Mathematical optimization comprises techniques that help businesses make highly complex decisions. But what does that actually mean? Here's a plain-language introduction to the field and why it matters.
What Is It?
Mathematical optimization works by capturing decision problems through an optimization model consisting of three components:
Objectives — what you're trying to achieve (minimize costs, maximize throughput, etc.)
Variables — the decisions you can make (how much to produce, which routes to take, when to schedule)
Constraints — the rules that must be followed (capacity limits, budget restrictions, physical laws)
How It Delivers Value
Optimal Solutions. It can generate decision sets representing the best possible outcome, often with mathematical proof of optimality.
Transparency. Organizations gain upfront visibility into plan quality regardless of their measurement criteria.
Multiple Objectives. It accommodates competing goals simultaneously, enabling quantification of necessary trade-offs.
Why Your Organization Should Adopt It
Data Monetization. Businesses with sufficient data for Excel-based planning can extract greater value through optimization.
Implementation Ease. Modern computational advances have made previously intractable problems solvable. Basic expertise suffices to begin, with expert support available for scaling.
Competitive Necessity. Major corporations globally—including Deutsche Bahn and PwC—already leverage optimization systems across industries, from hospitals to military operations.
The Bottom Line
Mathematical optimization is essential infrastructure for data-driven organizational planning. If you're making complex decisions with spreadsheets today, there's almost certainly a better way.
Written by
Jonasz Staszek