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Beyond the Route: How a Logistics Failure Became a Divine Intervention

A supply chain expert analyzes the night a GPS error in a Chicago industrial park defied the laws of probability.

The Daily Faithful Team
5 min read
A view from inside a car at night looking through a rain-streaked windshield, where bright headlights illuminate a lone, soaked woman sitting hunched on a street curb in a heavy downpour.

My entire professional life is built on the elimination of variance; I manage supply chains for a mid-sized distribution firm in Chicago, and my job is to ensure that Point A connects to Point B without a single wasted second or unallocated ounce of fuel. However, on the evening of October 14th, during that record-breaking storm that flooded the I-90 underpasses, my carefully calibrated life fell apart. I had just left a quarterly review where we discussed trimming "inefficiencies," leaving me in a foul, hyper-critical mood. Visibility was near zero, and the rain was coming down in sheets so thick it felt like driving through a car wash. Distracted by a work call I shouldn't have taken, I missed the exit for the western suburbs. For a logistics coordinator, missing an exit isn't just an accident; it’s a failure of planning. My GPS, struggling for a signal in the storm, attempted a reroute that sent me spiraling into the dark, labyrinthine streets of the old industrial district - an area of shuttered textile factories and unlit warehouses. I was furious. I gripped the wheel, watching my ETA climb by twenty minutes, then thirty. This was dead time. Wasted resources. The detour led me down a dead-end street near a defunct shipping depot, a place where no car should be at 8:00 PM. I pulled into the cul-de-sac to execute a three-point turn, mentally calculating the fuel cost of this mistake, when my high beams cut through the deluge. They didn't hit a street sign; they illuminated a person. A woman was sitting on the crumbling curb, completely exposed to the 40-degree rain, her head buried in her knees. In my line of work, we look for anomalies in the data. This was a massive anomaly.

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