Industry 4.0 and the Rise of Intelligent Analytics

After mechanization in the 1760s (First), mass production in the 1800s (Second), and automated production in 1970s (Third), the Fourth Industrial Revolution is building upon the Third with the rise of cyber-physical systems. The increase in data volumes, low-cost computational resources, new forms of human-machine interactions, and advanced analytics are all driving this digital transformation.

Hardware and software robots have begun to augment human workflow, making it more efficient and effective. Industry 4.0 is radically changing processes and is promising significant cost reduction, increased production and higher customer satisfaction.

Mechanization in 1760 gave way to mass production in 1840, computers in 1969, and networks and the Digital Workforce today.

Images of "The Jetsons" might be running through your mind right about now. Artificial intelligence (AI), particularly in the form of software robots, is making frequent appearances both in our homes and businesses. A great example of computers and automation coming together in an entirely new way can be seen in industrial robotics, where machines connect remotely to computer systems. These systems are equipped with machine learning (ML) algorithms, which allow them to learn and control the robots with very little input from human operators.

Industry 4.0

The core elements of the Fourth Industrial Revolution are analytics, ML, and AI based on big data and the Internet of Things (IoT). What does that look like on the ground? There are four fundamental design principles for systems in the new world of work, according to Forbes:

Interoperability — machines, devices, sensors and humans need to seamlessly connect through an ecosystem of connected devices

Information Transparency — sensors collect data about the physical world and use smart analytics to index this unstructured data to help with decision making.

Technical Assistance — Ssstems support people in the decision-making process and assist with tasks that are difficult or unsafe.

Decentralized Decisions — intelligent systems operate autonomously and can make simple decisions and resolve conflicts faster.

Are we there yet?

Not quite. The complexities of digital transformation manifest in many ways. The most common challenges to digital transformation, and meeting the four requirements on the factory floor, are:

  • Legacy systems: Systems of record will not change overnight. Tenured systems, many with proprietary applications, will continue to play central roles in business.

  • Big data and analysis: The term big data means “very large volumes of data” generated by sensors, digital systems existing equipment. These extreme volumes of data are difficult to analyze and visualize with traditional methods and systems of analysis. Organizations have limited resources to categorize and classify the data and derive actionable insights from it.

RPA is the answer, but what is the question?

The question is centered around the speed of change. All systems — from medical robots to enterprise resource planning (ERP) systems — are collecting information that is filed away for later use. For many manufacturers, this is the first thing that comes to mind when thinking about connecting machines on the production floor to back-office systems. They are concerned about drowning in unstructured data. And they’re right. There is an essential piece of the puzzle missing — one that uses software and analytics to create "organized data" to orchestrate actions.

Robotic Process Automation (RPA), in many ways, is plug and play. It does not require changes to existing IT systems, legacy applications or machines. It is ideal to indexing and structuring of large amounts of data for enterprises. AI-powered RPA with learning capabilities uses advanced data models to organize and classify all this information and make it valuable. RPA analytics platforms visualize these insights and eventually help predict outcomes in these ways:

  • Descriptive analytics: Describes or summarizes raw data and categorizes it into easily digestible information, allowing organizations to learn from previous events.

  • Predictive analytics: Provides companies with actionable insights based on data. It enables the use of existing data to train ML models to predict outcomes and estimate the likelihood of a future outcome.

  • Real-time analytics: While descriptive and predictive analytics provides insights based on what happened in the past, real-time analytics tells companies what is happening now. It provides value at the point of activity within your process. This enables course corrections and changes in real-time.

    • Data visualization: Real-time, streaming data shows what is occurring at all times.

    • Business insights: Important events immediately appear in dashboards. Alerts can be triggered based on a combination of rules and historical data.

RPA and analytics — better together

As manufacturers take steps toward Industry 4.0, many have turned to advanced machines to accelerate the digital transformation of their operations. With many pressures, such as improved productivity, meeting consumers’ expectations and driving continuous product innovation, companies are turning to AI-powered RPA platforms like Automation Anywhere Enterprise with advanced analytical capabilities to help lead the change.

RPA’s Digital Workforce combined with advanced analytics can discern trends and set benchmarks much more quickly, allowing them to use this data to surpass the competition — and succeed in the Industry 4.0.

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