I n t e g r i t y - S e r v i c e - E x c e l l e n c e AF Global Logistics Support Center (AFGLSC)...

I n t e g r i t y - S e r v i c e - E x c e l l e n c e

AF Global Logistics Support Center (AFGLSC)

1

Order Response Time

Mike McClure, 402 SCMS/GUSB

12 Mar 2013

I n t e g r i t y - S e r v i c e - E x c e l l e n c e 2

Order Response TimeAgenda

Review AFGLSC monthly ORT chartsCurrently distributed to OSD, AFMC, Depot Mx, AFGLSC, DLA

Mainly at the working level, with some going up to leadership as requested

Presentation normally contains 16 slides of performance, today only 5 slides

Review some recent ORT analysisWhat we are doing with ORT and what is it telling us?

Back-up Slides - Improving the CWT MetricORT developed by AFGLSC to overcome measurement and analysis issues with average CWT

Uses both closed and open orders ….. combines two traditional measures, CWT with open BOs

Uses customer order date, not closed date ….. real time, not delayed

Uses percentile buckets ….. much more accurate than the average of a non-normal distribution


AF Global Logistics Support Center (AFGLSC)

3

Order Response Time

Mike McClure, 402 SCMS/GUSB

13 Feb 2012


Measuring Enterprise Performance

AFGLSC 402nd, LIMS-EV Pipeline Analysis

Responsiveness to all customer demands AFGLSC, DLA, and Other SOS provides spares to customers

Best option is to have the part locally for immediate issue

Not all parts available locally, so backorder response time is key to deliver part within customer expectations

Order Response Time (ORT) Measures immediate

issue rate (same day) and backorder response times

90%

91%

87%

86%

85%

84%

87%

87%

86%

86%

85%

86%

86%

84%

84%

-20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000

0%10%20%30%40%50%60%70%80%90%

100%

FY08

FY09

FY10

Feb

11

Mar

11

Apr

11

May

11

Jun

11

Jul 1

1

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Mx

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Depot MXW

same day 1-2 days 3-10 days 11-30 days31-90 days 90+ days % 0-2 days Mx Orders


Measuring Enterprise PerformanceOrder Response Time (ORT), an improved CWT look

Order Response Time (ORT) previously called Calculated Issue Effectiveness (CIE)

What’s the data source? LIMS-EV CWT data

What’s different than CWT? Open orders included Performance grouped by open date, not closed date

Why ORT? Real-time responsiveness measure Much more accurate indicator of current support Customer focused by measuring percentage of immediate

issues and how long backorders are taking True “Tier” measure can be applied at multiple

Organizational levels

How to read the chart? The date axis represents customer order date Black line represents total customer orders Solid colors represent % of orders closed within time

period White % numbers represents % immediate issues

What about the goal? TDD like standard (customer gets x% of parts in y days) Calculated cumulative wait time (by order)

What does this metric tell us?• How often does a mechanic get a part when they order it?• If not, how long does it take to fill an order?• How many orders are placed in a given month?

ORT shows the real time, direct impact of supply support to the customer

90%

91%

87%

86%

85%

84%

87%

87%

86%

86%

85%

86%

86%

84%

84%

-20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000

0%10%20%30%40%50%60%70%80%90%

100%

FY08

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FY10

Feb

11

Mar

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Apr

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Mx

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Depot MXW



Depot Mx (available by MXW, MXG, Mx shops, workload & supply codes)

