Digital Transformation: Industry 4.0 Is Here to Stay

In the same way that new technologies are changing relationships and personal communications, information analysis in real-time, done online, over multiple systems and incorporating agile decision-making is landing in the industrial world and, therefore, is here to stay.

Called the Industrial Internet of Things (IIoT) or Industry 4.0, it involves connecting machines, systems and people to make te most of it. There are many benefits, from having real information at any time and anywhere, maintaining a reliable history, or combining both with machine learning systems for predictive and prescriptive purposes.

However, we have to keep in mind that any function that is developed in the framework of IIoT should focus on giving real value to customers. Having a lot of data is neither an automatic guarantee, nor a value. For example, these projects can fail when finding non-existent problems or focusing on technology instead of client needs.

One of the biggest challenges our industry faces is being more competitive in production efficiency. The availability of machines and the reduction of setups are some of the great opportunities where the technologies associated with IIoT can influence more by giving visibility to the processes and bottlenecks. Good management of the data from these machines can define preventive and predictive actions that will surely improve production.

Another very valuable aspect is the improvement of the machines through the digital twin concept, which is the virtual representation and simulation of a machine using real data to understand, learn and improve its performances. IBM is doing this in the aeronautics sector, for example, with a digital twin that offers real-time analysis of engine speed, air/fuel ratio, oil pressure, coolant temperature and more.

Live Data & Processed Data

Since 2008, with the popularization of smartphones, there has been a significant evolution of digital technologies and protocols. Today, machines generate large amounts of information that can be channeled as live or processed data.[perfectpullquote align=”right” cite=”” link=”” color=”#00FFFF” class=”” size=””]

We have to keep in mind that any function that is developed in the framework of the Industrial Internet of Things (IIoT) should focus on giving real value to customers.

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Live data can be considered the behavioral or operational Open Platform Communications (OPC) information of each machine, derived from the sensors or tags that control a part of it, like its temperature, speed, acceleration or pressures. These sensors or tags serve only to analyze a specific parameter of the machine, extracted with protocols such as OPC Data Access (OPC DA) or OPC Unified Architecture (OPC UA). This data can be used to determine the state of the machine in real time, as well as monitor its “health.”

Processed data defines who, what, when, where and how one thing has been done, and stores it in a structured way, inside or outside the machine, in order to extract indicators (like key performance indicators, or KPIs) or trends. Based on these indicators, adapted to the processes and particularities of any client, the real efficiency of a piece of equipment or a machine (its overall equipment effectiveness, or OEE) is obtained.

Specific to our industry, this could be the relation of incidences in a period of time for one or a set of flexographic presses, generated directly from the machine.

Data Management

Artificial intelligence (AI) can take a step forward managing and correlating these two types of data, simulating processes and making assumptions based on different variables with self-learning methods of use and detecting anomalies outside defined and verified patterns.

It is true these systems need a lot of data, but with large amounts of data, their reliability and accuracy increase. For this reason, it is important to start storing data for later analysis. With these intelligent algorithms, an IIoT system can provide the best possible configuration for each job, send alerts to prevent unplanned downtime (by predicting events), propose countermeasures, make more efficient technical and remote assistance, and—in the future—operate the machine autonomously.

All this data and all these numbers must be reliable, they must be properly prepared and must be represented in a simple, intuitive and direct dashboard that answers the needs of the client.

Connectivity

Connectivity is indispensable. The improvement in connections and security by means of encrypted communications has made more and more companies streamline the migration of their corporate tools toward services located in cloud platforms—services originally housed internally. But it’s not only the improvement in connections that has caused this change; improvements in security, availability, connectivity, the integration of other applications and continuous improvement have also played a role.

Companies such as Amazon (through its Amazon Web Services subsidiary), Google (whose Gmail guarantees 99.978 percent availability), Microsoft, IBM and Oracle (all three of which traditionally offered equipment and services for internal servers) are now providing these offerings as web services based in the cloud. That business model has completely migrated to the cloud.

The big difference in opting for a cloud service instead of local servers is that customers only have to worry about the direct value offered by the service, without being concerned about migrations, maintenance, having a contingency plan, security or unforeseen costs due to unexpected breakdowns.

As was seen at Mobile World Congress 2018 held in late February in Barcelona, Spain, ​​the next evolution of wireless data—5G—will enable a transfer speed of up to 2 GB/second with very low latencies. Together, these will enable us to interact in real time via any device, promoting a new acceleration in the transfer of what were once local services to the cloud. In addition, this kind of connectivity opens the doors to improving intracompany projects, not only for traceability and supply chain issues, but also to benefit from the new world of applications that exists. That is where investors are more focused nowadays.

Challenges, Both Personal & Professional

With all that said, there are also challenges in the adoption of these technologies.

On one hand, there is the need to be aware we are facing an important digital transformation. With that transformation, there is also a cultural change, an adjustment we have already carried out in our personal lives—for example, online shopping, online banking, email management, social networks, etc.

On the other hand, another important challenge is to converge the worlds of IT (information technology) with OT (operational technology). OT moves with patterns like stability or reliability while IT moves with patterns related with change, efficiency and optimization. This is why it is so important to define common case uses, focus on each client’s needs and define a flexible environment.

The industrial world has the opportunity to go one step beyond, implementing these technologies tested in our real life.

About the Authors: Jordi Sahun is chief innovation and technology officer at Comexi. Esteve Grassot is database architect at Comexi.

Comexi technologies discussed in this article include Comexi Cloud, a tool which can reduce setups, increase visibility in processes and uncover bottlenecks. Comexi Cloud can also gather data for preventive maintenance.