Utility Admin Dashboard (ONB-SC12)

Scenario 1 – Multi-Utility Configuration Crisis Management

Scenario Description A utility administrator discovers critical configuration gaps during peak billing season that threaten revenue collection and customer service delivery.

Objective (Why)

• Business Goal: Prevent revenue loss estimated at $250,000 due to incomplete billing configurations and ensure 99.5% billing cycle completion
• Operational Goal: Achieve 100% configuration completion across all utility services within 48 hours to avoid service disruptions
• Compliance Goal: Meet regulatory requirements for accurate billing and customer data management before the monthly audit

If Not Set – Business Impact

• Revenue Loss: Incomplete pricing configurations could result in $250,000 monthly revenue gap from unbilled consumption
• Compliance Violations: Missing service area mappings may lead to $50,000 regulatory penalties for improper territory management
• Customer Dissatisfaction: 35% increase in service requests due to billing errors and service interruptions

Scenario Explanation - in short

Case: Sarah Martinez, Utility Administrator at Metro Water & Electric, logs into her dashboard Monday morning to find overall setup completion at only 60%. Her billing cycle shows 76% completion with 15,774 unpaid bills out of 52,430 generated. The "Plans and Tariffs" section shows 33% completion, meaning residential customers like John Peterson at 123 Oak Street aren't being billed correctly for his $127.50 monthly electric usage. Sarah needs to immediately configure the missing residential rate of $0.12/kWh and commercial rate of $0.08/kWh to prevent a $250,000 revenue shortfall this month.

Audience (Why it Matters) - in short

• CSM: Must explain to customers like John Peterson why his bill is delayed and ensure accurate billing once configurations are complete
• QA: Must validate that rate calculations work correctly ($127.50 = 1,062 kWh × $0.12/kWh) and test billing cycle completion tracking
• Engineers/Interns: Must understand configuration dependencies - how tariff setup affects billing generation, which impacts revenue collection metrics

Does it fit in SMART360

YES - Perfect Fit

Step-by-step Application:

1. Dashboard Overview: Sarah views the 60% completion indicator and identifies "Plans and Tariffs" at 33%
2. Configuration Navigation: Clicks "Configure Section" on Plans and Tariffs card
3. Tariff Creation:
   • Creates "Residential Electric" tariff: $0.12/kWh base rate, $15 monthly connection fee
   • Creates "Commercial Electric" tariff: $0.08/kWh base rate, $35 monthly connection fee
4. Progress Updates: Returns to dashboard, sees Plans and Tariffs jump to 100%, overall completion rises to 76%
5. Billing Impact: Revenue collection metric updates showing projected $215,000 recovery
6. Validation: QA tests John Peterson's account: 1,062 kWh × $0.12 + $15 = $142.44 (corrected from $127.50)

Scenario 2 – Emergency Service Area Reconfiguration

Scenario Description A utility administrator must rapidly reconfigure service territories after a major infrastructure failure affects 15,000 customers across multiple zones.

Objective (Why)

• Business Goal: Restore service billing and work order management for 15,000 affected customers within 24 hours
• Operational Goal: Reallocate field crews efficiently across temporarily merged service areas to handle 450+ work orders
• Customer Goal: Minimize service interruption and ensure accurate billing despite territory changes

If Not Set – Business Impact

• Operational Chaos: 450 work orders become unassigned, extending outage time from 4 hours to 16+ hours
• Revenue Impact: $180,000 in unbilled services due to confused territory mappings and delayed meter readings
• Customer Trust: 15,000 customers face billing errors and extended outages, risking 12% customer churn

Scenario Explanation - in short

Case: Robert Chen, Utility Administrator at Valley Electric Cooperative, faces a transformer explosion that disabled the North Valley substation serving 15,000 customers. His dashboard shows 87 completed work orders but 110 new ones flooding in. He must merge North Valley territory (ZIP codes 85001-85003) with Central Valley territory temporarily. Customer Maria Rodriguez at 456 Pine Street (ZIP 85002) needs her power restored and accurate billing maintained at her usual $89.50/month rate. Robert uses the Service Area configuration to create temporary boundaries and reassign field crews from the Central Valley team to handle North Valley emergencies.

