Advanced Analytics Prepares Process Experts for Industry 5.0
In the early days of Industry 4.0, data analytics was still a mystery to companies in the process manufacturing industry. Today, it is well accepted that advanced analytics can provide actionable insights into manufacturing process behavior. Engineers using a state-of-the-art advanced analytics solution also find that they are well on their journey toward Industry 5.0.
What Is Industry 5.0?
Industry 5.0 is the ongoing digital transformation of traditional manufacturing and industrial practices. It represents a shift toward newer technologies, such as advanced analytics, the Industrial Internet of Things (IIoT), artificial intelligence (AI), and machine learning (ML). An extension of the digital transformation started during Industry 4.0, Industry 5.0 adds human intelligence (HI) to those new technologies.
While Industry 4.0 seeks to automate the factory floor, Industry 5.0 aims to improve communication and collaboration between humans and their automated counterparts. Like its predecessor, the extension of the Fourth Industrial Revolution also seeks to optimize manufacturing processes. The skill and experience of a process engineer are necessary to make operations decisions that are in the organization’s best interest.
For example, the smart systems might suggest a course of action that they believe is in the best interest of production. Yet, the systems fail to consider outside influences on process behavior. An engineer might need to take a process offline for maintenance or other business reasons, which could change the suggested action from the smart systems. Thus, an engineer’s intelligence is necessary to make a final decision on which action to take. The engineer can also adjust the smart systems to account for the downtime and provide more accurate recommendations.
Seamless integration between man and machine—or in this case, an engineer and the processes—does not happen instantaneously. Companies begin a series of phases when they start a digital transformation. Advancing to the next phase in the digitalization journey also represents a growing level of analytics maturity. Venturing beyond the horizon of Industry 4.0 requires organizations to close a mindset gap between human and artificial intelligence. A hybrid model applies the precision of artificial intelligence with the creativity and reasoning of human intelligence. This, then, is Industry 5.0.
Placing Engineers at the Center of Advanced Analytics
The core objective of an advanced analytics solution is to provide a process engineer with the information he or she needs to run that process optimally. Traditionally, the information was not available directly to an engineer. Analyzing sensor-generated operational data required the help of a data scientist. The collaboration between data scientists and engineers often was stressed. Engineers spent a lot of time explaining the processes to data scientists so that they could create data models and provide black box solutions.
With advanced analytics, engineers can solve more than 80% of their daily operations questions without the help of a data scientist. Data scientists still are necessary for modeling the 2-3% most critical assets and, often, the application of machine learning techniques. Advanced analytics allows data scientists and engineers to collaborate better in a unified, more powerful platform that gives engineers a complete overview of those data science efforts.
The advanced analytics solution also offers engineers a way to analyze operational data in conjunction with contextual data. Contextual data includes maintenance records, information stored in laboratory information management systems, shift reports, and other business-related information. This information frequently resides in separate third-party business systems that are maintained by other departments, which creates data silos. An advanced analytics solution breaks down those silos and makes the data available to everyone in the organization. By putting data into context, engineers can filter out the periods that are irrelevant, and thus provide a clearer analysis.
Engineers, who now have direct access to key operational performance parameters as well as contextual insights, are placed in the center position of control. Thanks to advanced analytics, engineers have the resources at their fingertips to improve process behavior, increase overall efficiency, and make a variety of other operational improvements. They also can achieve their organization’s sustainability goals.
Phasing Into the Next Evolution
Organizations beginning their Industry 4.0 journey start by taking control of the factory floor. As a result, a company realizes small gains immediately from improved safety measures. Each phase throughout the digital transformation includes adding a layer of technology to the previous layer and overcoming one of a series of mindset gaps. People are an afterthought to the addition of new technology, and it may feel as though automated systems will replace people outright.
This is not the case. Although Industry 4.0 focuses on technology, artificial intelligence was never meant to replace human intelligence. Instead, the advanced technology provides engineers with the information they need to make informed, data-driven decisions about process behavior and its effect on production over time.
A fully augmented factory is the last step in Industry 4.0. At this level, engineers, as well as others throughout the organization, have embraced advanced analytics and the digital revolution. Engineers can begin to automate tasks as information becomes available and apply artificial intelligence and machine learning techniques. Adding these layers of technical maturity empowers process experts to decrease repetition, create anomaly detection models, and get prescriptive recommendations to take corrective actions. Engineers also can plan the optimal schedule for maintenance to prevent costly downtime.
The augmented factory allows engineers to manage their processes by exception. This lays the foundation for the first phase of Industry 5.0. Although these phases have not been defined yet, each will add an additional layer of human intelligence to the artificial intelligence created during Industry 4.0. Engineers will use advanced analytics to collaborate not only with data scientists and others across the organization, but with their digital counterparts.
Preparing for the Next Part of the Journey
With an advanced analytics platform such as TrendMiner, engineers can grow in their analytics maturity levels as they gain valuable insights about process behavior. TrendMiner empowers process experts to solve many production challenges themselves. The solution provides engineers with suggestions for operational improvements based on clues found in sensor-generated data. With the integration of Python notebooks and the ability to analyze data in context with other departments, engineers also will find a strong collaborative tool that will help them prepare for more integration with automated systems as Industry 5.0 matures.
With the insight necessary to make a host of operational improvements, engineers are ready for the journey ahead.