How long did the roadmap take?

Four weeks from kickoff to executive sign-off, including workshops, discovery, and a phased rollout plan.

What about OT safety?

We avoided direct control changes in phase one, kept AI away from PLCs, and used read-only taps plus human-in-the-loop approvals for shop-floor actions.

What was the projected ROI?

$480k/year in time savings and scrap reduction across quality checks, maintenance scheduling, and reporting.

How did you handle change management?

We ran operator interviews, champion enablement, SOP updates, and a two-week shadow period before automation owned any step.

How We Built an AI Roadmap for a 50-Person Manufacturing Company

Week 1: Readiness assessment

We ran leadership and operator interviews, mapped current-state processes, and scored data readiness (quality, access, latency). OT safety was a red line: no changes to PLC logic, and AI could only read from historians and MES APIs. We also identified where data lived (ERP, MES, QMS, spreadsheets) and what was off-limits.

Week 2: Use-case prioritization

Quality checks
Vision-assisted inspection with human-in-loop approvals; target scrap reduction of 8%.

Maintenance scheduling
Predictive alerts based on run hours and sensor deltas; goal: cut unplanned downtime by 12%.

Reporting
Automated shift and OEE summaries; goal: reclaim 6 hours/week per supervisor.

Onboarding & SOPs
AI assistant for SOP lookup; reduce ramp time for new operators by 20%.

Week 3: Architecture & governance

Data stays on-prem/VPC; no plant data sent to public LLM APIs.
Role-based access for supervisors, quality, and maintenance teams.
Redaction before vectorization; embeddings stored in a private database.
Audit logs to SIEM; alerts to Slack/Email on anomalies.

Week 4: Roadmap and executive workshop

We presented a phased plan with costs, owners, and success metrics. Phase 1 pilots (reporting + SOP assistant) would pay back in ~4.5 months. Phase 2 (quality checks, maintenance alerts) brought the annual impact to $480k with guardrails to avoid downtime.

Pilot design highlights

Reporting bot: n8n pulls MES/ERP data nightly, generates shift reports, and pushes to Slack with variance alerts.
SOP assistant: Private RAG chatbot for operators with citation snippets and escalation to supervisors.
Maintenance alerts: Threshold-based in phase one; ML-based in phase two after 90 days of data.

Change management

Operators co-designed prompts and reviewed answers before go-live. Supervisors received a one-page “what changed” brief weekly. We ran a two-week shadow mode where humans kept ownership while automation ran in parallel to build trust.

“Nothing touches the line without an approval step. Zyphh’s plan respected our OT constraints and still delivered a clear ROI.” — Plant Manager

Metrics and ROI model

We modeled time saved (reporting, SOP lookups), scrap reduction, and downtime avoided. Costs included platform licenses, infra, and 8–12 hours/month for monitoring. The CFO saw break-even in month five and greenlit pilots.

What’s next for this client

Phase one launches with reporting and the SOP assistant. Phase two adds maintenance alerts and limited vision QC. Phase three evaluates automated reordering with human approvals. Every phase keeps a rollback plan and a human-in-loop checkpoint.

Build my AI roadmap See more Zyphh case studies

FAQ

Will AI touch production lines?

Not in phase one. We start with read-only, advisory outputs. Any control changes require explicit approvals and staged testing.

How do you handle vendor lock-in?

We keep orchestration in portable tools (n8n), store prompts/content in Git, and design APIs so components can be swapped.

What if data quality is poor?

We include a data-cleanup sprint: standardize tags, fix timestamps, and add basic validation before automation relies on it.

Can smaller plants use this?

Yes. We scale scope to 1–2 workflows first—often reporting and SOP lookup—before heavier automation.