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Case Study:

Case Study: . Business Results Utilizing Oracle Analytics Solutions. Jean Frelka Director, Meter to Bill Process We Energies. Who is We Energies?. Wisconsin and Michigan’s Upper Peninsula—22,000 square miles Population Served: 3.3 million

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Case Study:

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  1. Case Study: Business Results Utilizing Oracle Analytics Solutions Jean Frelka Director, Meter to Bill Process We Energies

  2. Who is We Energies? • Wisconsin and Michigan’s Upper Peninsula—22,000 square miles • Population Served: 3.3 million • Largest electric and gas company in Wisconsin: • 1.1 million electric customers • 1.1 million gas customers • 450 steam customers • Meter Reading facts: • 1.66 million daily reads • 560,000 monthly drive-by • 30,000 manual read • 3000 MV-90 • Employees: 4,600

  3. Background • High volume of billing exceptions each month • Length of Break to Fix too long (time from meter malfunction to rebill) • Too many rebills • Not taking advantage of daily reads • Not finding all metering issues • Limited field resources

  4. Project Description • Replace existing CIS monthly generated exceptions with meter data analytics exceptions-high volume, high risk or high complexity • Determine how to balance finding all the metering issues with limited resources • Send high probability meter error exceptions directly to field, others to Work Queues in CIS using existing processes • Use business/IT/vendor collaboration

  5. Work Plan

  6. Systematic Integrated Approach

  7. Iterative Algorithm Development Approach

  8. Approach • Establish new approach for a comprehensive and prioritized list of exceptions – Guiding Principles became Comprehensive, Manageable, Prioritized and Dynamic • Find the highest priority amongst total volume to reduce risk • Ability to add more exceptions from lower priority if billing resources available - Send high probability exceptions directly to the field • Change Management • Easy work replaced by lower volumes requiring complex analysis – impacts training needs, time per unit monitoring, resource planning, and employee engagement • Fear of missing one replaced with comprehensive and manageable volume approach • Staged transition from CIS to MDA process to allow for training, change management and clean-up of old work • Continuing performance monitoring

  9. How it Works - Deep Dive Example Identifying Slowing Consumption Meters in Meter-to-Bill Monthly register reads don’t easily reveal slowing consumption… 1 Daily data reveals there is a trend, but is it unusual, or weather driven? (yellow = temperature) 2 Comparison to rate class behavior (red = rate class aggregate) reveals that the pattern is specific to the meter 3 Inclusion of meter flag / event data seals the deal: meter is highly likely to be failing 4

  10. Results - Quantitative Reduction in Back Office Exceptions - AMR Read Needs Review volume 75% - Low Consumption 65% - Electric Diagnostic Flags 80% - No Consumption 38% Reduction in Break to Fix - 30-45 days shaved in process time for exceptions going directly to the field Undetected faulty equipment found and fixed - Over 2,000 gas AMR modules replaced due to Slow Consumption Reduction in old work from CIS - More than 58,000 CIS AMR Read Need Review down to 17,500 - More than 46,000 No Consumption Review down to 6,300

  11. Results - Qualitative SLA’s maintained for Billing exceptions Improved customer satisfaction Advancement in resource planning and prioritization of all back office work Cross trained back office staff using more analysis in their tasks Reduction in non-value added field investigations Flexibility to tweak algorithms for changing business needs Reduction in outbound calls for no consumption investigations Reduction in internal IT maintenance – annual scrubs to handle seasonal users as well as investigations into escalated situations

  12. Success factors • Acceptance at all levels within organization • Diverse, committed project team following project management methodologies • Continuing performance monitoring of the algorithms • A flexible tool and partnership with internal IT • Internal climate on “Big Data” and “Business Intelligence” • Continuing training and communication (shift in how work is measured and skills needed)

  13. Next Steps • Continue avoiding customer problems • Add algorithms for Primary meters • Deploy meter health test during all installations • Evaluate and refine new queues • Develop additional algorithms in the next highest complexity or volume areas

  14. In Summary Meter Data Analytics (MDA) is a high-value game changer for We Energies Key areas of value: Increased Efficiency: faster identification and resolution of a variety of issues (less We Energies resources) Improved Customer Satisfaction: catching problems before customers become aware of them Avoided IT Costs: MDA can take on reports, development and analysis that previously required IT time and costs.

  15. Thank you! Jean Frelka Director Meter to Bill Process We Energies Jean.Frelka@we-energies.com (414) 221-4115

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