Cardinal Operations is a unique big-data decision company in China. Founded by five Stanford professor and PhDs, and with a team of world-class decision making experts and data scientists, Cardinal Operations aims at using big data to provide solutions for firms under complex decision scenarios. Different from many other “big data” companies, we try to close the loop in the decision chain in the era of big data — from data collection, pattern analysis, all the way to the final decision. Particularly, with the decision making experts and the state-of-the-art models and algorithms, we fill the gap between data analysis results and actionable decisions, making the most use of data to create value for companies.
Supply Chain Management
An efficient supply chain enables companies to serve customers quicker and better, as well as to reduce operational costs. With extensive experiences in working with industry, we provide intelligent solutions that satisfy the needs of the complicated business world，ranging from inventory management, logistics management to warehouse management.
Pricing and Revenue Management
Revenue management helps firms make better pricing and sales decisions. We make recommendations on pricing based on historical data, and support decisions such as customer segmentation, dynamic pricing, promotional pricing, etc. We also provide solutions for emerging pricing needs such as non-standard product pricing and pricing in sharing economy.
Optimization and Machine
Learning Algorithm Suite
Developed by world-class optimization and machine learning scientists, we have two algorithmic toolkits - a mathematical programming suite and a machine learning suite. The suites enable the computation within our product and solution as well as provide efficient large-scale optimization and machine learning solutions for the community at large.
Quantitative decision making is playing an increasingly important role in today's financial industry. Examples include portfolio optimization, risk control in online lending and pricing of emerging financial products. With our state-of-the-art algorithms and sophisticated models, we can help financial firms maximize their returns while minimizing risks.
Chair Professor, Stanford University, John von Neumann Theory Prize Winner.
Ph.D. from Stanford GSB, Member of Global Experts of Beijing, Former VP of iheima.com.
Ph.D. in MS&E, Stanford University, Professor Dean, Research Institute for Interdisciplinary Sciences in Shanghai University of Finance and Economics.
Ph.D. in MS&E, Stanford University, M.S. in Math-Finance, Stanford University Assistant Professor, University of Minnesota.
Ph.D. in MS&E, Stanford University, Former Head of Business Analytics at Google.
Data-Driven Optimal Decision