Order Response Time

AFGLSC 402nd, LIMS-EV Pipeline Analysis, DLA Emall

90%

91%

87%

86%

85%

84%

87%

87%

86%

86%

85%

86%

86%

84%

84%

-20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000

0%10%20%30%40%50%60%70%80%90%

100%

FY08

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FY10

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Depot MXW


92%

93%

89%

88%

87%

88%

89%

89%

88%

88%

87%

88%

88%

87%

87%

-20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000

0%10%20%30%40%50%60%70%80%90%

100%

FY08

FY09

FY10

Feb

11

Mar

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Apr

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May

11

Jun

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Jul 1

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Mx

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Depot MXW - DLA SOS


84%

83%

81%

76%

80%

79%

81%

81%

78%

79%

78%

80%

80%

78%

76%

-5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000

0%10%20%30%40%50%60%70%80%90%

100%

FY08

FY09

FY10

Feb

11

Mar

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Apr

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May

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Jun

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Jul 1

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Depot MXW - AF SOS


84%

78%

73%

65% 69

%

54%

69%

65%

66%

66%

66%

63%

58%

49% 57

%

-5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000

0%10%20%30%40%50%60%70%80%90%

100%

FY08

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Feb

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Mar

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Apr

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Depot MXW - Other SOS



Operational Bases (available by WS, location & supply codes)

Order Response Time


61%

62%

62%

62%

63%

62%

62%

62%

62%

62%

60% 65

%

64%

65%

64%

-50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000 500,000

0%10%20%30%40%50%60%70%80%90%

100%

FY08

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FY10

Feb

11

Mar

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Jul 1

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Operational Bases

Base Issue 1-2 days 3-10 days 11-30 days31-90 days 90+ days % base issue Orders

57%

58%

59%

59%

59%

60%

59%

59%

59%

60%

57% 63

%

62%

62%

61%

-50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000 500,000

0%10%20%30%40%50%60%70%80%90%

100%

FY08

FY09

FY10

Feb

11

Mar

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Apr

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May

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Operational Bases - DLA SOS


76%

76%

74%

75%

74%

72%

74%

74%

75%

74%

74%

75%

75%

74%

74%

-20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000

0%10%20%30%40%50%60%70%80%90%

100%

FY08

FY09

FY10

Feb

11

Mar

11

Apr

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May

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Jun

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Jul 1

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Aug

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Operational Bases - AF SOS


57%

58%

59%

60%

62%

62%

61%

60%

59%

57%

57% 62

%

62%

63%

61%

-20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000

0%10%20%30%40%50%60%70%80%90%

100%

FY08

FY09

FY10

Feb

11

Mar

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Apr

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Operational Bases - Other SOS



89% 90%86% 86% 85% 84% 87% 87% 86% 86% 85% 86% 86% 84% 84%

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

0%

10%

20%

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40%

50%

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70%

80%

90%

100%

FY08

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FY10

Feb

11

Mar

11

Apr

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11

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Jul 1

1

Aug

11

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Ord

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% o

f Ord

ers

Depot Mx

Pass -Same Day Pass -Closed Pass -Open Fail -ClosedFail -Open % pass Standard Orders

2 day TDD std ORT measure the percent of customer orders meeting their TDD standards

ORT includes both open & closed orders in the month they were ordered

The dotted black line shows the ORT goal (read on the left axis)

The white diamonds & values show the percent of orders filled within their TDD standard (read on the left axis)

Solid black line shows the count of orders received (read on the right axis)

Colors show the percent of orders that either passed or failed during the month Dark Green/Pass – Base Issue = Immediately filled Light Green /Pass – Closed = Filled during the month within standard Light Red/Failed – Closed = Filled, but exceeded their TDD standard Dark Red/Failed – Open = Not filled and have already passed their TDD standard Yellow/Pass – Open = Not filled, but have not exceeded their TDD standard

TDD Standards: These standards are specific to the geographic location of the customer, the priority of the order, and the mode of transportation with one exception: AFGLSC uses a 2-day standard for all depot maintenance orders

