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Six Sigma Project Charter
Name of project: An Examination of Cohort Default Rates: A Causal Analysis for Prevention
Green belt: Jennifer Howells
Submitted by: Jennifer Howells e-mail: [email protected]
Date submitted: Draft 5/21/12
Final 10/4/12
I. Project Selection Process
Item Yes No Comments
Key business issue x Default rates are a very hot topic in the world of Financial Aid (at Purdue and nationally).
Linked to a defined process x Loan default rates are sent to Purdue annually.
Customers identified x Purdue University (rankings), students currently accepting loans, and DFA employees.
Defects clearly defined x We do not have a process to keep our loan default numbers in check.
I have described how and why the project was selected below and referenced the tools used.
II. Project Description
Project Title
Date Charted Target Completion Date Actual Completion Date
5/16/12 10/4/12 N/A
Project Leader Team Facilitator Team Champion
Jennifer Howells Jennifer Howells Joel Wenger
Estimated Cost Savings Actual Cost Savings Costs of implementing project
Although not a clear monetary savings for the office, this project will help reduce the risk of going over the 10% default rate, which keeps us in good graces with the Department of Education. If our rate would happen to skyrocket, we could lose eligibility for Title IV aid completely, costing the University more than 200 million dollars/year. Also, by having a low rate, we can offer early disbursement options to all students, including new freshman who would otherwise have to wait
N/A The costs incurred with
this project will be in work hours and minimal office supplies (paper, toner, etc.)
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30 days before getting their loan funds. The default rate is a comparison point between the Big Ten Universities and Peers, so any decrease would benefit Purdue’s reputation.
Team members
Jennifer Howells (analyst)
Joel Wenger (supervisor/will provide the initial default cohort list) Ted Malone (final approval at each stage)
Stephanie Fiddler (area expert, compiles cohort default rates for Peers & Big Ten)
Problem Statement
Our draft cohort default rate increased more than expected this year. We need a process to prevent students from appearing on the loan default list (lowering our cohort default rate). To this point, we have not identified common characteristics of these defaulters; we simply receive the names and SSNs for the borrowers who have defaulted. I have the ability to run the names through our Student and Financial aid databases (using Cognos) to add as many characteristics as
needed. I will use the characteristics that appear most often in the defaulters to identify an at-risk population. If we can focus education/intervention on the identified at-risk populations while they are still in school, we could reduce the number of borrowers who will default (lowering the cohort default rate). I will also attempt to identify loan servicer issues that may have an impact on the default rate.
Project Goal and Metrics
The goal of the project is to prevent the cohort default rate from increasing. More specifically, identify common characteristics of loan defaulters using the NSLDS default list. We can use those characteristics to identify current loan borrowers who may benefit from additional financial literacy or additional
counseling/intervention. Information gathered might also be used to alleviate loan servicer disparities.
Describe the challenges and support required
Time available for the project outside of Six Sigma class could be an issue. I will
need my supervisor’s support and will need to ask for assistance with the Six Sigma project and/or work duties as needed to ensure complete of this task.
Project Schedule
D1. Select the output characteristic. Date: 5/21/12
Y = Reduce the Cohort Default Rate
To make sure that there was a need for a Six Sigma Project, I used Pareto charts to plot the number of loan defaulters over the past 6 years and the number of
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3 people in loan repayment over the same time period. I lined the bars for
defaulters by year with the tallest bar on the left. Then to compare, I lined the bars for the number in repayment by year in the same order. They do not match, so there are other factors causing the increased number of loan defaults. It is not as simple as the number of defaulters increasing as the number in repayment increases (Attachment 1).
D2. Define the output performance standard. Date: 5/22/12
Our department would like to reduce next year’s cohort default rate as reported by NSLDS by one standard deviation. This is a one-sided spec for reduction only. The project passed the RUMBA questions 5 for 5 (Attachment 2).
D3. Describe the process. Date: 7/1/12 Required tools: SIPOC, Detailed process map
I used a SIPOC worksheet to define my process, inputs, outputs, suppliers, and customers (Attachment 3).
