Quantitatively Measured Process Improvements at Northrop Grumman IT. Craig Hollenbach Northrop Grumman IT

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1 Quantitatively Measured Process Improvements at Northrop Grumman IT Craig Hollenbach Northrop Grumman IT

2 Agenda Northrop Grumman IT Overview 2002 CMMI Approach & SCAMPI Appraisal Quantitatively Measured Improvements on: Inventory Tracking System (ITS) Software, Cost, and Schedule Quality Automatic Information Technology (AIT) Document Quality Joint Primary Aircraft Training System (JPATS) Training Integration Management System (TIMS) Maintenance Quality Synthetic Imagery Generation System (SIGS) Cost and Schedule Quality Conclusions November 19, Northrop Grumman IT 2003

3 Northrop Grumman Overview Northrop Grumman $26 B in revenue; 120,000 employees; 50 states; 25 countries Information Technology (IT) Sector $4 B in sales; 22,000 employees; 48 states; 15 countries Defense Enterprise Solutions (DES) Business Unit $548 M in sales; 3,045 employees, 23 states, 3 countries DES provides enterprise-wide technology solutions to the Defense marketplace Major Application Areas: Logistics Mission Support Science & Technology Simulation, Analysis, and Training Communications & Infrastructure 3

4 DES Maturity Pedigree Logicon LISS L3 ENABLER Logicon LAT L3 (to other units) Logicon LTS Logicon LIS LIEB LDES DES CMMI L5 L5 Litton PRC (to other units) L3 Litton TASC SPII (to other units) 4

5 2002 CMMI Approach To become CMMI Level 5 in 2003 while Forming DES from a merger of 3 major legacy organizations Contractually reconstituting all projects Using a newly developed accounting system Setting up a new management team Pursuing very aggressive financial targets DES implemented the following actions: PA Process Owners (FT EPI staff, organizational reps & subject matter experts) Resolved change requests from a CMMI Gap Analysis Worked with the NGIT sector to build umbrella processes to which legacy OSSPs comply Process Improvement Support and QA reps were assigned to each DES operating unit to upgrade L2-L3 processes Project reps implemented L4-L5 processes by participating on: Level 4 Working Group (QPM) Level 5 Working Group (OID Process) Metrics Office (OPP) Defect Prevention Working Group (CAR) Technology Change Management SIG (OID Technology) 5

6 2002 SCAMPI Appraisal Results Process Process Maturity Maturity CMMI CMMI SE/SW SE/SW Level Level 5 5 for for Staged Staged and and Continuous Continuous Representations Representations Software Software CMM CMM Level Level 5 5 also also Organizational Organizational Process Process Strengths Strengths Integrated Integrated SE SE and and SW SW Eng Eng Sharing Sharing and and reusing reusing process process assets assets Data Data driven driven corrective corrective action action Technology Technology Change Change Management Management Global Global Best Best Practices Practices Requirements Requirements Elicitation Elicitation Quantitative Quantitative Data Data Management Management Cost, Cost, Schedule Schedule and and Quality Quality Performance Performance JEDMICS JEDMICS exceeds exceeds software software quality quality goals goals (.0156 (.0156 vs vs defects/ksloc) defects/ksloc) PERMS PERMS exceeds exceeds revenue revenue goals goals by by 11% 11% AIT AIT has has 100% 100% on-time on-time delivery delivery Only Only 2% 2% of of total total defects defects found found in in delivered delivered SIGS SIGS product product ITS ITS is is on on cost cost and and schedule schedule (CPIm (CPIm = = & SPIm SPIm = =.985,.985, target target = = /- +/-.1).1) SIGS SIGS averages averages a a customer customer satisfaction satisfaction of of 98.5% 98.5% over over last last four four years years F1=Fully Implemented Out of Scope 6

