In today’s era of deep digital integration across all industries, operations analysis — a cross-disciplinary field focused on optimal decision-making — has become a key engine for solving industrial pain points, driving digital transformation, optimizing business operations, and empowering breakthroughs in scientific research. As an operations analysis expert with both deep academic grounding and full-chain industrial implementation experience, Haoran Cao has consistently adhered to the philosophy of ‘empowering industry through theory’. By embedding operations optimization theory into key areas such as industrial supply chains, quantitative finance, and technology products, she has delivered a series of innovative technological achievements and groundbreaking research contributions, injecting strong momentum into the high-quality development of the real economy.
In Cao’s view, solid academic research is the cornerstone of sustainable progress in operations analysis, providing indispensable theoretical support and methodological innovation. However, it is the hands-on implementation in complex real-world scenarios that gives the discipline its core vitality — the ultimate pathway to testing theoretical value and fulfilling the mission of the field. Consistently anchoring her research in industrial decision-making pain points, she has developed a portfolio of proprietary technological achievements. Among them, the Data-Driven Operations Research Modeling and Solution System for Industrial Enterprise Supply Chains serves as a quintessential embodiment of her core philosophy. As a practical and representative solution, it directly addresses widespread challenges prevalent across the industrial sector.
Global industrial enterprises are currently navigating the difficult waters of digital transformation. Traditional supply chain management models generally suffer from experience-reliant decision-making, slow market responses, imbalanced inventory control, and inefficient end-to-end coordination — shortcomings that make it difficult to meet the demands of complex, volatile markets and refined industrial upgrading. In response, Cao drew on core operations research theories and integrated cutting-edge technologies such as big data analytics, multi-objective optimization, and intelligent solving algorithms to lead the development of the Data-Driven Operations Research Modeling and Solution System for Industrial Enterprise Supply Chains. This system enables refined quantitative modeling of the entire supply chain — from procurement and production to warehousing and logistics — and quickly solves for globally optimal decisions under multiple constraints such as capacity, delivery time, cost, and risk. It effectively breaks the limitations of traditional experience-based decision-making and provides a deployable, reusable core support for the digital upgrading of industrial supply chains.
Alongside her breakthroughs in industrial supply chains, Cao has also extended the application of operations analysis into quantitative finance. In her project ‘Potential Variables Influencing Bond Index Prices: A Quantitative Analysis Using Correlation and Regression’, she established a systematic quantitative framework for operations analysis, deeply exploring variables that influence bond index prices and clearly revealing the underlying relationships between core variables and index prices. This work provides financial institutions with precise quantitative foundations for investment decisions and risk management.
Having delivered multiple applied technical solutions, Cao did not stop there. She continues to tackle critical technological bottlenecks that constrain future industry development. The project ‘Post-processing Bounding Box Prediction’ is another representative example of her technical breakthrough efforts. Focused on autonomous navigation — a core area in intelligent mobility — the project directly addresses long-standing industry pain points such as insufficient navigation perception reliability and low ground-truth data efficiency for complex environments.
During project execution, Cao took full responsibility for ground-truth data labeling, operations optimization and analytical validation, resolving several technical bottlenecks. In the data labeling phase, she applied full factorial experimental design and critical path optimization methods to optimize the labeling workflow and build the experimental matrix, achieving both data integrity and cost–efficiency improvements. Through multi-attribute decision analysis and cost–benefit optimization models, she determined the optimal configuration of core components under multiple constraints and established a full-process data quality control mechanism, laying a solid foundation for high-quality data. In the data analysis and algorithm optimization phase, using deep learning neuron networks and Bayesian theory, she overcame industry challenges related to insufficient object detection reliability in complex scenes. This advanced the iterative improvement of the bounding box perception framework, achieving a leap in core performance, proven by baseline comparison. The work carries significant industrial and social value and provides a replicable standardized paradigm for the industry.
Recently, Cao applied operations analysis techniques to the emerging industry of advanced manufacturing, establishing a quality control framework for manufacturing systems. She presented her research ‘Deep Learning Modeling for Detecting Anomaly Classes in Laser Powder Bed Fusion Based Smart 3D Printing’ at Rutgers University’s 2026 ISE Research Day, where she won second place. This study focuses on the quality inspection challenges of laser powder bed fusion for metals (PBF-LB/M), a core metal additive manufacturing process. By employing operations classification optimization and multi-objective decision theory, the study achieves high accuracy while significantly reducing the false positive rate. Owing to its outstanding innovative approach and application value, the study received high recognition from the judges and has set a new practical paradigm for the cross-disciplinary integration of operations analysis and advanced manufacturing. Additionally, her study demonstrated the significant economic benefits of early defect detection in additive manufacturing. For instance, detecting a defect at layer 50 rather than at the end of a 5000-layer print can prevent the production of the remaining 4500 layers, saving more than 98% of the build time. Depending on the part size and process, this can translate into tens of hours of avoided machine usage and cost saving ranging from hundreds to thousands dollars per failed build.
Whether achieving breakthroughs in the digital transformation of industrial supply chains, expanding the application boundaries of quantitative finance, tackling core technical bottlenecks of technology products, or pursuing innovative practices at the intersection of advanced manufacturing, Haoran Cao consistently places operations analysis at the core, driving continuous breakthroughs across every domain she pursues. Rooted in both cutting-edge research and front-line industrial implementation, she strengthens the discipline’s foundations through theoretical innovation and unlocks the value of technology through real-world application. By promoting the deep integration of operations optimization theory with the real economy, she continues to create new possibilities for high-quality growth across industries.
