keyword: LTTS, Industrial AI, Smart Manufacturing, Predictive Maintenance, ARC Advisory Group.
summary
AI holds great promise in software and product development, manufacturing operations, and engineering and design. Industrial AI is a subset of the broader field of artificial intelligence (AI), which is AI technology in industrial settings to augment the workforce in pursuit of growth, profitability, and more sustainable products and production processes. (including generative AI). Customer service and business results. Industrial AI leverages approaches such as machine learning, deep learning, and neural networks. Some of these technologies have been used for decades to build AI systems using data from various sources within industrial environments, including sensors, machines, industrial engineers, and field workers. .
Finding the right AI-based solution for the specific, often difficult requirements of manufacturing and industrial applications can be challenging. AI is not a product you buy; it exists in a myriad of applications, development frameworks, and solutions designed to accomplish specific functions. Leading industry organizations are aligning their AI efforts with broader business objectives to ensure maximum value from their technology investments. ARC analysts recently partnered with Larsen & Toubro Technology Services (LTTS), which has transformed its software and engineering expertise into a comprehensive suite of AI-based products specialized for a variety of use cases and applications, and Industrial AI Broadcasting. We talked about the impact.
The evolving role of industrial AI in digital transformation
Industrial AI is a subset of the broader field of artificial intelligence (AI), which is AI technology in industrial settings to augment the workforce in pursuit of growth, profitability, and more sustainable products and production processes. (including generative AI). Customer service and business results. Industrial AI leverages approaches such as machine learning, deep learning, and neural networks. Some of these technologies have been used for decades to build AI systems using data from various sources within industrial environments, including sensors, machines, industrial engineers, and field workers. .
Focus AI on business outcomes
For industrial organizations, achieving desired business outcomes requires a comprehensive approach that encompasses the critical trifecta of people, process, and technology. This framework is more than just a buzzword, it's a proven strategic blueprint that guides organizations to sustainable success. The convergence of AI with other technologies such as the Internet of Things (IoT) and edge computing opens new possibilities for distributed and embedded AI systems that can operate at the edge of the network, close to where data is generated. It will be done.
Industry leaders have identified areas where AI can have a significant impact, including generative design of sustainable products, production processes, and services, predictive maintenance, supply chain optimization, and quality control.
Industrial AI use cases
Industrial AI solutions and applications are emerging everywhere, but their ability to address the specific problems of industrial end users can be confusing. Different classes of competitors are entering the industrial AI market, from large automation suppliers to hyperscalers to small niche suppliers.
Industrial AI has multiple objectives. Aims to improve operational efficiency by automating repetitive tasks, improve accuracy by reducing human error, and enable real-time decision-making based on data-driven insights. . From generative design of products and production processes, to intelligent production operations maintenance and quality control, energy and supply chain optimization, to efficient sales and enhanced customer service, industrial AI is expanding its reach across a wide range of industrial operations. I'm finding a use for it.
Industrial AI has several benefits. Optimizing resource usage and improving process efficiency can significantly reduce operational costs. By enabling predictive maintenance, you can minimize downtime and extend the life of your machines. Real-time decision-making capabilities allow you to quickly respond to changing market demands and operational conditions, address your organization's skills gaps, and become more agile and competitive.
In the area of product and process design, AI-powered software enables more accurate and efficient design methods. Advanced AI algorithms analyze vast amounts of data to predict optimal design parameters, reducing trial and error and speeding time to market. Additionally, AI helps simulate and test product designs under different conditions to ensure robustness and reliability.
Production operations and maintenance have been significantly enhanced by AI. Predictive maintenance leverages machine learning algorithms to predict equipment failures before they occur, reducing downtime and maintenance costs. AI can also optimize factory operations by streamlining workflows, improving resource allocation, and enhancing quality control.
In a globalized economy, the logistics of supply chain management are becoming increasingly complex. This is where AI comes into play by adding predictive analytics for demand forecasting and inventory management to real-time visibility. It also optimizes transportation routing and scheduling, reducing costs and improving customer service.
LTTS approach to industrial AI
As a leading engineering services provider with significant strength in software development, LTTS is uniquely positioned to provide value-added solutions to the world of industrial AI. His LTTS knowledge of specific processes, plants and facilities can add significant value to your solution. LTTS views AI as a set of enabling technologies, along with IIoT, digital twins, simulation, and other technologies, that can deliver value in both the engineering, supply chain, and manufacturing domains.