7 Inventory Tracking System (ITS)

8 Inventory Tracking System Project Description: USAF/AFMC/MSG Inventory Tracking System (ITS) Modernization A 3.5-year, $11M Firm-Fixed Price project with a development staff of approximately 15 members Development Team uses SEI Personal Software Process (PSP) Implemented CMMI Level 5 quantitative management processes to dramatically improve the cost, schedule, and delivered quality of the software Currently in preparation for 1 st contractual customer driven test cycle Contractual Quality Goal is to deliver no known severity 1-3 defects (1-Critical, 2-Urgent, 3-Routine). 8

9 ITS Critical + Urgent Defect Density Peer Review - Builds 1-5 Quantitative Management Plan Goal: 5/KLOC for Critical + Urgent X /9/02 1/22/02 2/5/02 2/13/02 3/5/02 3/28/02 5/10/02 6/6/02 6/17/02 6/25/02 3/29/02 6/3/02 7/11/02 7/26/02 8/5/02 8/8/02 8/14/02 7/2/02 8/20/02 9/6/02 9/16/02 9/25/02 9/27/02 10/4/02 10/22/02 10/24/02 10/28/02 10/31/02 11/8/02 8/29/02 10/28/02 10/31/02 11/12/02 11/14/02 11/15/02 DDr DDr Mean UNPLX LNPLX U2S L2S U1.5S L1.5S KLOC = Thousand Lines of Code 9

10 Peer Review Defect Density (Critical + Urgent cont.) Defect Density (Defects/KLOC) DP 1 DP 2 DP Build 10

11 Cost Variance by Build DP 1 DP 2 DP 3 60% 41% Cost Variance 40% 20% 0% -20% -40% -46% -15% 11% 21% -60% Build 11

12 Schedule Variance by Build DP 1 DP 2 DP 3 Schedule Variance 60% 50% 40% 30% 20% 10% 0% 1% -1% 36% 38% 49% -10% Build 12

13 Return on Investment Construction Phase Hours invested: 124 Team training: 48 Conducting DP Cycles: 76 Defects avoided: If the Defect Density had remained at 6.6 (Build 1), we would have injected 110 more defects. Hours saved: At an estimated cost of 15 hours per defect this equals 1650 hours. Return: Hours:1650/124 = 1330% Customer satisfaction: Priceless! The contractor has always provided products and services with less defects that industry standards. Most have been provided with no defects. Personnel have been used that show a complete understanding of their subject area and are able to convey this information in a highly professional manner. 13

14 Inventory Tracking System Test Phase Need to understand Total Defect Density in Peer Review in Construction Phase to relate to Defect Density in Test Phase DP Cycles had an effect on Total defect density also Build 1 Total defect density = 21.6 defects/kloc Build 5 Total defect density = 13 defects/kloc Total Defect Density for Construction Phase = 19 defects/kloc Total Defect Density for Testing to Date = 4.5 defects/kloc Defect Density 400% Reduction 5 0 Construction Test 14

15 Inventory Tracking System Test Phase Management Goals in Test are being exceeded! Critical/Urgent Defect Total Defects DDt Unit.5/KLOC 2/KLOC DDt.25/KLOC 1/KLOC (all internal integration cycles) ITS Test Defects By Test Cycle Actual Defect Density by KLOC 12 DP Cycle Unit Test Build Build 0.7 Build 0.75 Build 0.80 (Informal) Critical + Urgent Total 15

16 Inventory Tracking System Test Phase Quantitative Management Plan Goals: DDs Defect Discovery 1/KLOC Defects Discovered Defect Discovery Rayleigh Curve All Test Defects Feb 03 Prediction = defects Feb 03 Actual = 15 defects.14 defects per K LOC/month Project Month Theoretical Curve Actual Values When the Total Defect Discovery rate falls under 1 defect.065 per KLOC per month the project manager and test lead have enough confidence.21 to stop test cycle. Defects Discovered Defect Discovery Rayleigh Curve Build.75 Test Defects Prediction = 1.44 defects Actual = 1 defect (last week of May) Project Month Theoretical Curve Actual Values 16

