Case File 06 · Cross-Industry
Coordinating agents across plant, planning, and paperwork
Representative — multi-site manufacturer
▸ ENGAGEMENT DETAILS
Representative scenario modeled on published industry benchmarks. Not a named client.
The Challenge
A representative multi-site manufacturer has three operational problems that are usually solved by three separate projects — and are in fact the same problem.
Asset health lives in historians, planning lives in the ERP, and supplier paperwork lives in mailboxes and a document store. Nobody sees them together, so a machine warning does not reach the production plan, and a late supplier confirmation does not reach the maintenance window. Each system is individually fine; the coordination between them is manual.
This is not a sixth practice area. It is what happens when the maintenance, automation, and document patterns we already build are composed into one engagement — which is also where most agent programs fail. Gartner expects more than 40% of agentic-AI projects to be cancelled by the end of 2027, and MIT NANDA found roughly 95% of enterprise GenAI pilots showed no measurable P&L return.
Our Agent Solution
We build a custom multi-agent system that composes the patterns from three of our practices: condition monitoring on the asset side, exception-aware process automation on the planning side, and document extraction on the supplier side. A coordination agent gives them one shared view of state, so an asset warning can reach the production plan and a supplier confirmation can reach the maintenance window. Every cross-system action is a proposal with the triggering evidence attached, routed to the human who owns that decision today.
Monitoring Agent
Tracks asset condition, order status, and supplier confirmations across the systems that already hold them
Prediction Agent
Forecasts asset and schedule risk from historical and live data, with confidence attached to each signal
Optimization Agent
Proposes revised plans and maintenance windows, showing the trade-off behind each option
Coordination Agent
Maintains one shared view of state and routes each proposal to the human who owns that decision
Implementation Timeline
A representative 20 weeks delivery path, from discovery to handover.
Discovery & Scoping
Mapped the operational handoffs between plant, planning, and supplier paperwork, and identified the data sources and integration points
Weeks 1-4
Architecture Design
Designed the multi-agent system, the inter-agent protocols, the shared state model, and the escalation rules
Weeks 5-8
Core Development
Built the agent system and integrated it with the ERP, historians, and document store; models tuned and evaluated on the client's own historical corpus under a signed data agreement
Weeks 9-14
Pilot & Evaluation
Ran a single-site pilot in shadow mode, scoring proposals against what the planning and reliability teams actually decided
Weeks 15-18
Handover & Rollout
Extended to remaining sites with dashboards, an operator runbook, alert thresholds, and a documented rollback path
Weeks 19-20
Target ranges — and today's baseline
Both sets of figures are drawn from published research, not from VelocityMind client results. Targets are the ranges this scenario is scoped against; baselines describe where the status quo sits. Actual results depend on your data, systems, and scope.
Less planning and coordination time
Source · Deloitte — predictive maintenance planning band
20–50%
Faster processing on the document-heavy flows
Source · Deloitte — IDP, 60-80% less processing time
70%
Less unplanned downtime on monitored assets
Source · McKinsey — 30-50% range for condition-based programs
40%
Where the baseline sits today
Published measurements of the problem, shown so the target ranges above can be read against something. These are not outcomes we delivered.
Of agentic-AI projects are forecast to be cancelled by 2027
Source · Gartner, 2025 — poll of 3,400+ respondents
>40%
What ships — and what stays human
The artifacts you own at handover, and the decisions the agents never take.
▸ DELIVERABLES
What you receive
- A coordinated agent layer running across your ERP, historians, and document store
- Shared state model, inter-agent protocol map, and documented escalation rules
- Evaluation suite scoring agent proposals against your teams' recorded decisions
- Operator runbook, alert thresholds, failure-mode plan, and a documented rollback path
- All source code in your repository, with the deployment owned by your team
▸ GUARDRAILS
What stays human
- Cross-system actions are proposals. The person who owns that decision today still makes it.
- Every proposal carries the triggering signal and the trade-off, so it can be overruled with the evidence visible.
- The pilot runs in shadow mode and is scored against real decisions before anything is put in the path of production.
- Agents read from the systems of record. Writes are scoped, logged, and reversible.
Method and sources
This scenario is constructed, not reported. Every figure on this page traces to one of the published sources below.
>40% of agentic-AI projects forecast to be cancelled by end of 2027
Gartner, 2025 (poll of 3,400+)
~95% of enterprise GenAI pilots show no measurable P&L return
MIT NANDA, 2025
Planning time -20-50% for condition-based maintenance programs
Deloitte
30-50% less unplanned downtime for condition-based programs
McKinsey
IDP: 60-80% less processing time
Deloitte
Ranges are published industry benchmarks. They describe what programs of this shape have achieved elsewhere, not a commitment for your environment — the range we would scope against for you is set after discovery.
A representative perspective
“Plant, planning, and supplier paperwork finally read from one view, and the handoffs that used to be somebody's inbox are now proposals we accept or reject.”
▸ NEXT STEP
Run this against your own workflow
Send us one workflow, its monthly volume, and the systems it touches. We reply within one business day with a first read on whether an agent system is the right tool, the agent shape we would propose, and an engagement range.
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