Order Response TimeTDD goal applied


Depot MxOrder Response Time


Depot TDD std set to 2 days

89% 90%86% 86% 85% 84% 87% 87% 86% 86% 85% 86% 86% 84% 84%

0

20,000

40,000

60,000

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120,000

140,000

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180,000

200,000

0%

10%

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40%

50%

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90%

100%

FY08

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Feb

11

Mar

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Aug

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% o

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Depot Mx


2 day TDD std

82% 82% 80% 76% 80% 79% 81% 81% 78% 79% 78% 80% 80% 78% 76%

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

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200,000

0%

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30%

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100%

FY08

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Depot Mx - AF SOS


2 day TDD std

83%77%

73%65%

69%

54%

69%65% 66% 66% 66% 63%

58%

49%

57%

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

0%

10%

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50%

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90%

100%

FY08

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FY10

Feb

11

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11

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11

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11

Jul 1

1

Aug

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Ord

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% o

f Ord

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Depot Mx - Other SOS


2 day TDD std

91% 92%88% 88% 87% 88% 89% 89% 88% 88% 87% 88% 88% 87% 87%

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

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200,000

0%

10%

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100%

FY08

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Depot Mx - DLA SOS


2 day TDD std


Operational BasesOrder Response Time – IPG 1


DoD Operational TDD std

88% 89% 90% 89% 90% 90% 90% 90% 90% 89% 89% 90% 89% 89% 90%

0

20,000

40,000

60,000

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120,000

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200,000

0%

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100%

FY08

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Feb

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Ord

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% o

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Operational Bases - IPG 1

Pass -Base Issue Pass -Closed Pass -Open Fail -ClosedFail -Open % pass Std (tracking) Orders

DoD TDD std

89% 90% 91% 90% 91% 92% 91% 91% 91% 91% 90% 91% 91% 91% 91%

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

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180,000

200,000

0%

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100%

FY08

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Operational Bases - IPG 1 - DLA SOS


DoD TDD std

92% 91% 91% 91% 91% 91% 91% 91% 92% 91% 92% 91% 90% 90% 90%

0

20,000

40,000

60,000

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0%

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100%

FY08

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Feb

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Mar

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% o

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Operational Bases - IPG 1 - AF SOS


DoD TDD std

78% 81% 83% 83% 83% 83% 82% 82% 81% 79% 81% 83% 81% 83% 83%

0

20,000

40,000

60,000

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100,000

120,000

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0%

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100%

FY08

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Operational Bases - IPG 1 - Other SOS


DoD TDD std


Operational Bases Order Response Time – IPG 2


DoD Operational TDD std

75% 77% 78% 80% 80% 81% 79% 79% 79% 79%73%

80% 76% 75% 76%

0

10,000

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FY08

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Operational Bases - IPG 2


DoD TDD std

77% 80% 81% 82% 83% 83% 82% 82% 82% 82%76%

82% 79% 78% 79%

0

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Operational Bases - IPG 2 - DLA SOS


DoD TDD std

76% 74% 73% 76% 74% 72% 72% 72%78% 78%

67% 71% 70%64% 63%

0

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Operational Bases - IPG 2 - AF SOS


DoD TDD std

64%67% 67% 69% 70% 70% 69% 70% 67% 66%

62%

72%65% 66% 67%

0

10,000

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FY08

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Operational Bases - IPG 2 - Other SOS


DoD TDD std


Order Response Time

Additional AnalysisExamples


Additional Analysis ExamplesMICAPs

Analysis Source: SMART & LIMS-EV

81%

80%

80%

78% 83

%

81%

82%

81%

82%

83%

81%

81%

83%

79%

79%

05,00010,00015,00020,00025,00030,00035,00040,00045,00050,000

0%10%20%30%40%50%60%70%80%90%

100%FY

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FY09

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Jan

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MIC

AP

Coun

t

MIC

AP

Resp

onse

Tim

e

Operational Bases

0-3 days 4-7 days 2 weeks 3 weeks4 weeks More % 0-7 days Total MICAPs


Additional Analysis ExamplesTier down to MD/MDS, Command, Base, SOS, etc.

Analysis Source: SMART & LIMS-EV

73%

60% 65

%

79% 86

%

83%

83%

83%

83%

80%

73%

74%

71% 75

%

74%

65%

61%

54%

0

500

1,000

1,500

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3,500

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0%

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Jun

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0

Aug

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10

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10

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CV-22 SOC

Base Issue 1-3 days 4-10 days 11-30 days31-90 days 90+ days % Base Issue Customer Orders

