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Agile werkt -  Hennie Huijgens - NESMA najaarsbijeenkomst 2012
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Agile werkt - Hennie Huijgens - NESMA najaarsbijeenkomst 2012

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    Agile werkt -  Hennie Huijgens - NESMA najaarsbijeenkomst 2012 Agile werkt - Hennie Huijgens - NESMA najaarsbijeenkomst 2012 Presentation Transcript

    • Agenda • Background • Set-up of the study • Data collection • Analysis of the core metrics • Analysis of the performance indicators • Characteristics of best practices • Characteristics of worst practices • Supplementary study on striking factors • Index numbersAgile werkt - © Goverdson 2012 2
    • Background • Hennie Huijgens • Working as a measurement & analysis expert since 20 years • Clients; large information-intensive organizations (e.g. banking, insurance, pension funds, government, telco) • Specialism; Measurement & Analysis, Information Management, Risk Management • NESMA board member 1999 to 2007 • MSc in Information Management (University of Amsterdam) in 2010Agile werkt - © Goverdson 2012 3
    • Set-up of the study Data AnalysisCore metrics Size Performance Indicators Best Practices & Duration Worst Practices Time-to-market Effort Success factors Productivity Cost Fail factors Process Quality & Product Quality Defects & IncidentsPredictability Project planning Scalability Delivery model Index Numbers Supplementary study on striking factors Agile werkt - © Goverdson 2012 4
    • Data collection• 2 Comparable information-intensive organizations• 278 finalized IT-projects; 23 running IT-projects• 249 Waterfall projects; 29 Agile projects (Scrum)• For all projects the size was measured according to the ISO/IEC 24570 (NESMA) counting practice (Function Points)• All assessed IT-projects were about solution delivery; focus at software development (new or enhancements); in some cases with hardware or middleware implementations within the project scope• The investigated population was divers in subject (e.g. internet, mobile apps, call enter solutions, marketing & sales, products, client based systems, transactional services, business intelligence)• Both organisations started of with a process improvement program (CMMI) and (at a later stage) moved from waterfall towards Scrum Agile werkt - © Goverdson 2012 5
    • Analysis of the Core Metrics Size (Functionpoints) Duration (Months) Effort / Cost (Hours / Euros) Quality (Defects / Incidents) Agile werkt - © Goverdson 2012 6
    • Size • Size measured in functionpoints Scalability based at numbers of projects70% • ISO/IEF 24570 (NESMA) counting practice60% • Smallest project was 9 FP; largest was 4.60050% FP40% Agile • 60% of the projects (presenting 32% of the30% Waterval project cost) was smaller than 200 FP (small) Totaal20% • 31% of the projects (42% of the project cost)10% was between 200 and 600 FP (medium)0% < 200 FP 200 - 600 FP > 600 FP • 9% of the projects was larger than 200 FP; representing 27% of the project cost (large) • Medium and large projects deliver most end- user functionality; resp. 41% and 37% of the functionpoints are delivered by these projects. Small projects delivered 21% of the functionpoints Agile werkt - © Goverdson 2012 7
    • Duration Duration (Months) versus Size (Functionpoints) 100 • Duration measured in months; from start-up phase to aftercare • Mean duration waterfall projects: 9,25 months Life Duration (Months) • Mean duration agile 10 projects: 7,94 months 110 100 1.000 10.000 Size (FP) Agile Waterval Avg. Line Style Agile werkt - © Goverdson 2012 8
    • Project Cost Project cost (euros) versus Size (functionpoints) 100.000 • Project cost measured in euro’s; from start-up phase to aftercare 10.000 • Including supplier cost; excluding investment Life Cost (EUR) (thousands) cost (e.g. software licences, hardware / 1.000 middleware investment) • Mean cost waterfall projects: € 781 K 100 • Mean cost agile projects: € 834 K 1010 100 1.000 10.000 Size (FP) Agile Waterval Avg. Line Style Agile werkt - © Goverdson 2012 9
    • Quality (Defects) Process Quality (Defects) versus Size (Functionpoints) • Quality measured in 10.000 number of defects (findings) during development (unit test 1.000 to go live) • Mean quality waterfall Errors (SysInt-Del) projects: 80 • 100 Mean quality agile projects: 128 10 110 100 1.000 10.000 Size (FP) Agile Waterval Avg. Line Style Agile werkt - © Goverdson 2012 10
