ASA Template Training

ASA Template Training

Training Objectives Prepare you to plan for data collection and analysis by using the ASA Data Collection Plan Discuss various ways to display and analyze data Describe how to construct various graphs as they apply to the ASA Template Review the basics of creating charts using Excel 1 ASA Data Collection Plan 2 PLAN before you act! Data collection can be time consuming Need to figure out the Who, What, Where, When, Why for each performance measure Distribute the data collection responsibility among your ASA Team Members Garbage in = Garbage out Plan now to prevent crisis down the road 3 ASA Data Collection Plan Takes ASA Template information and spells out the specifics of gathering data Performance objective Performance measure Methodology Point of Contact (POC) Source of Data Frequency Target

Recommend EACH ASA Team complete ONE Data Collection Plan Road map for the teams data collection efforts Distributes responsibilities among team members Allows the team to communicate their methods to others ASA Consultant, OQM, your Division/Office Director may request to review your Data Collection Plan 4 Completing the ASA Data Collection Plan Step 1: Review Common Performance Objectives and Measures Step 2: Add Unique Performance Objectives and Measures Step 3: Review/Determine Methodology Step 4: List Points of Contact (POC) Step 5: List Source of Data Step 6:

List Data Collection Frequency Step 7: List Target 5 Completing the ASA Data Collection Plan (cont.) Common performance objectives and performance measures are already listed Need to add Discrete Services as appropriate Need to add unique performance objectives and measures as appropriate Boxes with text can be edited if desired Shaded areas typically do not need to be filled in 6 Step 1: Review Common Performance Objectives and Measures Review common measures in each perspective If measurement is occurring at the Discrete Service level Enter your Discrete Services on the form Plan provides for 3 Discrete Services for common measures Add/delete rows for Discrete Services as necessary If measurement will occur at the Service Group level Change shaded boxes to white background on row that lists the common measure Enter information in that row Delete the rows for the Discrete Services

7 Step 1: Review Common Performance Objectives and Measures (cont.) ASA Data Collection Plan - 2002 Service Group: Customer Perspective Customer Performance Objective: Increase Understanding of Customer Base Methodology Performance Measure Customer segmentation of Discrete Services The ASA Team will meet to decide what makes most sense as the variable to use in segmenting our customers (e.g., NIH IC, customer location, type of customer). From there we will gather customer data from appropriate IT systems on products/services that will be needed to complete the customer segmentation chart. We will relate these product/service data to Discrete Services. We will then create graphs of our customer segments for each Discrete Service using Excel. Training on how to create the charts will be obtained in Data Analysis training. The charts will be included in our ASA Final Presentation. POC Source of Data Frequency Target

Shaded areas usually require no entry DS1 Once / FY02 One chart DS2 Once / FY02 One chart DS3 Once / FY02 One chart Common performance measures from ASA Template are listed for each of the four perspectives. 8 Step 1: Review Common Performance Objectives and Measures (cont.) ASA Data Collection Plan - 2002 Service Group: Customer Perspective Customer Performance Objective: Increase Understanding of Customer Base Methodology Performance Measure

Customer segmentation of Discrete Services The ASA Team will meet to decide what makes most sense as the variable to use in segmenting our customers (e.g., NIH IC, customer location, type of customer). From there we will gather customer data from appropriate IT systems on products/services that will be needed to complete the customer segmentation chart. We will relate these product/service data to Discrete Services. We will then create graphs of our customer segments for each Discrete Service using Excel. Training on how to create the charts will be obtained in Data Analysis training. The charts will be included in our ASA Final Presentation. POC Source of Data Frequency Target DS1 Once / FY02 One chart DS2 Once / FY02

One chart DS3 Once / FY02 One chart Type in Discrete Services for common measures as appropriate. 9 Step 1: Review Common Performance Objectives and Measures (cont.) To add rows Click left mouse button on DS3 DS1 Once / FY02 One chart DS2 Once / FY02 One chart DS3 Once / FY02 One chart DS1

Once / FY02 One chart DS2 Once / FY02 One chart DS3 Once / FY02 One chart Choose Insert, Row Select Columns A and B in new row Choose Format, Cells, Alignment, Merge Cells Click OK Re-label and edit rows as appropriate 10 Step 1: Review Common Performance Objectives and Measures (cont.) To delete rows Click left mouse button on DS3 DS1 Once / FY02 One chart

DS2 Once / FY02 One chart DS3 Once / FY02 One chart DS1 Once / FY02 One chart DS2 Once / FY02 One chart Choose Edit, Delete Click Entire Row Click OK 11 Step 2: Add Unique Performance Objectives and Measures Customer Performance Objective: Increase Customer Satisfaction Performance Measure

Customer satisfaction ratings from the ORS Customer Scorecard Methodology The ASA Team will meet to discuss the kinds of products/services we deliver to customers. Consideration will be given to the categories of products/services, the frequency of delivery of those products/services, and whom they are delivered to in planning for data collection. Customer segmentation data will be reviewed to identify possible groups to survey. Methods to distribute and collect the surveys will be determined. Prior to distribution of any customer surveys the methodology must be reviewed by OQM. OQM will provide our team with the results. We will interpret the results and include our assessment in our ASA Final Presentation. POC Source of Data Frequency Target Baseline Methodology POC

Source of Data Frequency Target Customer Performance Objective: Performance Measure Type in Unique Performance Objectives and Measures on Template for each perspective. Add Discrete Services as needed. 12 Step 3: Review/Determine Methodology Methodology is a summary of how you plan to gather and analyze the data For common measures, review the suggested methodology and edit as appropriate For unique measures, decide on appropriate methodology and type on template 13 Step 3: Review/Determine Methodology (cont.) Customer Performance Objective: Increase Customer Satisfaction Performance Measure Customer satisfaction ratings from the ORS Customer Scorecard Methodology The ASA Team will meet to discuss the kinds of products/services we deliver to customers. Consideration will be given to

the categories of products/services, the frequency of delivery of those products/services, and whom they are delivered to in planning for data collection. Customer segmentation data will be reviewed to identify possible groups to survey. Methods to distribute and collect the surveys will be determined. Prior to distribution of any customer surveys the methodology must be reviewed by OQM. OQM will provide our team with the results. We will interpret the results and include our assessment in our ASA Final Presentation. POC Source of Data Frequency Target Baseline Methodology POC Source of Data Frequency Target

