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Data migration (ONB-SC13)

Utility Data Migration Scenarios for SMART360

Scenario 1 – Water Utility Legacy System Migration

Scenario Description A regional water utility migrates 25,000 customer accounts from a 15-year-old billing system to SMART360, including meter readings, payment histories, and service connections.

Objective (Why)

  • Business Goal: Eliminate $50,000 annual maintenance costs of legacy system while improving billing accuracy from 78% to 98%
  • Consumer Goal: Enable online account access, accurate billing, and faster service request processing
  • Compliance Goal: Meet state regulatory requirements for digital meter reading records and billing transparency

If Not Set – Business Impact

  • Revenue Loss: $125,000 annual revenue leakage due to inaccurate meter-to-customer mapping and missed billing cycles
  • Operational Inefficiency: Customer service team spends 40% of time correcting billing errors instead of serving customers
  • Regulatory Risk: Potential $75,000 fines for non-compliance with state digital recordkeeping mandates

Scenario Explanation - in short

Springfield Water Authority has 25,847 residential customers using legacy meters with manual reading processes. Customer "Maria Rodriguez" (Account #WA-2024-15673) has a 15mm ZENNER meter (#12,133,911) at 456 Oak Avenue with inconsistent billing history - some months showing 0 usage due to reading errors. Her last accurate bill was $89.50 for 2,254 gallons, but system shows three $0.00 bills and one $245.67 bill due to data errors. Migration must correctly map her meter to account, transfer 24 months of corrected usage history, and establish proper billing premise connections.

Audience (Why it Matters) - in short

  • CSM: Must explain to customers like Maria why their billing history might show corrections post-migration and handle calls about updated account access methods
  • QA: Must validate that meter #12,133,911 correctly maps to customer account WA-2024-15673, consumption data matches physical readings, and billing calculations align with rate structures
  • Engineers/Interns: Must understand the relationship between meter_number, consumer_no, premise addresses, and rate structures to troubleshoot mapping issues and optimize AI field detection algorithms

Does it fit in SMART360

It fits perfectly. Here's the step-by-step implementation:

  1. Data Type Selection: Choose "Consumer Data" with required fields: Consumer No, Name, Address, Meter Number, Service Connection
  2. File Upload: Springfield uploads CSV with 25,847 rows containing: Account, Service, Surname/Given Name, Address, Meter#, Rate, Balance, Last Payment Date
  3. AI Column Mapping:
    • "Account" → Consumer No (95% confidence)
    • "Surname,Given Name" → Customer Name (needs Split transformation)
    • "Meter#" → Meter Number (95% confidence)
    • "Address" → Billing Premise (85% confidence)
  4. Data Transformation: Apply "Split" transformation on name field using comma delimiter
  5. System Data Mapping: Map "Rate" field to utility service plans (UD1 → Basic Water Service)
  6. Validation: System identifies 347 invalid meter-customer mappings requiring manual correction
  7. Migration Execution: Successfully migrate 25,500 records, flag 347 for review with specific error descriptions

Scenario 2 – Multi-Utility Gas & Electric Consolidation

Scenario Description City municipal utility consolidates separate gas and electric customer databases following acquisition of private utility, requiring complex data relationship mapping and duplicate customer resolution.

Objective (Why)

  • Business Goal: Reduce operational costs by $200,000 annually through unified customer service and consolidated billing systems
  • Consumer Goal: Single account management for both gas and electric services with unified billing and payment processing
  • Operational Goal: Eliminate duplicate customer records and establish proper service territory mapping

If Not Set – Business Impact

  • Customer Confusion: 15,000 customers receive duplicate bills for same address, leading to 300+ daily complaint calls
  • Revenue Leakage: $180,000 annual losses due to payment misallocations and unbilled services during transition period
  • System Maintenance: Maintaining two separate systems costs additional $85,000 annually in licensing and support

Scenario Explanation - in short

Metro City Utilities acquired EnergyPlus Corp, inheriting 18,500 gas customers and 22,300 electric customers with 8,200 addresses served by both utilities. Customer "David Chen" at 789 Pine Street has gas account #GAS-8847 ($156.30 monthly average) and electric account #ELEC-4429 ($89.45 monthly average). The migration must merge these into single consumer record CUST-789-PINE, maintain separate meter readings and service connections, establish proper rate structures (G-Residential for gas, E-Standard for electric), and transfer 18 months of payment history for both services.