17 Results CMMI Quantitative Management & Defect Prevention Cycles have a huge return on Investment in the Construction Phase. Specific results from the first coding cycle to the fifth are: Critical / Urgent defect density reduced by 68%, Cost Variance improved from -46% to +41%, and Schedule Variance improved from +1% to +49%. This return has a significant effect on the Test Phase Where most projects have the highest defect detection rate in Test, ITS has its lowest defect detection rate. Latent defect analysis estimates delivering a defect density of between 0.35 and 0.7; a total of 20 to 40. Understanding the quality of the product allows for better management decisions and with highly satisfied customers. 17

18 Automatic Information Technology (AIT) AIT

19 AIT Automatic Identification Technology: Set of task orders dealing with the identification and recording of logistical and personnel data support using a wide variety of sensing and input devices (including biometrics) Task orders deal with the design, implementation, operation, and maintenance of an AIT system which integrates with legacy systems Customer base includes all defense services Contract types are Firm Fixed Price and Time and Material 19

20 AIT Document Defect Data QM Began collecting data in Feb 02 as part of DP Cycle Six months data had defect rate vary from 4.9% to 11.6%, with one outlier higher Process improvement techniques per DP Cycle identified Use of CM controlled Templates for documents enforced for authors Type classification identified for defects: technical/non-technical Documentation Input Defect Report checklist completed Identify number of pages each document Identify document types Identify defects as technical/non-technical Management and personnel awareness of data collection and purposes Process Improvement implementation Resulted in re-evaluation of upper and lower limits Increased personnel and management focus on data Data for last 12 months has defect rate vary from 0.4% to 5.9% 20

21 Document defect data Feb 02 Jul 03 AIT QM Results 60% Document Input Defect X Control Chart 55% 50% 45% 40% 35% X 30% 25% 20% 15% 10% 5% 0% Feb-02 Mar-02 Apr-02 May-02 Jun-02 Jul-02 Aug-02 Sep-02 Oct-02 Nov-02 Dec-02 Jan-03 Feb-03 Mar-03 Apr-03 May-03 Jun-03 Jul-03 % Defects % Defects Mean UNPLX LNPLX U2S L2S U1.5S L1.5S Upper Spec Limit Northrop Grumman Information Technology Company Sensitive / Company Proprietary -- Subject to Nondisclosure 21

22 Joint Primary Aircraft Training System (JPATS) Training Integration Management System (TIMS) JPATS TIMS

23 JPATS TIMS Joint Primary Aircraft Training System (JPATS) Training Integration Management System (TIMS): Develop, deploy, and maintain the Training Integration Management System (TIMS) to the JPATS program, a joint Air Force and Navy program to replace the existing primary trainer aircraft (T-37/T-43) with a new trainer aircraft designated the T-6A Texan II TIMS is a specialized computer network information management system that is tailored to perform routine administrative functions, scheduling, grading, and resource management to aid instructors, students, and commanders in accomplishing training. The TIMS manages Undergraduate Flying Training (UFT) for Air Education and Training Command (AETC) and the Chief of Naval Education Training (CNET) with the Chief of Naval Air Training (CNATRA) as the lead agent for CNET. Customer base includes all defense services and a variety of commercial concerns Contract types are CPAF & FPLOE 23

24 JPATS Challenge JPATS Build 1.05 (June 2002) The first build to be released after progressing from Development to the Maintenance phase of the program. Builds were internal builds, not released for customer verification Used development style processes for fixing STRs Failed 9/30 (30%) of the on-site STR verification tests with customer witnesses. NOTE: STRs Fixed vs. STRs Accepted is a measure that is quantitatively measured by the JPATS Program As a Result Kicked off JPATS DP Cycle #1 GOAL: Reduce the STR verification failures to < 5% for JPATS builds 1.06 and

25 DP Cycle Findings Root causes included Lack of maintenance -style processes (e.g. streamlined for dealing with many (~30-130) STRs/build) Lack of maintenance -style build planning & tracking ~40 Countermeasures identified Many top-tier countermeasures focused on improving/updating our STR build processes Most of these were approved for action by the sponsor 25

