1. Introduction
DealerOps AI Command Center is a frontend-only enterprise demo that simulates AI-assisted service operations management for multi-store auto retail dealer groups, with a focus on Georgia-based operations.
The business narrative is operational visibility, decision acceleration, proactive issue detection, and service optimization. All data and AI outputs are generated from deterministic JavaScript simulation logic and mock JSON structures.
2. Feature Guide
Executive KPI Dashboard
Five KPI cards summarize appointment demand, delay pressure, technician loading, revenue exposure, and customer satisfaction risk.
Use this panel first to set overall operational context in under 20 seconds.
Operational Charts
Chart.js visualizations show service load distribution, delay trend trajectory, technician utilization pressure, and parts shortage hotspots.
Use this section to explain why risk is emerging and where to intervene.
Multi-Store Operations View
Store-level table provides appointments, delay risk, utilization, parts risk, revenue risk, and consolidated status indicator.
Color semantics: green = low risk, amber = moderate risk, red = high risk.
Real-Time Alerts + AI Insight
Alert feed simulates live SLA breaches, parts constraints, and customer escalations. AI Insight summarizes likely root causes and corrective opportunities.
Use this to show proactive intelligence beyond static reporting.
AI Copilot Panel
Chat-style assistant answers operational questions such as risk outlook, delay causes, priority customers, and shortages.
Suggested prompts are available for guided demos.
Executive Daily Brief
AI-style typed narrative summarizes disruption risk, revenue exposure, likely drivers, and recommended actions for morning reviews.
This section is ideal to close the value story with concrete action planning.
3. Demo Flow (8-10 Minutes)
- Open with business value: "DealerOps gives service leaders a live AI operations layer across all stores."
- Show KPI snapshot: highlight Revenue At Risk and Service Delay Risk to establish urgency.
- Explain root causes in charts: connect high load and utilization to delay trend growth.
- Drill into store table: identify top 2 red stores and explain risk decomposition.
- Review real-time alerts: show SLA breach, parts shortage, and escalation examples.
- Read AI insights: emphasize recommendations are generated from cross-signal analysis.
- Use AI Copilot: ask at least 2 prompts and narrate actionability of responses.
- Finish with executive brief: present AI summary as a morning leadership artifact.
4. Demo Script (Presenter Notes)
Opening Script (30 sec)
"This is DealerOps AI Command Center by FPT Software Auto Retail Operations Branch. It gives dealership groups a single live command center for service risk, technician capacity, inventory constraints, and revenue exposure."
KPI Script
"At a glance, we can see total demand, delay pressure, and financial impact. This lets leadership prioritize interventions by impact, not guesswork."
Charts Script
"Here we correlate store load and utilization with delay trend. The parts heatmap identifies inventory constraints that are likely to trigger SLA misses."
Copilot Script
"Instead of manually scanning dashboards, managers can ask: Which stores are at risk tomorrow? What is causing service delays? Which customers are high priority?"
Closing Script
"DealerOps accelerates decisions from reactive firefighting to proactive orchestration. The result is lower delays, protected revenue, and stronger customer trust."
5. Operator Guidelines
- Refresh the dashboard before each demo and wait for loading animation completion.
- Keep the copilot prompts focused on operations, capacity, and service risk.
- Avoid over-explaining AI internals; emphasize outcomes and management actions.
- Use the same 8-step demo flow for consistency across enterprise presentations.
- End with quantified value: risk reduced, exposure contained, faster intervention cycles.