Customer Performance Objective: Performance Measure Edit or develop methodology for data collection and analysis. 14 Step 3: Review/Determine Methodology (cont.) Edit methodology as appropriate Customer Performance Objective: Increase Customer Satisfaction Performance Measure Customer satisfaction ratings from the ORS Customer Scorecard Methodology The ASA Team will meet to discuss the kinds of products/services we deliver to customers. Consideration will be given to the categories of products/services, the frequency of delivery of those products/services, and whom they are delivered to in planning for data collection. Customer segmentation data will be reviewed to identify possible POC Source of Data Frequency Target Baseline Methodology

POC Source of Data Frequency Target Customer Performance Objective: Performance Measure To resize rows Position mouse at bottom of row number at left of screen A crosshair appears 15 Step 3: Review/Determine Methodology (cont.) Customer Performance Objective: Increase Customer Satisfaction Performance Measure Customer satisfaction ratings from the ORS Customer Scorecard Height: 236.25 Methodology The ASA Team will meet to discuss the kinds of products/services we deliver to customers. Consideration will be given to the categories of products/services, the frequency of delivery of those products/services, and whom they are delivered to in planning for data collection. Customer segmentation data will be reviewed to identify possible

groups to survey. Methods to distribute and collect the surveys will be determined. Prior to distribution of any customer surveys the methodology must be reviewed by OQM. OQM will provide our team with the results. We will interpret the results and include our assessment in our ASA Final Presentation. POC Source of Data Frequency Target Baseline Methodology POC Source of Data Frequency Target Customer Performance Objective: Performance Measure Click left mouse button to display row height Move mouse up or down to increase or decrease row height 16

Step 3: Review/Determine Methodology (cont.) Customer Performance Objective: Increase Customer Satisfaction Performance Measure Customer satisfaction ratings from the ORS Customer Scorecard Methodology The ASA Team will meet to discuss the kinds of products/services we deliver to customers. Consideration will be given to the categories of products/services, the frequency of delivery of those products/services, and whom they are delivered to in planning for data collection. Customer segmentation data will be reviewed to identify possible groups to survey. Methods to distribute and collect the surveys will be determined. Prior to distribution of any customer surveys the methodology must be reviewed by OQM. OQM will provide our team with the results. We will interpret the results and include our assessment in our ASA Final Presentation. POC Source of Data Frequency Target Baseline

To format cells for word-wrap Select cell(s) with mouse Choose Format, Cells , Alignment Click Wrap text, Merge cells Click OK 17 Step 4: List Points of Contact (POC) Decide who will have primary responsibility for data collection and analysis for each performance measure Serve as focal point regarding the data collection/analysis if several people are involved Can provide Team Leader with updates on how data collection and analysis is proceeding POC will ensure that data collection for that measure occurs in a timely fashion 18 Step 4: List Points of Contact (POC) (cont.) Internal Business Performance Objective: Identify Methods to Measure Processes Performance Measure Identify and report on process measures for Discrete Services Methodology The ASA Team will determine meaningful performance measures for each of the Discrete Services. Deployment process maps will be utilized to help pinpoint useful process measures. Our consultant

will help define the measures, the data to be collected, the source of the data, the frequency of data collection, and the methodology. Guidance on how to analyze and display the data will be obtained in Data Analysis training and Process Behavior Chart training. Our assessment will be included in our ASA Final Presentation. POC Source of Data Frequency POC Source of Data Frequency Target 100% of Discrete Services will identify at least one unique internal business process measure DS1 DS2 DS3 Internal Business Performance Objective:

Performance Measure Methodology Type in Point of Contact for each measure as appropriate. Target 19 Step 5: List Source of Data Identify where you will actually get the data for each measure Possible sources of data include: Ordering systems (ADB) Budgeting systems (OROS) Studies that have been conducted (Rate Studies) Hard copy order forms Tally of customer requests Email messages Phone calls Observations 20 Step 5: List Source of Data (cont.) If no known source exists Need to develop methods to collect data Check sheets useful for all kinds of data collection Constructed to whatever size, shape, format appropriate for you Easy to compile data in a way that can be readily graphed Record data for later analysis using bar, pareto, and run charts Provide historical record of the process over time Can be used to introduce data collection to workers who may not be familiar with it

May want to use simple check sheet to summarize data already collected but not tallied 21 Example Check Sheet Errors May June July Aug Wrong form used 11 1 1 11 6 1 1 2

Sept Total No CAN 1 1 111 11111 1 3 1111 18 No POC 11 1111 Incomplete information 22 Example Check Sheet July Email

Phone Aug Visit Email Phone Sept Visit Email Phone Total Visit Customer Contacts Other ORS Div/Office 1111 1111 11 NIH OD 1111 11 Clinical Center

111 111 111 NCI 1111 1111 1111 111 111 11 NHLBI 12 1111 111 8 111 111 111

18 111 111 111 30 11 6 11 -- NIAID Other NIH Institute 1 1 1 1 HHS Outside Agency 11 11

11 11 4 1111 1111 11111 111 11 11 111 11 21 12 23 Step 5: List Source of Data (cont.) For more information on designing data collection forms: Memory Jogger for an example of a Check Sheet (p.31) Statistical Methods for Quality Improvement (pp. 7 - 16) Guide to Quality Control (pp. 30-41) Basic Tools for Process Improvement Module 7: Data Collection (pp. 12-23) http://www.odam.osd.mil/qmo/pdf/datacoll.pdf Process Improvement Notebook for Data Collection Sheet and Check Sheet (pp. 66, 68) http://www.odam.osd.mil/qmo/pdf/pin.pdf

24 Step 5: List Source of Data (cont.) Internal Business Performance Objective: Identify Methods to Measure Processes Performance Measure Identify and report on process measures for Discrete Services Methodology The ASA Team will determine meaningful performance measures for each of the Discrete Services. Deployment process maps will be utilized to help pinpoint useful process measures. Our consultant will help define the measures, the data to be collected, the source of the data, the frequency of data collection, and the methodology. Guidance on how to analyze and display the data will be obtained in Data Analysis training and Process Behavior Chart training. Our assessment will be included in our ASA Final Presentation. POC Source of Data Frequency POC Source of Data Frequency

Target 100% of Discrete Services will identify at least one unique internal business process measure DS1 DS2 DS3 Internal Business Performance Objective: Performance Measure Methodology Target Type in Source of Data for each measure you have listed. 25 Step 6: List Data Collection Frequency Identify the frequency of data collection for each measure Examples include: Ongoing (e.g., ordering systems) Weekly (e.g., count of number of complaints) Monthly (e.g., utilization statistics) Quarterly (e.g., financial reports) Once per fiscal year (e.g., customer survey) Some Common Measures are filled in for you 26