26 DP Cycle Improvements Actions JPATS updated/developed the following processes specifically for the Contractor Logistics Support (CLS = maintenance) phase BP 100, CLS Software Build Process BP 200, Define a Build BP 300, Plan and Track a Build BP 400, Develop a Build UT 100, Unit Test Procedure PR 100, Peer Review Procedure (Code) RT 100, Regression Test Procedure BP 500, Deploy a Build JPATS developed a build planning and tracking matrix called the STR Big Board to track all the elements required by the process per STR across all STRs 26

27 DP Cycle Effectiveness Build 1.06 (July 2002) 0% STR verification failure rate (0/11) Goal of verification failure rate < 5% was met Build 1.07 (Oct 2002) 2.7% STR verification failure rate (3/113) Goal of < 5% verification failure rate was still met Subsequent builds have continued to perform well See next slide showing current JPATS QM measure for STR Verification 27

28 DP Cycle Effectiveness 40% 20% DP Cycle STRs Projected Fixed vs Actual Fixed: (Actual - Projected) / Projected Subsequent Results % Deviation 0% -20% -40% Problem Occurred -60% Build # DDrc Goal X-bar UCL LCL Northrop Grumman Information Technology Company Sensitive / Company Proprietary -- Subject to Nondisclosure 28

29 Synthetic Imagery Generation System (SIGS) SIGS

30 SIGS Synthetic Imagery Generation System: Continuous project since 1989 to build, deploy, and maintain the synthetic imagery system for use in training and wargaming Customer is the NRO Deployed at over 20 sites worldwide Current build is a major technology conversion from a Galaxy GUI to JAVA and also includes synthetic IR images and importing larger images Implementation began in 3/00, release scheduled for 6/02 Labor is billed to customer using Direct Equivalent Hours (DEH) Contract type is CPAF 30

31 SIGS Schedule Performance Situation: O&M project undertaking a major redesign of the system over multiple years using new technology At the beginning: unfamiliar technology meant that schedule estimates had large uncertainty since there was no available historical data to support the basis of estimate Process changes: introduced Earned Value (EV) tracking combined with statistical process control (SPC) techniques allowed better monitoring of progress against the plans and identifying when there are special causes of variation Improvement: by closely tracking the actual effort required to complete the earlier activities, we were able to feed that back into the estimates for the later activities and thus able to produce schedules with less uncertainty 31

32 SIGS Schedule Performance (Cont d) SPIm X Chart for SIGS 130% 120% 110% 100% Percent (%) 90% 80% 70% 60% 50% Mar-00 May-00 Jul-00 Sep-00 Nov-00 Jan-01 Mar-01 May-01 Jul-01 Sep-01 Nov-01 Jan-02 Mar-02 May-02 Jul-02 Sep-02 Nov-02 Jan-03 Date SPIm X bar UNPL LNPL Wt. Ave. Goal: SPI (X bar) of 85% in the 1 st third of each PoP, 90% in the 2 nd third, and 95% in the last third Actual: 92.1% over multiple PoPs; 88.3% at the end of the 1 st PoP; 88.7% at the end of the 2 nd ; and 96.8% at the end of the last PoP Highlights: Cost Performance (CPI) was 96.8% over the same period; Award Fee average was 99% over the same period 32

33 Conclusions CMMI Practices have a direct and positive influence on cost, schedule, and quality performance It helps to tie the quantitative goals and measures to customer needs and relevant project problems CMMI high maturity enables organizational and project agility in high priority areas Projects can improve dramatically in 6 months or less using DP cycles of 1 week or less The transition from SW-CMM to CMMI is not inherently difficult, costly, and time-consuming, but It pays to proactively improve against emerging models and standards It is much easier to attain the same maturity level the second time It is much easier to transition between models when you don t have to worry about meeting that next higher maturity level 33

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