Transforming Luxury Automotive Service
THE CHALLENGE
Service advisors couldn't predict repair times until after diagnosis, creating a "chicken-and-egg" problem: they needed to diagnose to schedule, but needed to schedule to diagnose.
Problem Context
Patricia
Service Manager, Lucid Service Center
Unpredictable Service Timing
Inefficient Capacity Planning
Growing Vehicle Backlog
Guesswork Scheduling
Customer Frustration
Administrative Overload
The Solution
I helped orchestrate a solution that integrated the Data Science team's machine learning model with operational workflows to solve the fundamental "chicken-and-egg" problem of service scheduling. The system translated everyday customer language into accurate repair time predictions.
Solution Flow
Customer Concern
Input
ML Engine
Processing
Scheduling System
Optimization
Appointment
Output
System Components
BRAIN
ML prediction engine that analyzed customer descriptions and predicted repair requirements and service times with 85% accuracy.
HOW IT WORKS
The system uses natural language processing to convert customer concerns into service predictions:
- Text Processing: Customer complaints are normalized, tokenized, and analyzed for key terms.
- Pattern Matching: The system compares concerns against thousands of historical service records.
- Service Prediction: Using TF-IDF and word embeddings, the model identifies the most likely repair operations.
- Time Estimation: The system calculates FRT using the 85th percentile of historical completion times for similar services, ensuring realistic scheduling.
- Confidence Scoring: Each prediction includes a confidence rating, with low-confidence cases flagged for advisor review.
CAPACITY ENGINE
Scheduling system that optimized technician workloads and service bay utilization based on predicted service times.
HOW IT WORKS
The capacity engine applies mathematical optimization to scheduling:
- Dynamic Capacity Calculation: Continuously calculates available capacity using the formula: Available Capacity = (Productivity% × Tech Hours) - Internal Hours - Same-Day Hours - WIP Hours.
- Constraint-Based Scheduling: Ensures appointments meet multiple constraints including buffer days (minimum 3-day lead time), technician availability, and service center intake hours.
- WIP Management: Tracks all vehicles currently in service, their estimated completion times, and dynamically adjusts available capacity as work progresses.
- Configuration Controls: Allows service managers to set productivity factors (typically 70-85%), reserve blocks for internal work, and allocate same-day emergency capacity.
- Queueing Theory Application: Uses Kingman's formula to balance utilization and wait times, proving mathematically that more precise FRT predictions allow both higher utilization and shorter waits.
REMOTE SERVICE
On-location service identification system that allowed pre-diagnosis of issues before vehicle arrival.
HOW IT WORKS
The remote service component optimized mobile service operations:
- Mobile Capability Assessment: Analyzes customer concerns to determine if issues can be resolved through mobile service rather than in-center.
- Drive Time Optimization: Calculates travel times between service locations using geospatial algorithms, optimizing technician routes to minimize non-billable travel.
- Mobile Capacity Planning: Incorporates both FRT and travel time when calculating mobile technician capacity, ensuring realistic scheduling.
- Territory Management: Maps service territories and coverage areas, identifying scheduling opportunities in "white space" areas where mobile service can expand reach.
- Mobile Parts Management: Predicts parts needed for mobile appointments to ensure technicians carry appropriate inventory for scheduled work.
USER INTERFACES
Intuitive screens for service advisors and customers that simplified scheduling and provided transparency.
HOW IT WORKS
The interface layer transformed complex data into actionable information:
- Appointment Creation: Streamlined interface where advisors enter customer concerns and receive instant FRT predictions with confidence indicators, surfacing appropriate appointment slots.
- Configuration Dashboard: Management interface for setting productivity factors, buffer days, intake hours, and other parameters that define scheduling constraints.
- Capacity Visualization: Interactive calendar showing available capacity across days and service centers, with real-time updates as appointments are booked.
- Technician Workload View: Visual representation of technician schedules, skill levels, and utilization rates to identify optimization opportunities.
- Customer Interface: Simplified version of the scheduling system for the customer mobile app, allowing self-service appointment booking based on the same capacity engine.
Process Timeline
Discovery
Conducted interviews with service advisors and analyzed 18 months of historical service data to identify patterns.
Design
Developed system architecture and applied queueing theory to optimize scheduling algorithms.
Prototype
Built interactive mockups and tested the ML model with real service advisors using historical data.
Documentation
Created detailed product requirements document and technical specifications for implementation team.
Interactive Solution Preview
Explore this service scheduling dashboard that I created myself using Claude and Next.js. This interactive prototype allows service advisors to visualize capacity, monitor technician utilization, and manage the workflow of vehicles in the service center.
Key Insights
Integration Over Creation
Queueing Theory Application
Cross-Functional Leadership
Challenges & Solutions
Challenge
Organizational Silos
Service, sales, and parts departments operated independently with no shared data access
Solution
Created a cross-functional team with representatives from each department to guide development
Challenge
UX Resources
Limited UX resources to design interfaces for both service advisors and customers
Solution
Prioritized service advisor interface first, then created simplified customer view from the same data model
Challenge
Technical Gaps
Legacy systems had no APIs for integration and limited historical data access
Solution
Developed a data extraction layer that could work with existing systems without requiring rewrites
Challenge
Competing Priorities
Management wanted both increased capacity utilization and reduced customer wait times
Solution
Applied queueing theory to demonstrate how precision in scheduling would achieve both goals simultaneously
The Results
Business Impact
Operational Excellence
Service departments could now manage their resources efficiently, improving both capacity utilization and worker satisfaction.
Customer Experience
Customers received more accurate time estimates and faster service, increasing satisfaction scores by 15%.
METRICS & PERFORMANCE
Prediction Quality
- Mean Absolute Percentage Error: <15% for routine maintenance, <25% for complex repairs
- Override Rate: Reduced from 65% to under 20% after 3 months
- Confidence Score Accuracy: 92% of high-confidence predictions completed within estimated time
Operational Efficiency
- Technician Efficiency: 20% improvement in FRT-to-actual-hours ratio
- Admin Time: 70% reduction in scheduling-related administrative tasks
- Mobile Service Optimization: 30% increase in mobile appointments completed per day
Business Impact
- Capacity Utilization: 30% increase in billable hours per bay
- Appointment Wait Time: Reduced from 14 days to 3 days average
- Aging Vehicles: 35% reduction in vehicles remaining over 10 days