Step 6: List Data Collection Frequency (cont.) Internal Business Performance Objective: Identify Methods to Measure Processes Performance Measure Identify and report on process measures for Discrete Services Methodology The ASA Team will determine meaningful performance measures for each of the Discrete Services. Deployment process maps will be utilized to help pinpoint useful process measures. Our consultant will help define the measures, the data to be collected, the source of the data, the frequency of data collection, and the methodology. Guidance on how to analyze and display the data will be obtained in Data Analysis training and Process Behavior Chart training. Our assessment will be included in our ASA Final Presentation. POC Source of Data Frequency POC Source of Data

Frequency Target 100% of Discrete Services will identify at least one unique internal business process measure DS1 DS2 DS3 Internal Business Performance Objective: Performance Measure Methodology Type in Frequency of Data Collection for each measure. Target 27 Step 7: List Target Identify the target performance level for this FY if possible Targets are usually identified after it is understood what the process is capable of doing Consult Service Level Agreements (SLA) if they exist Examples include: Response time within 3 business days 95% of reports error free Actual asset utilization within 10% of plan

Reduce use of vendors by 5% If performance measure is being defined for the first time this year True process capability is being defined for first time Type in Baseline under Target Targets for some Common Measures are filled in for you 28 Step 7: List Target (cont.) Internal Business Performance Objective: Identify Methods to Measure Processes Performance Measure Identify and report on process measures for Discrete Services Methodology The ASA Team will determine meaningful performance measures for each of the Discrete Services. Deployment process maps will be utilized to help pinpoint useful process measures. Our consultant will help define the measures, the data to be collected, the source of the data, the frequency of data collection, and the methodology. Guidance on how to analyze and display the data will be obtained in Data Analysis training and Process Behavior Chart training. Our assessment will be included in our ASA Final Presentation. POC Source of Data

Frequency POC Source of Data Frequency Target 100% of Discrete Services will identify at least one unique internal business process measure DS1 DS2 DS3 Internal Business Performance Objective: Performance Measure Methodology Type in Target performance for each measure listed on your Plan. Target 29 Methods of Analyzing Data With Graphs 30

There are Many Ways to Analyze Data Two general types of data Quantitative Qualitative Ways to analyze quantitative data Through visual displays - graphs Through process behavior charts Through statistical analyses Chi-square, t-tests, ANOVA, correlation, regression analyses, factor analysis Through predictive modeling LISREL 31 Common Graphs for Analysis Pie charts Bar charts Pareto charts Radar charts Line graphs Run charts Process behavior charts Scatter diagrams Correlation Gap analysis 32 Pie Charts Often used to summarize categorical data

Show the proportional size of items that make up a whole Convey the relative contribution of different categories to the total Usually used with percentages Good for simple descriptions, quick snapshots of some kinds of data 33 ORS Example Pie Chart Conference Services Survey Respondents N=564 34 Bar Graphs Useful for comparing different categories by contrasting heights of various bars Helpful in making comparisons among items Frequency, size, importance, satisfaction, dollars, etc. Often used to show comparisons on more than one dimension Categories of products ordered 35 ORS Example Bar Graph Conference Services: Scheduling Actions that Occurred N=564 36

ORS Example Bar Graph Categories of Photography Products Ordered by Year 37 Pareto Charts Type of bar chart Display bars in descending order Help to focus efforts on areas that offer the greatest potential for improvement Based on the Pareto principle Most problems are due to a minority of categories of causes For more information: The Memory Jogger for more information (p. 95) Building Continual Improvement (pp. 38-44) Statistical Methods for Quality Improvement (pp. 17-24) Basic Tools for Process Improvement Module 8 - Pareto Charts http://www.odam.osd.mil/qmo/pdf/pareto.pdf The Process Improvement Notebook for Pareto Chart of Causes of Quality form (pp. 70-73) http://www.odam.osd.mil/qmo/pdf/pin.pdf 38 Example Pareto Chart Improvement Ideas Supported by Customers N=250 39

Radar Charts Used to compare actual values on a series of categories to ideal values Allows comparison among data points Encourages identifying strengths and weaknesses See The Memory Jogger for more information (p. 137-140) 40 Example Radar Chart FY02 Customer Service Ratings: ORS Index = 6.9 Availability 10.0 Ideal Value 8.0 6.0 Handling of Problems 4.0 2.0 Responsiveness 0.0 Actual Value

Competence Convenience Data obtained from 3,000 customers 41 Line Graphs Used to study data for patterns Helpful in making comparisons over time Show changes in numerical amounts Identify sequences/changes in data Demonstrate performance before and after an intervention Can be used to predict future performance Run charts and process behavior charts are types of line graphs For more information: The Memory Jogger (pp. 141-144) Building Continual Improvement The Process Improvement Notebook for Run Chart and Control Chart forms (pp. 82-98) http://www.odam.osd.mil/qmo/pdf/pin.pdf 42 Example Line Graph Ratings of Responsiveness to Customer Complaints by Year N=125 43

ORS Example Line Graph NIH ID Cards Issued by Year 44 Scatter Diagrams Demonstrate the relationship between two variables Values on two variables are plotted on a graph to visually show the relationship For more information: The Memory Jogger (pp. 145-149) Statistical Methods for Quality Improvement (pp. 67-89) The Process Improvement Notebook for Scatter Diagram Worksheet (pp. 78-81) http://www.odam.osd.mil/qmo/pdf/pin.pdf 45 Example Scatter Diagram Gap Analysis of Customer Ratings of Satisfaction and Importance FY02 Service Group Importance and Satisfaction Ratings 10.00 Not Important, Satisfied 9.00 Important, Satisfied Reliability Satisfaction Mean Rating

Quality 8.00 Cost 7.00 Availability Competence Handling of Problems 6.00 Convenience 5.00 Timeliness 4.00 Responsivenes s 3.00 2.00 Not Important, Not Satisfied 1.00 1.00 2.00 Important, Not Satisfied 3.00

4.00 5.00 6.00 7.00 8.00 9.00 10.00 Importance Mean Rating Each symbol indicates both the importance and satisfaction rating for a variable, such as timeliness. 46 Analyzing Data With Graphs Measure Purpose Percentage Relative to Whole Relative Frequency, Size, Importance, Dollars Examples Percentage of customers by type Chart Type

Pie Bar Pareto Radar Line Run Control X X Scatter X Revenue by customer Number of errors by type X X Number of complaints by type Frequency of help desk calls by type Comparison of Actual Values to Ideal Values Customer satisfaction ratings Monitor Performance Number of defects in product by day over Time