Audience (Why it Matters) - in short

• CSM: Must communicate to Maria Rodriguez and 15,000 customers about temporary service changes and billing continuity
• QA: Must verify that work order assignments function correctly in merged territories and billing remains accurate across ZIP code changes
• Engineers/Interns: Must understand how service area boundaries affect work order routing and billing territory logic

Does it fit in SMART360

YES - Strong Fit with Enhancements Needed

Step-by-step Application:

1. Emergency Dashboard View: Robert sees 110 new work orders and declining service metrics
2. Service Area Configuration: Accesses Service Area section showing current territories
3. Temporary Merge: Creates "North-Central Combined" territory encompassing ZIP codes 85001-85005
4. Work Order Reassignment: System automatically reassigns 110 work orders to available Central Valley crews
5. Billing Continuity: Maintains existing rate structures ($0.11/kWh residential) despite territory changes
6. Progress Tracking: Work order completion rate improves from 44% to 78% within 6 hours

Gap Identified: Current system needs emergency territory management features for disaster scenarios.

Scenario 3 – Multi-Service Customer Integration Challenge

Scenario Description A utility administrator manages the complex onboarding of a large commercial customer requiring water, electric, and gas services with specialized billing across multiple premises.

Objective (Why)

• Business Goal: Secure $45,000 monthly revenue from TechCorp Industries across 4 building locations with combined utility services
• Operational Goal: Configure complex multi-service billing with different rates for office space ($890/month electric), manufacturing ($2,340/month electric), and cafeteria facilities ($156/month gas)
• Customer Goal: Provide seamless service activation and consolidated billing for TechCorp's facility manager

If Not Set – Business Impact

• Lost Revenue: $45,000 monthly contract at risk if services aren't configured properly within 5 business days
• Operational Inefficiency: Manual billing calculations increase processing time by 300% and create error risks
• Customer Experience: TechCorp may seek alternative providers, representing $540,000 annual revenue loss

Scenario Explanation - in short

Case: Linda Foster, Utility Administrator at Riverside Municipal Utilities, must configure services for TechCorp Industries' four buildings. Building A (offices) needs 8,100 kWh monthly electric at $0.11/kWh = $891. Building B (manufacturing) requires 26,000 kWh at $0.09/kWh = $2,340. Building C (cafeteria) uses 1,200 therms gas at $0.13/therm = $156. Building D (warehouse) needs basic electric at $445/month. Linda must create separate premises in her Service Area configuration, assign appropriate tariffs, and ensure consolidated billing totaling $3,832 monthly appears correctly in TechCorp's account.

Audience (Why it Matters) - in short

• CSM: Must coordinate with TechCorp's facility manager on service activation timeline and explain consolidated billing structure
• QA: Must validate complex billing calculations across multiple services and premises, ensuring $3,832 total matches individual building calculations
• Engineers/Interns: Must understand premise-based billing logic and how multiple utility services integrate within a single customer account

Does it fit in SMART360

PARTIALLY - Requires Enhancements

Current Fit:

1. Premise Configuration: Can add 4 premises through Service Area section
2. Tariff Setup: Can create different rates for electric ($0.11, $0.09) and gas ($0.13) services
3. Progress Tracking: Dashboard shows configuration completion across utility services

Gaps Identified:

• No consolidated billing view for multi-premise customers
• Limited multi-utility service integration in single dashboard view
• Complex commercial billing rules need more sophisticated configuration options

Enhancement Needed: Multi-premise billing consolidation feature and advanced commercial rate structures to fully support scenarios like TechCorp's complex requirements.

Scenario 4 – Revenue Analytics Deep Dive for Performance Recovery

Scenario Description A utility administrator uses advanced analytics to identify and resolve a 15% revenue decline by analyzing billing patterns, collection efficiency, and customer payment behaviors across multiple utility services.

Objective (Why)

• Business Goal: Recover $180,000 monthly revenue loss by identifying root causes through data analytics and implementing targeted fixes
• Operational Goal: Improve collection efficiency from 78% to 92% by analyzing aging buckets and payment patterns
• Strategic Goal: Use predictive analytics to prevent future revenue leakage and optimize billing cycle performance

If Not Set – Business Impact

• Revenue Hemorrhage: Continued $180,000 monthly loss could result in $2.16M annual revenue deficit
• Cash Flow Crisis: Outstanding receivables in 90+ day bucket growing from $450,000 to $890,000 within 6 months
• Regulatory Risk: Poor collection metrics may trigger utility commission investigation and potential rate adjustment restrictions

