1. Using Total Quality Management Tools to Improve the Quality of Crash Data John Woosley jwoosle@lsu.edu Louisiana State University

2. Phases of Process Improvement Phase I: Process Measurement Phase II: Process Analysis Phase III: Process Improvement Phase IV: Process Control

3. Phases of Process Improvement Phase I: Process Measurement Map process and identify KPIVs Key process input variable and KPOV’s Key process output variable Establish Process Capability Baseline Phase I: Process Measurement Map process and identify KPIVs Key process input variable and KPOV’s Key process output variable Establish Process Capability Baseline Phase II: Process Analysis Identity Potential Critical KPIV’s Perform Analysis using Quality Tools Phase II: Process Analysis Identity Potential Critical KPIV’s Perform Analysis using Quality Tools Phase III: Process Improvement Verify Critical KPIV’s Control Critical KPIV’s Phase III: Process Improvement Verify Critical KPIV’s Control Critical KPIV’s Phase IV: Process Control Implement Control Plan using Auditing Control Charts Continuously Improve Process Phase IV: Process Control Implement Control Plan using Auditing Control Charts Continuously Improve Process

4. Simplified Process Map Data Entry Crash Actions to Reduce Number of Crashes Actions to Reduce Number of Crashes Problem Identification Problem Identification Statistical Report Statistical Report Analysis Crash Report Merge Electronic Data Merge Electronic Data

5. Simplified Process Map for Data

6. Add the current operating specifications and process targets for the Controllable and Critical Inputs Data elements Errors Omissions Electronic data Errors Missing data elements Problems to be addressed New Laws Increased Enforcement High crash locations Report Margin of Errors When, Where, How Data Incomplete Information Missing data Statistical Analysis Police ReportData Entry Analysis Problem Identification KPOV KPIV Missing Data Errors Programming Errors Hardware Failures

7. Cause & Effect Matrix Process Output Missing Data Wrong Data Imprecise Data Process Input Importance rating 7105Total 1 Police reporting 935118 2 Data Entry Person 27189 3 Electronic Filing 996183 4 Data Merging 191102

8. Definition - FMEA A structured approach to: identifying the ways in which a process can fail identifying the ways in which a process can fail estimating the risk of specific causes with regard to these failures estimating the risk of specific causes with regard to these failures evaluating the current control plan for preventing these failures from occurring evaluating the current control plan for preventing these failures from occurring prioritizing the actions that should be taken to improve the process prioritizing the actions that should be taken to improve the process Concept: To identify ways the process can fail and then plan to prevent those failures.

9. Purposes of Process FMEA Assists in the analysis of processes Identifies deficiencies in the Process Control Plan so that actions can be taken to improve Establishes the priority of actions

10. Definition of Terms Failure Mode - What the Operator Sees - the way in which a specific process input fails If not detected and either corrected or removed, will cause Effect to occur. If not detected and either corrected or removed, will cause Effect to occur. Can be associated with a defect or a process input variable that goes wrong. Can be associated with a defect or a process input variable that goes wrong. Effect - What the Customer Sees - impact on customer requirements. Generally external customer focus, but can also include downstream processes. Generally external customer focus, but can also include downstream processes. Examples: Examples: DOTD and LHSC as user of data Analyst who prepares analysis.

11. Definition of Terms - Continued Cause Sources of process variation that causes the Failure Mode to occur. For example, Untrained police officer or data entry person, program error. Sources of process variation that causes the Failure Mode to occur. For example, Untrained police officer or data entry person, program error. Identification of Causes should start with Failure Modes associated with the highest severity ratings. Identification of Causes should start with Failure Modes associated with the highest severity ratings. Current Controls systematized methods/devices in place to prevent or detect failure modes or Causes (before causing effects). systematized methods/devices in place to prevent or detect failure modes or Causes (before causing effects). Prevention consists of foolproofing, automated control and set-up verifications Prevention consists of foolproofing, automated control and set-up verifications Controls consists of audits, checklists, Inspection, SOP’s Controls consists of audits, checklists, Inspection, SOP’s

12. FMEA Form - Initial Assessment

13. FMEA for Motor Carrier Reporting We have recorded when the action was taken and the impact on the RPN. Notice that this is a nice way to track past activities. The FMEA should group as new recommended actions are identified, completed and recorded.

14. Simple graphical techniques for problem solving Pareto Diagram Cause and Effect Diagram P-Chart

15. Pareto Chart

16. Pareto Chart using Cause-Effect Matrix Results

17. Pareto Chart

18. Cause and Effect Diagrams The Cause and Effects Chart is an excellent graphic tool to help prioritize which key input variables have an impact on a key output variable.

19. The p-Chart Count data (Number of errors found in audit) Used to determine the proportion of errors associated with specific problems or issues Ex. data entry, police omission, programming changes, etc. Ex. data entry, police omission, programming changes, etc. Quick way to determine if a process is “In- Control”

20. Example of p-Chart Step 1: Sample the Crash Reports Randomly sample crash reports that have been entered during a period Randomly sample crash reports that have been entered during a period Ex. 3 reports/week Ex. 3 reports/week Step 2: Count the Errors and Total Data Elements Count the number of data entry errors found in those reports and the number of data elements that should be entered Count the number of data entry errors found in those reports and the number of data elements that should be entered Ex. 4 errors in a possible 100 data elements Ex. 4 errors in a possible 100 data elements Step 3: Calculate the p-value for that Sample Divide the number of errors by the number of total elements possible to get the p-value for that sample of reports Divide the number of errors by the number of total elements possible to get the p-value for that sample of reports Ex. p-value = 4/100 =.04 Ex. p-value = 4/100 =.04 So, 4% of the data was in error due to data entry mistakes So, 4% of the data was in error due to data entry mistakes

21. Example of p-chart Step 4: Repeat Steps 1-3 Continue to sample the crash reports and perform these audits periodically Continue to sample the crash reports and perform these audits periodically Step 5: Determine the p-value of the Process Once several audits have been performed, calculate the overall process p-value. Once several audits have been performed, calculate the overall process p-value. Divide the total errors by the total data elements possible across all of the audit samples Divide the total errors by the total data elements possible across all of the audit samples Process p-value (p) = 20/1000 =.02 (2% data entry errors) Process p-value (p) = 20/1000 =.02 (2% data entry errors) Step 6: Construct the p-chart Plot the p-values for each sample Plot the p-values for each sample Use the process p-value as the center line on the chart Use the process p-value as the center line on the chart Use the following formula to determine the control value Use the following formula to determine the control value

22. Formula for Upper Control Limits

23. Example of p-chart using LA Crash Data Audit Results: Total Errors = 20 Total Errors = 20 Total Data Elements = 1000 Total Data Elements = 1000 Process p-value (p) = 20/1000 =.02 Process p-value (p) = 20/1000 =.02 n = 100 (total data elements possible for each audit sample) n = 100 (total data elements possible for each audit sample) SampleErrorsp-value 100 240.04 3150.15 400 500 600 710.01 800 900 1000 200.02

24. Upper Control Limit for p-chart Plug the numbers into the formula UCL =.061

25. p-chart for Data Entry Errors

26. Summary You can only manage what you measure. These tools allow for better identification of quality problems and the appropriate corrective responses.