When it comes to embracing new technology and digitising entire sectors of business, look no further than the Industry 4.0 revolution. All over the world, companies from the likes of manufacturing, warehousing and logistics are embracing Industry 4.0’s key technologies to open up new values and benefits.
However, Industry 4.0 trends shift and evolve as time goes on. Today, it’s much more than simple automation. As such, we want to take a look at Industry 4.0 technologies and projects in more detail.
What Is Industry 4.0?
We’ve previously discussed the topic of Industry 4.0 before, but here’s a quick recap. Originating from Germany, Industry 4.0 refers to the fourth Industrial Revolution, following the introduction of steam machinery (the 1st Revolution), electronic equipment (the 2nd Revolution) and initial IT support in the 1970s (the 3rd Revolution).
Each revolution, by its very nature, brings in new benefits and resolves old issues. So what’s the problem with current industrial practices? Well, traditional, linear manufacturing processes uses embedded systems to control machines for various purposes, such as to manage the speed of an assembly line or control Computer Numerical Control (CNC) machines, which are commonly used to perform specific actions. Such manufacturing systems have limited data concerning raw materials, process parameters or final products. Additionally, any collected data is analysed periodically, which causes delays in process improvements.
The Industry 4.0 Cycle
Industry 4.0 is a major transformation of the way such production and manufacturing operates. It enables a shift from linear process to integration and real-time data access.
Cyber-physical systems – those that integrate digital computation with real-world processes – enable the collection, integration and advanced analysis of data from an organisation’s machines and systems, using insights to manage physical processes. Instead of operating reactively, as it has been always done, companies can learn along the way and use insights to adjust processes in real time. Such systems can self-optimise performance and independently adapt to new conditions at a similar pace, running and improving entire production processes.
Throughout the cycle, data and actions flow continuously between physical and digital worlds. This cycle consists of three steps:
- Physical to digital: Here, companies capture information from the physical world and create a digital record
- Digital to digital: Advanced analytics are used, often alongside AI, to uncover meaningful insights
- Digital to physical: Businesses then translate digital-world decisions into physical actions and changes
Industry 4.0 & IoT
As you can see, the bridge between physical and digital is vital to industry 4.0 implementation. For this, sensors are a must, as they are able to accurately record all of the required information needed.
In other words, Industry 4.0 is an integration of the Internet of Things (IoT) within these industries. The IoT is a key connectivity enabler, enhancing IT technologies such as advanced analytics, artificial intelligence and augmented reality, in turn benefiting physical technologies, including the likes of additive manufacturing, robotics and advanced materials. Such integration enables physical systems to communicate and cooperate with each other, as well as with human workers, acting upon insights derived from the collected data.
To better describe this, the following table presents examples of technologies that are applied at each step of the physical-to-digital-to-physical loop.
Digital Twin – a Significant Element of Industry 4.0
When talking about Industry 4.0 technology, it’s worth looking at Digital Twin, as this is one of the most recent and fastest growing technologies available to businesses.
In simple terms, a digital twin is a virtual replica of a physical object, process, or product that is updated in real-time. We’ve covered the topic of digital twins before, but it is suffice to say that they can range from single piece of equipment to entire production line. A Digital Twin combines data from various sources, like real-time, real-world data measurements from IoT sensors, metadata, CAD designs, reports and documentation created during the lifecycle of the asset. Companies can use this data to run process simulations, react in real-time to parameter changes to prevent downtimes, manage production processes, optimise supply stocks and more.
In the aforementioned physical-to-digital-to-physical loop, A Digital Twin is a key link between the physical and digital world. Combined with modern-day massive computing power and advanced algorithms for real-time analytics, they enable fundamental design and process changes that would almost certainly be unattainable through current methods.
Who Uses Digital Twin Technology?
Digital Twins can be created for variety of assets to serve different objectives. One example could be the virtual replica of a single machine within a production line, created to monitor and optimise the performance of the singular asset.
However, the full potential and business value of Digital Twin is unlocked when combining different replicas across a specific process, where Digital Twins of single assets interact with each other. Here, they can exchange data with each other and provide a complete picture of the process and the dependencies between single steps. All of this enables new use cases, alongside opportunities for significant process improvements.
How Industry 4.0 Creates Value
Thanks to the digitisation of operations, manufacturing, supply chains and products, companies can combine the unique learnings and advantages from humans, machines, analytics and predictive insights to make better decisions, which ultimately leads to additional value for the business.
Looking at Industry 4.0 in manufacturing specifically, McKinsey has identified eight main value drivers that impact a company’s performance. They found that applying Industry 4.0 leavers on each of these value drivers lead to noticeable improvements.
So, with that in mind, let’s take a closer look at each.
Resources/process – Industry 4.0 mechanisms improve the effectiveness of manufacturing processes. ABB, for example, employed a computer-based system to control and optimise one of their customer’s cement kiln operations. The system mimics the behavior of an ‘ideal’ cement plant operator. It uses actual measures to calculate the process parameters required to meet target performance and adjusts the respective process in real-time. As a result, throughput yields saw a 5% optimisation improvement.
Asset utilisation – Industry 4.0 solutions such as predictive maintenance can provide value by decreasing machine downtime or changeover times. GE10 offers predictive maintenance, where IoT sensors collect and report data on the condition of respective machinery. Advanced analytics use this data to detect early signs of various problems, indicating which machines are – or will be – requiring maintenance. Predictive maintenance can reduce machine downtime by 30-50%, in addition to improving their lifespan by 20-40%.
