The Enterprise Manufacturing AI Vendor Selection Checklist: 47 Criteria Across 9 Dimensions
This checklist gives enterprise manufacturing procurement teams, technology leaders, and operations executives a structured framework for evaluating AI services vendors. It is organized into nine evaluation dimensions with specific, binary criteria — each one is either met or not met during the vendor assessment process.
Use this checklist in RFI and RFP processes, vendor scorecards, and proof-of-concept evaluation frameworks. Every criterion here reflects a real decision point that separates production-capable AI vendors from firms that succeed in demos but fail in deployment.
47
Binary criteria
9
Dimensions
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Dimension 1
Manufacturing Domain Expertise
The vendor can name at least three specific manufacturing customers with active production deployments.
The vendor demonstrates knowledge of OT systems, PLCs, SCADA, historians, MES, without prompting.
The vendor has direct experience with the manufacturing subsector relevant to your operations (discrete, process, automotive, electronics, food/beverage, aerospace, etc.).
The vendor has discussed shift variability, equipment reliability frameworks, and quality management standards in prior client engagements.
The vendor's team includes engineers or domain specialists with prior manufacturing operations experience, not only data scientists.
The vendor can define OEE, MTBF, MTTR, and AQL in operational context, not just as model targets.
Dimension 2
Technical Capability and Solution Delivery
The vendor can deliver end-to-end: data engineering, model development, system integration, and production deployment, not just modeling.
The vendor has built custom AI models, not solely configured off-the-shelf platforms.
The vendor offers edge deployment capability for plant-floor applications with latency requirements.
The vendor has experience with computer vision for quality inspection in manufacturing environments.
The vendor has deployed predictive maintenance models integrated with real CMMS or asset management systems.
The vendor has experience with demand forecasting models integrated with real ERP data.
The vendor can demonstrate a defined MLOps or AgentOps capability for ongoing model monitoring.
The vendor provides documented model validation methodology, not just accuracy metrics.
Dimension 3
ERP and System Integration
The vendor has successfully integrated AI solutions with at least one major ERP platform (SAP, Oracle, Infor, Plex, SYSPRO, QAD, or equivalent).
The vendor has integrated with at least one MES platform in production.
The vendor has documented experience connecting to OT data sources, historians (OSIsoft PI, Aveva), SCADA systems, or IIoT platforms.
The vendor can describe their data connector approach: prebuilt, API-based, or custom ETL.
The vendor has a defined approach to handling ERP master data quality issues during implementation.
The vendor's integration architecture does not require full ERP replacement or major system reconfiguration.
Dimension 4
Production History and References
The vendor can provide at least two reference contacts from manufacturing clients with active production deployments.
References can speak to what changed between pilot and production, not only to pilot success.
The vendor can provide quantified business outcomes — KPI improvements with baselines and measurement methodology — from at least one manufacturing deployment.
The vendor has at least one deployment that has been running in production for twelve months or more.
The vendor can describe a case where a deployment did not go as planned and how it was resolved.
Dimension 5
Data Governance and Security
The vendor holds relevant security certifications (SOC 2 Type II, ISO 27001, or equivalent).
The vendor can describe their data residency options — on-premises, private cloud, public cloud, or hybrid.
The vendor has a defined data access control model with audit logging.
The vendor can explain how they handle sensitive manufacturing data — throughput, yield, pricing, supplier data — and who has access during and after engagement.
The vendor has an incident response process for data security events.
The vendor can address OT network security considerations when integrating with plant-floor systems.
Dimension 6
Model Explainability and Auditability
The vendor can explain how their AI models produce recommendations in terms an operations engineer or quality manager can act on.
The vendor provides an audit trail for AI-driven recommendations or decisions.
The vendor has satisfied internal or external audit requests related to AI decision-making in prior engagements.
The vendor's explainability approach is documented — not dependent on a specific team member's institutional knowledge.
Dimension 7
Scalability and Multi-Site Capability
The vendor has deployed across multiple plants or sites for a single client.
The vendor can describe their architecture for maintaining central model governance with local operational adaptation.
The vendor supports multi-region deployment with appropriate data residency and compliance handling.
The vendor has experience with multi-language manufacturing environments.
Dimension 8
Post-Launch Support and Partnership
The vendor offers a defined post-launch support model, not just a hypercare period.
The vendor has a documented model retraining process with clear ownership.
The vendor provides SLAs for model performance degradation with defined response commitments.
The vendor's post-launch team has continuity with the implementation team, not a handoff to a generic support desk.
The vendor can describe how their engagement model evolves from project delivery to ongoing operational partnership.
Dimension 9
Commercial Structure and Total Cost of Ownership
The vendor provides a clear total cost of ownership estimate, including implementation, licensing, integration, ongoing support, and retraining.
The vendor's pricing model does not create structural disincentives to scale — no excessive per-user or per-model charges that penalize enterprise expansion.
The vendor can describe their exit portability approach — what happens to models, data, and intellectual property if the engagement ends.
How to Use This Checklist
Score Each Criterion
Go through every criterion in each dimension. Assign a binary score:
Criterion clearly met
Criterion not met
Vendor declined or gave insufficient answer
Unanswered criteria count as 0 — if a vendor avoids a question, that is itself a signal.
Calculate Total Score
Add up all scores across all 9 dimensions. The maximum possible score is 47.
Total Score Formula
Sum of all 1s across 47 criteria
Range: 0 – 47
Minimum threshold
Score below 35 / 47 → warrants significant scrutiny before proceeding to a proof-of-concept.
Apply Weighted Scrutiny
Even if a vendor hits 35+, scrutinise failures in these three dimensions more heavily — they represent where the gap between demo and production is greatest.
Dimension 1 · 6 criteria
Manufacturing Domain Expertise
Dimension 4 · 5 criteria
Production History & References
Dimension 8 · 5 criteria
Post-Launch Support & Partnership
This Checklist is a Floor, Not a Ceiling
This checklist is a starting point, not a contract. Your organisation's specific regulatory environment, existing technology stack, and operational complexity will add criteria uniquely relevant to your situation. Use this as the floor, not the ceiling, of your evaluation process.
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47
criteria covered
9
dimensions
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