Reaching Industry 5.0 through Advanced Analytics

Most operational experts are already on a digitalization journey, yet plans are in the works for the next factory of the future. Those just beginning a digital transformation start by automating the factory floor. They then go through a series of phases as they increase their level of digital and analytics maturity. But, once companies reach the end of their initial journeys, where do they go?

The answer is Industry 5.0. An extension of Industry 4.0, this leg of the journey adds human intelligence to automated systems. Operational experts who use advanced analytics to make data-driven decisions will find they are well on their way toward the factory of the future.

Establishing a Foundation on a Digitalization Journey

In short, the goal of Industry 5.0 is to pair human intelligence with artificial intelligence (AI) so the combined system can achieve levels of operational excellence not possible for either on their own. Rather than a new journey, the extension of the Fourth Industrial Revolution demonstrates a change toward innovative technology, such as advanced analytics, the industrial internet of things (IIoT), AI, and machine learning (ML).

Industry 5.0 is a concept that improves communication and collaboration between humans and their artificial counterparts. During Industry 4.0, companies enable autonomous decision-making processes, monitor assets and processes in real time, and enable equally real-time, connected value-creation networks through early involvement of stakeholders. The extension of the ongoing digital transformation seeks the same optimization goal as its predecessor but requires the skill of operational experts to make more accurate decisions.

For example, an automated system might suggest an action that an operational expert knows is not in the best interest of production. The expert’s reasoning skills are necessary to prevent a mistake. Operational experts also can adjust automated processes so that errors are less common.

Merging human and artificial intelligence does not happen instantaneously. It becomes part of the overall digital transformation. Companies just starting their journeys begin a series of phases for improvement and growth. Reaching a new phase represents an increasing analytics maturity level.

Process manufacturing companies also follow a similar path in their journeys.

• Automated Phase — In an automated factory, sensors report information back to a database known as a historian. Companies realize small improvements, particularly around safety.
  
  
• Data-Driven Phase — As factories start using advanced analytics to find hidden clues in their operational data, they enter the data-driven factory phase.
  
  
• Connected Phase — When all the data is connected, it is democratized throughout the organization. Operational experts can compare time-series data with contextual data from other business systems during the connected factory phase.
  
  
• Augmented Phase — The highest level of Industry 4.0 is the augmented factory. Here, companies begin to add ML techniques and manage by exception.

Reaching the augmented factory is a great achievement for process improvement but closing gaps between human intelligence and AI is necessary before a company can go further. A hybrid model, where people work alongside their smart systems, is the preferred approach.

Focusing on the Operational Expert

A data-driven factory has embraced advanced analytics software to provide operational experts with information to optimize their manufacturing processes. In the past, engineers did not have direct access to an analysis of process behavior. They relied on the expertise of a data scientist. This created friction, as data scientists use a language heavy in math and statistics.

Today, companies still use data scientists for the 2-3 percent most critical assets and to deploy algorithms inside advanced analytics software. However, advanced analytics empower operational experts to resolve about 80 percent of their daily questions for themselves. Adding contextual data, such as maintenance records or shift reports, gives them a more holistic view of process behavior.

Operational experts can make better decisions with direct access to key performance indicators. The advanced analytics software helps put resources at their fingertips to improve process behavior and increase overall efficiency. They also help a company accelerate the achievement of its sustainability goals.

For the most complex cases and to set up soft sensors, data scientists can apply machine learning techniques using an integrated Python notebooks hub within the advanced analytics software. With notebooks, data science and engineering teams can reduce roadblocks to communication and collaborate on even more opportunities for operational improvement.

Although Industry 4.0 focuses on technology, AI was never meant to replace people. Instead, it was meant to provide operational experts with better information to make more informed decisions. As operational experts begin to automate tasks and apply machine learning techniques, they also can decrease repetition, create anomaly detection models, and get prescriptive recommendations to take corrective actions.

Prepared for What’s Next in the Journey

Advanced analytics solutions help operational experts solve most production challenges themselves while providing a more complete picture of production and data science projects.

While most manufacturers start their digitalization journeys by automating the factory floor, by the time they become data driven, they can easily segue into the next step of realizing the benefits of Industry 5.0.