The fourth industrial revolution is trying to unfold itself over the past decade. Internet of things (IoT), machine learning (ML) and artificial intelligence (AI) can speed up things.

FREMONT, CA: Many argue that technologies like big data, analytics, and machine learning (ML) are merely abstract concepts with insignificant practical applications. While others who accept the growing prowess of the technologies are hesitant to invest their resources toward technology-driven transformation, those who have ventured out and opted against the convention have at least left an imprint for others to follow. Some of the transformations have reduced the gap in human-machine interactions. Others have enabled the companies to gain insights into customer behavior, relationships, nature, and sometimes even their thoughts, which might seem a bit too personal.

Often tagged as “Industry 4.0”, the presumably fourth industrial revolution is trying to unfold itself over the past decade, but the efforts toward such transformations have been relatively inconsistent. The major limitation stands out as the massive structural and cultural differences between the information technology and the operational technology residing at the heart of industrial automation since long. However, increasingly, companies are able to leverage precise, higher-quality manufacturing while minimizing overhead costs. Stepping further into Industry 4.0, smart factories are incorporating additive manufacturing such as 3D printing and other computer-driven manufacturing systems.

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Internet of Things (IoT), Machine Learning (ML) and Artificial Intelligence (AI) Driving the Transformation

It is possible to keep track of required components and order them based on demand patterns with the help of sensors. ML equipped applications and optical sensors monitor the quality of the constituents with higher consistency and accuracy. Robots are deployed that work as per human handling and easily accomplish delicate tasks that can be challenging for humans. While the entire supply chain can be affected with the introduction of a new product, changes in consumption pattern, and economic fluctuation, with the help of AI machines can forecast such trends and enable the companies to prepare accordingly.

The above technologies seem promising, but there are a couple of factors that companies must work upon to make such predictive systems work effectively as per their goals. There is an underlying gap between data and how and where they should be used. Further, the domain experts and the data scientists must interact to utilize the data as per the existing systems efficiently.

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