Audience (Why it Matters) - in short

  • CSM: Must handle customer calls from David Chen asking why he now has one account number instead of two, explain combined billing process, and resolve any service connection issues
  • QA: Must verify that gas meter #GAS-8847 and electric meter #ELEC-4429 both correctly link to unified customer record CUST-789-PINE, rate schedules apply correctly, and historical consumption data remains accurate
  • Engineers/Interns: Must understand multi-service customer data model, how to handle duplicate address detection, service-specific rate mapping, and the logic for maintaining separate utility services under unified customer records

Does it fit in SMART360

It fits with enhanced configuration. Here's the implementation:

  1. Utility Setup: Configure two utilities - "Gas Division" and "Electric Division" in SMART360
  2. Data Type Selection: Upload "Consumer Data" for each division separately
  3. Gas Migration:
    • Upload 18,500 gas customers with fields: Customer_ID, Name, Address, Gas_Meter, Service_Type, Rate_Schedule
    • AI maps fields with Split transformation for customer names
    • System Data Mapping assigns "G-Residential" service plans
  4. Electric Migration:
    • Upload 22,300 electric customers with similar structure
    • System Data Mapping assigns "E-Standard" service plans
  5. Duplicate Detection: System identifies 8,200 addresses with multiple services
  6. Relationship Mapping:
    • Map gas and electric services to same customer where addresses match
    • Validate service territory boundaries for each utility type
    • Establish proper meter-to-service connections
  7. Migration Execution: Create 28,600 unique customer records with appropriate multi-service connections

Scenario 3 – Asset Management System Integration

Scenario Description Regional wastewater treatment authority migrates infrastructure asset data from Excel spreadsheets and separate maintenance systems into SMART360's integrated asset management module.

Objective (Why)

  • Business Goal: Reduce asset maintenance costs by 25% through predictive maintenance and proper asset lifecycle tracking
  • Operational Goal: Centralize 15,000+ assets across 45 facilities with complete maintenance history and replacement scheduling
  • Compliance Goal: Meet EPA asset management requirements for infrastructure investment planning and rate justification

If Not Set – Business Impact

  • Maintenance Overruns: $350,000 annual cost overruns due to reactive maintenance instead of planned asset replacement
  • Regulatory Compliance: Risk $500,000+ EPA fines for inadequate asset management documentation during rate review proceedings
  • Operational Disruptions: 12% increase in unplanned service outages due to poor asset condition tracking and replacement planning

Scenario Explanation - in short

Metro Wastewater Authority manages 47 pump stations, 12 treatment plants, and 15,000 pipeline assets using Excel files and paper maintenance logs. Treatment Plant #3 has 247 individual assets including Pump Station PS-447 (installed 2018, $85,000 replacement cost) with motor M-447-01, controller C-447-01, and sensors S-447-A through S-447-D. Current maintenance schedules exist in separate Excel files, causing missed PM schedules. The migration must establish facility hierarchy, assign assets to correct locations, transfer maintenance history, set up replacement schedules based on useful life, and create proper asset relationships.

Audience (Why it Matters) - in short

  • CSM: Must explain to facility managers why asset numbers changed during migration and help them understand new predictive maintenance notifications
  • QA: Must validate that Pump Station PS-447 correctly belongs to Treatment Plant #3, maintenance schedules transfer accurately, and asset relationships (motor belongs to pump) are properly established
  • Engineers/Interns: Must understand asset hierarchy models (Facility → System → Asset), how useful life calculations drive replacement schedules, and parent-child asset relationships for maintenance planning

Does it fit in SMART360

It fits with comprehensive asset module usage. Here's the implementation:

  1. Data Type Selection: Use "Asset Data", "Plant Data", and "Unit Data" migration types
  2. Facility Migration:
    • Upload 47 facilities with: Facility_ID, Name, Type, Address, Manager, Capacity
    • AI maps facility types (Treatment Plant, Pump Station, Storage)
  3. Asset Migration:
    • Upload 15,000+ assets with: Asset_ID, Name, Facility_ID, Type, Install_Date, Cost, Useful_Life
    • System Data Mapping assigns assets to correct facilities
    • Establish asset class hierarchy (Mechanical, Electrical, Instrumentation)
  4. Relationship Mapping:
    • Map assets to parent facilities using Facility Assignment tab
    • Validate asset types match facility purposes
    • Create parent-child asset relationships (Motor belongs to Pump)
  5. Maintenance Data:
    • Upload historical maintenance records
    • Calculate remaining useful life for replacement scheduling
    • Set up predictive maintenance triggers
  6. Validation & Migration: Transfer all 15,000 assets with proper facility assignments, maintenance history, and replacement schedules

Scenario 4 – Customer Service Integration Migration

Scenario Description Electric cooperative migrates customer complaints, service orders, and payment history from three separate legacy systems into SMART360's unified customer service module.

