🎯
Business Objectives
- ›Give every role (owner, ops manager, dispatcher, customer) a single live view of the business
- ›Surface exceptions before they become escalations
- ›Reduce time-to-insight from hours of reports to seconds of conversation
⚙️
Operational KPIs
- ›Open WOs by status
- ›Vehicles in motion vs. dwelling
- ›Driver utilization
- ›Daily gate-in / gate-out
📈
Management KPIs
- ›Revenue MTD
- ›Avg margin per WO
- ›On-time delivery %
- ›Damage claim rate
🤝
Customer KPIs
- ›SLA compliance per customer
- ›ETA accuracy
- ›POD turnaround time
🗄️
Available Data
- ›Cross-module counts (users, drivers, vehicles, customers, work orders, yards)
- ›Status mix across the WO lifecycle
- ›User-applied filters and saved views
- ›Recent activity stream
✨
AI Use Cases
- ›Natural-language Q&A across all modules
- ›Auto-generated daily / weekly executive summary
- ›Personalized insight tiles per role
🔮
Predictive Analytics
- ›Forecasted week-ahead WO volume
- ›Predicted SLA breach risk per open WO
🚨
Anomaly Detection
- ›Unusual drop in gate-in volume
- ›Spike in on-hold WOs
🎁
Recommendation Engines
- ›Suggested next-best action per exception
- ›Recommended saved filter based on role
🤖
AI Copilot Features
- ›Ask: 'What changed since yesterday?'
- ›Ask: 'What should I look at first?'
💬
Natural-Language Reporting
- ›'Email me a Monday morning brief by 7am'
- ›'Show me everything above $5k that is late'
📊
Executive Dashboard Metrics
- ›Revenue, Margin, OTD, Damage, Yard Util — single strip
⚡
Workflow Automation
- ›Auto-create dashboards for new users by role
- ›Auto-pin breaches to top of feed
🛰️
Autonomous Agents
- ›Morning Briefing Agent posts to Slack/Email
- ›Exception Triage Agent assigns issues
🖥️
Recommended Dashboards
- ›Owner View
- ›Operations View
- ›Customer View
- ›Driver View
💡
Example Executive Insights
- ›Revenue tracking +12% MoM, driven by Enterprise lane
- ›3 SLA breaches forecast in next 48h — proactive action available
📦
Required Datasets
- ›work_orders
- ›users
- ›activity_log
- ›yards
- ›customers
📅
Historical Data Needed
12–24 months for seasonality
🧠
ML Models / AI Approaches
- ›LLM + RAG over portal data
- ›Time-series (Prophet / ARIMA) for volume