78%

79% 85

%

87%

86%

83%

83%

84%

82%

82%

84%

79%

79%

80%

78% 86

%

87%

82%

0

500

1,000

1,500

2,000

2,500

3,000

3,500

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0%

10%

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100%

Jun

10

Jul 1

0

Aug

10

Sep

10

Oct

10

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10

Dec

10

Jan

11

Feb

11

Mar

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AC-130U Fleet

Base Issue 1-3 days 4-10 days 11-30 days31-90 days 90+ days % Base Issue Customer Orders

74%

57% 60

%

78%

100%

0% 0% 0% 0% 0% 0%

65% 68

%

70%

68%

52%

54%

47%

-

500

1,000

1,500

2,000

2,500

3,000

3,500

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4,500

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0%

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Jun

10

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0

Aug

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10

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10

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10

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10

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11

Feb

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Jul 1

1

Aug

11

Sep

11

Oct

11

Nov

11

Cust

omer

Ord

ers

Ord

er R

espo

nse

Tim

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CV-22 Operational - 5806

Base Issue 1-3 days 4-10 days 11-30 days31-90 days 90+ days % same day Mx Orders

77%

77%

70% 77

% 80% 83

%

83% 90

%

84%

82%

80%

78%

77% 79

%

76%

72%

64%

64%

-

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Jun

10

Jul 1

0

Aug

10

Sep

10

Oct

10

Nov

10

Dec

10

Jan

11

Feb

11

Mar

11

Apr

11

May

11

Jun

11

Jul 1

1

Aug

11

Sep

11

Oct

11

Nov

11

Cust

omer

Ord

ers

Ord

er R

espo

nse

Tim

e

CV-22 Operational - DLA


WS B, MAJCOM

WS B, Base WS B, SOS

WS A, Fleet


Additional Analysis ExamplesBad Actors (identify NIINs, analyze, work root causes, chart)

62%

61%

64%

63%

64%

64%

63%

63%

63%

63%

64%

64%

66%

65% 69

%69

%69

%67

%

-50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000 500,000

0%10%20%30%40%50%60%70%80%90%

100%

Aug

10

Sep

10O

ct 1

0N

ov 1

0D

ec 1

0Ja

n 11

Feb

11M

ar 1

1A

pr 1

1M

ay 1

1Ju

n 11

Jul 1

1A

ug 1

1Se

p 11

Oct

11

Nov

11

Dec

11

Jan

12

Cust

omer

Ord

ers

Ord

er R

espo

nse

Tim

e

Operational Bases (Bad Actors removed)


60%

58%

61%

61%

62%

61%

59%

58%

58%

56%

54%

54%

46%

42% 46

%45

%45

%47

%

-50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000 500,000

0%10%20%30%40%50%60%70%80%90%

100%

Aug

10

Sep

10O

ct 1

0N

ov 1

0D

ec 1

0Ja

n 11

Feb

11M

ar 1

1A

pr 1

1M

ay 1

1Ju

n 11

Jul 1

1A

ug 1

1Se

p 11

Oct

11

Nov

11

Dec

11

Jan

12

Cust

omer

Ord

ers

Ord

er R

espo

nse

Tim

e

Operational Bases (Bad Actors)


82%

83%

83%

82%

84%

84%

83%

84%

83% 86

%88

%89

%91

%90

%91

%91

%89

%88

%

-20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000

0%10%20%30%40%50%60%70%80%90%

100%

Aug

10

Sep

10O

ct 1

0N

ov 1

0D

ec 1

0Ja

n 11

Feb

11M

ar 1

1A

pr 1

1M

ay 1

1Ju

n 11

Jul 1

1A

ug 1

1Se

p 11

Oct

11

Nov

11

Dec

11

Jan

12

Mx

Ord

ers

Ord

er R

espo

nse

Tim

e

Depot MXW (Bad Actors removed)

same day 1-2 days 3-10 days 11-30 days31-90 days 90+ days % same day Mx Orders

73%

73%

69%

69%

69%

68%

70%

67%

62%

61%

55%

47%

40%

32%

31%

27%

24% 32

%

-20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000

0%10%20%30%40%50%60%70%80%90%

100%

Aug

10

Sep

10O

ct 1

0N

ov 1

0D

ec 1

0Ja

n 11

Feb

11M

ar 1

1A

pr 1

1M

ay 1

1Ju

n 11

Jul 1

1A

ug 1

1Se

p 11

Oct

11

Nov

11

Dec

11

Jan

12

Mx

Ord

ers

Ord

er R

espo

nse

Tim

e

Depot MXW (Bad Actors)



Questions?


