It is already a decade when the idea of Industry 4.0 (or “Industrie 4.0”) was introduced on Hannover Fair in 2011. This is a good time to summarize what happened since that time and check the challenges that were observed during implementation.
Before we go to challenges, let’s remind what Industry 4.0 is, and what is going to provide to manufacturers and all people.
Industry 3.0 made, and is still making, a big revolution in many industry sectors. Shifting manufacturing to the digital world, by introducing computers on a production (e.g., Programmable Logic Controllers), involving robots, and boosting automation, has already increased performance dramatically. So why should we take another step? The better question is rather: “why not to move on with new level?”.
Imagine that all machines in your factory could communicate with each other, can collect data to one common place and there are mechanisms that allow you to analyze those data and, based on them, support the decision-making process. By easy interfaces all interested employees and consumers are well integrated with this virtual world and can, in an easy way, adapt to their needs. This is what Industry 4.0 is going to provide.
Why is Industry 4.0 Important in Manufacturing?
There are several key reasons and benefits why manufacturers would like to adopt to Industry 4.0:
- Cost Efficiency:
- Decreasing downtime, reducing a cost of supplying quality parts, by using Predictive Maintenance strategies; Solutions for Machine Monitoring or any other new technologies
- Reduction of the percentage of defects or shrinkage in the factories, since it will be possible to test the prototypes in a virtual way and the assembly lines will be optimized even before they become operational.
- Greater productivity and better management of resources
- Proper integration of all kind or resources also with people will boost a productivity and improve a working condition
- More efficient decision-making based on real information
- Proper analysis (supported by Artificial Intelligence, Machine Learning, Predictive Maintenance, Big Data solutions) of data coming from many machines, many different factories or even entire industries can help in taking the right decisions and moving production in a right direction.
- Operational Agility.
- With technologies provided in the context of Industry 4.0, manufacturers should be able react quicker to fluctuating demands, new product trends, the skills gap, and other unpredictable challenges. With the right technology in place, manufacturers have a greater likelihood of successfully pivoting, when experiencing adversity. It should increase flexibility, to achieve mass production (and personalized in real time).
- Retaining Customers
- Currently we can observe a trend of increasing expectations of consumers around service and product quality. To meet these rising demands, manufacturers will be forced to adopt technology, to support customization, product development, after-sales service, and more.
- Direct communication between clients and organizations, which means that we can better understand what customers need.
Items listed above are just a few examples, but already, by just analyzing them, we can easily see significant benefits.
What are the challenges?
Adapting to Industry 4.0 is not an easy task and requires time and a good strategy. Each manufacturer should prepare a solid, long-term plan, and needs to solve a long list of different challenges to be successful and enjoy the benefits provided by Industry 4.0. Some of those transformation-related topics would be:
- New business models — the definition of a new strategy
- Rethinking your organization and processes to maximize new outcomes
- Understanding your business case, understanding a problem that you want to solve
- Conducting successful pilots
- Helping your organization to understand where action is needed?
- Change management
- Examination of company culture
- The genuine interconnection of all departments
- Recruiting and developing new talent, technical skills
- The needs required of the workforce are evolving. Are your employees able to keep up? When looking to fill open positions, look for applicants who possess “digital dexterity” in that they understand both the manufacturing processes and the digital tools that support those processes. Only with the right workforce will business models be able to successfully implement new technology and maintain operations.
- Data Sensitivity
- The rise in technology has also led to increasing concerns over data and IP privacy, ownership, and management. A common example? To successfully implement an AI algorithm, data is required to train and test it. For this to happen, the data must be shared. However, many companies are reluctant to share their data with third-party solution developers (what is quite understandable). Furthermore, our current data governance policies for internal use within organizations are inadequate to support cross-organizational data sharing. Data is a powerful asset – make sure to keep it secure!
- Another significant issue is the lack of separation between protocols, components, products, and systems. Unfortunately, interoperability impedes companies’ ability to innovate. Furthermore, since they cannot easily “swap out” one vendor for another or one part of the system for another, interoperability also limits options to upgrade system components.
- Threats in terms of current and emerging vulnerabilities in the factory are another significant concern. The physical and digital systems that make up smart factories make real-time interoperability possible — however, it comes with the risk of an expanded attack surface. When numerous machines and devices are connected to single or even multiple networks in a smart factory, vulnerabilities in any one of those pieces of equipment could make the system vulnerable to attack. To help fight this issue companies need to anticipate both enterprise system vulnerabilities and machine level operational vulnerabilities. Companies are not fully prepared to deal with these security threats, with many relying on their technology and solution providers to scope out vulnerabilities.
- Handling Data Growth:
- As more companies become dependent on AI usage, companies will be faced with more data that is being generated at a faster pace and presented in multiple formats. To wade through these vast amounts of data, AI algorithms need to be easier to comprehend. Furthermore, these algorithms need to be able to combine data that might be of different types and timeframes.
What are the solutions?
Unfortunately, there is none, a common solution for all manufacturers.
Each manufacturer can build its own solution adapted to the factory from scratch. We could assume that this will be pretty cost full operation and will require a lot of maintenance throughout many areas.
There are also external providers of many components that may support the whole process.
The center of the IIoT infrastructure is a good platform, that helps with the integration process of all machines working in the factory.
Source: Gartner Magic Quadrant for Industrial IoT Platforms
Before the selection of the right one there is a need to analyze how much its support our business needs, e.g.
- what kind of communication protocols it uses e.g., MQTT, OPCUA, EtherCAT, EtherNET/IP, etc… and how easily it can be integrated with machines?
- What is the security level?
- What kind of analysis may provide, and which decision-making process may support?
- What is the level of integration with already existing consumer applications, ERP systems, Mobile applications, etc…
- What is the maintenance level?
The good information is that there are IT / Software companies that specialize in such transformation and support manufacturers on different levels like:
- Identifying the business needs
- Selecting and integration of the platform
- Implementation of required embedded, web, mobile software that make all parts running together
Did you know?
Codelab gained extensive IIoT experience in a multitude of industrial reference projects spanning industries such as injection molding, glass, (intra-) logistics, construction, chemical and medical.
Codelab’s IoT Center of Competence offers full range consulting for your industrial IoT projects. Whether you start from scratch and need an architect, want to retrofit and connect an existing base, need someone to optimize, secure and test your code — contact our experts Hubert Kaminski or Tomasz Brzozowski for a solution briefing.