Working Capital - > 90 Day DelinquencyProject – 0307-49-001

Heath PeacockFinanceTempe, AZ - CCC

3

Project Definition

Problem Statement:There is an opportunity to reduce delinquency for Trade Collection accounts with > 90 day delinquency in Tempe, AZ CCC. Excess delinquency leads to increased borrowing costs, reduced cash collected and additional reserve requirements.

Project Definition:Implement a series of improvements designed to increase collector and management effectiveness, enhance system utilization and reporting to reduce 90 day delinquency.

Project BenefitsReduce borrowing costs, increase cash and reduce reserves

$288,000 in reduced borrowing costs

7

Measurement System Analysis

Data type – Normal, continuous based on P = .536 Data Source – Data pulled directly from ARMS Goal – Verify Month end reported data is accurate Procedure – Pulled random sample of delinquency

bucket data and audited Sample – 135 random accounts, 15 in each

delinquency bucket Result – 100% accurate

Measurement System is Accurate!

8

DPMO and Sigma Level

Initial Capability Analysis Outcome Upper Specification Limit - 6.5% Sigma is short term based on 11 data points at month end DPMO – 350,000 based on delinquency in excess of Upper

Spec Limit Sigma Level – 1.88 Average delinquency – 10.28%

9

Data Collection Plan

Obtain calls by Collector – # of Calls, both incoming and outgoing

Delinquency Bucket by Collector Number of Accounts and invoices by Collector Time employed by IKON by Collector Sample data from IOSC collections history – number

of calls, Promise to Pay percentage and Percent Current

Leveraging IOSC Collection Experience

11

Tempe Data Correlation

1023.25

463.75

2746.5

1161.5

311.25

119.75

1248

526

Months with IKON

Customers Assigned

Total Invoices

Incoming Calls

External Calls

Correlation

Some Correlation

Minimal Correlation between Key Process Input Variables

12

Tempe Data Regression Analysis

Months with IKON – R-Sq (adj) – 0.4% Customers Assigned – R-Sq (adj) – 0.0% Total Invoices – R-Sq (adj) – 1.2% Incoming Calls – R-Sq (adj) – 0.0% But… We know, I know, the one thing that will most

definitely affect over 90% is the number of external calls by collector…

13

Tempe Data Regression Analysis

15001000 500 0

0.3

0.2

0.1

0.0

External Cal

Ove

r 90 p

erc

S = 0.0503701 R-Sq = 0.4 % R-Sq(adj) = 0.0 %

Over 90 perc = 0.0773945 - 0.0000103 External Cal

Regression Plot

!!!!!!

14

Tempe compared to IOSC

Compared Collector Activity to IOSC Collector Activity

Ho = Call volume is same Sample Size 175 and 168

over same time frame Mean – 32 and 45 Standard Deviation – 14 and

10 2 Sample – T test, P Value –

0.00Avg CallAvg Call

80

70

60

50

40

30

20

10

0

Tempe - IOSCOutbound Calls

Reject the null Hypothesis

15

Next Steps

Data is pointing out that the collectors may be on the phone but are not resolving the customers issues.

Establish new data collections Who are the collectors calling What issues are keeping them from collecting

Questions?