Continual Improvement Improvement Using Simulation

1. Continual ImprovementImprovement Using Simulation Gordon Clark Clark Solutions, Inc. The Ohio State University Lexington ASQ Section

2. Objectives • Simulation use in • Six Sigma • Lean • Design for Six Sigma • Validation of simulation models • Relation with designed experiments Lexington ASQ Section

3. Simulation • Imitates the system • Production line • Experiments Lexington ASQ Section

4. Simulation in Quality Improvement • Lean Six Sigma • Measure Phase • Determine KPIVs • Value Stream Map • Compare with real system • Improvement Phase • Reduce physical experimentation • Identify process changes • Design for Six Sigma Lexington ASQ Section

5. Emphasis by American Society for Quality (ASQ) • CSSBB BOK • Prior to 2007 • 2007 • Certified Six Sigma Black Belt Handbook (2005) • Breyfogle (2003). Implementing Six Sigma • The Quality Toolbox (2005) Lexington ASQ Section

6. Simulation Model Validation • Robert Sargent (2010) • Model purpose • Range of accuracy for successful validation • Conceptual model • Experts examine graphical model • Traces to track entities • Computer model • Operational validation Lexington ASQ Section

7. Operational Validity Lexington ASQ Section

8. Simulation-Based LSS Process Define Project Scope Define CTQ and Lean Measures Define Structure and Variables Define Develop Current State VSM Develop Simulation Conceptual Model Develop Simulation Model Measure View traces or an Animated Simulation Model Identify KPIVs and KPOVs Analyze Lexington ASQ Section

9. Simulation-Based LSS Process Measure Develop DOE Plan and Design Run Simulation Experiments Analyze Process Flow Analyze Optimize Process Parameters Develop Future State VSM Improve Control Lexington ASQ Section

10. Simulation-Based LSS Process Improve Develop Control Strategy Implement Control Plans Monitor Performance Over Time Control Simulation Predictions Accurate? Modify Simulation Model No Analyze Yes Continue Monitoring B. El-Haik and R. Al-Aormar (2006). Simulation-Based Lean Six-Sigma and Design for Six-Sigma Lexington ASQ Section

11. Simulation Model Validity • Compare output with actual data • Verify architecture with operating personnel • S. J. Shim and A. Kumar (2010). Business Process Management Patient Wait Times (Minutes) Lexington ASQ Section

12. Emergency Department Case Study • Paul, J. A. and L. Lin (2007). Society for Health Systems Conference • Simulation-based Lean Six Sigma • 40 minute reduction in length of stay • Adding one ED physician • Reducing admit time • Shortening lab turn-around time Lexington ASQ Section

13. Simulation Concepts • Dynamic entities • Inputs • Attributes • Activities • Delays • Queueing • Resources • Random variable generation Lexington ASQ Section

14. Moderate Acuity Process Bed Placement Clerk Discharge Patient Arrives I ns i de E D RN & MD Assessment Lab Tests Triage Discharge or Admit RN & MD Procedure Register RN & MD Discharge E. Beck, H. Balsubramanian, P. Henneman (2009). Proceedings of the 2009 Winter Simulation Conference Lexington ASQ Section

15. Severe Acuity Process Bed Placement Patient Arrives I ns i de E D RN & MD Assessment Lab Tests Triage Admit RN & MD Procedure RN & MD Discharge Clerk Discharge Lexington ASQ Section

16. Low Acuity Patient Service Times C. Johnson et al (2004). IIE Annual Conference and Exhibition 2004 Lexington ASQ Section

17. Low Acuity Patient Service Times Lexington ASQ Section

18. Time 198 Lexington ASQ Section

19. Animation Lexington ASQ Section

20. Current State • Resources • 10 beds • 3 RNs • 2 MDs • Incoming patients • 2 per hour • 40% severe Lexington ASQ Section

21. Performance Measures Simulated 300 8 Hour Shifts Lexington ASQ Section

22. Lexington ASQ Section

23. Current VSMResults from 300 8 Hours Shifts Triage Queue Clerk Queue Bed Queue Avg Time .97 Min Time 0 Max Time 25.6 Time Std. Dev. 2.76 Avg Length .032 Avg Time 2.27 Min Time 0 Max Time 68.21 Time Std. Dev. 5.41 Avg Length .121 Avg Time 3.34 Min Time 0 Max Time 158.8 Time Std. Dev. 15.2 Avg Length .111 RN Queue Lexington ASQ Section

24. Current VSMResults from 300 8 Hours Shifts Bed Queue RN Queue MD Queue Avg Time 2.07 Min Time 0 Max Time 84.1 Time Std. Dev. 6.39 Avg Length .207 Avg Time 3.88 Min Time 0 Max Time 217.2 Time Std. Dev. 10.7 Avg Length .387 Lexington ASQ Section

25. Lexington ASQ Section

26. Summary • Modified DMAIC process for simulation-based LSS • Use of VSM • Model validation • Emergency Dept. Simulation • Accuracy of actual case study • Lengthy observation times with real system DOE Lexington ASQ Section

27. Emergency Department Simulation • Long waiting times • Visits increasing • Key input variables • Doctors • Patients per doctor • Nurses • Queuing Priority • Arrival time • Severity Lexington ASQ Section

28. Assembly of Car Engines • Design for Six Sigma • Time to market • Work stations decoupled by buffers • Options • Even distribution • Bow Tie • Findings • Even better than Bow Tie • Maximum of 2 buffers • B. Tjahjono, J. Ladbrook, J. Kay (2009). Proceedings of the 2009 Winter Simulation Conference Lexington ASQ Section

29. Questions • Is simulation useful in • Six Sigma • Lean • Design for Six Sigma Lexington ASQ Section