The first evidence of the “wheel” dates back to 3,500 B.C. During that era, it served as a potter’s wheel. The idea gradually evolved, and in the next few hundred years, it found its use in chariots. Developments thereafter are well documented – from motor vehicles to aeroplanes to machines, and practically everywhere else. We never stopped innovating after inventing the wheel.
In 1950, English mathematician Alan Turing published a paper titled “Computing Machinery and Intelligence” which opened the doors to the field we know today as Artificial Intelligence. In 1956, John McCarthy, an American computer scientist and cognitive scientist, coined the term “Artificial Intelligence” or AI. After decades of research, computers are gradually moving closer to passing the Turing Test. The Turing Test is a method of inquiry in AI for determining whether a computer is capable of thinking like a human being. Here, we will explore the different building blocks of an AI system, the extent of AI adoption by businesses, and the next big leap. This document aims to help business leaders build a more focused strategy in their journey toward an Intelligent Factory.
Industry 4.0 and the AI Locomotive
According to McKinsey, 50% of companies that embrace AI over the next five to seven years have the potential to double their cash flow, with manufacturing leading all industries due to its heavy reliance on data (Source). Industry 4.0 makes factories and every entity on the shop-floor smarter, and AI plays a vital role in this journey. AI offers new ways to boost employee productivity and creativity, increase business agility, improve customer engagement, and jumpstart new product innovation. To infuse AI in the factory landscape, technology companies leverage the existing systems and also identify or create new avenues to make the systems more intelligent.
Integration of AI with recent emerging technologies such as Industrial Internet of Things (IIoT), big data analytics, cloud computing, and cyber-physical systems will enable operation of industries in a flexible, efficient, and sustainable way. If we consider the idea of an “AI locomotive” in the context of Manufacturing, we can break down the various components of this system as follows:
The Current state of adoption of AI/ML technology
Companies that have implemented Artificial Intelligence and Machine Learning have realized phenomenal gains in operational efficiency and revenue. Areas where they have demonstrated valuable outcome include:
The Future State: ‘Batch size 1’ production
‘Batch or Lot size 1’ refers to manufacturing a single product for an individual customer. This requires superior levels of customization of an item to match the buyer’s specifications. Achieving a production batch size of a single unit for any product with the current setup would require planned downtime of the production system with high probability of manual errors leading to high cost. Seamless customization is necessary for faster production at a reduced cost. With advancement in AI/ML technology, we are heading toward an environment of digital factories with networked machinery, equipment, and even workers, making it possible for these entities to interact and collaborate with each other. This setup enables an interconnected smart ecosystem capable of more accurate decision-making. As a result, individual products can be identified and necessary operations can be performed in an automated manner. The outcome will be distinct, tailored products with swift response to changes in demand in real-time.
There is a burning need for the systematic development and implementation of Artificial Intelligence to see its real impact in the next generation of industrial systems. As humans, we have always aimed for simplicity and a more productive lifestyle. With our intellect, we constantly brainstorm new ideas and innovate to simplify our lives, save time and resources, and in the end, define a transformed way of living.
Consultant, Manufacturing – Digital Practice
Mohit is an MBA graduate from IIM Indore and a Computer Science engineer with strong expertise in consulting, product messaging, and GTM for AI, IoT, and Cloud-based technologies. He also has prior experience in cloud computing and tools development.