Improving labour productivity – Industry 4.0 brings value by reducing the waiting time, or simply increasing the speed, of operations. For example, we can look at Etalex, a warehouse furniture manufacturer that introduced robots to increase labour productivity. These robots are used to support the company’s workers in physically straining tasks. Consequently, this human-robot collaboration increased productivity by 40% without increasing the employee base.
Inventory optimisation – This one is fairly straightforward, but the benefits are just as staggering. As a prime Industry 4.0 use case, Wurth’s iBins uses intelligent cameras to monitor the fill level of their supply boxes. Each box automatically reorders supplies based on accurate fill data. Such real-time supply optimisation has reduced inventory costs by 20-50%.
Improving quality – Toyota uses advanced process controls and data analytics tools for real-time error tracking and correction. These measures have resulted in the minimising of rework required, as well as generated scrap. Applying Industry 4.0 leavers in this fashion can decrease costs related to suboptimal quality by up to 20%.
Supply/demand match – In order to maximise the value captured from the market, every company has to understand their customer’s demands. Industry 4.0 gives companies the tools and means to use their full potential. Original Equipment Manufacturers in the automotive sector, for example, use online configuration tools to identify the products and options customers are willing to pay for. Such solutions help companies to remove unwanted options, focusing only on what customers seek. As a result, the time and costs of production are reduced, as wasteful options are eliminated entirely.
Time to market – A short time to market gives first-movers a strong advantage and Industry 4.0 leavers can help to speed up the development process. Local Motors, for example, produce cars through 3D printing, with designs crowd sourced from an online community. Because of these innovations, the company has been able to reduce the development cycle from six years – the industry average – to just one year. Additionally, they achieved significant R&D cost reduction. According to McKinsey, possible time to market reductions can range from 30-50%.
Service/aftersales – Maintenance and repair are important drivers when it comes to service costs so, naturally, reducing these costs for the customer opens potential for additional value creation. Secomea, for instance, uses remote maintenance to provide service to its clients. The company uses software that enables technicians to establish remote connections to industrial equipment at a customer’s premise and carry out diagnostics. Remote and predictive maintenance, the latter of which we’ve already mentioned, can reduce maintenance costs by 10-40%.
Other Technology & Market Trends
Of course, it’s not just about Digital Twin. There are a whole host of solutions to choose from, and many of these Industry 4.0 technologies are also on the rise. Here are some of the biggest Industry 4.0 trends to be aware of.
Over 50% of Industrial Assets in Factories Will Be Connected
Industry 4.0 is not a theoretical concept, but a real trend affecting how companies operate. An increasing number of manufacturers build industrial data collection solutions. According to a report by IoT Analytics, by 2020, over 50% of industrial assets will be connected to some kind of data collection system. The percentage of connected assets is only going to rise and is set to be a key driver of industrial connectivity market growth.
In last couple years Enterprise Resource Planning (ERP), Manufacturing Execution System (MES) and Supervisory Control And Data Acquisition (SCADA) systems have all moved to the Cloud. Due to this, new connectivity architectures have emerged, enabling direct edge to Cloud connectivity. Where traditional communication through SCADA and MES systems depends on Open Platform Communications (OPC) servers, advancements in connectivity technology (connectivity protocols), computing hardware (low cost edge computing that can run applications and enable edge-to-cloud connectivity) and software (software designed to run on edge devices as small as a Raspberry Pi) enable new architectures where edge devices can establish direct connection with the Cloud.
In recent years, enterprise computing has become increasingly Cloud-centric, for which there are many reasons. Cloud solutions are often cheaper and more powerful, in addition to being easier to implement, maintain and scale. Storing data in a centralised, yet off-premise server that is easily accessible simplifies collaboration, enables remote working and greatly improves flexibility.
However, business needs are evolving. The emergence of 5G and the increased popularity of IoT has resulted in the creation of large amounts of data, alongside real-time analytics. Subsequently, this has resulted in businesses look for alternative computing solutions. Despite the many advantages of the Cloud, the latency of hosted solutions is often insufficient for some Industry 4.0 use cases. As mentioned earlier, much of Industry 4.0 explores the assessment of data at speed, often with near real-time reactions. In situations such as this, the additional milliseconds in delay per action can ultimately make a noticeable dent in ideal improvement.
For time-critical processing, users are moving towards edge computing, which pushes data storage and processing closer to the point it is needed. Placing computing power as close as possible to the sensors capturing the data in question reduces amount of data sent to the Cloud, which simplifies security and decreases network response times.
Increased Popularity of AI and Advanced Analytics
Advanced analytics and artificial intelligences are becoming more and more capable, as well as increasingly cost-effective for businesses to utilise. Cloud solutions and other improved computing capabilities are making these technologies more accessible. Numerous companies have already realised that advanced analytics and AI can create significant value when applied to the manufacturing industry. Examples include predictive maintenance, digital quality management, and AI-driven demand forecasting. AI plays a key role in the smart factory, helping manufacturers predict demand and allocate resources.
Industry 4.0 is no longer just a concept, and neither is it ‘just’ about digitalising your data. It’s an entire technological revolution that encompasses IoT, Big Data, Analytics & Machine Learning to create new opportunities and advantages that simply weren’t available without such technology present.
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