Manufacturing Archives - Page 2 of 2

Blog Industry: Manufacturing

Empowering BI through GenAI: How to address data-to-insights’ biggest bottlenecks

Explore how integrating generative AI (GenAI) and natural language processing (NLP) into business intelligence empowers organizations to unlock insights from data. GenAI addresses key bottlenecks: enabling personalized insights tailored to user roles, streamlining dashboard development, and facilitating seamless data updates. Solutions like Tiger Analytics’ Insights Pro leverage AI to democratize data accessibility, automate pattern discovery, and drive data-driven decision-making across industries.

Why India-Targeted AI Matters: Exploring Opportunities and Challenges

The scope for AI-focused innovation is tremendous, given India’s status as one of the fastest-growing economies with the second-largest population globally. Explore the challenges and opportunities for AI in India.

Data-Driven Disruption? How Analytics is Shifting Gears in the Auto Market

The presence of legacy systems, regulatory compliance issues and sudden growth of the BEV/PHEV market are all challenges the automotive industry must face. Explore how Analytics can help future-proof their growth plans.

Data Science Strategies for Effective Process System Maintenance

Industry understanding of managing planned maintenance is fairly mature. This article focuses on how Data Science can impact unplanned maintenance, which demands a differentiated approach to build insight and understanding around the process and subsystems.

Waste No More: Making a Difference with Tiger Analytics’ Data-Driven Solution for a Greener Tomorrow

Improper commercial waste management devastates the environment, necessitating adherence to waste management protocols. Tiger Analytics’ solution for a waste management firm enhanced accuracy, efficiency, and compliance, promoting sustainable practices.

Maximizing Efficiency: Redefining Predictive Maintenance in Manufacturing with Digital Twins

Tiger Analytics leverages ML-powered digital twins for predictive maintenance in manufacturing. By integrating sensor data and other inputs, we enable anomaly detection, forecasting, and operational insights. Our modular approach ensures scalability and self-sustainability, yielding cost-effective and efficient solutions.

Building Data Engineering Solutions: A Step-by-Step Guide with AWS

In this article, delve into the intricacies of an AWS-based Analytics pipeline. Learn to apply this design thinking to tackle similar challenges you might encounter and in order to streamline data workflows.