Objective (Why)

  • Business Goal: Reduce customer service response time from 48 hours to 4 hours through integrated ticket management
  • Consumer Goal: Single point of contact for all service issues with complete interaction history and faster resolution
  • Operational Goal: Eliminate duplicate data entry across complaint, work order, and billing systems

If Not Set – Business Impact

  • Customer Satisfaction: CSAT scores remain at 67% due to slow response times and lack of service history visibility
  • Operational Inefficiency: Service representatives spend 35% of time searching across multiple systems for customer information
  • Lost Revenue: $95,000 annual revenue loss due to delayed service connections and unresolved billing disputes

Scenario Explanation - in short

Valley Electric Cooperative has 31,500 member-customers with service requests tracked in separate systems: complaints in ServiceDesk Pro, work orders in FieldForce, and payments in BillingMaster. Member "Sarah Johnson" (Account #VEC-31847) at 234 Maple Drive had power outage on March 15 (Complaint #C-2024-8847), technician dispatched via work order #WO-15663, and credited $23.50 for outage duration - but no system shows complete service history. Migration must link complaint, work order, payment credit, and customer account to provide complete service interaction timeline.

Audience (Why it Matters) - in short

  • CSM: Must access complete service history for Sarah Johnson to handle her call about outage credits, see previous complaints, and understand service patterns without switching between systems
  • QA: Must validate that Complaint #C-2024-8847 correctly links to Customer VEC-31847, work order completion triggers billing credit, and service timeline maintains chronological accuracy
  • Engineers/Interns: Must understand data relationships between customer accounts, complaints, work orders, and billing adjustments to build comprehensive service history views and automated workflow triggers

Does it fit in SMART360

It fits with multi-data type integration. Here's the implementation:

  1. Sequential Migration Approach:
    • Step 1: Upload "Consumer Data" (31,500 customer accounts)
    • Step 2: Upload "Complaints" data with customer ID references
    • Step 3: Upload "Service Orders" data linking to customers and complaints
    • Step 4: Upload "Payments" data including credits and adjustments
  2. Consumer Data Migration:
    • Upload customer accounts with: Consumer_No, Name, Address, Service_Connection
    • AI maps fields with high confidence for account numbers and addresses
  3. Complaints Migration:
    • Upload complaints with: Complaint_ID, Consumer_No, Issue_Type, Date_Reported, Status
    • System Data Mapping validates customer references exist
    • Map issue types to standardized complaint categories
  4. Service Orders Migration:
    • Upload work orders with: Order_ID, Consumer_No, Complaint_ID, Service_Type, Completion_Date
    • Relationship mapping links orders to complaints and customers
    • Validate technician assignments and completion status
  5. Payment Integration:
    • Upload payment records including credits: Payment_ID, Consumer_No, Amount, Type, Reference_Order
    • Link payment adjustments to originating service events
    • Validate billing credit calculations and applications
  6. Unified View Creation: System creates comprehensive customer service timeline showing complaints, work orders, and payment adjustments in chronological order

Scenario 5 – Smart Meter Data Historical Migration

Scenario Description Water utility migrates 5 years of interval meter reading data from smart meter vendor system to SMART360 for advanced analytics and billing optimization.

Objective (Why)

  • Business Goal: Implement time-of-use billing and leak detection analytics to increase revenue by $75,000 annually
  • Consumer Goal: Provide customers with detailed usage analytics and early leak detection alerts
  • Operational Goal: Centralize 2.5 million meter readings for advanced consumption analysis and demand forecasting

If Not Set – Business Impact

  • Missed Revenue Opportunities: Cannot implement time-of-use rates worth $75,000 annual revenue increase
  • Customer Service Issues: Unable to provide usage analytics leading to 200+ monthly calls about high bills
  • System Inefficiency: Maintaining separate meter data system costs $25,000 annually in vendor fees and integration complexity

Scenario Explanation - in short

Mountain View Water District deployed 12,500 smart meters in 2019 with 15-minute interval readings stored in vendor system MetraTech Pro. Customer "Robert Thompson" (Account #MVW-9847) at 567 Cedar Lane has smart meter #SM-445789 generating 96 readings daily (35,040 annually). Historical data shows unusual 3 AM consumption spikes indicating possible leak, but district cannot alert customers due to data isolation. Migration must transfer 5 years of interval data (12.5 million records), maintain meter-customer associations, preserve timestamp accuracy, and enable leak detection algorithms.