Average number of days to respond to customer requests by month Average holding time of animals by month Identify Relationships between Variables X % bills with errors by week Age of equipment and unscheduled repairs Availability of product and request turnaround time Problem resolution speed and % team fully trained X % error free bills and customer satisfaction ratings Gap Analysis of survey data Customer segmentation charts are good examples of these. 47 Analyzing Data With Graphs (cont.) Measure Purpose Percentage Relative to Whole Relative Frequency, Size, Importance, Dollars

Examples Percentage of customers by type Chart Type Pie Bar Pareto Radar Line Run Control X X Scatter X Revenue by customer Number of errors by type X X Number of complaints by type Frequency of help desk calls by type Comparison of Actual Values to Ideal

Values Customer satisfaction ratings Monitor Performance Number of defects in product by day over Time Average number of days to respond to customer requests by month Average holding time of animals by month Identify Relationships between Variables X % bills with errors by week Age of equipment and unscheduled repairs Availability of product and request turnaround time Problem resolution speed and % team fully trained X % error free bills and customer satisfaction ratings Gap Analysis of survey data Customer satisfaction results will be provided to Service Groups using radar charts. 48 Analyzing Data With Graphs (cont.) Measure

Purpose Percentage Relative to Whole Relative Frequency, Size, Importance, Dollars Examples Percentage of customers by type Chart Type Pie Bar Pareto X X Radar Line Run Control X X Scatter X

Revenue by customer Number of errors by type Number of complaints by type Frequency of help desk calls by type Comparison of Actual Values to Ideal Values Customer satisfaction ratings Monitor Performance Number of defects in product by day over Time Average number of days to respond to customer requests by month Average holding time of animals by month Identify Relationships between Variables X % bills with errors by week Age of equipment and unscheduled repairs Availability of product and request turnaround time Problem resolution speed and % team fully trained X % error free bills and customer satisfaction ratings Gap Analysis of survey data

Internal Business Process measures often require run charts or control charts. Control charts are a special type of run chart. Training on process behavior charts will be available (http://www.nih.gov/od/ors/od/oqm/asa/asa_training.htm) 49 Analyzing Data With Graphs (cont.) Measure Purpose Percentage Relative to Whole Relative Frequency, Size, Importance, Dollars Examples Percentage of customers by type Chart Type Pie Bar Pareto Radar Line Run Control X X

Scatter X Revenue by customer Number of errors by type X X Number of complaints by type Frequency of help desk calls by type Comparison of Actual Values to Ideal Values Customer satisfaction ratings Monitor Performance Number of defects in product by day over Time Average number of days to respond to customer requests by month Average holding time of animals by month Identify Relationships between Variables X % bills with errors by week Age of equipment and unscheduled repairs Availability of product and request turnaround time Problem resolution speed and % team

fully trained X % error free bills and customer satisfaction ratings Gap Analysis of survey data Scatter charts depict the correlation between 2 variables and can be used to investigate hunches you may have about relationships. 50 Tips for Graph Analysis Realize that analyzing data is a skill With experience you will get better Analysis is both a science and an art Common methods to interpret graphical data Compare size of categories to each other Compare self to target Compare self to others who are similar Compare self to industry standards Compare self over time General things to look for in data Highlight similarities and differences Identify trends or patterns Notice if anything is missing (e.g., customer groups) Look for themes 51 Tips for Graph Analysis (cont.) Make conclusions based on graphs

Summarize what you learned from the data Diagnose problems identified by data Identify any potential solutions suggested by data Generate potential actions based on what you have learned from the data Can you make changes to address what you learned from data? How might you implement those actions? State actions in terms of recommendations 52 Summarizing Analyses in your ASA Presentation Organize your graphs and conclusions to tell the story of your ASA Who are your customers (i.e., customer segments) Are you customers satisfied with your products/services (i.e., customer satisfaction)? What have you learned about your internal business processes from the process maps and measures? Learning and Growth analyses and conclusions Financial findings regarding unit costs, asset utilization Recommendations for improvement based on data gathered and analyzed ASA Presentation Template and tips will be available this summer via the ASA web page 53 Summarizing Analyses in Your ASA Presentation (cont.) Beware of excess detail Do NOT place all graphs in main section of presentation

Select only the most informative graphs for main portion of ASA Presentation Insert narrative slides that include your conclusions of the graphs, findings from the data Place graphs not used in main presentation in Appendices to your main presentation Try to see the forest through the trees Look for themes and major findings as a result of the ASA Include recommendations and proposed follow-on actions Make realistic recommendations that could actually be implemented If have power to implement suggested changes, do so 54 Constructing Graphs from the ASA Template 55 Customer Perspective Graphing and Analyzing Customer Segmentation Data 56 Customer Segmentation 1. Select customer characteristic(s) to segment NIH IC Location On-campus, Off-campus Building Type of customer NIH employees, contractors, visitors 2. Select product/service dimension

New equipment sales Trouble reports Requests for service Frequency of use 3. Determine time frame Current fiscal year, several fiscal years, current month, several months 57 Customer Segmentation (cont.) 4. Generate chart(s) 5. Review and interpret chart(s) Which segments are your primary customers? Is there any pattern to your chart(s)? Which NIH customers are not currently your customers? Why? What does the information say about who is and who is not your customer? 58 Pie Chart Customer Segmentation of Discrete Services Discrete Service = Manage concession services program, contracts, and use agreements Customer characteristic = Customer Type Product/Service dimension = Frequency of Use Time frame = January through April, 2001 Methodology Surveys handed out to all willing participants at point of sale at 8 Dining Halls on campus for one day during the data collection period

59 Pie Chart Customer Segmentation of Discrete Services (cont.) 1. Collect data and enter in Excel List Customer Type Horizontally Federal Government Employees # Sales Label Frequency of Use Measure 13250 Fellows/Visiting Researchers 5500 Visitors/ Conference Attendees Contractors 2500 Other 1500 2250 Enter Data

60 Pie Chart Customer Segmentation of Discrete Services (cont.) 2. Generate Chart Select all labeled cells and data using mouse Choose Insert, Chart, Pie Click Finish # Sales Federal Government Employees Fellows/Visiting Researchers Contractors Visitors/ Conference Attendees Other 61 Pie Chart Customer Segmentation of Discrete Services (cont.) 3. Format Chart Click on chart Click right mouse button Choose Chart Options Choose Titles, Chart titles: Type in chart title (e.g., Concession Service Customers)

