Common Use Cases for Mathematical Optimization
Exploring the applications & benefits of this prescriptive analytics technique across industries & disciplines
What is mathematical optimization and how do organizations across industries use this type of prescriptive analytics? Download our guide to get an overview of the most common and impactful use cases.
Mathematical optimization is a subset of artificial intelligence and a type of prescriptive analytics. It determines ways in which business processes should evolve or be modified, providing implementable solutions with known cost and/or benefit.
What are some of the most common use cases for mathematical optimization? How can this type of prescriptive analytics be applied to lower costs, reduce carbon emissions and build more resilient supply chains? Find out in this Guide.
This Guide is ideal if you:
- Are curious about the different application areas for mathematical optimization
- Want ballpark estimates of value and benefits achieved through optimization
- Want examples where optimization is used in combination with other types of AI techniques, such as machine learning
Mathematical optimization, applied
Optimization complements other AI techniques like machine learning. It enriches the analytics toolbox and is a highly adaptable tool that helps you improve decision making in a broad range of areas and across industries.
The benefits of a low code approach
AIMMS offers a mature and flexible toolset that empowers you to optimize almost any business problem. Our low code platform enables you to develop and deploy mathematical optimization apps at speed. For over 30 years, decision makers in leading organizations have used AIMMS to analyze and compare different scenarios and make optimal decisions with confidence.
Guide to Mathematical Optimization and Modeling
Don't miss your copy
Many of the world’s fortune 500 companies trust AIMMS to make optimized decisions
“AIMMS is an attractive tool that has made optimization accessible to everyone across the company”
Jan André, Operations Research & Data Science Team Manager
“Using AIMMS to optimize the increasing amount of changeovers helps my team to go home on time.”
Paul Groen, Head of Supply Chain Planning