Artificial Intelligence has transformed both our personal and professional lives. The adoption of AI is a critical feature of industry 4.0 because of its potential.  It has brought many opportunities and challenges to various sectors. Many AI-powered innovations have also been built to boost the economy dramatically through improved quality of life. In India, AI is a significant contributor to various economic fields, such as agriculture, manufacturingand services such as banking, which pushes GDP growth significantly.However, it is possible to optimise the gains and minimise losses by implementing the requisite resources and regulations. While many countries have agreed on their AI policy, a strategy has not yet been drawn up in India.

Artificial Intelligence systems can not only simplify operations in manufacturing but also give early warnings, contribute towards quality management and quality control. They can also predict machinery breakdowns and apparent use cases such as automation and robotics. Many companies implement different AI algorithms for decision-making in real-time for their industrial IoT (IIoT) applications.. In AI-based applications, it's important to realise that this data is the ruler. The main feature of using AI to optimise organisation and derivative insights is collecting, cleaning and preparing data.

The manufacturing industries are under pressure from industry 4.0 to digitise and grow their supply chain operations via the Internet of Things (IoT). Moreover, by integrating greater visibility, stability and organisational competence in the supply chain network, the manufacturing industries have transformed using AI and ML techniques. Solid market forecasts and better decisions by systematic scenario analysis are also included. It also utilisesmathematical modelling techniques forthe inventory optimisation process. The stock amount and the missed revenue scenario study are performed.The manufacturing units adopting AI optimise their operations by improved monitoring and process correction, resulting in obsolete machines being detected and adjusting parameters to achieve better results in the process. All of these contribute to reducing the cost of production in the manufacturing industry by quantifying tacit and concrete labourcosts and finished product costs. AI and robotics in the production and supply chain industries depend upon the government and the intervention of the private sector.

Glass bottle manufacturing in progress at AGI glaspac.

Edge Artificial Intelligence for Embedded Systems

Edge Artificial Intelligence is an essential part of manufacturing's overall AI growth. Instead of using a central database or processing node connected over the Internet, Edge AI processes data locally on a hardware computer. A back-end server receives data from many Internet-connected devices and sensors for most IoT solutions. The servers host machine learning algorithms that process the input data and creates any value provided by the AI solution.

For a variety of sectors, Edge AI is vital. One example is autonomous cars in which Edge AI can reduce battery power use. The Edge AI model also benefits from surveillance technologies, robotics and many other industries.

Predictive Maintenance Machine Learning

Predictive maintenance is a particularly lucrative area for machine learning and AI to enhanceoutput. According to a Capgemini report, almost 30 per cent of AI's production implements are linked to machinery and tool repair. Itmakes predictive maintenance the most commonly used case in today's production. Its speed and precision are the key advantages of ML-based predictive maintenance. AI can easily and reliably detect mechanical faults and enablecorrections before crashes and errors occur.For example, General Motors uses cameras mounted on its more than 5000 robots, and it has detected scores of components.

Predictive maintenance methods may be used withdifferent models and techniques.These range fromregression models and classification models using historical evidence to forecast faults to anomaly detection models that study processes tomodules that seek signs of strain or abnormality.

Computer Vision for Quality Control

The industry of automotive and consumer goods faces the challenging requirements of regulators. Every year, the costs of high-quality cameras are reducing, and AI image detection and processing software continue to advance quickly. As a result, AI-based inspection approaches for businesses have become increasingly appealing. This technology was adopted by the automotive industry in particular, withGerman automaker BMW leading the way. BMW uses a newly manufactured vehicle to compare its order details and data with its AI application as the last step of its inspection process. Nissan is another carmaker that integrates AI visual inspection models in its QA systems.

An estimated 8 billion pieces of IoT equipment were installed in 2019, which is expected to be 41 billion by 2027. The AI assessment is expected to increase more than 15 times from around $1.1 trillion to $16 trillion by 2026 in manufacturing.

Machine learning and artificial intelligence applications are characteristics of effective manufacturing – standardisation, economies of scale, task automation and speciality. In the coming years, AI incorporated in IoT devices will feature in all major production processes.

About AGI glaspac:

The Packaging Products Division of HSIL Ltd., AGI glaspac (better known as AGI), was established in the year 1972 and is one of the leading container-glass manufacturers in the country. AGI has two state-of-the-art facilities, one in Hyderabad and the other in Bhongir, Telangana. They are engaged in the manufacture of high-quality glass containers to meet the stringent and demanding quality standards of the packaging needs of the food, pharmaceuticals, soft drinks, spirits, beer, wine and other industries. With an in-house design studio, mould manufacturing and ACL (Applied Ceramic Labeling) facilities, AGI has fully integrated operations, which enables the manufacture of quality products and ensures timely deliveries. With the Hyderabad and Bhongir facilities put together, AGI melts around 1600 tonnes of glass per day. With four furnaces, AGI can commit to availability of flint, amber and green glass throughout the year.  Apart from the host of the multinationals who comprise a part of AGI's Indian market, it also has a large customer base in North America, Europe, Africa and the APAC regions.

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About Rrajesh Khosla

Rrajesh K Khosla is President & CEO of AGI glaspac, a part of Packaging Products Division (PPD) at HSIL Limited, makers of the iconic brand 'Hindware'.  Khosla brings to the table a passion for business development that reflects in his stellar work ethic that focuses on practical implementation, his end-to-end approach towards business growth that entails employee development, financial analysis, and debottlenecking. His ability to create convergence between innovation and creativity has helped the PPD business at HSIL to scale newer heights. Khosla is the Current Chairman of EFSI for Telangana & Andhra Pradesh Region. 

His ability to induce relevant micro-cultures within departments and align their operations accordingly has catalyzed overall business efficiency for the Container Glass business at HSIL. He also has rich experience of Joint Ventures, Anti-Dumping Duty & Market Development.