Choose Data Labels: Click Show percent Click OK Concession Service Customers 9% Federal Government Employees 6% Fellows/Visiting Researchers 10% Contractors 53% 22% Visitors/ Conference Attendees Other 62 Pie Chart Customer Segmentation of Discrete Services (cont.) 4. Add important notes to chart Ensure that Drawing toolbar is available to you Choose View, Toolbar, Drawing

Click on chart Click on Text Box from Drawing Toolbar Place text box on chart Type in note and format text Concession Service Customers Federal Government Employees 9% 6% Fellows/Visiting Researchers 10% Contractors 53% 22% Visitors/ Conference Attendees Other Data obtained from 564 customers at all concessions during January - April, 2001. 63 Bar Graph Customer Segmentation of Discrete Services Discrete Service = Conduct collaborative bioengineering and physical science research Customer characteristic = NIH IC

Product/Service dimension = Number of Collaborations Time frame = FY01 Data Collection Data collected on all collaborations during FY01 64 Bar Graph Customer Segmentation of Discrete Services (cont.) 1. Collect data and enter in Excel List Customer Type Horizontally NCI # Collaborations Type Measure Label NIA 16 NIAID 1 NIMH 7 NINDS 5 18 Enter Data 65

Bar Graph Customer Segmentation of Discrete Services (cont.) 2. Generate Chart Select all labeled cells and data using mouse Choose Insert, Chart, Column Click Finish # Collaborations 20 18 16 14 12 10 8 6 4 2 0 # Collaborations NCI NIA NIAID NIMH NINDS 66 Bar Graph

Customer Segmentation of Discrete Services (cont.) 3. Format Chart Click on chart Click right mouse button Choose Chart Options Choose Titles, Chart title: Type in chart title (e.g., # Collaborations with NIH ICs) Choose Category (X) Axis: Type in Customer Type (e.g., NIH IC) Choose Category (Y) Axis: Type in Measure Label (e.g., # Collaborations) # Collaborations WIth NIH IC # Collaborations 20 18 16 15 10 7 5 5 1

Choose Legends: Click Show legend Choose Data Labels: Click Show value Click OK 0 NCI NIA NIAID NIH IC NIMH NINDS 67 Bar Graph Customer Segmentation of Discrete Services (cont.) 3. Format Chart (cont.) Click on gray area on chart (e.g., plot area) Click right mouse button Choose Format Plot Area In the Area section, click on the white box. Click OK Click on gridlines in center of chart Click right mouse button Choose Clear # Collaborations With NIH ICs 20 # Collaborations

18 16 15 10 7 5 5 1 0 NCI NIA NIAID NIH IC NIMH NINDS 68

Bar Graph Customer Segmentation of Discrete Services (cont.) 4. Add important notes to chart Ensure that Drawing toolbar is available to you Choose View, Toolbar, Drawing Click on chart Click on Text Box from Drawing Toolbar Place text box on chart Type in note and format text # Collaborations With NIH ICs 20 # Collaborations 18 16 15 10 7 5 5 1 0 NCI

NIA Data based on 47 collaborations in FY01 NIAID NIMH NINDS NIH IC 69 Pareto Chart Customer Segmentation of Discrete Services Discrete Service = Conduct collaborative bioengineering and physical science research Customer characteristic = NIH IC Product/Service dimension = Number of Collaborations Time frame = FY01 Data Collection Data collected from all DBEPS collaborators on the number of collaborations during FY01 Note: Data is same as bar graph example, but pareto chart will re-order NIH ICs from the greatest to the fewest number of collaborations. 70 Pareto Chart Customer Segmentation of Discrete Services (cont.) 1. Collect data and enter in Excel (see slide 65) NCI NIA

# Collaborations 16 NIAID 1 NIMH 7 NINDS 5 18 Select IC and Data Cells using Mouse Click Data, Sort Ensure correct row (e.g., row with data) is displayed in Sort By box. If not, select correct row Click Descending Click OK NINDS # Collaborations NCI 18 NIAID 16 NIMH 7

Data is resorted by IC in descending order NIA 5 1 71 Pareto Chart Customer Segmentation of Discrete Services (cont.) 2. Generate Chart See Slide 66 3. Format Chart See Slides 67-68 # Collaborations # Collaborations With NIH ICs 20 18 16 15 10 7 5 5 1

0 NINDS NCI Data based on 47 collaborations in FY01 NIAID NIMH NIA NIH IC 72 Bar Graph with Added Dimension Customer Segmentation of Discrete Services Discrete Service = Conduct collaborative bioengineering and physical science research Customer characteristic = NIH IC Product/Service dimension = Number of Collaborations Time frame = FY01 and FY02 Data Collection Data collected on all collaborations during FY01 and FY02 Added Dimension 73 Bar Graph with Added Dimension Customer Segmentation of Discrete Services (cont.) 1. Collect data and enter in Excel

List Customer Type Horizontally NCI NIA NIAID NIMH NINDS FY01 16 1 7 5 18 FY02 18 3 12 8 18

Type Measure Labels Enter Data 74 Bar Graph with Added Dimension Customer Segmentation of Discrete Services (cont.) 2. Generate Chart Select all labeled cells and data using mouse Choose Insert, Chart, Column Click Finish 20 18 16 14 12 FY01 10 FY02 8 6 4 2 0 NCI NIA

NIAID NIMH NINDS 75 Bar Graph with Added Dimension Customer Segmentation of Discrete Services (cont.) 3. Format Chart Click on chart Click right mouse button Choose Chart Options Choose Titles, Chart title: Type in chart title (e.g., # Collaborations With NIH ICs by Fiscal Year) Choose Category (X) Axis: Type in Customer Type (e.g., NIH IC) Choose Category (Y) Axis: Type in Measure Label (e.g., # Collaborations) # Collaborations With NIH ICs by Fiscal Year # Collaborations 20 16 18 18 18

15 12 10 7 5 1 3 FY01 8 FY02 5 Choose Data Labels: Click Show value Click OK 0 NCI NIA NIAID NIH IC NIMH

NINDS 76 Bar Graph with Added Dimension Customer Segmentation of Discrete Services (cont.) 3. Format Chart (cont.) Click on gray area on chart (e.g., plot area) Click right mouse button Choose Format Plot Area In the Area section, click on the white box. Click OK Click on gridlines in center of chart Click right mouse button # Collaborations With NIH ICs by Fiscal Year Choose Clear 20 # Collaborations 16 18 18 18

