Industry 4.0 and data management

Industry 4.0 is the clear materialization of a business trend that transitions from process-based to data-driven production. However, this transition is not yet complete.

In 2020, only 13% of manufacturing companies were digitally transforming their processes. Meanwhile, many operational and service organizations have already taken advantage of the benefits of data as a driver of growth.

In fact, many manufacturing companies are still betting on mega-factories and offshoring. In these cases, the cost savings are already debatable, as wages are being updated in the various nations where this offshoring is taking place. What is certain is that in this atomized production it is difficult to materialize coordinated efforts for digitization. This makes this 87% of companies continue to maintain the same approach for five decades.

Industry 4.0 and the pre-eminence of data

Industry 4.0 has been dubbed the "Fourth Industrial Revolution" and with good reason. At the time, the first and second revolutions were significant, with the mechanization of production using steam engines and mass production with electrically driven lines, respectively.

The third revolution introduced computers and automation. Industry 4.0 will enrich its predecessor with intelligent and autonomous systems based on data and Machine Learning. Consequently, a reinvention of processes around data capabilities is currently taking place.

Steps to transition

Consolidating a manufacturing company as Industry 4.0 requires a transition of three key stages:
  • Incorporate advanced manufacturing technologies. Specifically: robotics, augmented reality and the simulation of production processes with 3D scanners, virtual reality and energy simulators.
  • Redesign processes, products and services based on Big Data and causal analytics. This with the intention of studying the volumes of data generated by the production processes themselves.
  • Test the operational efficiency provided by the Internet of Things (IoT) networks. As well as improving system control.
In other words, this requires a holistic reassessment of the entire manufacturing process. In this way, automation through cyber-physical systems and data exchange through end-to-end operation (IoT) will be at the core of the manufacturing plant architecture. Not an improvised or ancillary idea.

A necessary cohesion

Until now, manufacturing technology has been classified into operational and information technology. On the one hand, operational technology (OT) focused on sensors and software that monitored the manufacturing process. On the other hand, information technology (IT) provided the separate function of data processing and analysis. In contrast, Industry 4.0 will realize the cohesion of OT and IT. Both will interact in real time, streamlining processes and providing analytics.

However, to achieve this cohesion, it is essential to rethink the manufacturing process through a data architecture. This is the only way to assimilate the vast amount of information provided by IoT sensors and devices from other technologies in real time. In addition, this would facilitate nanosecond control of the entire production environment. Two vital elements must come together here: time and the adherence of each element of the process to the control exercised by the central management system.

The role of data in Industry 4.0

Undoubtedly, the core of a state-of-the-art manufacturing plant is its central control system. Understanding that time is a vital variable in its operation, the right way to provide accuracy is through a time series database.

Incorporating Industry 4.0 criteria into a manufacturing facility requires a process of adherence to data standards that ensure the seamless flow of information. In this sense, having process applications supported by time series databases would provide two essential conditions:
  • Maintain the efficient operation of the production line.
  • Reduce downtime.
The control and sequencing of the steps in the manufacturing process define the efficiency of the production line. And for this control to work, it needs to capture large amounts of data from multiple sensors. It will then be able to transmit real-time instructions to cyber-physical systems. As well as to other points along the line. For this purpose, it is critical to change the backend systems of the time when OT and IT systems operated independently. Beyond that, a time series database architecture must be incorporated to accommodate the scale and accuracy required.

Benefits of data for the new industry

Reducing production line downtime is now feasible thanks to data analysis. Data analysis makes it easier to anticipate potential problems and equipment failures. Predictive analytics prevents problems and makes it possible to take measures to reduce the risk of unscheduled downtime. Therefore, the advantages provided by a time series database are:
  • Capacity for accurate monitoring of events with possibilities of reaching nanoseconds.
  • Control of various data sources.
  • Providing context to the data. This makes it possible to retain huge volumes of highly accurate data for short periods of time. On the other hand, it allows preserving low-accuracy data for longer or indefinitely.
Scalability and open data exchange in Industry 4.0

In itself, the data generated by an industrial facility is very diverse and its volume could be unpredictable. So the time series database must assimilate the high performance of the data. It must also always allow real-time queries. Otherwise, the total operability of the line would be seriously compromised.

For all these reasons, open data exchange ensures the optimal operation of manufacturing processes in a 4.0 environment. In particular, the use of sensors that provide data for real-time process adjustment is essential to ensure production continuity. But incorporating an inconsistent data architecture would generate dangerous data silos that could prevent the availability of critical data to optimize processes in a timely manner.

In short, not taking on the transformation to the Industry 4.0 model can lead to losses for manufacturers. Precisely because they continue to rely on post-event analysis to verify the efficiency of their operations. This would deprive them of optimizing elements in real time and obtaining predictive failure intelligence.

Data management is essential in 4.0 environments and in any enterprise.

Today, data management is a priority for any company that needs to streamline its processes through automation. In the case of companies that work with personal data from customers, suppliers and contacts, data quality is key. For this, applying solutions such as DEYDE's MyDataQ avoids the inconveniences derived from name and address errors.

MyDataQ is a versatile system that normalizes, deduplicates and enriches your organization's database. Contact us and get to know all the features of this tool in detail!