ASQ World Conference on Service Data Quality and Improvement Quality: When Session T31 99.7% Isn’t Good May 17, 2016 Enough Jennifer Wilson Dodd Starbird
Learning Objectives 1. Discuss the value of using quality data for process and performance control, and understand the critical difference between defects per opportunity (DPO) and defects per unit (defective rate). 2. Discuss the change management challenges that a team overcame in creating visibility and team buy-in to improve performance, and learn how to create a positive culture of human error prevention. 3. Integrate key concepts of Lean (visible metrics) with key concepts of Six Sigma (sampling and process capability), all with the common foundation of Deming Quality to tie the solution together!
Engaging the Team in Quality Employee Engagement is: • Every team member actively caring about the team’s performance and outcomes for customers • Working together to improve • The key to Quality! It’s a culture, not a program! We took an Engaged Team Performance (ETP) approach…
Differentiation: Data-driven Insights Energy Carbon Water Waste Telecom Lease Data-driven insights 1,000s of Utilities (demand to impact) 100,000s of Facilities 1,000,000s of Households Capital Impact Demand Consumption Expense
Why We Needed a Data Quality Project Data Entry QA data from December, 2014: • 99.7% line item accuracy… only 3 defects per 1,000 opportunities (3,000 DPMO) • Data Entry team was only using this number • But only 88.3% bill-level accuracy! • 11.7% defective bills (1 or more defects) • Data Entry leaders had stopped using this number “because it made people feel bad” • Estimated impact of 800 downstream people spending 25% of their time scrubbing and fixing data: 200 FTE! Sources: Dec 2014 QA files and data user survey Mar 2015
Survey Question #1 How does your organization measure quality? Pick the closest answer… A. Mostly from the customer perspective (output quality, final yield, complaints, etc.) B. Mostly from an internal perspective (defects per hundred or million, individual quality, etc.) C. Both an internal and an external perspective D. We don’t measure quality all that much! Survey ¡Link ¡#1
Data Collection Plan Designed two data collection spreadsheets (account & bill) • • Designed and ran proof of concept • Tested repeatability and reproducibility with 4 collectors • Re-designed and re-piloted Collected population stratification factors for all accounts • (all channels of data entry were in scope) • Sampled 1,500 accounts, 650 sites, 3 months of bills • Estimation of defect rates to precision of +/- 3% Purpose: • • Validate defect rate; correlate causal factors Timing: • • Deployed: week of March 9 (starting with pilot) • Gathered data with 8 data collectors over a month • Results analysis: April 13–15, 2015
Measured Data Quality: Stratified Defects Yields: 89.6% Bill Data Entry Quality overall 93.3% Bill Quality from EDI (Automated) Data Entry 43% Bill Quality on Post-Audit* resolved bills * Post-Audit bills were a subset that the computer system identified as potentially defective, but were queued for future review with substantial backlogs
Data Entry Process Improvements Turned off an automated edit resolution (computer • based data scrubbing) program that was allowing known defects to get deferred instead of fixed (52% of auto-resolved edits were defects!) Launched a project to use Optical Character • Recognition (OCR) technology to expand on benefits of Electronic Data Interchange (EDI) Trended causes of defects using Quality Control • data and delegated assignments to teams to discuss causes and solutions in daily huddles Example key cause and solution: Inactive accounts • Open inactive accounts cause reporting errors downstream • Closing inactive accounts was perceived to be a lower- • priority task and was backlogged 30 days on some teams Catching up on that backlog has avoided $75,000 in • rework labor costs annually
Data Entry Performance Improvements Started to communicate “bill-level” quality scores • Quality Control caught up to real-time to produce • weekly team quality scores v. sharing data just monthly Added new quality metrics to team whiteboards • (shown below) to balance efficiency and quality focus Sharing • “quality stories” in daily huddles Quantified • cost of quality in same terms as production… “We probably made 3,000 defects yesterday!”
Data Entry QC Scores Monthly Data Entry Quality Scores 99.0% 97.0% 95.0% 93.0% 91.0% 89.0% 87.0% 85.0%
Data Entry Quality: Before and After Individual QC Scores Process and Performance Improved! 89.5% Bill Quality in May 93.8% Bill Quality in August/Sept Shift in performance and training!
Nik Wallenda Who is Nik Wallenda?
Nik Wallenda Nik is accountable for his performance.
Quality Sampling Principles All work should have some chance of being checked (to drive individual accountability) But individuals should know the work is likely to go right out the door (no safety net!) Sample sizes for checking should be statistically calculated to deliver an appropriate precision of resulting quality measurements by (at least) work type, client, team, & individual
Sampling Strategy Lots of sources for sampling equations. Use software…! One for continuous data, one for discrete…
Approach Collect the minimum sample size per month (usually…), in order to get: • Appropriate precision by individual for purposes of an annual review • Appropriate precision by client for quarterly reviews and/or required service-level reporting • Appropriate precision by task type every month at a team level, for: - Trending of performance - Ongoing monitoring - Root cause analysis of defects
Deming’s Point
Survey Question #2 Where is your organization’s most significant opportunity to improve your quality measures? A. Balancing internal and external quality measures B. Gathering process (root cause) data to improve process quality at the source C. Collecting the right sample size to get the most effective data as efficiently as possible D. All of the above! E. None of the above Survey Link #2
Discussion of Learning Objectives 1. Discuss the value of using quality data for process and performance control, and understand the critical difference between defects per opportunity (DPO) and defects per unit (defective rate). 2. Discuss the change management challenges that a team overcame in creating visibility and team buy-in to improve performance, and learn how to create a positive culture of human error prevention. 3. Integrate key concepts of Lean (visible metrics) with key concepts of Six Sigma (sampling and process capability), all with the common foundation of Deming Quality to tie the solution together!
Presenters Jennifer Wilson Director, Quality and Business Process Improvement, Ecova Inc. jgarciawilson@ecova.com Dodd Starbird Managing Partner, Implementation Partners LLC dodd@implementationpartners.com
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