Engineering analytics is the process of collecting, processing, and analyzing data generated by industrial equipment and systems to extract insights for optimizing performance and reducing costs.
We are a leading Engineering Analytics solutions provider with vast experience in improving engineering processes with data-driven decision-making. Our exposure to engineering analytics challenges and knowledge of cutting-edge analytics technologies will help you find a data-centric solution for your business needs.
Improved product design and performance
Increased operational efficiency and productivity
Reduced downtime and maintenance costs
Enhanced safety and reliability of products and systems
Faster time-to-market for new products
Increased profitability and competitiveness
Improved quality control and process optimization
Enhanced decision-making and strategic planning capabilities
Our solutions are designed to optimize production schedules, reduce waste, and improve quality, increasing the overall productivity and profitability of your operations.
Our comprehensive analytics solutions enable you to identify and address issues before they cause downtime, optimize maintenance schedules, and increase asset uptime.
We provide flexible, adaptable solutions, and most importantly, customized to meet the specific needs of your business.
Our engineering analytics solutions are designed using cutting-edge tools and techniques to help you optimize your operations and make data-driven decisions that drive growth.
Our solutions are designed to be cost-effective, providing you with the maximum return on investment for your engineering data analytics needs.
We leverage advanced analytics, AI & ML to assist you in making informed decisions faster than your rivals and enable you to stay ahead of the competition.
We have assisted organizations across industry verticals to manage complex systems with our engineering analytics services. Some of these industries include:
Banking and financial services
Four Pillars of Data Governance shape excellence in data quality & decision-making. Know about their core principles in this guide.
Challenges in data migration testing affect an organization's data integrity & business operations. Uncover some of the key challenges here.
Engineering analytics refers to data analysis and statistical methods to optimize engineering processes and improve product performance.
Engineering analytics can use data from various sources, including simulation models, laboratory tests, and field measurements. The data can include stress, strain, temperature, and other variables.
Engineering analytics can help organizations identify opportunities for improvement in their engineering processes, improve product quality, reduce costs, and increase efficiency.
Engineering analytics focuses on solving engineering problems, whereas other data analytics techniques may have a broader focus. Engineering analytics often uses simulation models and engineering knowledge to predict product performance.
Data Engineering and Data Analytics are two different but related fields in data science. Data Engineering focuses on the technical aspects of collecting, storing, and processing large amounts of data. In contrast, Data Analytics focuses on using that data to gain insights and make informed decisions.
Engineering analytics can use various tools and techniques, including statistical methods, machine learning algorithms, and simulation models. The specific tools and techniques will depend on the specific goals and needs of the organization.
Organizations can implement engineering analytics by establishing a data collection and management infrastructure, data analysis and visualization tools, and developing models and algorithms to predict product performance. It may also involve working with external consultants and experts to provide additional support and expertise.
Engineering analytics can be integrated into product development using data analysis and simulation models to inform design decisions, validate prototypes, and optimize product performance. The results of engineering analytics can be used to make adjustments and improvements throughout the product development process.
Working with the Phygital data engineering team has been a game-changer for our company. They helped us identify our data ingestion problems and implemented a centralized system to tackle them. We highly recommend their services.
Technical Product Manager
We needed support to help grow our retail business with data-driven recommendations. We found it in Phygital Insights’ Data Analytics Services. They studied our data, uncovered hidden patterns, and enabled us to anchor decisions on data-driven insights. Don’t look beyond Phygital for all your data needs.
Product Manager, US-based Retail Firm
We were struggling to increase our combined yield for all processes. After several futile efforts, we turned to Phygital. Their end-to-end analysis helped us identify hidden bottlenecks in the production line. We plugged them in to cut down on wastage and improve overall efficiency. Their service was indeed a game-changer.
Senior Operations Manager, US-based Manufacturing Company