Manufacturing Cloud + Field Service Lightning
AI pipelines for
Manufacturing
Get the right technician to the right job the first time, automate warranty claim decisions, and cut CPQ cycle time — all on Manufacturing Cloud.
Field Service Work Order Triage
The Problem
Dispatchers manually read work order descriptions and assign technicians based on memory of who knows what. Wrong skill assignments result in truck rolls that can't complete the job. Each repeat visit costs $300–800 in labor and transport. SLA breaches trigger penalty clauses.
Why not DIY?
Querying available technicians with matching skills, ranking by proximity and availability, and routing to dispatcher review only for ambiguous cases — that's three SOQL queries, a scoring algorithm, and conditional routing. Each piece is straightforward. The orchestration isn't.
Pipeline Stages
FlowMason Components
Realistic Outcomes
| Metric | Before | After |
|---|---|---|
| First-time fix rate | 65% | 82% |
| Repeat visits | Baseline | -40% |
| Dispatcher decision time | 8 min | 90 sec |
Illustrative based on observed patterns. Your results depend on your data and implementation.
Warranty Claim Analysis
The Problem
Warranty analysts manually review claim descriptions, cross-reference product failure databases, determine coverage eligibility, and draft approval/denial letters. Processing takes 3–7 business days. Inconsistent decisions create dealer disputes. Fraudulent patterns go undetected.
Why not DIY?
Extracting structured data from claim PDFs (DocumentProvider), calling an external parts DB (FMCircuitBreaker for resilience), running two parallel LLM stages, and merging outputs (CombinerNode) is a 12-week DIY project. FlowMason ships all four primitives.
Pipeline Stages
FlowMason Components
Realistic Outcomes
| Metric | Before | After |
|---|---|---|
| Claim cycle time | 5 days | 4 hours |
| Coverage consistency | Variable by analyst | Policy-driven |
| Fraud flag rate | Unmeasured | Measurable for first time |
Illustrative based on observed patterns. Your results depend on your data and implementation.
Configure-Price-Quote Intelligence
The Problem
Complex industrial quotes require engineers to manually review configurations for feasibility, compatibility, and margin. A single review takes 2–4 hours. Sales cycles stretch to weeks. Engineers are bottlenecks. Non-standard configurations miss discount approval steps.
Why not DIY?
Routing quotes through different approval paths based on margin percentage, product type, and customer tier — and making that logic maintainable — is exactly what ConditionalFlowControl is built for. In raw Apex, that's a deeply nested if-else tree that breaks on the next pricing model change.
Pipeline Stages
FlowMason Components
Realistic Outcomes
| Metric | Before | After |
|---|---|---|
| Engineer review queue | Baseline | -60% |
| Quote cycle time | 8 days | 3 days |
| Discount compliance | Baseline | +90% |
Illustrative based on observed patterns. Your results depend on your data and implementation.
Chat with your Manufacturing data.
Beyond the pipeline patterns above, FlowMason ships a chat surface admins drop on any Lightning page. Manufacturing users type plain-English questions; FlowMason generates SOQL, validates through the 8-gate sanitiser, runs under FLS, returns rows. Read-only by default.
Try asking:
- › "show me work orders blocked > 24 hours"
- › "count quality holds by line and shift"
- › "which suppliers have OTD < 90% this quarter?"
FLS-aware. Allowlist-governed. Permset-gated. No data leaves the org.
Ready to build Manufacturing pipelines?
Tell us your use case. We'll show you exactly which pipeline pattern fits.
Talk to us about Manufacturing