Scenario Explanation - in short

Case: David Park, Utility Administrator at Coastal Energy Services, notices his Revenue Collection dashboard showing concerning trends. Monthly revenue dropped from $1.2M to $1.02M over 3 months. His analytics reveal: Collection Efficiency at 78% (target: 92%), 0-30 day aging bucket at $320,000 (26%), 30-60 days at $285,000 (23%), 60-90 days at $198,000 (16%), and 90+ days at $427,000 (35%). The analytics show residential customer Jennifer Walsh's account ($89.50/month) moved to 60-90 day bucket, while commercial customer Metro Manufacturing ($4,230/month) is now 90+ days overdue. David uses the analytics to identify that rate plan changes in March weren't properly communicated, causing customer confusion and delayed payments.

Audience (Why it Matters) - in short

• CSM: Must contact Jennifer Walsh and Metro Manufacturing with data-driven explanations of billing changes and payment assistance options
• QA: Must validate analytics calculations showing Collection Efficiency = ($942,000 collected ÷ $1,208,000 billable) × 100 = 78%
• Engineers/Interns: Must understand how aging bucket percentages connect to cash flow projections and revenue forecasting algorithms

Does it fit in SMART360

YES - Strong Analytics Foundation

Step-by-step Analytics Application:

1. Revenue Trend Analysis: David views 3-month revenue decline chart showing $1.2M → $1.15M → $1.08M → $1.02M
2. Collection Efficiency Drill-down:
   • Total Billable: $1,208,000
   • Total Collected: $942,000
   • Efficiency: 78% (14% below target)
3. Aging Bucket Analysis:
   • 0-30 days: $320,000 (26%) - Normal range
   • 30-60 days: $285,000 (23%) - Above normal 18%
   • 60-90 days: $198,000 (16%) - Critical threshold
   • 90+ days: $427,000 (35%) - Crisis level
4. Root Cause Identification: Analytics show rate change correlation with payment delays
5. Recovery Action Plan: Target 60-90 and 90+ day accounts with communication campaign
6. Predictive Modeling: System forecasts 12% improvement in collection efficiency within 60 days

Scenario 5 – Operational Performance Analytics for Service Optimization

Scenario Description A utility administrator leverages comprehensive operational analytics to optimize work order completion, reduce service request volume, and improve customer satisfaction scores through data-driven decision making.

Objective (Why)

• Business Goal: Reduce operational costs by $125,000 annually through improved work order efficiency and proactive service management
• Performance Goal: Increase work order completion rate from 78% to 95% and reduce average resolution time from 4.2 days to 2.1 days
• Customer Goal: Decrease service requests by 30% through predictive maintenance and improve customer satisfaction from 72% to 88%

If Not Set – Business Impact

• Operational Inefficiency: Work order backlog grows from 142 to 400+ orders, overwhelming field crews and extending outages
• Customer Exodus: Poor service metrics drive 18% customer churn, losing $340,000 in annual recurring revenue
• Regulatory Compliance: Service quality metrics below standards trigger $85,000 in regulatory penalties and mandatory improvement plans

Scenario Explanation - in short

Case: Amanda Rodriguez, Utility Administrator at Highland Water District, analyzes her operations dashboard revealing critical patterns. Work order completion shows 87 completed vs 25 in-progress and 30 overdue (78% completion rate). Service requests jumped 22% with 89 high-priority issues. Her analytics identify that Sector 7 (Hillcrest neighborhood) generates 35% of service requests despite having only 12% of customers. Customer Betty Thompson at 789 Maple Drive has filed 4 service requests in 60 days for low water pressure. The analytics show correlation between aging infrastructure in Sector 7 (installed 1987-1992) and service issues. Amanda uses predictive analytics to schedule proactive main line replacements, reducing reactive service calls by 28%.