15 12 10 7 5 1 FY01 8 FY02 5 3 0 NCI NIA NIAID NIMH NINDS NIH IC 77

Bar Graph with Added Dimension Customer Segmentation of Discrete Services (cont.) 4. Add important notes to chart Ensure that Drawing toolbar is available to you Choose View, Toolbar, Drawing Click on chart Click on Text Box from Drawing Toolbar Place text box on chart Type in note and format text # Collaborations With NIH ICs by Fiscal Year 20 # Collaborations 18 18 18 16 15 12 10 7 5 1

FY01 8 FY02 5 3 0 NCI NIA Data based on 47 collaborations in FY01 and 59 in FY02 NIAID NIMH NINDS NIH IC 78 Customer Perspective Graphing and Analyzing Customer Satisfaction Ratings 79 Customer Satisfaction Ratings 1. Review methodology with OQM before distributing surveys to

any customers 2. Completed surveys will be analyzed by OQM and summary charts will be provided 3. Review the radar charts and interpret the findings Compare rating dimensions on each chart What is highest? Why? What is lowest? Why? 4. Review the scatter diagram (gap analysis) What do customers feel is most important? What was their satisfaction on the most important dimensions? What actions can be taken to address the situation? 5. Focus first improvement efforts on areas most important to customers with lower satisfaction ratings 80 Radar Chart Customer Satisfaction Ratings FY02 Product/Service Ratings: ORS Index = 7.4 FY02 Product/Service Ratings: Service Group Index = 7.7 Cost 10.0 Reliability Cost 10.0 8.0

8.0 6.0 6.0 4.0 4.0 2.0 2.0 0.0 Timeliness Data obtained from 3,000 customers Quality Reliability 0.0 Quality Timeliness Data obtained from 200 customers Mean Product/Service satisfaction ratings provided for ORS overall on the left and the Service Group on the right. 81

Radar Chart Customer Satisfaction Ratings (cont.) FY02 Customer Service Ratings: ORS Index = 6.9 FY02 Customer Service Ratings: Service Group Index = 7.3 Availability 10.0 Handling of Problems Availability 10.0 8.0 8.0 6.0 6.0 4.0 2.0 Responsiveness Handling of Problems 0.0

Competence Data obtained from 3,000 customers 4.0 2.0 Responsiveness 0.0 Convenience Competence Convenience Data obtained from 200 customers Mean customer service ratings provided for ORS overall on the left and the Service Group on the right. 82 Scatter Diagram: Gap Analysis Customer Importance and Satisfaction Ratings FY02 Service Group Importance and Satisfaction Ratings 10.00 9.00 Not Important, Satisfied Important, Satisfied Reliability

Satisfaction Mean Rating Quality 8.00 Cost 7.00 Availability Competence Handling of Problems 6.00 Convenience 5.00 Timeliness 4.00 Responsivenes s 3.00 2.00 1.00 1.00 Not Important, Not Satisfied 2.00 Important, Not Satisfied

3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 Importance Mean Rating Mean satisfaction and importance ratings for the Service Group. 83 Internal Business Process Perspective Graphing and Analyzing Process Measures 84 Internal Business Process Measures 1. Identify process measures using deployment process maps, known problem areas, customer feedback 2. Implement methodology to collect data 3. Collect data 4. Generate chart(s) 5. Review and interpret chart(s)

What do the process measures tell us? Can we determine why problems are occurring? Is process improving or declining? Why? What can be done to impact process? 6. Identify and report on areas for improvement 85 Run Chart Internal Business Process Measures Unique Measure = Reduce number of bills processed with errors Methodology = Count number of bills returned by customer noting an error Point of Contact = Billing Supervisor Source of Data = Log of bills returned classified by customer and type of error Frequency = Quarterly Target = 5% reduction in bills returned 86 Run Chart Internal Business Process Measures (cont.) 1. Collect data and enter in Excel List Frequency Intervals Horizontally Q1 FY01 Q2 FY01 Q3 FY01 Q4 FY01 Q1 FY02 Q2 FY02 Q3 FY02 Q4 FY02 # Bills Processed with Errors

Label Data Being Collected 58 62 54 68 57 48 25 22 Enter Data 87 Run Chart Internal Business Process Measures (cont.) 2. Generate Chart Select all labeled cells and data using mouse Choose Insert, Chart, Line Click Finish # Bills Processed with Errors 80 70 60 50 40

30 20 10 0 # Bills Processed with Errors Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 FY01 FY01 FY01 FY01 FY02 FY02 FY02 FY02 88 Run Chart Internal Business Process Measures (cont.) 3. Format Chart Click on chart Click right mouse button Choose Chart Options Choose Titles, Chart title: Type in title (e.g., # Bills Processed With Errors by Quarter) Choose Legend: Click Show legend Choose Data Table: Click Show data table

Click OK # Bills Processed with Errors by Quarter 80 60 40 20 0 # Bills Processed w ith Errors Q1 FY01 Q2 FY01 Q3 FY01 Q4 FY01 Q1 FY02 Q2 FY02 Q3 FY02 Q4 FY02 58

62 54 68 57 48 25 22 89 Run Chart Internal Business Process Measures (cont.) 3. Format Chart (cont.) Click on gray area on chart (e.g., plot area) Click right mouse button Choose Format Plot Area In the Area section, click on the white box. Click OK Click on gridlines in center of chart

Click right mouse button Choose Clear # Bills Processed with Errors by Quarter 80 60 40 20 0 # Bills Processed w ith Errors Q1 FY01 Q2 FY01 Q3 FY01 Q4 FY01 Q1 FY02 Q2 FY02 Q3 FY02 Q4 FY02

58 62 54 68 57 48 25 22 90 Run Chart Internal Business Process Measures (cont.) 4. Add important notes to chart Ensure that Drawing toolbar is available to you Choose View, Toolbar, Drawing Click on chart Click on Text Box from Drawing Toolbar Place text box on chart Type in note and format text # Bills Processed with Errors by Quarter Note: Process

changed in Q2 FY02 80 60 40 20 0 # Bills Processed w ith Errors Q1 FY01 Q2 FY01 Q3 FY01 Q4 FY01 Q1 FY02 Q2 FY02 Q3 FY02 Q4 FY02 58

62 54 68 57 48 25 22 91 Run Chart Internal Business Process Measures (cont.) Run Chart shows that the change in process in Quarter 2 of FY02 appears to have resulted in fewer errors in billing # Bills Processed with Errors by Quarter Note: Process changed in Q2 FY02 80 60 40 20 0 # Bills Processed w ith Errors Q1 FY01