Audience (Why it Matters) - in short

  • CSM: Must explain to Robert Thompson how new leak alerts work, help him understand detailed usage reports, and resolve questions about time-based billing changes
  • QA: Must verify that meter #SM-445789 reading timestamps preserve accuracy, consumption data aggregates correctly for billing, and leak detection algorithms trigger appropriate customer alerts
  • Engineers/Interns: Must understand interval data storage structures, time-series data validation, aggregation logic for billing calculations, and algorithm requirements for anomaly detection

Does it fit in SMART360

It fits with meter data specialization. Here's the implementation:

  1. Data Type Selection: Use "Meter Data" migration type for interval readings
  2. Meter Setup Migration:
    • Upload 12,500 smart meters with: Meter_Number, Consumer_No, Install_Date, Read_Frequency
    • System validates meter-customer relationships exist
  3. Historical Data Migration:
    • Upload 12.5 million interval readings with: Meter_Number, Reading_Date, Reading_Time, Consumption_Value, Read_Status
    • AI mapping handles timestamp formatting and consumption unit conversion
    • Data transformation standardizes time zones and reading intervals
  4. Data Validation Process:
    • System validates reading sequences for gaps or anomalies
    • Flags suspicious consumption patterns for review
    • Validates meter number references against customer accounts
  5. Analytics Enablement:
    • Historical data enables leak detection algorithm calibration
    • Time-of-use billing calculations using interval data
    • Customer usage analytics and comparison reporting
  6. Migration Execution: Successfully migrate 12.5 million readings with timestamp accuracy, enabling immediate analytics and billing optimization

Scenario 6 – Regulatory Compliance Data Migration

Scenario Description Municipal electric utility migrates environmental compliance data, outage reports, and regulatory filings from multiple spreadsheets to meet state PUC reporting requirements through SMART360.

Objective (Why)

  • Compliance Goal: Automate quarterly PUC reporting to eliminate 40 hours of manual report compilation per quarter
  • Business Goal: Avoid $150,000+ regulatory fines through accurate, timely compliance reporting
  • Operational Goal: Centralize outage tracking, customer impact analysis, and service reliability metrics

If Not Set – Business Impact

  • Regulatory Penalties: Risk $150,000+ annual fines for late or inaccurate reliability reporting to state PUC
  • Manual Process Costs: 160 hours annually spent compiling spreadsheet data for regulatory reports
  • Data Accuracy Issues: 15% error rate in manual reporting leads to regulatory scrutiny and additional audit requirements

Scenario Explanation - in short

City Power & Light serves 48,500 customers with state PUC requirements for quarterly reliability reporting including SAIDI, CAIDI, and outage cause analysis. Recent storm caused 47 separate outages affecting 12,847 customers for total duration of 156,780 customer-minutes. Data currently exists in separate Excel files: OutageLog.xlsx (outage events), CustomerImpact.xlsx (affected accounts), and RepairLog.xlsx (restoration timeline). Migration must link outage events to affected customers, calculate reliability metrics automatically, and enable automated PUC report generation.

Audience (Why it Matters) - in short

  • CSM: Must handle regulatory auditor questions about specific outage events, provide customer impact documentation, and explain reliability metric calculations
  • QA: Must validate that outage #OUT-2024-0847 correctly links to 2,156 affected customers, duration calculations match actual restoration times, and SAIDI/CAIDI metrics calculate according to PUC formulas
  • Engineers/Interns: Must understand regulatory reporting requirements, outage-to-customer relationship modeling, reliability metric calculations (SAIDI = total customer-minutes / total customers), and automated report generation logic

Does it fit in SMART360

It fits with custom data relationships and reporting. Here's the implementation:

  1. Multi-Data Type Migration:
    • Outage Events: Upload outage records with Event_ID, Start_Time, End_Time, Cause, Affected_Circuits
    • Customer Impact: Upload customer-outage relationships with Event_ID, Customer_No, Outage_Duration
    • System Data: Upload circuit and customer geographical data
  2. Relationship Mapping Process:
    • Map outage events to affected customer accounts
    • Validate geographical relationships (customers on affected circuits)
    • Link restoration activities to outage resolution
  3. Data Validation & Calculation:
    • System calculates customer-minutes automatically (customers × duration)
    • Validates duration calculations against start/end times
    • Computes reliability metrics (SAIDI, CAIDI, CAIFI) per PUC requirements
  4. Regulatory Reporting Integration:
    • Enable automated quarterly PUC report generation
    • Historical trending analysis for reliability improvement tracking
    • Outage cause analysis and customer impact reporting
  5. Migration Success Metrics:
    • 100% of outage events properly linked to customer impacts
    • Automated calculation of all PUC-required reliability metrics
    • Reduction of reporting preparation time from 40 hours to 2 hours per quarter

Scenario 7 – Waste Management Route Optimization Migration

Scenario Description Municipal solid waste department migrates customer service routes, vehicle assignments, and collection schedules from paper-based tracking to SMART360's integrated route management system.

