Case Studies/Manufacturing/Southeast Michigan

Physics Simulator for Bin Optimization

We engineered a real-time physics simulation engine that predicts optimal packing configurations for irregular machined parts, eliminating guesswork from inventory planning and recovering 15-20% of previously wasted bin capacity.

Physics SimulationOptimization AlgorithmsAI Engineering
Challenge

This precision machine shop produces thousands of irregularly-shaped metal parts daily. Workers packed bins by instinct—often underfilling to avoid jams or overfilling and causing damage. The result: 20% of bin capacity went unused, shipping costs ballooned, and inventory forecasts were unreliable. Management had no way to predict how many bins a production run would require.

Solution

We developed a custom physics engine that simulates gravity, collision, and settling behavior for parts of any shape. The system ingests CAD models or scanned geometries, runs thousands of virtual packing iterations in seconds, and outputs optimal bin configurations. Workers now see exactly how to load each bin before touching a single part.

Impact

Bin utilization jumped from ~65% to over 85%. The shop reduced shipping frequency, cut packaging material costs, and gained predictable inventory planning. What once required hours of trial-and-error now takes seconds of simulation.

Results

Tangible outcomes, not just prototypes

  • •15-20% improvement in bin space utilization.
  • •Shipping frequency reduced by eliminating wasted space.
  • •Inventory forecasting accuracy improved dramatically.
  • •Worker time reclaimed from trial-and-error packing.
Bin Utilization
+20% capacity
Planning Speed
Seconds vs. hours

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