Process – Students accept loans for educational expenses, then drop out of
school or graduate. The loan recipient enters repayment of their loans and after two years if they stop paying, they appear on the default list. NSLDS provides the report of loan defaulters and our official default rate to our data area. Purdue would have severe ramifications if the cohort default rate were to increase
drastically. I will request the default list from the data area, convert it to a flat file, Query for student ID’s, pull all financial aid & student information using Cognos, combine that data with the default list. The remainder of the project will be to analyze and identify similar characteristics using the Cognos and Banner Systems.
Inputs – The default list, Cognos for data retrieval, Banner to confirm Cognos
reports and FAFSA information, TIME to complete the project. Later, I will be examining characteristics such as gender, age, race, college, SAP status, full time vs. part time, degree vs. dropout, EFC, GPA, etc. (BRAINSTORMED with team Attachment 4).
Outputs – The information from NSLDS and the at risk populations identified by
common defaulter characteristics.
Suppliers – NSLDS, data area, champion, team
Customers – Purdue University (rankings among the Big Ten & Peers), The
Division of Financial Aid, and Purdue students (loan borrowers).
M1. Validate the measuring system. Date:8/23/12 Required tools: Gage R&R/Attribute Agreement Analysis
I will use existing documents (ten years of the default reports). It is in a form that can be used easily (I have converted the flat file to an Excel spreadsheet, and downloaded the Department of Education’s Cohort Default Rate Guide).
It is the most current data available. It has a consistent collection time each year. The data is representative. The data comes from a federal agency so repeatability and reproducibility will be difficult to test.
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I used the Attribute Agreement Analysis to check the last date of attendance for the students on the reports to confirm cohort reproducibility. The
Cohort was correctly chosen with all students selected not having enrollment past the expected date. I completed two reports to check for enrollment and used Minitab to confirm the results (Attachment 5).
M2. Establish current process capability for the output. Date:8/23/12 Required tools: Process capability, Control chart
To show where we were before the project, I created a Binomial Process
Capability Analysis of Defaulters using Minitab showing the number of defaulters and the number entering repayment over the years (Attachment 6).
The process is out of control as seen by the points falling outside of the control limits.
M3. Determine project objectives. Date:8/23/12
I determined the project objectives based on the results of M2. We know that there is more to the story, there is not a direct correlation between the number of defaulters increasing as the number in repayment increases.
The 50-90 Rule does not work in this case, it would not be realistic to reduce the number of defaulters by 90%. We have agreed that a slight reduction (approximately 0.5%) is more reasonable.
A1. Identify and list all potential causes (inputs). Date:9/28/12 Required tools: Process map, Brainstorming, Fishbone diagram, Cause and effect
matrix, Potential “X” matrix
In the root cause analysis, I will identify potential student characteristics that may have an impact on loan defaults. My inputs “X’s” will be determined by the most popular characteristics of the 2010 Two Year Cohort Defaulters. I have used a Fishbone diagram (Attachment 7). My brainstorming attachment (#4) is referenced in Section D3.
A2. Screen potential causes. Date:10/1/12 Required tools: See A1
I will try to use a Cause & Effect Matrix to prioritize the buckets of Xs (specific characteristics) to a potential reduction in the Cohort Default Rate. The Cause & Effect Matrix is not an ideal tool for this project due to having only one output; a default on a loan, but I have attached one to show understanding of the tool (Attachment 8).
I have used practice data to this point as the actual default results for the 2010 Two-Year Cohort Defaulters were released at the end of September. These processes will be redone using actual data and will take several months.
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A3. Determine the f(x) – key input variable(s) Date:10/1/12 Required tools: One factor at a time experiment
I will try to use a Potential “X” Matrix to analyze the remaining Xs (specific
characteristics) to further drill down to a potential reduction in the Cohort Default Rate.
(Attachment 9).
I have used practice data to this point as the actual default results were released at the end of September. These processes will be redone using actual data and will take several months to complete.
I-1. Establish operating tolerances for key inputs and output. Date:10/1/12 Describe how the solution was derived and how it will be implemented. Describe the operating tolerances and how they were selected.
The team will brainstorm and build a Solution Prioritization Matrix in December to determine our action. Ideas such as…
1)New method of borrower education? 2)Contact with advisors?
3)Communication with Dept of Ed?
4)Limiting loan awards to upper student levels? (Attachment 10).
