This is shaping up to be a year of transformation for manufacturing. With slowly rising demand and the steep digitization curve, we are all poised for a good year. At the heart of this is AI, a technology that over 93 percent of manufacturers think will be pivotal to drive growth and innovation across manufacturing (According to a Deloitte survey). So what is AI in manufacturing? AI in manufacturing is making use of AI’s ability to analyze millions of data points simultaneously to unearth a wealth of insights to make manufacturing assets, processes & workforce perform to their potential.

AI in Manufacturing – Deciphering the Hype

Why is AI so important in manufacturing?

With the rise of connected plants, manufacturing is expected to generate 1812 Petabytes (PB) of data every year, more than banking, communications, financial services and many other industries. As digital data accumulation increases, the decision-making process also becomes increasingly complex. AI helps manufacturing enterprises analyze, process and use the gathered data meticulously to discover patterns and anomalies that can uncover a wealth of insights. Apart from this, it can also enable the automation of repetitive activities. The average automation potential for a business in the US is around 60 percent (Mckinsey). Implementing AI solutions for these tasks can expand your human workforce’s capacity for value-adding, growth-driving activities. Further, AI can provide intelligence by learning the best practices and making them available to the operators when they need them the most. This can have drastic effects on your productivity and process efficiency, and, consequently, your bottom line.

So, given the impact that AI can drive across manufacturing, how is AI used in manufacturing?  The maximum impact can be divided into three categories:

1. Product & Service improvements

With cut-throat competition, rising innovation, and detailed customer demand, product and service improvements are a priority today. To ensure that your product considers every real-world scenario possible, a rule-based system is never adequate. So conventional systems are not able to fully enable autonomous decision-making. But, adding self-learning elements (AI/neural networks) to this system can ensure that your system is continuously learning and can apply that learning to a similar scenario.

The aim is to create fully integrated, learning-based systems bolstered further by AI algorithms. This makes the overall process of sensor data processing, data interpretation, planning and decision making, as well as execution, seamless.

For example, in the case of an AI-powered autonomous vehicle, AI application can be implemented by having human beings train the system initially on a set of basic scenarios and then letting the vehicle learn from all real-world situations encountered. The lessons and accumulated knowledge can be shared through a centralized platform and used to improve all connected vehicles.

2. Manufacturing Operations or Process Improvement:

For most manufacturers, this is where AI can make the most difference. As per Mckinsey, 37 percent of companies observed a decrease in manufacturing costs by up to ten percent following AI implementation.  These are the top scenarios where AI can make a mammoth difference:

      • Predictive Maintenance: The technique of predicting machine failure before it actually happens, predictive maintenance involves studying all aspects of an interconnected, dynamic system, and ascertaining which symptoms might lead to downtime. Using a traditional rule-based system to handle these thus leads to unimagined chaos. AI-based predictive maintenance solutions help to separate noise from relevant data, continuously learning from all situations and then using them to predict further downtimes and guiding decisions. This can lead to around a 20 percent savings in your annual maintenance costs and reduced downtime and inspection costs by a staggering 10 and 20 percent respectively.
      • Collaborative Robots: Manufacturers are leveraging the power of AI to build flexible, non-special-purpose robots that need less configuration time and are easier to incorporate into specific environments. Applying these robots to work on repetitive menial tasks like an assembly line, can then free up your workforce to be engaged in significant growth-driving activities. This can provide up to a 20 percent increase in your workforce productivity per year.
        • Yield enhancement: Decreasing the percentage of defective/imperfect pieces in the manufacturing process is critical, particularly for the industries like semiconductor manufacturing, aerospace manufacturing where high quality is critical & testing costs and yield losses can cost up to 20 to 30 percent of the total production cost. Leveraging AI at production Lines to identify the root causes of defects can be used for quality control that can be avoided through process improvements and optimized design. The use of AI can mean up to a 30 percent decrease in yield detraction (including reduced scrap rates and testing costs).           
        • Automated quality testing: AI-driven automation testing can ensure early detection of detects that conventional approaches could only detect further downstream. This can create around a 50 percent savings in productivity and around a 90 percent improvement in detecting defects.

      3. Business Processes:

      Many business processes have the scope to enhance and automate with AI to improve Overall Equipment Effectiveness (OEE) and workforce productivity. Here are some departments/ processes that can benefit:

      • Supply Chain Management: With a constantly fluctuating external and internal environment, accurate forecasting and inventory management is almost never possible. AI enables an automated, self-adjusting decision-making system for the supply chain management. Demand spikes are accurately predicted, and the routes and volumes of material flows are adjusted automatically.
      • R&D Project Management: These projects are often affected by external environment changes and ambitious internal go-to-market targets and budget limitations. Given the long-term time frame and the highly irregular outputs, it is difficult to decide when to discard a project in favor of a promising one. AI collates data from sources like CRM, HR, and CAD to predict project performance early so managers can react before it is necessary. This can provide a 10 to 15 percent productivity gain to R&D projects.
      • Support Function Automation: The resource-intensive support functions like finance, HR and IT face challenges like high-cost pressures, competition, and force majeure circumstances like the current pandemic, where automation is now a necessity. AI-driven IPA (Intelligent Process Automation) builds upon the organization’s knowledge via human beings and employs continuous learning to adapt a customized ticket resolution for similar issues. This ensures high accuracy and consistency, increased scalability and speed and traceability of all actions — all with 100 percent availability 24/7. AI can help realize around 30 percent automation potential for support functions across the enterprise.


There is no doubt that AI is a game-changer for manufacturing. Especially for manufacturing operations, around 37 percent of companies surveyed by Mckinsey saw manufacturing costs reduced by up to 10 percent after an AI implementation. But it is equally important to make sure that the implementation of AI also handled well. Here is a suggested approach for this.

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