Optimization is often highlighted in supply chain contexts – routing, scheduling, inventory, and other tangible, visible problems. This post explores a few use cases in computing and software.
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Identify how updates to your model will impact business metrics using batch experiments. Plus, add more context to your apps by naming your runs, viewing the run input directly in the console, and more.
Welcome to the what-if wonderland of parallel universes. What if you expand your delivery region? Or fulfill orders from stores and distribution centers? Or hire more staff? Scenario tests help provide the answers. And, no, it’s not the same as simulation.
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You need to change your decision model, but you’re not sure how it’ll impact your KPIs. Will there be unexpected effects? Batch experiments allow for exploration through summary statistics to orient yourself to impacted metrics.
You’ve got a production decision model and an updated decision model. Should you ship the new model to prod? Will the new model meet your acceptable KPI thresholds? Acceptance testing provides the answers by delivering a documented, repeatable decision-making process.
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.