I-2. Re-evaluate the measuring system. Date: FUTURE Required tools: Gage R&R/Attribute Agreement Analysis
In October of 2013, I will use an Attribute Agreement Analysis to confirm the last date of attendance with the new Cohort Default List, as I did in 2012. Once I am confident that the population is accurate, I will analyze the student characteristics of the defaulters. Using a Cause & Effect Matrix and a Potential “X” Matrix, I will ensure that we are still focusing our efforts on the right students to keep them from defaulting in the future. Most of our efforts for current students will not be evident for many years. If we target enhanced loan entrance awareness, financial literacy education, and changes to academic counseling for freshman we will not see the results for seven years (four years until they graduate and three additional years until they would appear on the default list). Changes to loan servicing would have a faster impact on our default rate, but may be out of our control.
I-3. Establish final capability for key input(s) and the output. Date: FUTURE Required tools: Process capability, Control chart
I will not have the results at the end of the Six Sigma time frame. We are hoping to see an impact before the 2013 and 2014 release of default rates, but will not likely see large changes for seven years.
C1. Implement process controls for the key inputs. Date: FUTURE Required tool: Four levels of control, error proofing
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0, 1, 2, or 3.
Our process control will be Level 1 – Our department needs to continue to monitor the default rate and adjust the solution as needed. If we see increases in the default rate we will need to screen for new X’s and adjust accordingly. We need to remain dedicated to reallocating resources where necessary and using Six Sigma tools will validate the cost and effort.
Follow-up to ensure effectiveness. Date: FUTURE
We will continue to analyze the characteristics of future loan defaulters every year.
Comparative Pareto Charts ‐ Comparing the growing number of students in default to the number of students in loan repayment Attachment 1 Purdue ‐ West Lafayette 2009 2008 2007 2006 2005 2004 Number in Default 87 51 81 67 68 39 Number in Repayment 5,081 4,138 4,914 7,471 8,220 4,495 Rate 1.7 1.2 1.6 0.8 0.8 0.8 Sorted for Number in Default 2009 2007 2005 2006 2008 2004 Number in Default 87 81 68 67 51 39 Number in Repayment 5,081 4,914 8,220 7,471 4,138 4,495 Rate 1.7 1.6 0.8 0.8 1.2 0.8 Sorted Number in Repayment to Match Number in Default 2009 2007 2005 2006 2008 2004 Number in Repayment 5,081 4,914 8,220 7,471 4,138 4,495 Number in Default 87 81 68 67 51 39 Rate 1.7 1.6 0.8 0.8 1.2 0.8 I used the Pareto charts to compare two sets of data over 6 years (the number of loan defaulters and the number in repayment). This disproves that the number of loan defaulters is simply in line with the number of people in loan repayment. The Division of Financial Aid receives an annual cohort default rate from NSLDS. There would be repurcussions if our cohort default rate were to skyrocket, but to date we have not considered any causes for defaulting on loans. Before beginning a project, I wanted to rule out that the number of people in default on their loans was simply a reflection of the number of people in default. 0 20 40 60 80 100 2009 2007 2005 2006 2008 2004
Number in Default 2004 ‐ 2009
Number in Default 3,000 4,000 5,000 6,000 7,000 8,000 9,000 2009 2007 2005 2006 2008 2004Number in Repayment 2004 ‐ 2009
Number in RepaymentAttachment 2 RUMBA Reduce next year's Two‐Year Cohort Default Rate by one standard deviation Reasonable? I believe this project is realistic and necessary. Understandable? The project and the goals are defined in the problem statement. Measurable? Believable? Attainable? The default rate is measurable with a clear formula that is provided by the Department of Education.
Our draft cohort default rate increased more than expected this year. We need a process to prevent students from appearing on the loan default list (lowering our cohort default rate). To this point, we have not identified common
characteristics of these defaulters; we simply receive the names and SSNs for the borrowers who have defaulted. I have the ability to run the names through our Student and Financial aid databases (using Cognos) to add as many characteristics as needed. I will use the characteristics that appear most often in the defaulters to identify an at-risk population. If we can focus
education/intervention on the identified at-risk populations while they are still in school, we could reduce the number of borrowers who will default (lowering the cohort default rate). I will also attempt to identify loan servicer issues that may have an impact on the default rate.
The goal is believable and attainable if we invest the time needed
Attachment 3
SUPPLIERS
INPUTS
PROCESS
OUTPUTS
CUSTOMERS
List Suppliers, internal and external.