Air ForceGlobal Logistics Support Center (AFGLSC)

17

Improving the CWT Metric

Mike McClureOperations Research Analyst

AFGLSC 402 SCMS/GUSB


BLUF

18

Customer Wait Time (CWT) is an established metric, but is sensitive to extreme-value effects, volume bias and is lagging in nature

Order Response Time (ORT) has been designed to be more illustrative and actionable of real-time customer support

ORT was designed to overcome the shortfalls of CWT


Customer Wait Time (CWT)

Customer Wait Time (CWT) Defined in DoD 4140.61.3.2…a measurement of the total elapsed time

between the issuance of a customer order and satisfaction of that order Data is restricted to closed orders Excludes open orders, cancellations, and partial fills – significant

INFORMATION loss! Presented in the month the order was closed, therefore reflects problems

after the fact Typically, top-driver issues have already been fixed

19

M1 M2

Order 1

Order 2

Order 3Order 4

M3When CWT is calculated in M2, both order 3 and 4 are omitted from CWT calculation


0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

010

020

030

040

050

060

070

080

090

010

0011

0012

0013

0014

0015

0016

0017

0018

0019

0020

0021

0022

0023

0024

0025

0026

0027

00

Freq

uenc

y

CWT Days

Volume

0100200300400500600700800900

1,000

0 10 20 30 40 50 60 70 80 90 100

Customer Wait Time (CWT)

Customer Wait Time (CWT) Typically reported as an average

with a target Average is skewed by extremes –

Penalty for closing old orders! Missing a target could be a

function of a single bad actor out of 100,000+ orders

Average is skewed by volume change, immediate issues offset longstanding backorders

Significantly more meaningful in percentile buckets

20

Typical CWT distribution

Average is a poor representation of the CWT distribution

Volume

Extreme values


Contribution of Closed Orders to CWT

21

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

0

2

4

6

8

10

12

14

16

18

20

Oct

06

Nov

06

Dec

06

Jan

07Fe

b 07

Mar

07

Apr

07

May

07

Jun

07Ju

l 07

Aug

07

Sep

07O

ct 0

7N

ov 0

7D

ec 0

7Ja

n 08

Feb

08M

ar 0

8A

pr 0

8M

ay 0

8Ju

n 08

Jul 0

8A

ug 0

8Se

p 08

Oct

08

Nov

08

Dec

08

Jan

09Fe

b 09

Mar

09

Apr

09

May

09

Jun

09Ju

l 09

Aug

09

Sep

09O

ct 0

9N

ov 0

9D

ec 0

9Ja

n 10

Feb

10M

ar 1

0A

pr 1

0M

ay 1

0Ju

n 10

Jul 1

0A

ug 1

0Se

p 10

Oct

10

Nov

10

Dec

10

Jan

11Fe

b 11

Mar

11

Apr

11

May

11

Jun

11Ju

l 11

Aug

11

Sep

11

Ord

ers

CWT

CWT Contribution by Closed Orders

Base Issues 1-3 days 4-10 days 11-30 days 31-90 days 91-180 days 180+ days CWT Orders

Volume skew

Extreme value skew + Next to impossible to

interpret real trendsIssue date

aggregation +


Order Response Time (ORT)

Order Response Time The percent of orders falling within pre-designated wait time buckets, DoD’s real intent? LIMS-EV CWT data source Both open and closed orders Data attributed to the customer order date Open orders seen as they age, data will update until the 90+ day population establishes

itself Performance directly attributed to the period in which it happened, making trend

analysis valid Not skewed by extreme values or by large volume changes Goals established using a TDD like standard (customer gets x% of parts in y days) or

by calculating a cumulative wait time (by order)

