Measuring Time An earned value simulation study
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1 Measuring Time An earned value simulation study Mario Vanhoucke Presentation for the Annual Earned Value Conference for the UK London - June 1-11, 28
2 An EVM introduction The EV terminology A case study The research project Static drivers of forecast accuracy Dynamic drivers of forecast accuracy Time sensitivity and corrective actions Top-down or bottom-up project tracking The software Cover might be subject to change upon publication
3 An EVM introduction The EV terminology A case study The research project Static drivers of forecast accuracy Dynamic drivers of forecast accuracy Time sensitivity and corrective actions Top-down or bottom-up project tracking The software
4 An EVM introduction The EV terminology A case study The research project Static drivers of forecast accuracy Dynamic drivers of forecast accuracy Time sensitivity and corrective actions Top-down or bottom-up project tracking The software
5 EVM metrics Earned Value Key Parameters Planned Value (PV) Actual Cost (AC) Earned Value (EV) Earned Schedule (ES) Earned Value Performance Measures Cost Performance Index (CPI) Cost Variance (CV) Schedule Performance Index (SPI) Schedule Variance (SV) Schedule Performance Index (SPI(t)) Schedule Variance (CV(t)) Translation to time units Time Variance (TV) Earned Duration (ED) Earned Value Forecasting Indicators Cost Estimate At Completion (EAC) Duration Estimate At Completion (EAC(t)) Duration Estimate At Completion (EAC(t))
6 delay over budget 225 delay under budget W W1 W2 W3 W4 W5 W6 W7 W8 W9 W W1 W2 W3 W4 W5 W6 W7 W8 W9 4 3 ahead over budget ahead under budget W W1 W2 W3 W4 W5 W6 W7 W8 W9 W W1 W2 W3 W4 W5 W6 W7 W8 W9 PV AC EV
7 Earned Schedule ES (new!) Find t such that EV > PVt and EV < PVt +1 ES = t + (EV - PVt) / (PVt+1 - PVt) PVt+1 EV 75 PVt W W1 W2 W3 W4 W5 W6 W7 W8 W9 Planned Value Earned Value ES AT
8 Earned Schedule ES Find t such that EV > PVt and EV < PVt +1 ES = t + (EV - PVt) / (PVt+1 - PVt) Behind schedule (in monetary terms!) 75 W W1 W2 W3 W4 W5 W6 W7 W8 W9 Planned Value Earned Value ES AT Behind schedule (in a time dimension!)
9 Earned Schedule ES ES measures time in hours, days, weeks or months! Find t such that EV > PVt and EV < PVt +1 ES = t + (EV - PVt) / (PVt+1 - PVt) Behind schedule (in monetary terms!) 75 W W1 W2 W3 W4 W5 W6 W7 W8 W9 Planned Value Earned Value ES AT Behind schedule (in a time dimension!)
10 Performance Planned Duration PC PV AC EV W W1 W2 W3 W4 W5 W6 W7 W8 W9 W1 W11.% 2.67% 1.% 18.33% 26.67% 4.% 5.% 75.% 8.% 87.% 95.% 1.% Late! ES SPI SPI(t) CPI W W1 W2 W3 W4 W5 W6 W7 W8 W9 W1 W11 PV AC EV
11 Performance Cost performance: cost overrun! Correct measure PC PV AC EV W W1 W2 W3 W4 W5 W6 W7 W8 W9 W1 W11.% 2.67% 1.% 18.33% 26.67% 4.% 5.% 75.% 8.% 87.% 95.% 1.% ES SPI SPI(t) CPI Cost Performance Budget overrun!.5.25 W W1 W2 W3 W4 W5 W6 W7 W8 W9 W1 W11 CPI
12 Performance Time performance: quirky behavior! PC PV AC EV W W1 W2 W3 W4 W5 W6 W7 W8 W9 W1 W11.% 2.67% 1.% 18.33% 26.67% 4.% 5.% 75.% 8.% 87.% 95.% 1.% ES SPI SPI(t) CPI Schedule Performance On time?.85 Late! W W1 W2 W3 W4 W5 W6 W7 W8 W9 W1 W11 SPI SPI(t)
13 Performance ES measures real time performance until the end of the project! Time performance: quirky behavior! PC PV AC EV W W1 W2 W3 W4 W5 W6 W7 W8 W9 W1 W11.% 2.67% 1.% 18.33% 26.67% 4.% 5.% 75.% 8.% 87.% 95.% 1.% ES SPI SPI(t) CPI Schedule Performance On time?.85 Late! W W1 W2 W3 W4 W5 W6 W7 W8 W9 W1 W11 SPI SPI(t)
14 SPI = EV / PV End of project: EV = PV SPI = 1 (always!) SPI(t) = ES / AD End of project: ES = PD ES > AD (early) ES = AD (on time) SPI(t) = 1 ES < AD (late) 1. Schedule Performance On time?.85 Late! W W1 W2 W3 W4 W5 W6 W7 W8 W9 W1 W11 SPI SPI(t)
15 Unreliable region for performance measurement! Inaccurate time forecast! 1. Schedule Performance On time?.85 Late! W W1 W2 W3 W4 W5 W6 W7 W8 W9 W1 W11 SPI SPI(t)