List Inputs to Process: Data, information, materials, manpower, environment,
equipment, resources.
List Outputs to Process: Data, information, materials, manpower, environment,
equipment, resources.
List customers, internal and external.
Lendors Student Data from Cognos Lendor Info from NSLDS Students
NSLDS Financial Aid Data from Cognos at risk populations Department of Education (they publish rates)
Students Loan App Info Purdue
Financial Aid Office Banner DFA
Cognos Big Ten
DFA Time FAFSA info
SIPOC Loan Default Process
Purdue would have severe ramifications if the cohort default rate
were to increase drastically If after two years in
repayment the recipient stops paying their loan,
they are in default
NSLDS provides a report of loan defaulters
(and default rate) to Purdue annually Map Process Below.
Do not get led by the form! List as many steps as necessary to descriibe the
MACRO process. The purpose of his exercise is to examine scope, to list primary inputs and outputs, and to list
high-level customer expectations.
Students accept loans for educational
expenses
Those students drop out of school or graduate from Purdue
The loan recipient enters repayment of
their loans
# of defaulters
Brainstorming for potential inputs on SIPOC Attachment 4 Residency Indiana Resident Non‐residents Demographics Gender Age Race Marital Status Number of Dependents First Generation Student (keep in mind for future years) Legacy? Fin Aid Characteristics SAP Status EFC SSACI Levels Parent Income Student Income Dependency Number of Budget Adjustments Pell Eligible Indicator Education Tax Credits Vet Benefits Child Support Paid Amount unmet need Family Size Number in Household Number in College SWT? Merit Scholar? Work Study Other Purdue Employment Funds Loan Characteristics Lender Servicer Type of Loan Amount of Loan (set levels) College/Advising College Major GPA Advisor Name Dean's List CODO (undecided in the beginning) Total Credits Student Level Part Time vs. Full Time Degree vs. Drop Out Other SAT/ACT High School GPA Course Credits Earned in HS Extra Curricular Coop/Frat/Sorr
Attachment 5 ————— 8/23/2012 2:40:45 PM ———————————————————— Attribute Agreement Analysis for Last Date of Attendance Between Appraisers (two Cognos reports by Jennifer Howells) Assessment Agreement # Inspected # Matched Percent 95% CI 5567 5567 100.00 (99.95, 100.00) # Matched: All appraisers' assessments agree with each other. Fleiss' Kappa Statistics Identical assessments. Cannot compute kappa. * NOTE * Single trial within each appraiser. No percentage of assessment agreement within appraiser is plotted.
The Fishbone Diagram
Attachment 7 College/AdvisingDemographics Residency College
Gender Indiana Resident Major
Age Non‐residents GPA
Race Advisor Name Marital Status Dean's List First Generation Student CODO (undecided in the beginning) Legacy? Total Credits Number of Dependents Student Level Part Time vs. Full Time Degree vs. Drop Out SAP Status EFC SSACI Levels Parent Income SAT/ACT
Lender Student Income High School GPA
Servicer Dependency Course Credits Earned in HS
Type of Loan # of Budget Adjusts Extra Curricular Amount of Loan (set levels) Pell Eligible Indicator Coop/Frat/Sorr Loan Characteristics Education Tax Credits Other
Vet Benefits Child Support Paid Amount unmet need Family Size Number in Household Number in College SWT? Merit Scholar? Work Study Other Purdue Employment Funds Fin Aid Characteristics Cohort Default Rate
