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.
There are two aphorisms that should register for optimization practitioners: 1) Your model provides no value until it’s in production making decisions — until then it’s simply a cost and 2) people don’t trust decisions they can’t understand. Decision optimization is a powerful technique that unlocks game-changing business efficiency and ROI, but it is also subject to failed rollout and stakeholder skepticism due to fragmented decision workflows and tooling.
“Why did we schedule it this way?” “Wouldn’t the routes be better if we did this?” “How did we arrive at this price point?” When you get these questions, you need a complete audit trail to provide answers and user interface to communicate outcomes. That’s why it’s important every optimization run needs to be logged, tracked, and explainable.
In this techtalk, join William Wirono, Senior Decision Scientist at Aimpoint Digital, and Tiffany Bogich, Head of Product at Nextmv, as they survey the optimization ecosystem, the tooling and approach required for building end-to-end decision workflows, and explore a batch schedule processing example that uses Gurobi, Nextmv, and Streamlit.
Key topics:
- 00:57 - Nextmv + Aimpoint and Ecosystem Overview
- 02:27 - Aimpoint Digital Optimization Approach
- 06:05 - Batch Production Scheduling Example Overview
- 08:31 - Demo of Production Scheduling Optimization
- 23:11 - Mapping of Gurobi and Nextmv to Optimization Approach
- 26:57 - Q&A
Get started on Nextmv for free and learn more in the documentation. Learn more about Aimpoint Digital and their solutions.
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