    • Analysis of the Performance Indicators Size Time-to-market (Functionpoints) (Days / FP) Duration Productivity (Months) (Cost / FP) Effort / Cost (Hours Process Quality / Euros) (Defects / FP) Product Quality Quality (Incidents / FP) (Defects / Incidents) Not in the study Agile werkt - © Goverdson 2012 11
    • Time-to-market Time-to-Market (Calendar Days per FP) versus Size (FP) 1.000 • Time-to-market is expressed in (calendar) days per functionpoint 100 (‘how fast is a function- point delivered?’) • Mean Time-to-market Calender Days/FP 10 waterfall projects: 2,91 days / FP 1 • Mean Time-to-market agile projects: 1,93 days / 0,1 FP • Remark; an alternative measure for TTM is a10 100 1.000 0,01 10.000 weighted average of days Size (FP) per FP, where size is the weighting factor. Agile Waterval Avg. Line Style Agile werkt - © Goverdson 2012 12
    • Productivity Productiviy (Euros per FP) versus Size (FP) • Productivity is expressed 100 in project cost per functionpoint (the price of one functionpoint) • Mean Productivity 10 waterfall projects: 4.613 Life Cost/FP (thousands) euro / FP • Mean Productivity agile projects: 3.360 euro / FP • Not in scope of the study: 1 net versus gross productivity • Remark; an alternative measure for productivity 0,1 is a weighted average of10 100 1.000 10.000 Size (FP) cost per FP, where size is the weighting factor. Agile Waterval Avg. Line Style Agile werkt - © Goverdson 2012 13
    • Process Quality Process Quality (Defects per FP) versus Size (FP) • Process Quality (in- 10 process product quality) is expressed in number of defects per functionpoint • Coherence with Product 1 Quality (before versus after Go Live) Defects/FP • Mean Process Quality waterfall projects: 0,38 defects / FP 0,1 • Mean Process Quality agile projects: 0,30 defects / FP • Remark; an alternative 0,01 measure for Process10 100 1.000 10.000 Size (FP) Quality is a weighted average of defects per FP, where size is the Agile Waterval Avg. Line Style weighting factor. Agile werkt - © Goverdson 2012 14
    • Best Practices & Worst PracticesAnalysis of the performance scores based at Star-rating• For every performance indicator that scores better than average (under the trend line in the figures) a project gets a star. For every performance indicator that scores above Sigma+1 (above the highest dotted line in the figures) a project loses a star• 3-star projects: performed better than average for all 3 performance indicators  Characteristics of best practices• 0-star projects: performed worse than average for all 3 performance indicators (or no data was measured)  Characteristics of worst practices Agile werkt - © Goverdson 2012 15
    • Success factors for IT-projects Project type was The 7 reason’s behind Best Practices*: Business Intelligence 5% 1. Single application (no application clustering) Dedicated test Single application (no 2. Working in releases resources application clustering) 7% 26% 3. Fixed and experienced project teamClose cooperationwith external party 4. Scrum 7% 5. Close cooperation with external party (same party Scrum mentioned several times) 15% 6. Dedicated test resources (e.g. test environment, deployment tools) Working in releases 20% 7. Project type was Business Intelligence Fixed and experienced project team 20% *) Based at 30 measured projects that scored better than average for both productivity, time-to-market and process quality. Agile werkt - © Goverdson 2012 16
    • Fail factors for IT-projects The 10 reason’s behind Worst Practices*: Package with 1. Complex environment (e.g. back- customization Bad performing 5% Complex offices, infrastructure, many stakeholders) external supplier environment 5% 18% 2. New technology (causing technical problems)Dependencies with other domains 3. Dependencies with other projects 7% 4. Preconditioned or ‘ technical’ projectsScope changes 7% 5. Pilot or PoC in project (incl. complex RFP/RFI) New technology 14% Complex legacy 6. Complex legacy environment (e.g. bad environment 9% documentation) dependencies with 7. Scope changes during project (exceptions) Pilot or PoC other 11% 13% 8. Dependencies with other domains Preconditioned or technical projects 9. Bad performing external supplier 11% 10. Package with high amount of customization *) Based at 15 measured projects that scored worse than average for both productivity, time-to-market and process quality. Agile werkt - © Goverdson 2012 17