Q2 FY01 Q3 FY01 Q4 FY01 Q1 FY02 Q2 FY02 Q3 FY02 Q4 FY02 58 62 54 68 57 48 25

22 Process behavior charts (i.e., control charts) use formulas to help determine whether the difference is significant Training on process behavior charts will be available. Check the ASA web page (http://www.nih.gov/od/ors/od/oqm/asa/asa_training.htm) 92 Scatter Diagram Relationship Between Two Measures After completing data analyses on other measures, a scatter chart may be generated Further examine relationships between 2 measures Relationship may be suggested by other analyses Example - examine the relationship between two unique measures Unscheduled repairs and age of equipment Graph data and study relationship Positive in nature Negative in nature 93 Scatter Diagram Relationship Between Two Measures (cont.) 1. Collect data and enter in Excel List Categories and Definitions Horizontally Repairs Age of Equipment 1 = Routine Maintenance 2 = Unscheduled Repair

Round off age of equipment to nearest year 1 1 1 1 2 2 1 1 1 2 2 2 1 2 1 2 2 2 2 2 1 1 1 2 3 4 5 8 1 1 2

10 4 25 2 4 2 12 5 7 6 5 1 1 Enter Data 94 Scatter Diagram Relationship Between Two Measures (cont.) 2. Generate Chart Select all data cells using mouse Choose Insert, Chart, XY (Scatter) Click Next Choose Data Range. Click Columns Click Finish 30 25 20

15 Series1 10 5 0 0 0.5 1 1.5 2 2.5 95 Scatter Diagram Relationship Between Two Measures (cont.) 3. Format Chart Click on chart Click right mouse button Choose Chart Options Choose Titles, Chart title: Type in chart title (e.g., Repair Calls by Age of Equipment) Choose Value (X) Axis: Type in X axis title (e.g., Type of Repair Call) Choose Value (Y) Axis: Type in Y axis title (e.g., Age of Equipment)

Choose Legend Click Show legend Click OK Repair Calls by Age of Equipment Age of Equipment 30 25 20 15 10 5 0 0 0.5 1 1.5 2 2.5 Type of Repair Call 96 Scatter Diagram Relationship Between Two Measures (cont.) 4. Format Chart (cont.) Click on chart

Double click left mouse button on values on X axis Choose Scale Choose Minimum: Type in 0 Choose Maximum: Type in 3 Choose Major Unit: Type in 1 Choose Minor Unit: Type in 1 Repair Calls by Age of Equipment Age of Equipment 30 25 20 15 10 5 0 0 1 2 3 Type of Repair Call 97 Scatter Chart Relationship Between Two Measures (cont.)

4. Format Chart (cont.) Click on gray area on chart (e.g., plot area) Click right mouse button Choose Format Plot Area In the Area section, click on the white box. Click OK Click on gridlines in center of chart Click right mouse button Repair Calls by Age of Equipment Choose Clear 30 Age of Equipment 25 20 15 10 5 0 0 1 2 3

Type of Repair Call 98 Scatter Chart Relationship Between Two Measures (cont.) 5. Add important notes to chart Ensure that Drawing toolbar is available to you Choose View, Toolbar, Drawing Click on chart Click on Text Box from Drawing Toolbar Place text box on chart Type in note and format text Repair Calls by Age of Equipment 30 Age of Equipment 25 20 15 10 5 0 0 1 = Routine Maintenance 2 = Unscheduled Repair

1 2 3 Type of Repair Call 99 Scatter Chart Relationship Between Two Measures (cont.) 6. Run Regression Analysis on Data Choose Tools, Data Analysis, Regression Click OK Click inside Input Y Range box Select all Y axis data cells (e.g., repair data cells) Click inside Input X Range box Select all X axis data cells (e.g., age of equipment cells) Click Output Range. Click in Output Range box Click on blank cell on data sheet. Click OK 100 Scatter Chart Relationship Between Two Measures (cont.)

6. Run Regression Analysis on Data (cont.) Correlation = number between 0 (no relationship) and 1 (1 to 1 relationship) Significance level = .05 indicates 95% confidence level Correlation = .61 SUMMARY OUTPUT Regression Statistics 0.61241 Multiple R R Square 0.37505 Adjusted R Square 0.3438 Standard Error 0.41456 22 Observations Number of Observations = 22 ANOVA df Regression Residual Total 1 20 21 SS MS F Significance F 2.062755747 2.062756 12.00238 0.002448

3.437244253 0.171862 5.5 Significance Level = <.002 101 Learning and Growth Perspective Graphing and Analyzing Turnover, Sick Leave, Complaints, Awards 102 Turnover, Sick Leave, Complaints, Awards 1. Data is being obtained from ORS HR IT systems 2. OQM will graph data and provide to ASA Teams 3. ASA Teams meet to review and discuss the data 4. Highlight conclusions from the discussion in the final ASA presentation 103 Learning and Growth Perspective Analysis of Readiness Index 104 Qualitative Analysis of Readiness Index 1. The Readiness Index will be available this summer via the ASA web page 2. ASA Teams meet to review and discuss the questions on the Readiness Index 3. Prepare a short narrative summary of conclusions included as Appendix in final ASA presentation 4. Highlight conclusions in the final ASA presentation

105 Financial Perspective Graphing and Analyzing Unit Cost 106 Unit Cost 1. Obtain guidance from OBSF on the definition of unit cost measures for your Discrete Services 2. Determine how you can obtain your unit cost data 3. Gather your unit cost data 4. Graph your unit cost data and interpret the findings Are your costs going up? Why? Are your costs going down? Why? 107 Run Chart Unit Cost (cont.) Discrete Service 1: Unit Cost 15 14 13 Dollars 12 11 10 9 8 7 6 5

Unit Cost Q1 FY01 Q2 FY01 Q3 FY01 Q4 FY01 Q1 FY02 Q2 FY02 Q3 FY03 Q4 FY04 $10.15 $10.52 $10.22 $10.98 $11.25 $11.35 $11.88 $11.13 Quarter

Unit Cost by Quarter Data might be displayed using a run chart (shown) or control chart. 108 Financial Perspective Graphing and Analyzing Asset Utilization 109 Asset Utilization 1. Obtain guidance from OBSF on how to define assets for your Discrete Services 2. Determine how you can obtain your planned and actual asset utilization data 3. Gather your data 4. Graph your asset utilization data and interpret the findings 110 Run Chart Asset Utilization 25% 25% 20% 20% 15% 15%

