Industry 4.0 & Industrial Digitization: what is it and why should you care?
Over the past few years, the organizations have faced a rapidly changing world. Digitization of processes, products and services, as well as customer’s expectations have increased. Furthermore, this digital transformation has recently experienced an extreme acceleration. Industry 4.0 and Industrial Digitization are here to stay.
Even if most companies understand the need and urgency to digitize their operations, some might feel confused and ‘unsecure’ on what is behind Industry 4.0 and industrial digitization. In this article we will first zoom in on what exactly Industry 4.0 stands for.
Then we will clarify why data is critical and the value you can get from data analytics in your Industry 4.0 journey. Finally, we will explain why now, more than ever, is the time to explore the full potential that digital brings.
What is Industry 4.0?
Industry 4.0 refers to the fourth industrial (r)evolution. After the first (water and steam), second (electricity) and third (electronics and IT) wave, this fourth phase is based on data and connectivity. While Industry 4.0 and the Internet of Things (IoT) were still relatively new concepts, innovations in these domains focused on “proof of concepts”. Today, on the other hand, digitization and new technologies are accelerating the increase of efficiency and quality in manufacturing and supply chain.
To summarize in other terms, Industry 4.0 allows your factories to become ‘smart’. Different technologies like cyber-physical systems and Internet of Things (IoT) interacting with each other and adapting based on the latest input. It is this adaptability and flexibility that allows companies to position themselves for the challenges that lay before them.
In the Industry 4.0 context, the digital factory (Smart Factory or Factory of the Future) is key. This digital factory has a number of essential characteristics.
First, data is leveraged consistently and exhaustively. Second, new manufacturing technologies and assistance systems are used throughout operations. Third and finally, the vertical and horizontal integration of all departments and divisions (from R&D to Sourcing, Manufacturing and Customer Care), allows for an extremely adaptive, customizable and efficient process. Furthermore, the rise of integrated digital tools linked to more computing and simulating power increase end to end transparency in the overall supply chain.
A second point of element related to Industry 4.0, industrial digitization and smart technologies is the advent of smart products and services. Smart, once again, referring to the fact that these products are interconnected and have their own data processing capacities. This allows smart products, on one hand, to work autonomously and coordinate with one another through cloud platforms. On the other hand, it lets them respond in real-time to new external events and tasks. In and of itself, these smart products enable and monetize new applications. Next to this, they are the core for smart services. Examples of this are data-based business models, Smart City, Smart Agriculture or Smart Healthcare.
Last point is that, when embracing industrial digitization, it is essential to introduce new ways of working and organizing yourself. Concepts such as are integrated with LEAN principles and introduced into the supply chain and manufacturing processes. Industry 4.0 requires a shift in the way teams collaborate, a new cultural paradigm and the development of new competences. Most importantly, the corporate strategy needs to be oriented towards improving the digital maturity of the organization.
By capturing, understanding and leveraging data, cyber-physical systems are created. Cyber-Physical Systems (CPS) are integrations of computation, networking, and physical processes. Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa. This allows for new processes, services and entire business models to be created.
A data-based approach to Industry 4.0
The most important pre-requisite to be able to leverage the full advantages offered by Industry 4.0 is the maturity an organization has with regards to its data. At EFESO Consulting, we use the Industry 4.0 Maturity Pyramid to assess current readiness and first improvement opportunities on the way to fully connected operations. It reflects five levels of maturity:
Level 1: Connectivity
The pre-requisite to Industry 4.0 is simply the fact that data is available. Data can be accessed from the full range of processes, assets, people and products. In the event that (some) data is unavailable, there are a number of options to remedy this: retrofitting, edge computing, APIs, …
A key enabler to unleash the full potential of Industry 4.0 is the horizontal and vertical integration of your data sources.
Horizontal data integration involves the internal and external partners throughout the value chain. This stabilizes the planning, adds responsiveness and allows for real-time event detection. In short, it aids the decision process.
Vertical data integration is about connecting the machine level to the highest-level ERP interface. Throughput times are minimized, interfaces are harmonized and the data is reliable and transparent.
Level 2: Information
One step beyond data being simply available, is ensuring this data can be used as information. It is clear what the origin of the data is and it is available in real-time.
Level 3: Knowledge
Data is transformed into knowledge when it is understood why certain results are the way they are. This knowledge can aid the problem-solving process and the continuous improvement culture at the organization. In practice, dashboards and BI-tools will be the concretization of this step.
Level 4: Prediction
The data is not only used to react to situations. Data helps start anticipating future events and pro-actively select the right course of action. This allows to avoid problems instead of solving them. We use advanced (predictive) analytics and Artificial Intelligence (Deep and Machine Learning) to support decisions and actions.
What is AI?
Artificial Intelligence, in essence, allows us to find or determine a logic or patterns in an enormous amount of data. When starting from big data, AI is often a lot faster and more correct than humans. Two main categories can be discerned: weak and strong AI.
- Weak AI is focused on very specific, easy-to-define problems or situations. The main benefit of weak AI lies in automating time-consuming, repetitive tasks. Examples include Amazon’s suggested purchases, Siri or Alexa.
- Strong AI is broader than its weak counterpart. In this case, the AI is able to assess and cluster data beyond specified input-output scenarios. This type of AI mimics our classical definition of intelligence much more closely.
