Enterprise Operations Analytics | Workforce Planning | Capacity Optimization | Decision Support | SQL • Python • Tableau
📍 Houston, Texas 🔗 LinkedIn
Most analytics projects explain what happened.
Mine are designed to answer a different question:
What should we do next?
My career has been built inside operations—ATM network management, workforce planning, contact centers, and high-volume service environments where decisions affect customers, service levels, and revenue in real time.
That background shapes every project I build.
I don't view analytics as reporting.
I view analytics as decision support.
A low-volume casino ATM located 142 miles from the nearest branch with only 0.8 days of cash remaining is operationally more critical than a high-volume urban ATM with six days of runway.
Traditional reporting treats them differently.
Operational analytics should not.
4 Enterprise Analytics Projects
2 Operational Domains
18+ Years of Enterprise Operations Experience
Specializing in transforming operational data into actionable business decisions.
Analytics & Engineering
- SQL
- Python
- Pandas
- NumPy
- Matplotlib
- Tableau
- Business Intelligence
Domain Expertise
- Workforce Management
- Workforce Capacity Planning
- Intraday Staffing
- Erlang C Modeling
- Shrinkage Planning
- Service Level Management
- Call Center Forecasting
- ATM Network Operations
- Cash Management Analytics
- Operational Risk Intelligence
- Executive Decision Support
Two parallel tracks.
One philosophy.
Operational analytics creates the greatest value when it moves beyond describing operations and begins improving operational decisions.
→ View Repository
Tools
Python • Pandas • NumPy • Matplotlib • Erlang C • Jupyter Notebook
Given real contact-center arrival data, can we build an enterprise workforce planning workflow—from interval-level forecasting through staffing recommendations that leadership can act upon?
The most valuable insight wasn't the forecast itself.
It was what happened after the forecast.
| Module | Description | Status |
|---|---|---|
| Forecast Engine | Interval-level forecasting with holdout validation (21.2% WAPE) | ✅ |
| Capacity Planning | Erlang C staffing, shrinkage modeling, FTE sizing | ✅ |
| Staffing Gap Analysis | Required vs. scheduled staffing by interval | ✅ |
| Workforce Optimization Engine | Staffing redistribution without additional hiring | ✅ |
| Decision Lab | Scenario simulator for staffing decisions | ✅ |
| Multichannel Planning | Voice, Chat, Email planning methodologies | ✅ |
| Executive Dashboard | Leadership KPI dashboard | 🔄 |
| Executive Brief | Executive summary and recommendations | 🔄 |
| Metric | Result |
|---|---|
| Forecast Accuracy | 21.2% WAPE |
| Staffing Variance | -1.6% |
| Understaffed Intervals | 53% |
| Overstaffed Intervals | 44% |
| Balanced Intervals | 3% |
| Delivered Service Level | 62.4% |
| Target Service Level | 80% |
These findings were independently validated to ensure that every business conclusion accurately reflects the underlying calculations.
Total scheduled staffing was within 1.6% of calculated requirements.
Yet:
- 53% of operating intervals were understaffed.
- 44% were overstaffed.
- Service level achieved only 62.4% against an 80% target.
The issue wasn't total staffing.
It was interval-level workforce distribution.
Redistributing existing staffing—without adding a single FTE—recovered approximately 20 percentage points of service level and exceeded the 80% target.
The project demonstrates how workforce analytics can improve operational performance before recommending additional hiring.
| Scenario | Projected Service Level | 80% Target |
|---|---|---|
| Current Schedule | 62.4% | ❌ |
| Redistribute Existing Staff | 82.5% | ✅ |
| Add Five FTE | 95.5% | ✅ |
| Reduce AHT 15% | 76.2% | ❌ |
| Reduce Shrinkage 4 Points | 69.3% | ❌ |
| Combined Improvements | 94.4% | ✅ |
Peer-reviewed by an experienced Workforce Management practitioner (Encore Capital Group, 13+ years).
Three projects.
One operational argument.
Transaction volume alone is an incomplete measure of operational risk.
→ View Repository | → Live Tableau Dashboard
Predictive cash forecasting designed to identify which ATMs will become operationally critical within the next 72 hours—and recommend proactive replenishment priorities.
Highlights
- Revenue at Risk: $830,880
- Critical Locations: 5
- Immediate Dispatches: 5
- Average Time to Failure: 0.6 Days
One of the project's signature concepts is the Refund Rush Effect—a real operational scenario demonstrating how external business changes can invalidate historical demand assumptions almost overnight.
Composite operational risk scoring framework combining:
- Cash Position
- Distance
- Location Type
- Revenue Impact
- Operational Criticality
into a single enterprise prioritization model.
Foundational SQL analytics demonstrating why transaction volume alone is an incomplete measure of ATM operational priority.
| Project | Focus | Primary Tools | Status |
|---|---|---|---|
| ATM Network Analysis V2 | ATM Analytics | SQL | ✅ |
| ATM Network Risk Intelligence | Risk Intelligence | SQL | ✅ |
| ATM Predictive Demand Model | Predictive Analytics | Python • SQL • Tableau | ✅ |
| Enterprise Workforce Planning & Optimization Engine | Workforce Planning | Python • Erlang C | 🚧 |
MCI Telecommunications
Intraday Workforce Management for a 350+ agent multi-site contact center.
Experience includes:
- Intraday staffing
- Abandonment-rate management
- Schedule exception coding
- IEX TotalView
- Workforce coordination across multiple time zones
Cardtronics / NCR
Supported one of North America's largest ATM networks.
Experience includes:
- 45,000+ ATM terminals
- 98% contractual uptime SLA
- ~$8M annual theft-loss avoidance coordination
- Executive operational reporting
- Enterprise cash logistics
Margo / PowerCoin
Built cash operations from the ground up for an early-stage Bitcoin ATM operator.
Independent Operations Analytics Consultant focused on enterprise workforce planning, operational analytics, forecasting, and decision support.
I'm interested in remote opportunities involving:
- Workforce Planning
- Capacity Planning
- Operations Analytics
- Business Intelligence
- Data Analytics
- Fintech
- HealthTech
- SaaS
- Payments
particularly where analytics directly support operational decision-making.
🔗 LinkedIn — Sean Codner
Portfolio datasets are either real (Technion "Anonymous Bank" call-center data) or synthetic datasets designed to model realistic enterprise operating environments. All synthetic workforce assumptions are explicitly identified throughout the projects.