16 Forecasting General idea: based on current performance Forecasting methods Traditional methods (PV, AC and EV) Novel method (ES) Planned Value Method Earned Duration Method Earned Schedule Method Current Project Performance PF = 1 Future follows the schedule EAC(t)PV1 EAC(t)ED1 EAC(t)ES1 PF = SPI or SPI(t) PF = SCI or SCI(t) Future time performance = Current time performance Future time performance = Current time/cost performance EAC(t)PV2 EAC(t)ED2 EAC(t)ES2 EAC(t)PV3 EAC(t)ED3 EAC(t)ES3
17 Forecasting ES predicts the future in a better way? General idea: based on current performance Forecasting methods Traditional methods (PV, AC and EV) Novel method (ES) Planned Value Method Earned Duration Method Earned Schedule Method Current Project Performance PF = 1 Future follows the schedule EAC(t)PV1 EAC(t)ED1 EAC(t)ES1 PF = SPI or SPI(t) PF = SCI or SCI(t) Future time performance = Current time performance Future time performance = Current time/cost performance EAC(t)PV2 EAC(t)ED2 EAC(t)ES2 EAC(t)PV3 EAC(t)ED3 EAC(t)ES3
18 Forecasting ES predicts the future in a better way? General idea: based on current performance Forecasting methods Traditional methods (PV, AC and EV) Novel method (ES) Planned Value Method Earned Duration Method Earned Schedule Method Current Project Performance PF = 1 Future follows the schedule EAC(t)PV1 EAC(t)ED1 EAC(t)ES1 PF = SPI or SPI(t) PF = SCI or SCI(t) Future time performance = Current time performance Future time performance = Current time/cost performance EAC(t)PV2 EAC(t)ED2 EAC(t)ES2 EAC(t)PV3 EAC(t)ED3 EAC(t)ES3
19 An EVM introduction The EV terminology A case study The research project Static drivers of forecast accuracy Dynamic drivers of forecast accuracy Time sensitivity and corrective actions Top-down or bottom-up project tracking The software
20 Research project General conclusions instead of often subjective case study statements! Research topic 1: Static forecast accuracy drivers Research topic 2: Dynamic forecast accuracy drivers Research topic 3: Guide the corrective action decision making process Study 1 Determinants of forecast accuracy Study 2 Project structure (topological indicators) Baseline schedule (activity criticality) Schedule adherence (p-factor) Review period (percentage completed) Concept Project definition Project scheduling Project execution Project control Project termination Forecast accuracy Study 3 Feedback loop (earned value performance management) Management s attention (activity or project based) Corrective action decision making
21 An EVM introduction The EV terminology A case study The research project Static drivers of forecast accuracy Dynamic drivers of forecast accuracy Time sensitivity and corrective actions Top-down or bottom-up project tracking The software
22 Methodology Network indicators (4) Project network Generate project networks with a pre-defined structure Project schedule Construct an earliest start schedule (ESS) using forward calculations Planned Value (PV) Simulation scenarios (9) Project execution Monte-Carlo simulation for each activity s duration and cost Actual Cost (AC) Earned Value (EV) Project monitoring Measure the forecasting accuracy at each review period
23 Project network Topological structure of a network Number of activities, precedence relations, Influence on risk of delay, computer speed, schedule quality, resource consumption, etc... Wide and diverse set of project networks Idea: span the full range of complexity Parameter: SP indicator to measure the degree between a serial/parallel network
24 Baseline schedule Construction of baseline schedule Critical path method: ESS Planned Value PV S-curve Point-of-reference: The real purpose of a project schedule is to act as a predictive model of a team of resources and the project work destined for that team
25 Simulation scenarios Critical activities Non-critical activities SPI(t) = Legend: SPI(t): average project early warning performance signal > 1: average positive signal (ahead of schedule) < 1: average negative signal (schedule delay) RD: Real project duration
26 Simulation scenarios The true scenarios Critical activities Non-critical activities SPI(t) =
27 Simulation scenarios The true scenarios Critical activities Report: Project ahead Reality: Project ahead Non-critical activities SPI(t) =