The Cause & Effect Matrix Attachment 8 This will be updated as further examintation of defaulters continues. Rating of Importance 1 The stength of the relationship of each Input will be rated 0, 1, 3 or 9 based on the number of defaulters with those characteristics. Default List Overall Rating Indiana Resident 3 3 Non‐residents 3 3 Male 3 3 Female 3 3 URM 1 1 Married 1 1 Single 1 1 First Generation Student (keep in mind for future years) 1 1 Legacy? 0 0 Negative SAP 9 9 zero EFC 3 3 SSACI Level 1 1 Dependent 1 1 Budget Adjustments >1 9 9 Pell Eligible Indicator 1 1 Child Support Paid 1 1 Unmet need 9 9 Number in Household >5 0 0 Number in College >2 0 0 SWT 0 0 Merit Scholar 1 1 Work Study 1 1 Other Purdue Employment 1 1 DL Servicer 9 9 FFEL Servicer 9 9 Type of Loan 9 9 Amount of Loan>10,000 9 9 Amount of Loan>20,000 9 9 Amount of Loan>30,000 9 9 College 3 3 Major 3 3 High GPA 3 3 Low GPA 3 3 Dean's List 1 1 CODO (undecided in the beginning) 1 1 Total Credits 1 1 Student Level 1 1 Part Time 1 1 Degree vs. Drop Out 9 9 SAT/ACT 0 0 Course Credits Earned in HS 0 0
Potential X Matrix Attachment 9 This will be updated as further examintation of defaulters continues replacing values with recent data. (X) Rating from C&E Measurement, Technique and Units Currently Collected? Statistical Test
1 Indiana Resident 3 Attribute Yes/No Yes Chi-square
2 Non‐residents 3 Attribute Yes/No Yes Chi-square
3 Male 3 Attribute Yes/No Yes Chi-square
4 Female 3 Attribute Yes/No Yes Chi-square
5 URM 1 Attribute Yes/No Yes Chi-square
6 Married 1 Attribute Yes/No Yes Chi-square
7 Single 1 Attribute Yes/No Yes Chi-square
8 First Generation Student (keep in mind for
future years) 1 Attribute Yes/No Yes Chi-square
9 Legacy? 0 Attribute Yes/No Yes Chi-square
10 Negative SAP 9 Attribute Yes/No Yes Chi-square
11 zero EFC 3 Attribute Yes/No Yes Chi-square
12 SSACI Level 1 Attribute Yes/No Yes Chi-square
13 Dependent 1 Attribute Yes/No Yes Chi-square
14 Budget Adjustments >1 9 Attribute Yes/No Yes Chi-square
15 Pell Eligible Indicator 1 Attribute Yes/No Yes Chi-square
16 Child Support Paid 1 Attribute Yes/No Yes Chi-square
17 Unmet need 9 Attribute Yes/No Yes Chi-square
18 Number in Household >5 0 Attribute Yes/No Yes Chi-square
19 Number in College >2 0 Attribute Yes/No Yes Chi-square
20 SWT 0 Attribute Yes/No Yes Chi-square
21 Merit Scholar 1 Attribute Yes/No Yes Chi-square
22 Work Study 1 Attribute Yes/No Yes Chi-square
23 Other Purdue Employment 1 Attribute Yes/No Yes Chi-square
24 DL Servicer 9 Attribute Yes/No Yes Chi-square
25 FFEL Servicer 9 Attribute Yes/No Yes Chi-square
26 Type of Loan 9 Attribute Yes/No Yes Chi-square
27 Amount of Loan>10,000 9 Attribute Yes/No Yes Chi-square
28 Amount of Loan>20,000 9 Attribute Yes/No Yes Chi-square
29 Amount of Loan>30,000 9 Attribute Yes/No Yes Chi-square
30 College 3 Attribute Yes/No Yes Chi-square
31 Major 3 Attribute Yes/No Yes Chi-square
32 High GPA 3 Attribute Yes/No Yes Chi-square
33 Low GPA 3 Attribute Yes/No Yes Chi-square
34 Dean's List 1 Attribute Yes/No Yes Chi-square
35 CODO (undecided in the beginning) 1 Attribute Yes/No Yes Chi-square
36 Total Credits 1 Attribute Yes/No Yes Chi-square
37 Student Level 1 Attribute Yes/No Yes Chi-square
38 Part Time 1 Attribute Yes/No Yes Chi-square
39 Degree vs. Drop Out 9 Attribute Yes/No Yes Chi-square
40 SAT/ACT 0 Attribute Yes/No Yes Chi-square
Solution Prioritization Matrix Attachment 10 Our team will meet in December after looking at the results of the analysis to populate this matrix. #1 #2 #3 #4 #5 #6 SUM Reduction in Default Rate 0 Reduction in Defaulters with Fin Aid Characteristics 0 Reduction in Defaulters with Advising Characteristics 0 Reduction in Defaulters with Loan Characteristics 0 Other 0 SUM 0 0 0 0 0 0 0 #1 New Method of Borrower Education ‐ loan counseling #2 Finacial Literacy Courses for Borrowers #3 Training for Advisors #4 Communication with the Department of Ed #5 Changes to Loan Awarding Procedures #6 Other
