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AI - The Prime Mover For Industry 4.0

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Dr. R. Chandran, CIO, and Sudhadeep Sugunadoss, Head- Quality, Bahwan Cybertek

Established in 1999, Bahwan CyberTek is a global provider of digital transformation solutions in the areas of Predictive Analytics, Digital Experience and Digital Supply Chain Management, and has been driving innovation through outcome-based business models, proven and powerful IP solutions.

Automation and robotics provide the impetus for Industry 4.0, AR/VR, cameras and other sensors provide the senses and data & connectivity are its central nervous system. The real prime mover is AI.

Industry 4.0
Industry 4.0 refers to a new phase that focuses heavily on interconnectivity, automation, machine learning, and real-time data. i.e., revolutionizing the way your entire business operates and grows. Industry 4.0 extents the complete product life cycle and supply chain management—Requirement, Design, Development, Quality, Sales, Schedule, Inventory and customer service.

Why AI in Industry 4.0
• More competitive, especially against disruptors
• More attractive to the younger workforce.
• Stronger and more collaborative team
• Address potential issues before they become big problems.
• Trim costs, boost profits, and increase efficiency
• Better visibility across asset tracking accuracy, supply chain visibility, inventory optimization. And increased product quality
• Richer and more timely analytics
• Improve customer satisfaction and customer experience
• Real-time inputs help to make better and faster decisions on day to day business operation
• Real time observing of the operations on the production environment helps in providing idea on production schedule performances.

AI’s Impact on Industry 4.0
Artificial intelligence’s impact on AI can be organized into following categories:

a. Maintenance
Predictive maintenance systems depend on AI technique to formulate predictions. Implementing predictive analytics in AI, allows to forecast and avoid production waste, by identifying areas of weakness and suggesting focused action items that reduce product defects and inefficiencies.

b. Quality 4.0
Manufacturers are finding it harder than ever to maintain consistently high levels of quality which is the need of the hour. This is due to rising complexity in products development and very short

time to market. This new pursuit for quality has been named as Quality 4.0 and AI is at its forefront. Quality issues cost companies a lot of money, but with the use of AI algorithms developed through machine learning, manufacturers can be alerted of initially minor issues causing quality drops.

c. Human-robot collaboration
As the adoption of robotics in manufacturing increases, AI will play a major part in ensuring the safety of human personnel as well as giving robots more focus to make decisions that will increase the process excellence based on real-time data collected from production environment. In simple, AI shall facilitate robots to assist human to supplement his capability and relieves from hard work.

d. Generative design
Manufacturers can also make use of artificial intelligence in the design phase. With a clearly defined design brief as input, designers and engineers can make use of an AI algorithm, generally referred to as generative design software, to explore all the possible configurations of a solution.

As the adoption of robotics in manufacturing increases, AI will play a major part in ensuring the safety of human personnel


e. Market adaptation / Supply chain
Artificial intelligence algorithms are used to optimize the supply chain of manufacturing operations and to help them better respond to, and anticipate, changes in the market.

How AI can be implemented in Industry 4.0
The key elements to be considered for Implementation AI in Industry 4.0are
• Analytics
• Big data
• Cloud or Cyber
• Domain knowledge
• Evidence

Analytics is the essential part of AI, which will bring value if other related elements are present.

Big data technology and Cloud are both essential elements, which provide the source of the information and a platform for Industrial AI.

Domain knowledge is to understanding the problem, usage of industrial AI solution, collection of right data with the right quality, parameters associated with the physical characteristics of a system / process.

Evidence is an essential element for testing AI models and trains them with cumulative learning ability. By gathering data patterns and the evidence associated with those patterns can optimize the AI model to become more accurate, comprehensive and robust.

Challenges
The expectations from Manufacturing AI are versatile and enormous and even a partial fulfillment of these expectations would represent unique and real challenges of applying AI to industries.

Among the existing challenges and complexities, the following ones are of higher importance and priority:
• Machine-to-machine interactions
• Data quality
• Cybersecurity

Conclusion
To leverage AI for significant optimization in manufacturing performance, it’s critical that we account for the entire production process.

It goes without saying Cyber-physical systems and machine learning are cornerstones of the Industry 4.0 philosophy.

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