The Path to AI for Industry is a Digitalization Journey

Successful adoption of AI for industry brings opportunities and challenges

Manufacturers have been on a digitalization journey since the early days of Industry 4.0, which is the path to successful adoption of AI for industry. Factories are constantly adapting to an ever-changing digital landscape. Those that continue to make digital improvements also go through a series of phases. Each of these represents growing digital and analytics maturity.

There are ways to make operational improvements without the help of industrial AI. Manufacturers that have been through the digitalization journey learned during their adventure that they could optimize process behavior with help from operational data. As companies begin to see success from their efforts, however, they also want to find more opportunities. The goal of the digitalization journey is to help manufacturers manage change more effectively so they can find those opportunities. It also provides a roadmap for greater agility when rolling out industrial AI applications.

There are four phases in the digitalization journey. Each of them provides new opportunities to find improvements, but they also have challenges—or gaps—between them that companies must address before they can move to the next phase.

Becoming Data-Driven: Step 1 for AI for Industry

Today’s new factories start in the Automated Factory phase. Sensors throughout the plant collect data. Historians or industrial cloud solutions provide storage for them, and a control room monitors operations. Engineers can begin to use operational data for deeper analysis.

To become data-driven, a company needs to empower operational experts with a solution that leverages the data, such as advanced industrial analytics software. Companies that use traditional analytics methods or spreadsheets might find resistance to innovative solutions.

As companies move from an Automated to an Augmented factory, they go through a series of phases that represent agrowing digital and analytics maturity. Each phase comes with challenges—gaps—that companies must address before moving to the next phase.

Thus, an organization must bridge the Mindset Gap before diving into operational data. Companies use change management techniques, open communication, and education to make the transition easier. Once an organization has reached the Data-Driven Factory phase, the focus shifts to gaining deeper insights from operational data. This data will be used later when incorporating industrial AI solutions.

Step 2: Adding Contextual Insights

Operational context is important when analyzing time-series data. Adding contextual data provides better clarity of production events. Examples include maintenance logs, shift reports, laboratory information systems, and even weather updates. For instance, sensors do not generate data when maintenance takes equipment offline, but those events could appear as unplanned downtime in a visual analysis. Adding maintenance logs filters out those periods and provides a cleaner result.

As plants reach the Connected Factory phase of their digitalization journey, companies begin to use contextual data to provide better insights on time-series events.

With the rise of data lakes and modern historians, the availability of data across sites and to remote workers has improved. This shift in technology helps close the IT/OT Gap that may have prevented better access to the right data, which is a prerequisite for using AI. Once a company has democratized data to those who need it, it is ready to consider AI for industry.

To be a fully Augmented Factory, companies must bridge The Organizational Gap. They align people, processes, and technology at local operations with the skills and solutions of data scientists in central teams. Collaboration is essential to achieve higher efficiency in solving complex use cases. In an augmented environment, engineers work with data scientists on advanced solutions that drive operational efficiency farther than either could alone.

Step 3: Adopting AI for Industry at the Augmented Level

The Augmented Factory phase is as high as you can go in Industry 4.0. Companies in this phase begin to see the culmination of their efforts. Engineers at local sites take a more comprehensive approach to data analysis. They work with data scientists on central teams to use AI for industry, such as augmented analytics and machine learning. These advanced solutions help operational experts understand, predict, and optimize processes in real-time. Moreover, they put their companies on the path toward Industry 5.0 in pursuit of the ultimate smart factory.

From our Blog

The Organizational Gap

Bridging the Organizational Gap in Pursuit of an Augmented Factory

By closing the Organizational Gap, manufacturers can do more to improve operations with machine learning models and AI-powered systems.

Achieve Better Operational Storytelling with a Connected Factory

A Connected Factory unlocks data silos and provides a more holistic view of operations to help engineers make better decisions.
The smart factory with the help of IT OT Convergence

Navigating the IT/OT Convergence in Pursuit of a Smart Factory

The IT/OT convergence facilitates a more innovative, efficient, and adaptable manufacturing environment in a digital transformation.