Objective (Why)

  • Business Goal: Reduce fuel costs by 20% ($85,000 annually) through optimized route planning and reduce overtime costs by 30% ($120,000 annually)
  • Consumer Goal: Provide customers with reliable pickup schedules, missed collection notifications, and special pickup request tracking
  • Operational Goal: Optimize 47 collection routes serving 38,500 residential and 2,400 commercial customers across 156 square miles

If Not Set – Business Impact

  • Operational Inefficiency: Current paper-based routing leads to 15% longer routes, wasting $85,000 in fuel and $120,000 in overtime costs annually
  • Customer Complaints: 450+ monthly calls about missed pickups due to poor route tracking and communication
  • Regulatory Issues: Cannot demonstrate compliance with environmental route efficiency requirements for state waste management permits

Scenario Explanation - in short

Metro Waste Services operates 23 collection trucks across 47 routes using paper manifests and manual scheduling. Route 15-A covers Hillside neighborhood with 847 residential stops including customer "Jennifer Martinez" at 892 Oak Street (Account #MW-15A-0234) with weekly Wednesday pickup, recycling on alternating Wednesdays, and yard waste monthly. Driver Tony Rodriguez uses paper route sheet showing stop sequence, but recent construction detour created 45-minute delays affecting 234 downstream customers. Migration must digitize route sequences, customer stop information, service frequencies, vehicle assignments, and enable real-time route optimization with traffic integration.

Audience (Why it Matters) - in short

  • CSM: Must handle Jennifer Martinez's call about missed pickup, provide accurate next collection date, and process special pickup requests through integrated system instead of paper forms
  • QA: Must validate that customer MW-15A-0234 appears correctly in Route 15-A sequence, service frequencies match billing cycles, and route optimization maintains all required stops
  • Engineers/Interns: Must understand geographic route sequencing, vehicle capacity constraints, service frequency business rules, and real-time route adjustment algorithms for traffic and road closures

Does it fit in SMART360

It fits with route and service order integration. Here's the implementation:

  1. Customer Data Migration:
    • Upload 40,900 customers with: Customer_No, Address, Service_Type, Pickup_Frequency, Route_Assignment
    • AI maps service addresses to geographic coordinates for route optimization
  2. Route Structure Migration:
    • Upload 47 routes with: Route_ID, Route_Name, Vehicle_Assignment, Service_Days, Stop_Sequence
    • System Data Mapping assigns customers to correct routes based on geographic zones
  3. Service Configuration:
    • Map service types (Weekly Trash, Bi-weekly Recycling, Monthly Yard Waste) to pickup schedules
    • Configure vehicle capacity constraints and route duration limits
    • Set up special pickup request workflows
  4. Route Optimization Integration:
    • Enable GPS tracking integration for real-time route adjustments
    • Traffic pattern analysis for dynamic route optimization
    • Customer notification system for schedule changes

Scenario 8 – Multi-Tenant Housing Complex Migration

Scenario Description Regional utility migrates complex apartment and condo communities where individual units have separate utility accounts but shared infrastructure requires specialized billing and service coordination.

Objective (Why)

  • Business Goal: Reduce billing errors in multi-tenant properties by 85% and eliminate $45,000 annual revenue loss from incorrect unit assignments
  • Consumer Goal: Ensure apartment residents receive accurate bills for their specific unit usage, not averaged building consumption
  • Property Management Goal: Enable property managers to track utility costs per unit for lease management and cost allocation

If Not Set – Business Impact

  • Revenue Loss: $45,000 annual losses due to incorrect unit-to-meter mapping causing billing disputes and uncollected charges
  • Customer Dissatisfaction: 25% of multi-tenant customers file billing complaints monthly due to shared meter confusion
  • Administrative Burden: Property management companies threaten to switch utilities due to billing complexity and error rates

Scenario Explanation - in short

Harbor View Utilities serves 156 apartment complexes and condo buildings with 8,247 individual units using various metering configurations. Sunrise Apartments (Property #HV-APT-0089) has 64 units with individual electric meters but shared water service with allocation based on unit square footage. Tenant "Michael Chen" in Unit 3B (Account #HV-3B-1247, 850 sq ft) receives electric bill based on his meter #EM-3B-4478 but water allocation of 7.2% of building usage. Migration must maintain unit-level billing, property-level shared service calculations, square footage allocation factors, and property manager access to all unit data.