Artificial Intelligence covers various technologies and methods which can be useful in analytics and big data projects. At EFESO, we employ the latest methods to solve specific problems in Big Data Analytics projects. A non-exhaustive list of examples includes:
- Natural Language Processing (NLP) typically covers ways to handle speech recognition and generation. Next to the previously mentioned examples of Siri and Alexa, NLP can be combined with bots to be employed as an interactive chatbot. This is becoming more and more popular in service environments.
- A classic type of AI is the area of image processing and recognition. Such techniques make it now possible to transform pictures of text to actual editable text.
- Machine learning is repeatedly confused with AI or used as a synonym of AI. In short, it implies creating new insights from specific data or experiences. As such it can be seen as a type of AI.
- Deep learning, next, is a type of machine learning. The method is based on the functioning of the human brain, allowing to extract much more detailed information from raw data.
- Using expert systems, existing knowledge can be formalized and organized.
- A typical planning exercise can also be automated with AI supported algorithms. Nearest-neighbor is one example of such an algorithm.
Level 5: Autonomy
The entire system becomes self-adjusting. Process insights and decision speed accelerates beyond human limitations.
One of the key value enablers in the application of and transformation towards Industry 4.0 and Smart Factory will rely on data analytics. It is only when an organization has the capabilities to manage and leverage the massive amounts of data being created by IoT (Internet of Things) environments, that they can create value with this data. The latest approaches and data analytics methods allow you to actually use unstructured data in real-time and even change its structure at any time.
Unlocking the full potential of data not only makes it possible to make more effective and immediate decisions. This also facilitates typical forward-looking processes such as planning maintenance through advanced analytics and predictive maintenance. Customer requirements can actually be understood and acted upon. In short, data analytics helps an organization to put in place its strategy and check on progress, while continuously improving its day-to-day operations.
It is through the combined efforts of data analysis and AI (learning systems) that the biggest potential for automation can be exploited. A specific and powerful example of this opportunity is a digital twin. These are models that are defined as the virtual counterpart of objects, processes or even networks. The digital twin maps its physical counterpart, which makes it possible to run detailed simulations. Scenarios can be compared on opportunities and risks with a high transparency and accuracy.
A final core characteristic of modern-day data analytics is decentralization. Sensors on specific products provide real-time aggregated insights and create new intelligent products (Smart Products & Services). This makes decisions at the highest level easier. However, it is important that the analysis of different processes and components happens as close as possible to the data source. On one hand, this relieves central divisions of the gargantuan task of keeping on top of every detail. On the other hand, most importantly, it empowers local teams and personnel to control and improve their own scope, benefitting the overall organization.
Why is the time now for Industry 4.0?
As a matter of fact, digitization efforts and focus on Industry 4.0 has accelerated tremendously in the past few years. But why is Industry 4.0 so disruptive? What is at stake?
Four key areas can be identified, each contributing to the importance of Industry 4.0. First and foremost, the impact which can already be made with Industry 4.0 is substantial. Second, the technological advancements in recent years truly support more and easier digitization. Third, Industry 4.0 creates new business and revenue models. Fourth, Industry 4.0 can be seen as the global equalizer in manufacturing and operations.
Let’s take a look at each of these factors in turn.
Companies and organizations embracing the opportunities Industry 4.0, IoT and Data Analytics have to offer, have already been reporting significant improvements. From a productivity point of view, OEE (Overall Equipment Effectiveness) and output per FTE rise, while scrap and energy costs drop. Companies also become more agile, reducing leadtimes, inventories and change-over times. Below numbers are from actual clients we supported.
Costs and size of technologies enabling Industry 4.0 and digitization have never been lower. Meanwhile, performance of these technologies has never been that high.
Sensors, for example, have approximately decreased by 50% in price over the past 10 years. Connectivity is made easier due to bandwidth being around 40 times less expensive compared to 10 years ago. Data storage, processing power, … All these components have become less expensive and more performant.
Besides cost, flexibility, quality and safety, Industry 4.0 offers the opportunity to expand business and change your business model. These smart technologies allow organizations to change from selling products to selling services, as truly understanding the right data can be valuable for a number of external parties. Below infographic as an example.
Finally, Industry 4.0 can be seen as a true global equalizer. This can be observed from 2 perspectives. First of all, for the mature economies. Second, for developing economies. Traditionally, the former was strongest in quality, while the latter competed on costs.
Industry 4.0 and the previously discussed technological opportunities, such as Digital Twins, enable the digitization of industrial operations. This produces a key step change in both efficiency and productivity, as we have shown with the numbers above. Next, the increased flexibility allows mass customization. In summary, it becomes possible to deliver higher value to your customers.
Coming back to the topic of the difference between mature and developing economies: with the right use of Industry 4.0, mature economies will be able to compete on costs, while developing economies can compete on quality.
Where to start with Industry 4.0?
If you take anything away from this article, we hope it is that there is no one-size fits all answer to this question. First of all, it will be important to understand where your maturity is with regards to your data. From there onwards, some possible roads can be taken to progress further. However, you should realize that the root causes preventing you from moving up the Maturity Pyramid may vary a lot. It will always be critical to consider a holistic approach from a Process, Human and Digital Perspective.
We know how challenging it can be to determine the best approach to the Industry 4.0 journey. We at EFESO have a team of multi-disciplinary experts at the ready to help you figure out how to progress to the next step of your journey. Please feel free to contact us with any question you might have.