Audience (Why it Matters) - in short

• CSM: Must proactively contact Betty Thompson and Sector 7 customers about planned infrastructure improvements and temporary service impacts
• QA: Must validate analytics showing Work Order Completion Rate = (87 completed ÷ 112 total) × 100 = 78% and verify predictive maintenance algorithms
• Engineers/Interns: Must understand correlation analysis between infrastructure age and service request patterns for predictive maintenance scheduling

Does it fit in SMART360

YES - Excellent Operational Analytics Match

Step-by-step Analytics Implementation:

1. Work Order Performance Dashboard:
   • Completed: 87 orders
   • In Progress: 25 orders
   • Overdue: 30 orders
   • Completion Rate: 78%
2. Service Request Analytics:
   • Total Requests: 156 (22% increase)
   • High Priority: 89 requests
   • Sector 7: 55 requests (35% of total)
3. Geographic Heat Map: Shows Sector 7 concentration of issues
4. Predictive Maintenance Model: Correlates infrastructure age with failure probability
5. Proactive Work Order Generation: System creates 15 preventive maintenance orders for Sector 7
6. Performance Impact Measurement:
   • Service requests decrease 28% in targeted areas
   • Work order completion improves to 91%

Scenario 6 – Billing Cycle Analytics and Revenue Optimization

Scenario Description A utility administrator uses advanced billing analytics to optimize revenue collection, reduce billing errors, and improve cash flow through data-driven billing cycle management and customer payment behavior analysis.

Objective (Why)

• Revenue Goal: Optimize billing delivery to achieve 98% bill delivery rate and reduce days sales outstanding (DSO) from 45 to 32 days
• Efficiency Goal: Decrease billing errors by 60% through analytics-driven quality control and automated exception handling
• Cash Flow Goal: Improve monthly cash collection by $280,000 through optimized billing timing and payment method analytics

If Not Set – Business Impact

• Cash Flow Strain: Extended DSO costs $95,000 monthly in financing charges and working capital inefficiency
• Customer Disputes: Billing errors generate 340+ monthly disputes, consuming 120 CSR hours and creating customer dissatisfaction
• Revenue Recognition Delays: Poor billing delivery affects financial reporting accuracy and regulatory compliance metrics

Scenario Explanation - in short

Case: Michael Chen, Utility Administrator at Valley Municipal Power, analyzes his billing cycle dashboard showing concerning metrics. Of 52,430 bills generated, only 49,856 were delivered (95% delivery rate) and just 36,701 were paid (70% payment rate). His billing analytics reveal that Tuesday-generated bills have 89% payment rates versus Friday-generated bills at 62%. Customer segments show residential customers like Susan Martinez ($67.80/month) pay within 12 days when billed Tuesday but 28 days when billed Friday. Commercial customer TechStart Inc. ($2,340/month) consistently pays in 15 days regardless of billing day. Michael uses analytics to optimize billing schedules, moving 15,000 residential accounts to Tuesday cycles and analyzing payment method preferences (auto-pay customers: 95% on-time rate vs manual pay: 71%).

Audience (Why it Matters) - in short

• CSM: Must communicate billing schedule changes to Susan Martinez and explain auto-pay benefits for improving payment timing
• QA: Must validate billing analytics calculations showing 70% payment rate = (36,701 paid ÷ 52,430 generated) × 100 and test optimized scheduling logic
• Engineers/Interns: Must understand payment behavior algorithms and how billing timing affects cash flow optimization models

Does it fit in SMART360

YES - Advanced Billing Analytics Perfect Fit

Step-by-step Analytics Implementation:

1. Billing Cycle Performance Analysis:
   • Generated Bills: 52,430
   • Delivered Bills: 49,856 (95% delivery rate)
   • Paid Bills: 36,701 (70% payment rate)
   • Cycle Completion: 76%
2. Payment Timing Analytics:
   • Tuesday Billing: 89% payment rate, 12-day average payment time
   • Friday Billing: 62% payment rate, 28-day average payment time
   • Auto-pay Customers: 95% on-time rate
   • Manual Pay Customers: 71% on-time rate
3. Customer Segmentation Analysis:
   • Residential: 42,000 accounts, $89 average bill, 25-day payment cycle
   • Commercial: 8,200 accounts, $420 average bill, 18-day payment cycle
   • Industrial: 2,230 accounts, $2,180 average bill, 15-day payment cycle
4. Revenue Optimization Actions:
   • Move 15,000 residential accounts to Tuesday billing
   • Launch auto-pay campaign targeting manual-pay customers
   • Implement dynamic billing schedules based on customer payment history
5. Projected Impact Analytics:
   • Payment rate improvement: 70% → 83%
   • DSO reduction: 45 days → 32 days
   • Monthly cash flow improvement: $280,000

Advanced Analytics Features:

• Predictive payment modeling based on customer history
• Billing error pattern recognition and prevention
• Dynamic billing schedule optimization
• Payment method preference analysis
• Revenue forecasting with confidence intervals