Think mathematical optimization is only for large corporations? These two case studies from smaller enterprises prove otherwise—with annual savings measured in hundreds of thousands of euros.
Case Study 1: Singaporean Freight Forwarder
A logistics company faced profitability challenges due to severely underutilized truck capacity—operating at only one-third of available space. The company knew they were leaving money on the table but couldn't figure out how to solve the packing problem manually.
Mathematicians from NUS and Nanyang Polytechnic developed an optimization algorithm for cargo placement. The results were dramatic: savings of up to S$567k, or EUR370k per annum, through improved cargo placement and increased average vehicle load.
The key insight: this wasn't about buying more trucks or hiring more staff. It was about using existing resources more intelligently.
Case Study 2: Brazilian Furniture Factory
A small furniture manufacturer pursued operational efficiency improvements without capital investment. They faced two interconnected problems: determining optimal production sequences and quantities (lot-sizing), and optimizing how wooden boards are cut (cutting-stock).
By integrating both problems into a single optimization model, the company reduced production costs by approximately 4%, translating to roughly EUR 50,000 annually. Not transformational on its own, but significant for a small manufacturer—and achieved without buying new equipment.
The SME Opportunity
These examples demonstrate that mathematical optimization isn't exclusively for large corporations with dedicated analytics teams. SMEs worldwide can achieve substantial savings by applying these techniques to their specific operational challenges.
The common thread: both companies had data (shipment records, production logs) and well-defined operational constraints. They just needed the mathematical tools to extract value from what they already knew.
If your business involves complex scheduling, routing, allocation, or planning decisions, there's likely an optimization opportunity waiting to be discovered.
Written by
Jonasz Staszek