A business-driven approach to industrial AI
Rather than focusing solely on technology, LTTS aligns its service offerings with the business needs of its customers. By understanding the pain points and cost drivers of different industries, LTTS can provide customized AI solutions that address specific problems and deliver desired results. LTTS has demonstrated the value of AI in increasing operational efficiency, reducing costs, improving quality, and increasing customer satisfaction.
Industrial AI as an application layer
LTTS leverages engineering and software development expertise to create AI-powered applications that turn data into knowledge. By applying machine learning, deep learning, neural networks, and other AI techniques, LTTS can build intelligent systems that can analyze data, generate insights, and automate tasks.
Industrial AI as a partner ecosystem
LTTS works with technology providers such as Qualcomm, Nvidia, Google, AWS, and Intel to deliver innovative and customized AI solutions. LTTS also works with customers to co-create and co-innovate AI applications that meet their specific needs and goals.
Empower workers with industrial AI
Rather than replacing employees, LTTS uses AI to improve their skills and productivity. LTTS provides frontline employees with her AI tools and training, enabling them to make better decisions, increase efficiency, and reduce human error. LTTS plans to train his 2,000 engineers in Gen-AI using Nvidia technology.
AiKno metadata extraction
One factor that all industrial companies have in common is the sheer amount of paperwork and data that was manually entered in the past. The LTTS Cognitive Meta Data Extraction Module automates the tedious task of digitizing physical data, increasing efficiency by 92-95 percent and reducing time by 85 percent.
What makes this solution unique is its ability to extract metadata from complex engineering documents such as 2D drawings, legacy documents, and scanned images. OCRs are trained in engineering symbols, such as GD&T symbols, which are frequently used in the industry. Continuously self-learning systems can perform automatic corrections and drive semantics-based rules based on human feedback without the need for re-engineering. A case in point: AiKno helped a major transportation company optimize his PO/RO processing. It takes his team 233 hours to manually digitize 1,000 documents. With AiKno, this time was reduced by 60-80% to just 42 hours.
Predictive analytics
The LTTS AiKno predictive analytics framework provides real-time insights into equipment health, identifying anomalies and failures long before they actually occur. Using built-in AI/ML models, service requests are automatically initiated or machines run self-diagnostic programs to resolve issues. The LTTS AI framework can automatically preprocess and run various ML algorithms and evaluate models based on key metrics. The best possible model is automatically selected, reducing manual model creation effort.
Using AI to create an asset health framework for a global food and beverage company
LTTS is currently actively implementing AI-based projects with tangible results. The company recently implemented an asset health forecasting framework for a large global beverage company and identified key issues related to asset performance management, such as change management and lack of consistency when deploying solutions across multiple plants. has been addressed. Unplanned downtime causes significant losses for the industry, especially in areas such as bottling and filling lines.
End users wanted to move from a preventative maintenance mindset to a predictive maintenance mindset to improve uptime and OEE. The user wanted a solution that could be deployed across multiple factories in the United States. LTTS delivered an AI-based predictive maintenance solution across 14 critical assets within one plant in areas such as blow molders, fillers and cappers, labelers, packers, and ammonia chillers. The solution features over 200 sensors with 34 edge gateways, providing real-time health monitoring of over 100 asset components. LTTS brings analytics to the edge, enabling early failure detection combined with reduced latency and data congestion. Edge analytics was introduced using pre-built models along with custom machine learning to increase accuracy. The impact on business was significant. Production line availability has been significantly increased, over 17 hours of unplanned downtime avoided over an eight-month period, resulting in estimated savings of approximately $300,000.
conclusion
The ability to understand the potential impact and benefits of AI and leverage it effectively is critical for industry organizations. Artificial intelligence can be a daunting task, but with the right strategy and approach, manufacturers can increase their chances of successfully implementing AI into their operations. While software providers have a lot of technical expertise, often focusing on technology, a comparison that allows them to both implement and maintain the solution is the real key to capturing business value. has a small services and solutions business. Finding a solution provider with the right experience implementing these solutions for your specific use case can be difficult.
LTTS is unique among many companies providing AI solutions for industrial applications because of its depth in both engineering expertise and software development. The company's engineering expertise gives it a unique ability to access engineering data. LTTS has extensive expertise in digitizing data and transforming it into useful information that AI models can ingest to create effective solutions.
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