learning techniques employ an emerging class of algorithms that actually learn from the data presented to them and automatically construct the best possible model for each dataset. As such, it empowers analysts who have little expertise in statistics and modelling to solve complex problems otherwise beyond their reach. These developments have directly resulted in product quality improvements and reduced waste or product rework. Applying data analysis on a multitude of productions parameters helps to understand the best setup of machineries for a specific order or avoiding machinery settings that actually may produce a bad quality and lead to waste.

Interoperability and artificial intelligence

The maturity of cyber-physical systems allows humans, the product itself and smart-factory machines to connect and communicate with each other and derive insights in real time. Not only is there human-machine interaction, but with decentralized cyber-physical systems, machines can make decisions on their own. A great example of human to machine interaction comes from the automotive industry. Highly specialized workers wear bracelets which can track their movement and alert them when a move in a wrong direction happens or during assembly when a torque applied is enough. This not only enhances security purposes but also avoids repeated wrong movements that could lead to work injuries and may worsen over time.

Key areas of application of the Industry 4.0 principles are:

• Manufacturing Operations
• OEE & Factory Productivity
• Predictive maintenance
• Real Time equipment & process monitoring
• Process Optimization
• Real time Quality monitoring
• Product Yield & Root Cause analysis
• Reliability & Warranty

By introducing data analytics, machine learning and AI capabilities, an Industry 4.0 enabled factory is often defined as a Smart Factory or referred to as Intelligent Manufacturing. In a smart factory, equipment 'learns' to predict anomalies and make decentralized decisions in real-time to respond to events. Many manufacturers are already using components of a Smart Factory such as augmented reality to help repair machines, but a true Smart Factory is a more holistic endeavor.

Using a Smart Factory system, all relevant data is aggregated, analyzed, and acted upon. Within the modular structured smart factories, cyber-physical systems monitor physical processes, create a virtual copy of the physical world, and make decentralized decisions. Over the Internet of Things, cyber-physical systems communicate with each other and with humans in real time and via the Internet of Services. Sensors, devices, people, and processes are part of a connected ecosystem providing:

• Reduced downtime
• Minimized surplus and defects
• Deep insights
• End-to-end real-time visibility
• Digital Twin of the factory

Manufacturers that are able to continuously monitor processes, equipment, people, suppliers and make automated predictive decisions will improve productivity and gain a competitive advantage over those who take a siloed approach. The need for “smart” machines will only to continue to grow and organizations need to implement a solution that incorporates data analytics to support operations; predictive or preemptive analytics; big data integration with big data sources; real-time analytics and actions; and IoT real time integration.

The goals of Smart Manufacturing, manufacturing resources (machines, equipment, people and factories) and the processes they carry out are better when automated, integrated, monitored and continuously evaluated to enable people to work smarter, make timely informed decisions and run operations that are more efficient.

The next step will come with the introduction of 5G cellular technology. This will more and more allow manufacturers to move towards cloud centric technologies due to its increased speed and lower latency. A decrease in the latency means a much higher data capacity to allow machines and systems to react promptly in real time. The 5G network ensures connection reliability. Operators can work with machine controls anywhere on the shop floor with confidence that connections will not be lost. For the first time, 5G offers a data rate and reliability comparable to wired communication.

The shift from traditional, linear ways of receiving information to real time analytics and intelligence could shift the entire way products get manufactured. Industry 4.0 is more than just all the technologies listed above. It is about how organizations can harness them, bring them together and improve operations and growth. Organizations must figure out how to best use these new technologies to stay competitive.

Industry 4.0 is much more than just sensing and learning - it’s the delivery of interconnected automated global workflows that automate processes to improve quality and increase machinery availability. It is the combination of physical and digital worlds that enable collaboration between departments, partners, and people. It is the evolution of factories to self-healing, self-running ecosystems using automation, data virtualization, and wireless connectivity and IoT sensors. With Industry 4.0, the manufacturing process is faster and more efficient than in the past, due to analyzing a huge amount of production data and applying machine learning and artificial intelligence.

The availability of low-cost sensors that can be retrofitted to older machinery, data storage, big data analytics, intelligent devices, and cloud technology is now allowing manufacturers to have real-time fine grained visibility into manufacturing operations across equipment, people, suppliers, processing lines, and manufacturing sites. Without analyzing a huge amount of data and acting in real time when critical conditions encountered, there’s little room to improve the product.

Many industry experts agree that only around 5% of all available data sources in a factory are currently being tapped. Most companies just collect process data and typically use it just for logging purposes rather than a basis for improving operations. There are many challenges facing manufacturers today that are demanding a change. First, there is a proliferation of products. There are more options for consumers which make it difficult for producers to differentiate. Shorter product life cycles require manufacturing processes to be constantly changing and updating. Products going to the market are becoming more complicated and require more aspects of the business to be working together to succeed. And, it’s harder to get a competitive product to the market these days. Products are coming to the market much quicker now, the market has more competition and there are more options than ever.

Another trend is the demand of customers for highly personalized products. There’s a shift from mass productions towards mass customization. Eyewear, shoe manufacturers and many others allow their customers to fully personalize their product and select between multiple choices directly on their websites. Once the customization is done, a new production order is generated only for that single item.