Audience (Why it Matters) - in short

  • CSM: Must explain to Michael Chen why his water bill varies monthly based on building usage while electric bill reflects his exact meter, and help property managers understand allocation calculations
  • QA: Must validate that Unit 3B correctly receives 7.2% of building water costs, electric meter EM-3B-4478 bills only to Chen's account, and property manager dashboard shows accurate per-unit utility costs
  • Engineers/Interns: Must understand multi-tenant billing models, shared service allocation algorithms, unit-to-meter mapping hierarchies, and property-level reporting requirements for management companies

Does it fit in SMART360

It fits with enhanced property management features. Here's the implementation:

  1. Property Structure Migration:
    • Upload 156 properties with: Property_ID, Property_Name, Manager, Total_Units, Shared_Services
    • Upload 8,247 individual units with: Unit_ID, Property_ID, Square_Footage, Occupant_Name
  2. Multi-Level Account Setup:
    • Create property-level accounts for shared services (water, common area electric)
    • Create unit-level accounts for individual services (unit electric, gas)
    • System Data Mapping links units to parent properties
  3. Meter Assignment Migration:
    • Individual meters map directly to unit accounts
    • Shared meters map to property accounts with allocation rules
    • Configure allocation factors (square footage, occupancy, equal division)
  4. Billing Configuration:
    • Set up unit-level billing for individual meters
    • Configure shared cost allocation and distribution to unit accounts
    • Enable property manager reporting for all units

Scenario 9 – Emergency Response Integration Migration

Scenario Description Electric utility migrates critical customer data including medical equipment dependencies, accessibility needs, and emergency contact information to enable priority restoration during outages.

Objective (Why)

  • Safety Goal: Ensure customers with life-supporting medical equipment receive priority restoration within 4 hours during outages
  • Regulatory Goal: Meet state PUC requirements for vulnerable customer protection and emergency response planning
  • Community Goal: Provide emergency services with accurate customer information during natural disasters and grid emergencies

If Not Set – Business Impact

  • Safety Risk: Potential liability exposure for delayed restoration to medical equipment customers during emergencies
  • Regulatory Penalties: $250,000+ fines for non-compliance with vulnerable customer protection regulations
  • Emergency Response Issues: First responders lack critical customer information during natural disasters, delaying rescue operations

Scenario Explanation - in short

Southwest Power Cooperative serves rural area with 12,500 customers including 347 customers with registered medical equipment dependencies. Customer "Dorothy Williams" (Account #SWP-MED-0156) at 1247 County Road 15 has home dialysis equipment requiring 24/7 power, backup generator with 8-hour capacity, and emergency contact daughter Lisa Williams (555-0987). During last ice storm, Dorothy's power remained off for 16 hours beyond generator capacity because utility didn't prioritize medical customers. Migration must flag medical equipment customers, track equipment types and power requirements, maintain emergency contacts, and integrate with dispatch system for priority restoration.

Audience (Why it Matters) - in short

  • CSM: Must handle Dorothy Williams' registration updates, verify medical equipment information annually, and coordinate with emergency dispatchers during outage events
  • QA: Must validate that medical flag triggers priority restoration protocols, emergency contact information remains current, and dispatch system receives accurate medical customer lists during outages
  • Engineers/Interns: Must understand medical equipment power requirements, priority restoration algorithms, emergency contact notification systems, and integration with dispatch and field service systems

Does it fit in SMART360

It fits with customer service enhancement and emergency management. Here's the implementation:

  1. Medical Customer Migration:
    • Upload 347 medical customers with: Customer_No, Medical_Equipment, Power_Requirements, Backup_Duration
    • Flag accounts with medical priority status
    • System Data Mapping assigns priority restoration categories
  2. Emergency Contact Integration:
    • Upload emergency contacts with: Customer_No, Contact_Name, Relationship, Phone, Alt_Phone
    • Enable multiple contacts per customer account
    • Configure automated notification systems
  3. Equipment Tracking:
    • Catalog medical equipment types (dialysis, oxygen, CPAP, refrigerated medications)
    • Track power consumption and backup requirements
    • Set up annual verification workflows
  4. Dispatch System Integration:
    • Generate priority customer lists for emergency dispatchers
    • Enable rapid identification during outage events
    • Configure automated alerts for extended outages affecting medical customers

Scenario 10 – Rate Structure Migration and Billing Optimization

Scenario Description Municipal electric utility migrates from simple flat-rate billing to complex time-of-use, seasonal, and demand charge structures requiring historical usage analysis and customer segmentation.

