Turn Hannover-era AI narratives into defensible payback and cumulative net scenarios.

How to use this forecaster

1. Enter your best current estimate of annual unplanned downtime cost in USD.
2. Set an achievable downtime reduction percent from AI maintenance, scheduling, or vision QA.
3. Add annual process savings (scrap, rework, energy, throughput) as a separate line to avoid double counting.
4. Bundle hardware, software, integration, and training into implementation cost.
5. Choose analysis years to match your capital approval horizon, then copy or share results for review.

Formulas Used 📐

Key Takeaways

• • Downtime and quality savings compound differently by sector — split the inputs.
• • Payback months explode if annual benefit is overstated by even 20%.
• • Implementation cost should include integration, not only software list price.
• • Multi-year views expose whether ROI depends on unrealistically long adoption curves.

Did You Know?

How The Model Works

Step-by-Step

Governance Checklist

Expert Tips

Frequently Asked Questions

Official Data Sources

Diligence checklist (printable)

• Baseline downtime hours from CMMS tickets, not from memory.
• Separate planned maintenance from unplanned stops in the baseline cost.
• Confirm downtime $/hour includes margin on lost shipments, not only labor.
• Ask vendors for reference customers with similar line topology.
• Require a data availability assessment before accepting model accuracy claims.
• Map each promised saving to a KPI owner on the operations side.
• Document assumptions for energy prices if energy is bundled into process savings.
• Run a workshop with finance to align on capex versus opex treatment.
• Include cybersecurity incremental cost for new connected devices.
• Plan retraining hours for operators and maintenance techs.
• Identify legacy PLC constraints that block faster inference loops.
• Check export control and cloud residency rules before choosing hosting.
• Stress-test payback if adoption reaches only 50% of projected savings.
• Add a downside case where integration slips two quarters.
• Compare AI initiative to lean projects competing for the same attention.
• Validate double-counting between quality AI and downtime AI narratives.
• Capture baseline scrap codes for before/after comparisons.
• Align on currency and inflation assumptions for multi-year views.
• Book a quarterly review to replace static spreadsheets with actuals.
• Escalate legal review if outputs feed investor communications.
• Track spare-parts spend changes when reliability programs shift.
• Model tax treatment of software versus hardware separately with advisors.
• Confirm insurance or warranty impacts when changing maintenance strategy.
• Review union or works-council clauses on monitoring technologies.
• Set kill criteria for pilots that fail to move MTTR or scrap in 90 days.
• Archive vendor decks with version dates for audit trails.
• Pair ROI outputs with a one-page risk register for leadership.
• Use sensitivity charts in this tool as attachments to capex forms.
• Recompute after major product mix changes that alter cycle time.
• Treat Hannover Messe quotes as upper-bound storytelling until validated.
• Document data retention policies for sensor streams used in models.
• Verify IT capacity for backup and restore of new inference servers.
• Schedule calibration routines for cameras and analog sensors.
• Benchmark electricity draw of new GPU racks against facility limits.
• Clarify who pays for model retraining when recipes change.
• Add contingency percent to implementation cost for unknown civil work.
• Link to ESG reporting only when savings are metered and attested.
• Compare outcomes across shifts to catch uneven adoption.
• Revisit scenario after major supply shock changes priority SKUs.
• Close the loop: tie board-approved numbers back to this calculator version.

Vendor due diligence prompts

Use these prompts in RFP workshops and pilot kickoffs. They are generic and do not replace legal, security, or procurement review.

1. Show anonymized before/after MTBF for three plants in our industry.
2. What latency can you guarantee at the edge with our PLC scan rates?
3. How do you handle model drift when raw material grades swing?
4. What is your SOC2 or IEC 62443 posture for plant connectivity?
5. Provide a detailed bill of materials for hardware you resell.
6. Who owns the IP on fine-tuned weights after our data is used?
7. What happens to service levels if we pause cloud spend mid-quarter?
8. List every third-party API your stack calls during inference.
9. How are false positives on quality alerts tuned over the first 90 days?
10. What training data rights did you secure for the base model?
11. Describe rollback if a bad deploy increases scrap by more than 1%.
12. How do you price incremental plants after the pilot?
13. What field engineering hours are included in year one?
14. Can outputs be exported to our data lake without vendor lock-in?
15. How do you separate PII from operator HMI captures?
16. What is the disaster recovery RTO for cloud inference?
17. Provide references where the project was killed—lessons learned matter.
18. How is labeling quality audited for computer vision use cases?
19. What firmware signing process protects edge devices?
20. How do you attribute savings when multiple initiatives run in parallel?
21. What is the minimum viable dataset size you need from us?
22. Describe your change management collateral for unionized sites.
23. How are GPU thermals managed in cabinets near furnaces?
24. What KPIs do you use in your own customer success reviews?
25. How frequently must we refresh baseline data to keep models honest?
26. What penalties exist if accuracy SLAs are missed?
27. How does pricing scale with number of lines versus number of plants?
28. Can we run shadow mode without writing back to PLCs at first?
29. What is the upgrade path when we replace legacy cameras?
30. How do you support air-gapped sites with periodic model drops?
31. What logging is available for regulator or insurer audits?
32. How are seasonal shutdowns handled in forecasting modules?
33. What is the carbon footprint of your training versus inference footprint?
34. How do you price professional services versus software seats?
35. What languages are supported for operator-facing alerts?
36. How do you integrate with SAP PM or Maximo work orders?
37. What is your policy on using our data to improve other customers models?
38. Describe security testing performed before each release.
39. How are edge containers updated with minimal downtime?
40. What analytics prove adoption, not just model accuracy?
41. How do you handle multi-tenant cloud segregation for competitors?
42. What is the exit plan if we terminate after year two?
43. How are spares stocked for proprietary accelerators you ship?
44. What training certifications exist for our maintenance teams?
45. How is pricing impacted if we expand to regulated pharma lines?
46. What roadmap items are funded versus aspirational in decks?
47. How do you measure energy reduction separate from throughput gains?
48. What legal templates do you provide for worker notification?
49. How are pilot success criteria written to avoid moving goalposts?
50. What community or open-source dependencies carry license risk?
51. How quickly can you stand up a sandbox with our anonymized sample?