28 Simulation scenarios The true scenarios Critical activities Report: Project ahead Reality: Project ahead Non-critical activities SPI(t) = Report: Project delay Reality: Project delay +
29 Simulation scenarios The true scenarios Critical activities Report: Project ahead Reality: Project ahead Non-critical activities SPI(t) = Report: Project delay Reality: Project delay + Report: Project on time Reality: Project on time
30 Simulation scenarios The misleading scenarios Critical activities Non-critical activities SPI(t) =
31 Simulation scenarios The misleading scenarios Critical activities - + Report: Project ahead Reality: Project on time Non-critical activities SPI(t) =
32 Simulation scenarios The misleading scenarios Critical activities - + Report: Project ahead Reality: Project on time Non-critical activities SPI(t) = Report: Project delay Reality: Project on time
33 Simulation scenarios The false scenarios Critical activities Non-critical activities SPI(t) =
34 Simulation scenarios The false scenarios Critical activities - + Non-critical activities SPI(t) = Report: Project ahead Reality: Project delay +
35 Simulation scenarios The false scenarios Critical activities - + Report: Project delay Reality: Project ahead Non-critical activities SPI(t) = Report: Project ahead Reality: Project delay
36 Forecast accuracy Project schedule and real-life execution: Murphy strikes! Real project duration Forecast accuracy = quality of average EAC(t) prediction = difference between average EAC(t) and RD 35 PV AC EV SPI(t) per period EAC(t) per period Average EAC(t) over complete horizon
37 Results (1) The earned schedule method outperforms, on average, the more traditional method (planned value method and earned duration method) EAC(t) estimate is too low EAC(t) estimate is too high Scenario 1 Scenario 2 True scenarios (1, 2, 5, 8, 9) ES >> PV and ED Scenario 3 Scenario 4 Scenario 5 Scenario 6 Misleading scenarios (4, 6) ES < PV and ED Scenario 7 Scenario 8 Scenario 9 Planned Value Earned Duration Earned Schedule False scenarios (3, 7) ES << PV and ED -2.% -1.%.% 1.% 2.% 3.% 4.%
38 Results (2) The network structure has a clear influence on the forecast accuracy 2 16 Early project 7.5 Late project SP low (parallel networks) Low accuracy SP high (serial networks) High accuracy Planned Value Earned Duration Earned Schedule From a parallel to a serial network From a parallel to a serial network
39 Results (3) Accuracy along the completion stage (beginning, middle or late) All forecasting methods have a relatively low accuracy at the project start. So what? The earned schedule method outperforms the other methods from the beginning of the project All other methods make the quirky mistake from the 5% à 6% percentage completed Early project Late project Early stage Middle stage Late stage Early stage Middle stage Late stage Planned Value Earned Duration Earned Schedule
40 An EVM introduction The EV terminology A case study The research project Static drivers of forecast accuracy Dynamic drivers of forecast accuracy Time sensitivity and corrective actions Top-down or bottom-up project tracking The software
41 Earned Value Management = project based tracking approach Project Objective Item Item Item Item Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity
42 Earned Value Management = project based tracking approach Project Objective SPI or SPI(t) below a critical threshold? Item Item Item Item Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity In danger! Watch out! No problem Requires action Requires attention Under control
43 EVM: when? Project based vs. activity based project tracking approach Activity based tracking SPI(t) is unreliable EAC(t) is inaccurate Project based tracking SPI(t) is reliable EAC(t) is accurate 1% parallel network 1% serial network
44 Schedule Risk Analysis = activity based tracking approach Project Objective Positive effect on project objective? Item Item Item Item Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Individual activity control as a trigger for corrective actions = Obtain with the minimal effort the maximal return!
