Session 6 Drishti Technology

The ability to measure and monitor every aspect of a manufacturing process, including individual products, employee actions, and production stations, in real-time.

Traditional quality improvement methods relying on data collected from a representative sample of products, processes, or employees.

The limitations of traditional sampling include its potential for misinterpretation, difficulty in implementing it accurately in large operations, and inability to target specific products or pinpoint the source of issues in real-time.

The difficulty in managing human errors in manufacturing despite significant advancements in robotics.

The use of digital automation to improve process visibility and simplify operations on the factory floor.

The Internet of Things (IoT) is the network of physical objects that are connected to the internet, enabling data collection, storage, and analysis.

The high cost of implementing IoT devices and the associated infrastructure for data collection, storage, and analysis in the early 2000s.

The slow adoption of technology solutions in manufacturing industries due to factors like high implementation costs and concerns about simplicity and process visibility.

The process of gathering and analyzing data from a factory floor to understand how well a product is being made, including its quality and safety.

Traditional methods like charts and dashboards made by hand to visualize production data.

Data related to how fast a product is made, its quality, and safety issues.

Key factors that businesses measure to see how well their production is going.

The ability of a machine to run without problems, the quality of a product, the safety of workers, how much work is done, how many products need to be fixed, and how much product is made in total.

Techniques like observing workers' motions, using control charts to track data, displaying charts for everyone to see, and having ongoing programs to improve things.

Training workers to follow instructions precisely and minimize differences in product quality.

Significant improvements in how factories work through the use of traditional methods.

Drishti Technologies Inc. is a company that specializes in using computer vision, AI, and video analytics to help businesses improve their operational efficiency.

Drishti focuses on serving clients in the automotive, medical devices, and electronics industries. They have notable clients like Ford, DENSO, and HELLA.

Drishti aims to provide solutions that deliver a significant return on investment (ROI) for their clients. They often report ROI realization within a short timeframe.

One of Drishti's products is called 'Trace'. It offers features like live video streaming, replay, video sharing, and time-based studies to help understand and analyze processes.

The 'Flow' product from Drishti uses big data (human data) to identify bottlenecks and opportunities for optimization within a manufacturing process.

Drishti's 'Assist' product aims to help line workers perform their tasks consistently and effectively.

Drishti's solutions are designed to provide a deeper understanding of the manufacturing process. This includes root cause analysis, process design enhancement, and operator training.

Drishti's approach emphasizes not just improving efficiency but also enhancing workforce capabilities. This includes helping operators learn new skills and contribute more effectively.

Drishti is a software solution that uses computer vision, AI, and video analytics to help companies manage their operations.

Drishti's capabilities are designed to assist employees by identifying potential issues and providing immediate feedback to prevent costly rework and optimize processes.

Drishti uses Trace, Flow, and Assist capabilities to help improve operational efficiency. Trace tracks the flow of work and identifies bottlenecks, Flow analyzes the speed and efficiency of each step in a process, and Assist helps employees by providing real-time guidance and feedback.

Drishti leverages the fact that humans are good at problem solving but sometimes slow, while machines are fast but not as insightful. It allows humans to focus on complex tasks while machines handle repetitive and data-intensive work for faster and better decision-making.

Drishti helps businesses identify and address problems early on by providing real-time insights and feedback, enabling companies to improve processes and avoid costly rework. This allows companies to optimize their operations and save money.

Drishti's capabilities have been implemented by companies like DENSO and HELLA, leading to improvements in cycle time, productivity, and overall equipment effectiveness. This highlights the potential of Drishti to transform businesses across various industries.

Drishti was developed with input from its first customers, Flex and DENSO, to address specific needs that had not been met by existing solutions. This collaborative development approach ensures that Drishti provides real-world value.

Before introducing its innovative solution, Drishti demonstrated the existence of problems that needed to be solved to showcase the value proposition of its technology to potential customers.

A business valuation based on expected future growth rather than current profitability, common in early-stage companies.

The process of understanding and responding to customer needs and technical requirements for support.

The time it takes to complete a sale, from initial contact to contract signing, involving back-and-forth negotiation about price and value.

The process of incorporating new features and services into the pricing structure of a product.

Determining the priority of developing new features and supporting services based on their value to customers and the company's goals.

Finding a balance between offering a standard product with essential features and allowing for some customization to meet individual customer needs.

A systematic approach to expanding a business, focusing on the key stages of development and growth.

Prioritizing the development of core product technology as the foundation for future growth and expansion.

A type of artificial intelligence (AI) where machines are taught to learn and adapt from data. They can recognize patterns, make predictions, and improve their performance over time.

A specific type of machine learning where machines are trained using large amounts of labeled data to identify patterns and make predictions.

A subset of machine learning where algorithms are trained on unlabeled data to discover hidden patterns and structures.

A type of machine learning where algorithms learn from data and continuously improve through feedback and interaction with the environment.

A type of AI that mimics the human brain's neural networks to process information and learn.

A technique used in deep learning where multiple layers of artificial neurons are stacked to learn hierarchical representations of data.

A massive dataset containing millions of labeled images used to train computer vision models.

A specific type of AI that analyzes video data to understand actions performed by humans or objects.