    • Supplementary study on 4 striking factorsFactors that patently obvious influenced the performance:• Predictability• Project planning• Scalability• Delivery model Agile werkt - © Goverdson 2012 18
    • Predictability F/A Plot (Cost) F/A Plot (Schedule) 2.50 2.00 2.00 1.50 1.50Forecast / Actual Forecast / Actual 1.00 1.00 0.50 0.50 0.00 0.00 0.00 0.20 0.40 0.60 0.80 1.00 0.00 0.20 0.40 0.60 0.80 1.00 Project completion Project completion Cost predictability Schedule predictability • On average almost a perfect match • On average the planned Go Live date was between planning and realisation of 3 months to early project cost • The measure shows a bias on • However: good steering on cost expenditure is not the same as aiming for underestimating the delivery date a good performance…. Agile werkt - © Goverdson 2012 19
    • Project planningThe period preceding the big-bang towards Scrum; 3 dilemma’s occurred:1. Experts do not plan for improvement2. Managing uncertainties is not on the agenda3. Managers steer at cost expenditure; ‘Flying an airplane with only one instrument… a fuel meter…’The result: planning realised; performance declined Agile werkt - © Goverdson 2012 20
    • Scalability: the effect of size Schaalgrootte op basis van aantal projecten Number of projects70% More than half of the projects are small projects60% (size less than 200 FP). Slightly less than one third of50% the projects are medium sized (between 200 and 60040% Agile FP).30% Waterval Totaal20%10%0% < 200 FP 200 - 600 FP > 600 FP Schaalgrootte op basis van omvang (FP) Added value expressed in function points70% However medium and large sized projects deliver the60% greater part of the value, measured in end-user50% functionality (the number of function points). Large40% Agile agile projects (> 600 FP) even deliver more than 60%30% of the value. Waterval20% Totaal Lesson: The majority of finalized projects is small as10% to size; while conversely medium and large sized 0% projects deliver the greater part of the value for the < 200 FP 200 - 600 FP > 600 FP end-user. Agile werkt - © Goverdson 2012 21
    • Delivery model: agile wins Schaalgrootte op basis van kosten per FP Productivity€ 5,000 The production of a function point in small waterfall projects cost on average more (euro 4.728) than in medium sized€ 4,000 (euro 3.344) and large projects (euro 2.423). Agile projects show another result; one function point in a small project cost€ 3,000 on average 3.787 euros, while the production cost for a Agile Waterval function point in medium and large sized projects is almost€ 2,000 Totaal equal (respectively 1.515 euro’s and 1.475 euro’s).€ 1,000 Lesson: Large projects are cheaper than small projects. Lesson: Agile projects show a better productivity than €0 waterfall projects. < 200 FP 200 - 600 FP > 600 FP Schaalgrootte op basis van dagen per FP Time-to-market3.00 The delivery of a function point within both waterfall and agile projects takes more time in small projects than in2.50 medium sized projects. Large projects accordingly deliver2.00 faster than medium sized projects. What strikes the most is Agile that agile projects in all cases deliver a function point faster1.50 Waterval than waterfall projects.1.00 Totaal Lesson: Larger projects deliver a function point faster than small projects.0.50 Lesson: Agile projects show a better time-to-market than0.00 waterfall projects. < 200 FP 200 - 600 FP > 600 FP Agile werkt - © Goverdson 2012 22
    • Some index numbers Waterfall Agile ImprovementTime-to-Market (Calendar Days / FP) 2,91 1,93 34%Productivity (Cost in Euros / FP) 4.613 3.360 27%Process Quality (Defects / FP) 0,38 0,30 21%Standard Error (avg. of 3 indicators) 0,78 0,63 19%Time-to-Market (Calendar Days / FP)* 1,13 0,56 51%Productivity (Cost in Euros / FP)* 3.374 1.814 46% * Weighted average with size as weighting factor Agile werkt - © Goverdson 2012 23
    • SummaryLessons from the study:• The majority of finalized projects is small as to size; while conversely medium and large sized projects deliver the greater part of the value for the end-user• Large projects are cheaper, deliver a function point faster, and show less defects per functionpoint than small projects• Agile projects show a better productivity, time-to-market and process quality than waterfall projects, meaning;• Agile teams work faster, cheaper and deliver better quality• Standard error (r2): agile trends are more reliable as a source for project estimates Agile werkt - © Goverdson 2012 24