Product Acceleration, Software Development and Data Analytics

Pirelli is one of the world’s largest manufacturers of premium car tires. The company owns 24 factories in 13 countries.

Challenge: Enhancing the tire manufacturing process to increase efficiency and precision while minimizing waste.

Technology: The technology used in this case included advanced machine learning algorithms and data analytics tools. These technologies were specifically implemented to enhance the analytical capabilities of the manufacturing process, allowing for more precise decision-making and efficient use of materials. The integration aimed to optimize the production workflows, reduce waste, and improve overall operational efficiency in tire manufacturing.

Results: Gains in production precision and reduced material wastage, showcasing a clear improvement in operational KPIs such as cost savings and production speed.

Classmate is an Asian brand of student stationery products under the ITC Group. Classmate produces and sells pens, pencils, mechanical pencils, and geometry boxes and is one of the biggest D2C brands in Asia.

Challenge: Reduce human touchpoints to zero in their online sales process, focusing on streamlining operations to enhance efficiency and customer satisfaction. 

Technology: Conversational AI-enhanced by NLP to automate user interactions and support advanced search, and AI-powered decision engines leverage ML to make informed decisions. 

Results: Manual intervention in the online sales process was reduced by 0%, customer service response time was reduced from 3 hours to 1.3 seconds, and conversion rates increased by 21%. 

Electrosoft is a US company recognized as a premier electronics contract manufacturing firm. It provides manufacturing solutions in marketplaces such as cable, enclosure, and wire harness assembly and the production of printed circuit boards.

Challenge: Reduce the time to select suppliers, improve the accuracy of contract analysis, and find opportunities to reduce costs.

Technology: Machine Learning (ML) interprets and processes unstructured data in contract lifecycle management. An AI-powered decision engine utilizes ML insights to streamline decision-making processes and manage data orchestration into a centralized data lake, thereby improving the efficiency and accuracy of contract analysis and management.

Results: 80% reduction in supplier selection time, 95% improvement in supplier selection accuracy, and more than 30% savings by identifying spending patterns.