The Problem Begins: A Flood of Inventory Correction Requests
Last Wednesday, the Windows device notifications wouldn't stop. Inventory adjustment requests had surpassed 100. Normally we'd process them by priority (urgency, quantity, partner tier), but that method required at least 3 hours.
What if we tried reverse order instead?
The Reverse Processing Experiment
I instructed the Claude Code bot:
Start with the lowest priority items (reverse chronological order)
For each request, perform only: correction reason + quantity validation + final record.
What happened?
**Completed in 90 minutes**. All 100 items processed.
Why It Was Faster
• **Fewer context switches**: Priority-first processing required extra judgment at each step, like "What's this partner's usual policy?" Reverse processing maintained the previous item's context.
• **Pattern repetition**: When similar correction types appeared consecutively, the bot learned faster. "Ah, this pattern again."
• **Psychological ease**: Starting with low priority meant no rush. Confidence built toward the final items.
An Unexpected Discovery
Midway through reverse processing, an interesting pattern emerged.
"Over 30 correction requests from the past 3 months were for the same reason, repeating."
If we'd processed by priority, fatigue would've set in after the first 20 items, risking missed patterns. Reverse processing inadvertently let us observe cumulative patterns starting from the 'small stuff.' That was the real surprise.
Conclusion: More Than Just Speed, It's Discovery
In automation, "the right order" depends on context. This reverse approach didn't just accelerate processing. It revealed hidden data patterns.
Next week, we'll add preventive rules to the bot for this recurring pattern. 100 inventory requests turned out to be quite the gift.