AI Innovation
For 40 years, Princeton has developed and deployed AI, Optimization, and Machine Learning solutions. Global interest in AI is white hot, and executives trust us to build new tools to advance functionality and outcomes, and to integrate emerging technologies into existing systems.
Case Study
AI Innovation: Network Optimization
UPS was building a new-generation logistics network, one that is smart, integrated, flexible and global. Princeton led the highly successful design and development of the core simulation capability for the “NPT” (Network Planning Tool) for the UPS linehaul operations. Benefits include:
- Efficiently route and reroute package flows using a series of sophisticated algorithms.
- Move volume to lower-cost transportation modes to optimize margins without affecting service.
- Estimated $200 million in annual savings and cost avoidance.
Case Study
AI Innovation: Healthcare Scheduling
Leaders of a national healthcare company with 3,000 facilities launched a program to deliver superior patient experience, while optimizing workforce costs and processes, by creating a best-in-class scheduling management software.
Scheduling was taking place locally at the facilities: less than 50% used the legacy scheduling application; the majority performed scheduling manually on spreadsheets. Princeton built a custom optimization solution that enables facilities to optimally schedule their patients and staff in advance.
The Princeton and client teams are working to drive adoption of the future state solution via automation, enhanced usability, and seamless systems integration.
Case Study
AI Innovation: Digital Twin
Intermodal operations requires balancing multiple objectives including asset utilization, travel time, loading and grounding time, train schedule adherence, policies and guidelines.
Princeton utilized AI and an intermodal digital twin to optimize a Railroad’s intermodal terminals to:
- Provide insight into operational needs
- Test, validate and fine tune the optimization solutions in a realistic, dynamic environment without disrupting operations
- Allow many difference scenarios and possibilities to be explored at a fraction of the cost of testing them in the real world
Case Study
AI Innovation: Subscription Box Service
Leaders at Birchbox, the trailblazing subscription box service, sought to improve customer experience and core operations. The legacy model that assigned products to a box could not handle the additional mathematical complexity related to greater operational flexibility.
Princeton developed a new modeling technique powering a reformulated optimization model that generated better results, reduced the required number of box configurations to meet subscriber needs, and slashed run time by more than 99%.
“With this new model, Birchbox has truly entered a new operating universe,” said David Bendes, Director, Personalization and Operations Technology at Birchbox.
Case Study
AI Innovation: Bulk Transportation
A national trucking company must transport bulk commodities safely and profitably, which is made highly complex by hundreds of constraints on tankers, drivers, and cargos.
Princeton developed an innovative dispatch optimization solution for 1,500 orders, 800 tankers and 500 drivers with a five-day rolling horizon of less than 10 minutes.
The application reduced overall miles driven by more than a million miles, saving several million dollars annually. The company serves customers more cost-effectively, delivers their products faster, and improved its service quality.
Case Study
AI Innovation: High Frequency Finance
Leaders of quantitative hedge funds retain Princeton for optimization strategies to improve targeted areas along the full business spectrum, from data cleaning to signal generation to trading.
For one hedge fund, Princeton won the first Amazon Web Services (AWS) Spotathon for its innovative approach to quantitative research.
Alpha and trading strategies developed for clients with Princeton’s technical assistance profitably trade many millions of dollars every day.