Impact of Generative AI on Ceramics Manufacturing

Manish Chandegara, CIO, Simpolo Ceramics

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Manish engaged in a conversation with the CIOTechOutlook magazine in order to answer queries on how Generative AI is bringing about transformation in the ceramics manufacturing. He is currently the Group CIO for Simpolo Group responsible for all IT operations and digital transformation of the organization. He has deep expertise and experience in AI and IoT. He has successfully assisted the top management of organizations in planning IT strategies, and leveraging technologies for rationalizing manpower, enhancing organizational productivity and improving efficiency of operations.

In what ways is generative AI optimizing the manufacturing process of ceramics? How does generative AI contribute to reducing waste and improving material efficiency in ceramic production?

There are multiple ways by the help of which we can reduce material waste. These are helping optimize manufacturing processes, material selection, and recipe development, improving product quality and process development and design. The industry is able to reduce energy consumption and improve material efficiency. Technologies have been implemented in order to bring about additive manufacturing optimization and these have been some of the latest developments how AI is helping transform the manufacturing processes in the ceramics industry.

If we talk about how Generative AI is helping organizations in faster development cycles, we can see that systems are helping in the development of new ceramic formulations and recipe by suggesting promising options for testing and reducing the time taken in trial and error. This is providing a faster product launch and distribution in the market. AI is also helping organizations optimize firing schedules, reductive maintenance and minimizing energy waste. This is leading to considerable cost saving in energy expenditure.

In case of defect minimization, AI systems are able to identify the pattern correlated with the defect and can also help in preventing manufacturing and process issues even before they occur. Predictive maintenance is one of the core functions that AI can do which is changing processes across industries.  

What tools and software are available for ceramic designers to integrate generative AI into their workflow?

As of now, specific tools are not available in the market but organizations are using various kinds of AI tools in order to optimize the manufacturing process. Adobe has recently implemented AI in their tools which are being extensively used in this industry along with Autocad. 

How is generative AI contributing to sustainable practices in the ceramics industry? What are the potential environmental benefits of integrating generative AI in ceramic production processes?

The first objective here is to reduce material cost. After that organizations have to look after optimizing material selection and minimizing waste. Repurposing waste in order to improve product efficiency is also one of the methods being implemented by organizations. These processes are helping in achieving faster development cycles and reducing energy consumption. Another aspect that is being taken care of is labor cost saving by automation of repetitive tasks. Initial investment data availability and modal maintenance is also necessary in this regard. In case of strategic material selection, generative AI is helping organizations analyze vast data sets for identification of the most cost effective material combinations for design. Organizations are able to implement trial and error method with the help of AI and achieving the goal of minimizing waste through this. This is having a direct impact on cost saving for raw materials. When it comes to repurposing waste, generative AI technologies are analyzing the whole process and suggesting about possible repurposing options within the process itself. It is one of the sustainability measures that generative AI is helping organizations take in order to bring about transformation in the manufacturing process.

What are the cost benefits of using Generative AI in ceramic manufacturing compared to traditional methods?

There are a few instances that can be discussed in this regard. AI systems are helping organizations in minimizing the running cost. First, the systems are reducing labor cost by performing mundane and repetitive tasks round the clock. Automation of repetitive tasks is something that is new in the manufacturing sector that wasn't there before. There are various repetitive tasks that needed to be performed through manual labor are prone to errors. Those processes have been completely automated by AI systems with no room for error at all. This is providing a way to structure employee training programs and teach them about the functions of the AI technologies and how to supervise these systems. Faster on-boarding of new employees are being carried out and skill development programs can also be done with the help of AI systems. 

It is true that initial investments are there for implementing the AI model and data needs to available a well for the preparation of work models and data sets. Cost is also incurred by organizations in terms of model maintenance as well. But, Gen AI does offer significant cost benefits to organizations after successful implementation. 

What are the technical challenges associated with implementing Generative AI in ceramic design and production?

Currently, there are a few limitations in implementing Gen AI into manufacturing processes. First is data acquisition and quality. There are limited data sets available in the market which needs to be analyzed in order to bring out the effectiveness of the Gen AI models. The ceramic industry typically lacks in the availability of standard data sets on material properties, firing parameters and desired product characteristics. Secondly, data sharing and collaboration is a challenge for small scale manufacturing companies and they are unable to collect comprehensive and actionable data sets. Third is data labeling and standardization where data needs to be standardized for accurate AI impressions and it is really essential to optimize the manufacturing process.

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