Objective (Why)

  • Revenue Goal: Increase utility revenue by $2.3 million annually through optimized rate structures that encourage off-peak usage
  • Grid Management Goal: Reduce peak demand by 15% through time-of-use pricing incentives
  • Customer Fairness Goal: Ensure heavy users pay proportional costs while protecting low-income customers through appropriate rate classes

If Not Set – Business Impact

  • Revenue Loss: Maintaining flat rates costs $2.3 million annually in foregone revenue optimization opportunities
  • Grid Stress: Peak demand continues growing 5% annually without pricing signals, requiring $8 million infrastructure investments
  • Cross-Subsidy Issues: Light users overpay by $180 annually while heavy users underpay by $340, creating equity concerns

Scenario Explanation - in short

Capital City Electric serves 67,500 customers using single $0.12/kWh rate structure. Analysis shows residential customer "Patricia Johnson" (Account #CCE-4578) uses 1,247 kWh monthly with 67% consumption during peak hours (4-8 PM), while "Senior Center" (Account #CCE-COM-0089) uses 4,500 kWh with only 23% peak usage. New rate structure includes off-peak ($0.09), mid-peak ($0.14), on-peak ($0.18), summer seasonal multiplier (1.15x), and demand charges for commercial customers. Migration must analyze 5 years of usage history, segment customers by usage patterns, calculate bill impacts, and transition billing system to multi-tier rate calculations.

Audience (Why it Matters) - in short

  • CSM: Must explain to Patricia Johnson why her bill increased $23/month due to peak usage patterns and help her understand time-shifting strategies to reduce costs
  • QA: Must validate that peak/off-peak time assignments calculate correctly, seasonal multipliers apply to appropriate months, and bill calculations match rate tariff requirements exactly
  • Engineers/Interns: Must understand time-of-use rate calculations, customer segmentation algorithms, historical usage analysis methods, and complex billing calculation logic with multiple rate components

Does it fit in SMART360

It fits with advanced billing and rate management capabilities. Here's the implementation:

  1. Historical Usage Analysis:
    • Upload 5 years of interval usage data for all 67,500 customers
    • AI analyzes usage patterns to identify peak consumption behaviors
    • Customer segmentation based on usage profiles and bill impact analysis
  2. Rate Structure Configuration:
    • Configure time-of-use periods (Off-peak: 10 PM-6 AM, Mid-peak: 6 AM-4 PM, On-peak: 4 PM-10 PM)
    • Set seasonal rate multipliers and demand charge thresholds
    • System Data Mapping assigns customers to appropriate rate classes
  3. Bill Impact Analysis:
    • Calculate bill changes for each customer under new rate structure
    • Identify customers with significant increases/decreases
    • Generate customer communication materials with personalized impact analysis
  4. Billing System Integration:
    • Configure complex rate calculation engine
    • Enable time-differentiated billing with interval data
    • Set up customer portal showing usage by time period and cost optimization recommendations

Scenario 11 – Cross-Border Utility Service Migration

Scenario Description Regional utility serving customers across state lines migrates customer data while maintaining compliance with different state regulations, tax requirements, and service territory boundaries.

Objective (Why)

  • Regulatory Goal: Maintain compliance with three different state PUC regulations while serving customers across state borders
  • Tax Management Goal: Automate state-specific tax calculations and regulatory fee assessments
  • Service Territory Goal: Accurately track which customers fall under which state jurisdiction for reporting and rate-setting purposes

If Not Set – Business Impact

  • Regulatory Violations: Risk $500,000+ in fines across three states for incorrect jurisdictional reporting and rate applications
  • Tax Collection Errors: Manual tax calculation errors result in $75,000 annual over/under-collection requiring expensive reconciliation processes
  • Rate Confusion: Customers receive incorrect rates based on wrong state jurisdiction, causing billing disputes and regulatory complaints

Scenario Explanation - in short

Tri-State Power Authority serves customers in Colorado, Nebraska, and Wyoming with different regulatory requirements for each state. Customer "Ranch Properties LLC" operates facilities at three locations: Colorado ranch (Account #TSP-CO-4567, 15,000 kWh monthly, subject to CO renewable energy surcharge), Nebraska grain elevator (Account #TSP-NE-8901, 45,000 kWh monthly, industrial rate, state efficiency rebates), and Wyoming office (Account #TSP-WY-2345, 2,500 kWh monthly, different sales tax rate). Migration must maintain state-specific rate schedules, tax calculations, regulatory fees, and jurisdictional reporting while ensuring customers receive correct bills based on service location.

