What is Industry 4.0?
One of the first times the term ‘Industry 4.0’ was used was in 2013 in a memo of the German government, which promoted the computerization of manufacturing. However, it only became mainstream in 2015 after Germany’s chancellor Angela Merkel used it in Davos at the World Economic Forum, calling “Industry 4.0” the way to “ quickly deal with the fusion of the online world and the world of industrial production.”
Concretely, Industry 4.0 refers to a new phase in the industrial revolution that focuses heavily on interconnectivity, automation, machine learning, and real-time data. It can also be referred to as “smart manufacturing”. The term translates to machine-to-machine communication and to large-scale Internet of Things (IoT) deployments to provide increased automation as well as improved communication and monitoring. The smart machines can also independently analyze and diagnose issues without the need for human intervention.
The best way to understand smart manufacturing is to think about how it could be applied. Let’s go over some of the main use cases the industry is already applying successfully.
Two common applications
According to recent studies, half of the resources spent on preventive maintenance goes to waste. Manufacturers are turning to the Internet of Things for a smarter approach, continuously analyzing behavior data to create actionable insights that predict product failure, increase uptime, and improve asset efficiency.
Concretely, while preventive maintenance decided almost arbitrarily on the frequency of checks, predictive maintenance uses Artificial Intelligence to analyze all the data relative to a machine to detect patterns that would indicate machine downtime that a human could not have detected.
Quality control is a fundamental part of any manufacturing process. However, it is generally suffering from a lot of different problems: it is often slow, not scalable, and sometimes even inaccurate. All of these problems are largely due to human input.
Moreover, just like in software development, detecting defects early in the process is crucial in reducing their impact and eventual costs. It’s no wonder that improving the efficiency of quality control and defect detection has long been one of the leading research domains in the manufacturing industry. There are two keys to this improvement: automation and increased accuracy.
This is where Artificial Intelligence, and in particular Deep Learning, comes into play. Deep learning is behind the recent huge progress in computer vision that has greatly impacted medical imagery or enables autonomous cars. Considering that visual inspection plays a big part in defect detection for manufacturing, it is natural to think of deep learning to automate the process. And the COVID 19 crisis did nothing but speed up the adoption process.
Challenges of Industry 4.0
Although the value smart manufacturing brings is clear many industry players are still studying how to adopt it. Indeed, while the opportunities it creates are numerous, it also comes with a few challenges. Here are the most common ones that need to be overcome for a smooth implementation:
Will my data/system/machines be safe?
The question of security is a question the Cloud industry knows very well. Many companies were reluctant to switch out of fear their systems could be breached more easily in the cloud. The Cloud industry has successfully answered this question and has considerably allayed companies’ fear. However, the heavy reliance of smart manufacturing on IoT can create renewed anxiety. According to a recent study, more than half of IoT devices are vulnerable to severe attacks.
If the network can be accessed, machines could potentially be hacked, the production process could be interfered with, and even halted. Once again, the question of security will have to be tackled thoroughly to convince companies to make the switch.
At Allcloud, our experience as cloud enablers has made us extremely sensitive to the security needs of our clients. They are always at the top of our agenda, especially for IoT or smart manufacturing projects.
Do I have the skills to make it happen?
Does the company have the skills in place to design, implement and maintain an IoT deployment? The range of skills required are considerable, from cloud architects with knowledge of manufacturing environments to systems integration specialists who can facilitate the implementation of the hardware into the manufacturing environment or data analysts and data scientists.
Unfortunately, this puts off many companies who simply don’t have the internal knowledge for such projects or can’t undertake such a training/recruitment effort. Fortunately, technical or knowledge gaps are no longer roadblocks to execute projects. These gaps can be overcome by outsourced resource companies like Allcloud. Our teams of architects, data engineers and data scientists can bring to life what seemed unrealistic projects.
Will I be able to handle the growth in data?
As plants get more connected, companies will be faced with more data being generated ever more quickly and in multiple formats. Will their architecture be able to scale to handle this growth? According to all big data studies, in order for a data platform to live for 10 or 15 years, it will probably have to scale by a factor of at least several hundreds, if not one thousand.
Again it can be difficult for an organisation which is not specialized in big data to know how to design such an architecture. At Allcloud, we deal with clients from all industries and domains and work with them on many different use cases. As such, we have the experience to quickly build the most appropriate data architecture for any type of project.
All of these challenges are significant and need to be addressed before making the transition to smart manufacturing. However, these efforts are absolutely worth it considering the huge value and competitive advantage Industry 4.0 can give you. If you feel these projects are beyond your capabilities, don’t forget that Allcloud has the technical skills, the experience and the know-how to make them happen.