“We were hitting every target and losing money”
The dashboard was green across the board. Every single KPI glowing with success.
Pick rate: 15% above target. Orders processed: Record high. Fulfillment speed: Best in company history. Employee utilization: 97%.
The operations team celebrated. Bonuses were paid. The VP stood in front of the company and announced they’d “cracked the code on efficiency.”
Then the quarterly financials came out.
The company was losing money. Not a little. A lot. And nobody could explain why.
“We’re doing everything right,” the operations manager insisted, pulling up the dashboard. “Look at these numbers.”
The CFO looked. Then she asked a question nobody had thought to ask: “What are we optimizing for?”
Silence.
“Speed,” someone finally said. “We’re optimizing for speed.”
“And is speed profitable?”
That’s when someone pulled the returns data. Then the replacement shipment costs. Then the customer service hours. Then the write-offs from damaged inventory that had been rushed through quality control.
The picture that emerged was damning they’d built a magnificently efficient machine for producing errors at scale.
They were picking faster than ever and picking the wrong items 11.2% of the time. They were processing more orders and processing them incorrectly. They were hitting every operational target while hemorrhaging money on corrections, replacements, and lost customers who quietly took their business elsewhere.
The team had optimized for speed because speed was measurable. Speed was exciting. Speed made dashboards turn green. What they hadn’t measured was whether they were shipping the right things to the right people.
“We’re not running a warehouse,” someone said quietly. “We’re running a very expensive mistake factory.”
That realization didn’t land softly. Pride turned to defensiveness. Defensiveness turned to denial. But the numbers didn’t lie, and eventually, neither could they.
The next eight months were humbling. They slowed down. They added verification steps. They rebuilt processes they’d “optimized” into oblivion. The dashboard turned yellow, then red. Pick rates dropped. Speed metrics fell. The team that had celebrated record efficiency watched those records disappear.
But something else happened: mistakes plummeted. Returns dropped. Customer complaints dried up. The panicked calls about wrong shipments stopped. And slowly, carefully, quietly, the company started making money again.
By month eight, they were profitable. Really profitable. The kind of profitable that comes from doing things right instead of doing things fast.
They updated the dashboard. Added new metrics. Error rates. Net margin per order. Customer retention. Cost of corrections. Suddenly, green didn’t just mean fast, it meant sustainable.
At the next quarterly meeting, the VP stood up again. “Last year, I told you we’d cracked the code on efficiency,” he said. “I was wrong. We’d cracked the code on speed. This year, we cracked the code on profitability. Turns out, they’re not the same thing.”
The Numbers Tell the Story
Error Rate
- Before: 11.2% of picks incorrect
- After: 0.8% of picks incorrect
- Impact: From shipping mistakes at scale to getting it right the first time
Profit Margin
- Before: -2.3% (losing money on operations)
- After: +8.7% (healthy, sustainable profitability)
- Impact: An 11-point swing from red to black
The Hidden Costs Revealed
- Returns processing: $340K annually
- Replacement shipments: $280K annually
- Customer service overhead: $190K annually
- Rush corrections and expedited fixes: $150K annually
- Total cost of speed: $960K per year in preventable losses
Time to Transformation
- 8 months from “hitting every target” to hitting the targets that mattered
What Changed
It wasn’t technology. It wasn’t staff. It wasn’t investment.
It was asking one simple question: What are we optimizing for?
Speed looks impressive on a dashboard. Accuracy shows up on a balance sheet.
They learned the difference the hard way. But they learned.
We were hitting every target and losing money. Because we were measuring everything except what mattered: whether we were getting it right.
EN 
FR