10% 10% 5% 5% 0% 0% -5% -5% -10% -10% -15% -15% -20% -20% -25% Q1 FY01 Q2 FY01

Q3 FY01 Q4 FY01 Q1 FY02 Q2 FY02 Q3 FY02 Q4 FY02 Deviation (Quarter) -13% -3% -6% -5% -8% -4% -2% -3% Deviation (Cumulative) -13% -8%

-7% -7% -7% -6% -6% -5% Cumulative % Deviation (Line) % Deviation by Quarter (Bar) Asset Utilization: % Deviation From Plan -25% Quarter Data might be displayed using a run chart showing % deviation from plan. 111 Run Chart Asset Utilization 1. Calculate the % deviation from plan by quarter Quarterly Actual Planned % Deviation From Plan

Quarter 1 80 90 (80 - 90)/80 = -13% Quarter 2 87 90 (87 - 90)/87 = -3% 2. Calculate the % deviation from plan cumulatively Cumulatively Actual Planned % Deviation From Plan Quarter 1 80 90 (80 - 90)/80 = -13% Quarter 2 (80 + 87) = 167 (90 + 90) = 180 (167 - 180)/87 = -8% 112 Run Chart Asset Utilization (cont.) 1. Collect data and enter in Excel List Data Collection Intervals Horizontally Q1 FY01

Q2 FY01 Q3 FY01 Q4 FY01 Q1 FY02 Q2 FY02 Q3 FY02 Q4 FY02 Deviation (Quarter) -13% -3% -6% -5% -8% -4% -2% -3% Deviation (Cumulative) -13%

-8% -7% -7% -7% -6% -6% -5% Label Deviation Measures Enter Deviation Percentages 113 Run Chart Asset Utilization (cont.) 2. Generate Chart Select all labeled cells and data using mouse Choose Insert, Chart, Custom Types, Line - Column on 2 Axes Click Finish 0% 0% Q1 FY01 Q2 FY01 Q3 FY01 Q4 FY01 Q1 FY02 Q2 FY02 Q3 FY02 Q4 FY02 -2%

-2% -4% -4% -6% -6% Deviation (Quarter) Deviation (Cumulative) -8% -8% -10% -10% -12% -12% -14% -14% 114 Run Chart Asset Utilization (cont.) 3. Format Chart

Click on chart Click right mouse button Choose Chart Options Choose Titles, Chart title: Type in chart title (e.g., Asset Utilization: % Deviation From Plan) Choose Category (X) Axis: Type in data collection interval (e.g., Quarter) Choose Value (Y) Axis: Type in title (e.g., % Deviation by Quarter (Bar)) Choose Second value (Y) Axis: Type in title (e.g., Cumulative % Deviation (Line)) Choose Legend, Click Show legend Choose Data Table, Click Show data table Click OK 115 Run Chart Asset Utilization (cont.) 0% 0% -2% -2% -4% -4%

-6% -6% -8% -8% -10% -10% -12% -12% -14% Q1 FY01 Q2 FY01 Q3 FY01 Q4 FY01 Q1 FY02 Q2 FY02

Q3 FY02 Q4 FY02 Deviation (Quarter) -13% -3% -6% -5% -8% -4% -2% -3% Deviation (Cumulative) -13% -8% -7% -7%

-7% -6% -6% -5% Cumulative % Deviation (Line) % Deviation by Quarter (Bar) Asset Utilization: % Deviation From Plan -14% Quarter 116 Run Chart Asset Utilization (cont.) 3. Format Chart (cont.) Click on gray area on chart (e.g., plot area) Click right mouse button Choose Format Plot Area In the Area section, click on the white box. Click OK Double Click left mouse button on left Y axis

Choose Scale In Minimum box type -.25 In Maximum box type .25 In Major unit box type .05 Click OK Repeat for right Y axis 117 Run Chart Asset Utilization (cont.) O% = perfect planning (no deviation) Bars show deviation each quarter 25% 20% 15% 10% 5% 0% -5% -10% -15% -20% -25% Q1 FY01 Q2 FY01 Q3 FY01

Q4 FY01 Q1 FY02 Q2 FY02 Q3 FY02 Q4 FY02 Deviation (Quarter) -13% -3% -6% -5% -8% -4% -2% -3% Deviation (Cumulative)

-13% -8% -7% -7% -7% -6% -6% -5% 25% 20% 15% 10% 5% 0% -5% -10% -15% -20% -25% Cumulative % Deviation (Line) % Deviation by Quarter (Bar) Asset Utilization: % Deviation From Plan

Quarter Solid line shows cumulative deviation 118 All Perspectives Graphing and Analyzing Unique Measures 119 Unique Measures Use any of the graph types already discussed as appropriate for unique measures you have added Refer to slides 47-50 to select the type of graph best suited to the data Try other graph types to see if different displays highlight different results Interpret the data Identify potential actions to address problems areas, issues, improvement opportunities Make recommendations for improvement and plan actions accordingly Include in Final ASA Presentation In main part of presentation if major finding In Appendices if back-up material 120 Summary Data collection can be time consuming PLAN before you act Each ASA Team should complete a Data Collection Plan Analyzing data is both a science and an art Use graphs to summarize data

Pie, bar, pareto, radar, line, run, scatter ASA Team review of the graphs is basis of the data analysis and interpretation Organize your graphs and conclusions to tell the story of your ASA Look for themes and major findings Identify potential actions to address problems areas, issues, improvement opportunities Make recommendations for improvement and plan actions accordingly 121 Resources Brassard, M., & Ritter, D. (1994). The memory jogger. Salem, NH: GOAL/QPC. Culbertson, A., Houston, A., Faast, D., White, M., Aguirre, M., & Behr, C. (1997). The Process Improvement Notebook (TQL 97-01). Washington, DC: Department of the Navy. http://www.odam.osd.mil/qmo/pdf/pin.pdf Ishikawa, K. 1982. Guide to quality control. Tokyo, Japan: Asian Productivity Organization. Kume, H. (1989). Statistical methods for quality improvement. Tokyo, Japan: Association for Overseas Technical Scholarship. Navy Total Quality Leadership Office. (1996). Basic Tools for Process Improvement. Washington, DC: Department of the Navy. http://www.odam.osd .mil/qmo/pdf/pareto.pdf Wheeler, D. J. (2000). Understanding variation: The key to managing chaos. Knoxville, TN: SPC Press. Wheeler, D. J., & Poling, S. R. (1998). Building continual improvement. Knoxville, TN: SPC Press. 122

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