Industry 4.0 Technology Enablers

The main features of the suggested guidelines of the new high tech strategies are:

Interoperability: cyber-physical systems (software embedded in hardware such as sensors, processors and communication technologies) allowing humans and factories to connect and communicate with each other.

Virtualization: the creation of a virtual copy of the factory by linking sensor data with virtual plant models and simulation models; known as a Digital Twin of the factory.

Decentralization: ability of cyber-physical systems to make decisions on their own and to produce locally thanks to technologies such as 3d printing.

Real-Time Capability: the capability to collect and analyze data and provide the derived insights immediately.

Service Orientation: the services are available over the Internet of Services (IoS) and can be utilized by other participants. We refer to IoS as APIs to exchange information between B2C and B2B.

Modularization: flexible adaptation of the factories to changing requirements by replacing or expanding individual modules.

To be successful, siloed information created in the past needs to be broken down. Connecting to multiple different data sources, unifying the underlying data and avoiding redundant information allows manufacturers to interconnect multiple departments and businesses to operate on factory wide data.

IT is becoming more integral to the manufacturing process. In the past, IT had the role to support the manufacturing process, now that has changed. IT is not only supporting but also has a primary and pervasive role in the entire manufacturing process.

The main use cases of Industry 4.0

Predictive Analytics

McKinsey study corroborates the promise: “A big data/advanced analytics approach can result in a 20 to 25 percent increase in production volume and up to a 45 percent reduction in downtime.” Downtime is expensive and lowers your OEE KPI. Moving from a reactive to a proactive approach will be key for strongly competing.

Machine Learning

Advances in machine learning have led to the increasing adoption of lean manufacturing and Six Sigma practices. Machine learning techniques employ an emerging class of algorithms that actually learn from the data presented to them and automatically construct the best possible model for each dataset. As such, it empowers analysts who have little expertise in statistics and modelling to solve complex problems otherwise beyond their reach. These developments have directly resulted in product quality improvements and reduced waste or product rework. Applying data analysis on a multitude of productions parameters helps to understand the best setup of machineries for a specific order or avoiding machinery settings that actually may produce a bad quality and lead to waste.

Interoperability and artificial intelligence

The maturity of cyber-physical systems allows humans, the product itself and smart-factory machines to connect and communicate with each other and derive insights in real time. Not only is there human-machine interaction, but with decentralized cyber-physical systems, machines can make decisions on their own. A great example of human to machine interaction comes from the automotive industry. Highly specialized workers wear bracelets which can track their movement and alert them when a move in a wrong direction happens or during assembly when a torque applied is enough. This not only enhances security purposes but also avoids repeated wrong movements that could lead to work injuries and may worsen over time.

Key areas of application of the Industry 4.0 principles are:

• Manufacturing Operations
• OEE & Factory Productivity
• Predictive maintenance
• Real Time equipment & process monitoring
• Process Optimization
• Real time Quality monitoring
• Product Yield & Root Cause analysis
• Reliability & Warranty

By introducing data analytics, machine learning and AI capabilities, an Industry 4.0 enabled factory is often defined as a Smart Factory or referred to as Intelligent Manufacturing. In a smart factory, equipment 'learns' to predict anomalies and make decentralized decisions in real-time to respond to events. Many manufacturers are already using components of a Smart Factory such as augmented reality to help repair machines, but a true Smart Factory is a more holistic endeavor.

Using a Smart Factory system, all relevant data is aggregated, analyzed, and acted upon. Within the modular structured smart factories, cyber-physical systems monitor physical processes, create a virtual copy of the physical world, and make decentralized decisions. Over the Internet of Things, cyber-physical systems communicate with each other and with humans in real time and via the Internet of Services. Sensors, devices, people, and processes are part of a connected ecosystem providing:

• Reduced downtime
• Minimized surplus and defects
• Deep insights
• End-to-end real-time visibility
• Digital Twin of the factory

Manufacturers that are able to continuously monitor processes, equipment, people, suppliers and make automated predictive decisions will improve productivity and gain a competitive advantage over those who take a siloed approach. The need for “smart” machines will only to continue to grow and organizations need to implement a solution that incorporates data analytics to support operations; predictive or preemptive analytics; big data integration with big data sources; real-time analytics and actions; and IoT real time integration.

The goals of Smart Manufacturing, manufacturing resources (machines, equipment, people and factories) and the processes they carry out are better when automated, integrated, monitored and continuously evaluated to enable people to work smarter, make timely informed decisions and run operations that are more efficient.

The next step wil come with the introduction of 5G cellular technology. This will more and more allow manufacturers to move towards cloud centric technologies due to its increased speed and lower latency. A decrease in the latency means a much higher data capacity to allow machines and systems to react promptly in real time. The 5G network ensures connection reliability. Operators can work with machine controls anywhere on the shop floor with confidence that connections will not be lost. For the first time, 5G offers a data rate and reliability comparable to wired communication.

The shift from traditional, linear ways of receiving information to real time analytics and intelligence could shift the entire way products get manufactured. Industry 4.0 is more than just all the technologies listed above. It is about how organizations can harness them, bring them together and improve operations and growth. Organizations must figure out how to best use these new technologies to stay competitive.