22

Actionable, top-drivers can be restricted to aging open orders, the future drivers of CWT

True “Tier” measure can be applied at multiple Organizational levels

Customer-focused, real-time responsiveness measure

M1 M2

Order 1

Order 2

Order 3

Order 4M3

When ORT is calculated in M2, M3 is still unknown, but order 3 and 4 current is used


Order Response TimeExamples

23

89% 90%86% 85% 86% 85% 84% 87% 87% 86% 86% 84% 86% 84% 82%

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FY08

FY09

FY10

Jan

11

Feb

11

Mar

11

Apr

11

May

11

Jun

11

Jul 1

1

Aug

11

Sep

11

Oct

11

Nov

11

Dec

11

Ord

ers

% o

f Ord

ers

Depot Mx

Pass -Same Day Pass -Closed Pass -Open Fail -ClosedFail -Open % pass Std (proposed) Orders

2 day TDD std

85% 86% 87% 87% 87% 88% 88% 87% 87% 87% 87% 85% 88% 86% 85%

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FY08

FY09

FY10

Jan

11

Feb

11

Mar

11

Apr

11

May

11

Jun

11

Jul 1

1

Aug

11

Sep

11

Oct

11

Nov

11

Dec

11

Ord

ers

% o

f Ord

ers

Operational Bases

Pass -Base Issue Pass -Closed Pass -Open Fail -ClosedFail -Open % pass Std (proposed) Orders

DoD TDD std

61%

62%

62%

63%

62%

62%

62%

62%

62%

62%

62%

60% 65

%

64%

64%

-50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000 500,000

0%10%20%30%40%50%60%70%80%90%

100%

FY08

FY09

FY10

Jan

11

Feb

11

Mar

11

Apr

11

May

11

Jun

11

Jul 1

1

Aug

11

Sep

11

Oct

11

Nov

11

Dec

11

Cust

omer

Ord

ers

Ord

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espo

nse

Tim

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Operational Bases


87%

87%

83%

82%

82%

82%

80%

83%

84%

83%

83%

82%

83%

82%

79%

-20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000

0%10%20%30%40%50%60%70%80%90%

100%

FY08

FY09

FY10

Jan

11

Feb

11

Mar

11

Apr

11

May

11

Jun

11

Jul 1

1

Aug

11

Sep

11

Oct

11

Nov

11

Dec

11

Mx

Ord

ers

Ord

er R

espo

nse

Tim

e

Depot MXW



84%

82%

83%

83%

82%

84%

84%

83%

84%

83% 87

%88

%90

%91

%91

%91

%90

%88

%

-20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000

0%10%20%30%40%50%60%70%80%90%

100%

Jul 1

0A

ug 1

0Se

p 10

Oct

10

Nov

10

Dec

10

Jan

11Fe

b 11

Mar

11

Apr

11

May

11

Jun

11Ju

l 11

Aug

11

Sep

11O

ct 1

1N

ov 1

1D

ec 1

1

Mx

Ord

ers

Ord

er R

espo

nse

Tim

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Depot MXW (Bad Actors removed)


75%

73%

73%

70%

69%

70%

69%

70%

66%

59%

59%

53%

40%

35%

29%

27%

25%

24%

-20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 200,000

0%10%20%30%40%50%60%70%80%90%

100%

Jul 1

0A

ug 1

0Se

p 10

Oct

10

Nov

10

Dec

10

Jan

11Fe

b 11

Mar

11

Apr

11

May

11

Jun

11Ju

l 11

Aug

11

Sep

11O

ct 1

1N

ov 1

1D

ec 1

1

Mx

Ord

ers

Ord

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espo

nse

Tim

e

Depot MXW (Bad Actors)


ORT Top DriversExample

ORT Analysis

Found the worst performers for last 18 months for AF SOS ORT

Upper left chart is looking at just the bad actors

Lower left chart are when bad actors are filtered out

AF SOS constraints analyzed from the bad actors are below

Bad actors

Best performers

I n t e g r i t y - S e r v i c e - E x c e l l e n c e AF Global Logistics Support Center (AFGLSC)...

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