Audience (Why it Matters) - in short

  • CSM: Must handle Ranch Properties' questions about different rates across their three locations and explain state-specific charges and rebate programs
  • QA: Must validate that Colorado location receives renewable energy surcharge, Nebraska location gets industrial rate and efficiency rebates, and Wyoming location has correct state tax calculations
  • Engineers/Interns: Must understand multi-jurisdictional billing logic, state-specific tax and fee calculations, regulatory reporting by state, and rate schedule assignment based on service territory boundaries

Does it fit in SMART360

It fits with multi-jurisdictional utility management. Here's the implementation:

  1. Geographic Service Territory Setup:
    • Configure three state jurisdictions with boundaries and regulatory requirements
    • Upload customer locations with precise geographic coordinates
    • System validates service addresses against jurisdictional boundaries
  2. State-Specific Rate Configuration:
    • Configure different rate schedules for each state
    • Set up state-specific surcharges, taxes, and regulatory fees
    • Enable jurisdiction-based rate assignment validation
  3. Customer Migration by Jurisdiction:
    • Migrate customers with location-based jurisdiction assignment
    • System Data Mapping validates rate schedule compatibility with jurisdiction
    • Configure automatic tax calculation based on service location
  4. Regulatory Reporting Setup:
    • Enable state-specific reporting capabilities
    • Segregate customer data and financial metrics by jurisdiction
    • Configure automated regulatory filing generation for each state

Scenario 12 – Disaster Recovery Data Restoration Migration

Scenario Description Gulf Coast electric cooperative restores customer data, outage history, and billing records from backup systems following hurricane damage to primary data center, requiring careful data validation and customer communication.

Objective (Why)

  • Business Continuity Goal: Restore full customer service operations within 72 hours of disaster recovery site activation
  • Data Integrity Goal: Validate 100% data accuracy from 30-day backup restoration while identifying any missing transactions
  • Customer Service Goal: Maintain accurate billing cycles and customer communications despite 6-day system outage during hurricane response

If Not Set – Business Impact

  • Service Disruption: Extended outage beyond 72 hours costs $50,000 daily in lost revenue and customer goodwill
  • Data Loss Risk: Incomplete data restoration could result in unbilled services worth $200,000+ and billing disputes lasting months
  • Regulatory Reporting: Cannot meet state emergency response reporting requirements without complete outage and restoration data

Scenario Explanation - in short

Coastal Electric Cooperative's primary data center was destroyed by Hurricane Maria, affecting 23,500 member accounts. Disaster recovery site has 30-day-old backup data, missing recent transactions including new customer "Harbor Marina Inc." (Account #CEC-NEW-1247) connected 15 days ago, payment from "Rodriguez Family" (Account #CEC-4589) made 8 days ago, and storm damage service orders for 1,247 customers. Migration must restore backup data to SMART360, identify missing transactions, reconcile billing cycles, update customer service records, and validate outage restoration data for regulatory reporting.

Audience (Why it Matters) - in short

  • CSM: Must handle calls from Rodriguez Family about missing payment credit and Harbor Marina about service connection not showing in system, while managing high call volume from storm-affected customers
  • QA: Must validate that backup data restoration completed successfully, identify all missing transactions, and verify that outage records accurately reflect actual storm response activities
  • Engineers/Interns: Must understand backup data validation procedures, transaction reconciliation processes, data integrity checking algorithms, and customer communication workflows during disaster recovery

Does it fit in SMART360

It fits with disaster recovery and data validation capabilities. Here's the implementation:

  1. Backup Data Assessment:
    • Upload 30-day-old backup data for all customer accounts, billing, and service records
    • System identifies data cutoff date and potential missing transaction periods
    • Generate data gap analysis report
  2. Transaction Reconciliation:
    • Upload available transaction logs from point-of-sale systems, payment processors, and field service devices
    • AI matching algorithms identify missing payments, new connections, and service changes
    • System flags accounts requiring manual review and customer contact
  3. Data Validation Process:
    • Cross-validate backup data against external system records
    • Identify inconsistencies in customer accounts, meter readings, and billing calculations
    • Generate customer notification lists for data verification
  4. Emergency Operations Integration:
    • Upload storm response data including outages, restoration activities, and customer impacts
    • Validate outage duration calculations for billing credits
    • Generate regulatory compliance reports for emergency response documentation
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