Regional factory context (illustrative)

Use this table to sanity-check whether your downtime and implementation assumptions match local realities. It is not exhaustive.

Glossary snapshot

Sector downtime context (illustrative)

Pilot exit criteria snippets

Mix-and-match for steering committees; not a substitute for written success metrics in your charter.

• MTTR improves measurably on the top three recurring fault codes.
• Scrap or rework dollars drop versus a frozen 90-day baseline.
• OEE uplift is visible on a single shift before scaling to the plant.
• Edge inference latency stays within PLC scan budget under peak load.
• Cybersecurity review closes with documented compensating controls.
• Operator trust survey shows comprehension, not just satisfaction.
• Maintenance backlog age decreases without hiding work orders.
• Spare parts inventory turns improve without stockouts on critical SKUs.
• Energy per good unit trends down when production mix is held constant.
• Quality holds are fewer and faster to clear with traceable root causes.
• Vendor professional services hours trend down after month three.
• Data labeling throughput meets the weekly target without shortcuts.
• False positive rate on alerts is below the agreed threshold.
• Integration incidents do not repeat after root-cause closure.
• Finance signs off that savings tags map to GL accounts cleanly.
• IT confirms backup and restore tested on inference servers quarterly.
• Works council or union concerns have written resolutions on monitoring.
• Safety incidents related to new workflows remain at zero in the pilot window.
• Customer OTIF is unchanged or better while pilot runs.
• Model cards are published internally with drift monitoring owners.
• Engineering change orders slow down — fewer surprise recipe edits.
• Downtime pareto shifts — previously dominant failure modes shrink share.
• Throughput per staffed hour rises without increasing injury rates.
• Water or coolant usage per unit is flat or down where instrumented.
• Noise and vibration sensors show no alarming new bands post-deploy.
• Camera coverage gaps are closed on the critical path stations.
• Analog sensor calibration records are current and auditable.
• PLC program diffs are reviewed and signed after each deploy.
• MES timestamps align with edge logs within agreed skew.
• Cloud spend per million inferences stays inside the pilot cap.
• GPU thermals remain below throttle thresholds in summer peaks.
• Airflow in cabinets meets OEM guidance after cable management fixes.
• Remote support tickets drop after runbooks are updated.
• On-call pages for model errors stay below the agreed weekly count.
• Executive readout deck matches numbers exported from this calculator version.

OT data governance reminders

High-level checklist for IT/OT and vendor teams when models touch plant data.

• Tag every training image with line, station, and date range.
• Separate PII from operator footage before upload to cloud trainers.
• Document retention for vibration streams with legal sign-off.
• Encrypt data at rest and in transit; rotate keys on schedule.
• Restrict model registry access to CI/CD roles with MFA.
• Log every manual override of model outputs for audit trails.
• Version datasets alongside model weights in lockstep.
• Anonymize supplier names in shared benchmark decks.
• Mask serial numbers in screenshots used for training materials.
• Establish a data quality SLA with operations, not only IT.
• Publish a RACI for who approves new sensors on the network.
• Run tabletop exercises for ransomware impacting historians.
• Backfill missing timestamps before training — do not interpolate silently.
• Quarantine corrupted OPC tags automatically when checksums fail.
• Align OT and IT patch windows to avoid surprise reboots.
• Document acceptable use for contractor laptops on plant Wi-Fi.
• Require signed driver packages for any new edge device image.
• Track consent for any biometric or gait analytics if applicable.
• Keep a map of all outbound API calls from inference stacks.
• Review third-party subprocessors annually with procurement.
• Store offline backups for air-gapped lines with periodic restore drills.
• Classify recipes and yields appropriately before cross-plant sharing.
• Red-team prompt injection paths if LLMs touch operational text.
• Maintain an asset inventory for cameras including lens replacements.
• Track who can push OTA firmware to gateways — two-person rule.
• Segment VLANs so vision systems cannot reach unrelated ERP subnets.
• Document disaster recovery ownership between vendor and plant IT.
• Keep change records when scaling GPU counts affects power permits.
• Archive model evaluation reports with the same rigor as financial audits.
• Ensure studentized residuals on quality models are reviewed weekly.
• Pair SOC alerts with physical security for edge cabinet tamper switches.
• Maintain a kill switch procedure if models recommend unsafe setpoints.
• Record training data provenance when synthetic data supplements real.
• Validate that edge containers run read-only root filesystems.
• Store secrets in vaults — not in plaintext env files on HMIs.
• Review cross-border data flows when using global cloud regions.
• Publish a simple flowchart for incident response when models misbehave.