Signature tool · COO AI Operating Engine
Ops scenario simulator
Pick a disruption — a demand spike, a supplier failure or a staffing gap — and set its size, duration and your baseline volume, cost, service level and lead time. The tool compares your CURRENT response with an AI-ASSISTED one on extra cost, service level and time to recover, using simple formulas you can read. The AI improvements (earlier detection, smaller forecast error, faster rerouting) are illustrative assumptions: replace them with your own pilot numbers.
Every default — the baseline, the disruption, today's response and the AI improvements — is an illustrative assumption, not a benchmark. We found no published, comparable ranges for 'days of earlier detection', 'forecast-error reduction' or 'rerouting speed-up'; the evidence below shows what real programs report. Use your own pilot numbers.
1. The disruption
2. Baseline and responses
Cost and recovery assumptions
3. Current vs AI-assisted
| Metric | Current | AI-assisted | Difference |
|---|---|---|---|
| Days exposed before the response works | 17 days | 11.6 days | 5.4 days sooner |
| Units lost (not served) | 18,176 | 14,697 | 3,479 avoided |
| Extra cost of the disruption | $1,207,040 | $1,046,682 | $160,358 saved |
| · of which lost units | $1,090,560 | $881,818 | |
| · of which expediting | $81,536 | $130,243 | |
| · of which mis-sized response | $34,944 | $34,621 | |
| Service level during the disruption | 64.7% | 70.5% | +5.8 pts |
| Time to recover (backlog cleared) | 77.6 days | 60.6 days | 17 days faster |
- Current$1,207,040
- AI-assisted$1,046,682
- Current64.7%
- AI-assisted70.5%
- Current77.6
- AI-assisted60.6
Earlier detection only helps when the response is ready: if lead time or set-up dominate, invest in pre-qualified alternatives and flexible capacity, not just better signals. Run the same disruption with your own pilot numbers before you build a business case.
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