“What is your AI strategy? How are you investing in AI? Where are you incorporating AI into your everyday workflows?” These are popular questions right now — and while your thinking might go straight to GenAI, LLMs, or machine learning, you should consider mathematical optimization as part of your answer.
Mathematical optimization (which goes by other names — operations research, decision science, decision intelligence, etc.) is a mature discipline prevalent among leaders in logistics, ecommerce, transportation, and supply chain for anything from vehicle routing to workforce planning and beyond. It is a powerful, longstanding tool for efficiently making optimized decisions in the face of complexity derived from operational scale, business constraints, and outcome quality.
The applications of mathematical optimization have increased in relevance alongside the prevalence of AI/ML tools for predictive analytics and beyond: A machine learning model can forecast increased order demand, but it does not decide how to adapt employee staffing or order assignments — that’s where mathematical optimization comes in.
This talk is primarily focused on introducing mathematical optimization to business users and data scientists looking to learn more about the opportunities this approach can provide in their existing tech stacks. Join Carolyn Mooney, Nextmv CEO, and Yash Puranik, Aimpoint Digital Director of Decision Sciences, for a presentation and conversation on this topic that will include time for questions.
Key topics:
- Optimization overview: what it is, use cases, place in AI/ML landscape
- Optimization touchpoints: Interactions across modelers, data scientists, and business users
- Optimization starter steps: How to know when you need it, where to learn more, protips
Get started on Nextmv for free and learn more in the documentation. Learn more about Aimpoint Digital and their solutions.
Have questions? Reach out to us to talk with our team.
