How do logistics and automation intertwine? What are common misconceptions about decision automation? How do efficiency and sustainability power the circular economy? We asked startup founders these questions and more.
It’s been one year since we launched the new and improved Nextmv platform. That was just the start of an exciting dash to arrive at the product we have today. We’ve come a long way, and there's so much more to come.
Decision optimization technology plays a critical role in everyday operations — food delivery, healthcare staffing, and subscription box assembly — but it can often stand in its own way. It doesn’t have to.
Do you launch decision model A or B? What happens if an operator makes a manual override? How does a new optimization model perform against real-world data? Testing and experimentation has the answers, but getting them has traditionally been a challenge.
Are AI, ML, and OR the same or different? Is operations research a solved problem? How do you know if you’re ready for optimization? We asked industry veteran Karla Hoffman these questions and more.
NVIDIA GPU-accelerated decision optimization has elevated the conversation around decision intelligence. Join a live Q&A with the NVIDIA cuOpt team to learn more.
“I wouldn’t pair these products.” “How much better is this optimized schedule than mine?” Human review and feedback is part of any decision workflow...
Historian and philosopher Hannah Arendt once said, “Storytelling reveals meaning without committing the error of defining it.” While good stories of operations research and data science can come from practitioners of all kinds...
The operations research and decision science space has a diverse portfolio of open source projects, including Pyomo and HiGHS. Recently, new momentum is building around project adoption...
“I’d like help deploying this decision model. Does this .ipynb work? Or would you prefer .zip?” If you work on decision science projects, it is possible you’ve asked or been asked this question...
Good. Fast. Cheap. Pick two, they say. But (true to form) decision intelligence teams strive to maximize for all three — project success often hinges on it. Balancing these objectives often comes down to a team’s agility ability. What does this look like? And how is it put into practice?
Two operations research PhDs with varied real-world experiences explore skillsets, actions, and considerations for entering into and practicing in OR and decision science in industry analytics settings.
Integrated, holistic optimization approaches are critical for long-term success — without them your project will fail. Learn what these workflows look like through a batch production scheduling example.
“What is your AI strategy? How are you investing in AI? Where are you incorporating AI into your everyday workflows?” Before you think “GenAI” and “LLMs”, have you considered optimization?
Learn how to leverage data and AI for better operational decisions.
How do optimization teams get decision models live into business processes faster as managed services? We explore this through the lens of dedicated DecisionOps workflows.
What approaches are available to decision scientists and operations researchers to incorporate more randomness and uncertainty into their models? We explore this, ML + OR, and stochastic optimization with Nextmv and Seeker.
The CEOs and founders of two startups sit down with Carolyn Mooney to discuss logistics and automation, navigating the evolving world of AI technology, and the benefits of efficiency and sustainability.
Is optimization a solved problem? How does it fit into modern business models such as on-demand delivery? What does it mean to model like an operator? We’ll ask Dr. Hoffman these questions and more.