45 Simulation study Input Simulation run Output Project data Run simulation Measure activity sensitivity Action threshold Run simulation with corrective actions % Control Measure improvement Unit contribution Total contribution
46 Management s focus Action threshold as a function of activity sensitivity Determines the %Control Sensitivity Chart Sensitivy Action threshold Full control ( th percentile) Action threshold No control (1 th percentile)
47 Results Activity based tracking SPI(t) is unreliable EAC(t) is inaccurate Project based tracking SPI(t) is reliable EAC(t) is accurate low SP - low action threshold high SP - low action threshold ? CI SI CRI SSI CI SI CRI SSI low SP - high action threshold high SP - high action threshold ? CI SI CRI SSI CI SI CRI SSI %C TC
48 An EVM introduction The EV terminology A case study The research project Static drivers of forecast accuracy Dynamic drivers of forecast accuracy Time sensitivity and corrective actions Top-down or bottom-up project tracking The software
49 Ideal WBS level? WBS levels EVM: top-down SRA: bottom-up Project Project performance problem! Negative effect on project performance? item Project Objective Project Objective Item Item Item Item Item Item Item Item Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activity Activities Which activities are critical and responsible for the problem? Highly sensitive activities in trouble!
50 Construct baseline schedule Measure activity sensitivity Start project execution (t = ) Increase review period (t = t + 1) Measure project performance (SPI and SPI(t)) Simulate activity time/cost variation at current time t Determine set of activities in progress Top-down project monitoring no no Below project performance threshold? yes Delays in critical activities? yes Take corrective actions yes Above activity sensitivity thresholds? yes Delay in sensitive activities? no no Bottom-up project monitoring (a) (b) Project finished? no yes Measure efficiency
51 Results 5% 4% Tracking Efficiency 3% 2% 1% % Parallel networks Serial networks TD-SPI TD-SPI(t) BU-SRA Top-down project tracking using EVM with SPI(t) is particularly useful for serial networks - Bottom-up project tracking using SRA is particularly useful for parallel networks
52 An EVM introduction The EV terminology A case study The research project Static drivers of forecast accuracy Dynamic drivers of forecast accuracy Time sensitivity and corrective actions Top-down or bottom-up project tracking The software
53 ProTrack CRITERIA ProTrack ProTrack ProTrack ProTrack Standard Sensitivity Scan Time Shuttle Smart Version Project Planning CPM based planning Network visualization Automatic Project Creation yes yes yes yes - yes yes yes - yes yes yes Project simulation options Activity Time/Cost Simulation Schedule Risk Analysis EVM forecast accuracy Analysis - Advanced module Standard Module Standard/Advanced modules - yes - yes - - yes yes Project Tracking Earned Value and Earned Schedule Earned Value Forecasting** Automatic Tracking Generation p-factor calculation yes yes yes yes yes yes yes yes - - yes yes - - yes yes Book Measuring Time Price yes
54 General conclusions References Future research avenues
55 General conclusion Activity based project tracking (bottom-up time sensitivity) Project based project tracking (Top-down earned value) Parallel project networks Yes! Focus on a subpart of activity set No! Inaccurate time forecasts Serial project networks No! Focus not possible Yes! Accurate time forecasts using Earned Schedule
56 Contact Research Faculty of Economics, Ghent University, Tweekerkenstraat 2, 9 Gent (Belgium) mario.vanhoucke@ugent.be Vlerick Leuven Gent Management School, Reep 1, 9 Gent (Belgium) mario.vanhoucke@vlerick.be Publications Cover might be subject to change upon publication 2 published: International Journal of Project Management, Journal of the Operational Research Society 4 research manuscripts under submission (June 28) Book: Measuring time (to appear) Software (coming soon) OR-AS, Operations Research - Applications and Solutions, Hoorndriesstraat 1, 982 Merelbeke (Belgium) Web: info@or-as.be
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