Complementing Approaches in ERP Effort Estimation Practice: an Industrial Study

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Complementing Approaches in ERP Effort Estimation Practice: an Industrial Study - PROMISE 2008

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  • Complementing Approaches in ERP Effort Estimation Practice: an Industrial Study

    1. 1. Complementing Approaches in ERP Effort Estimation Practice: an Industrial Study Maya Daneva
    2. 2. Table of Contents <ul><li>Why ERP effort estimation is difficult? </li></ul><ul><li>The solution proposal </li></ul><ul><li>The case study </li></ul><ul><li>Validity threats </li></ul><ul><li>Future activities </li></ul>
    3. 3. <ul><li>ERP projects are notorious for delays, budget overruns, cancellations </li></ul><ul><li>Customization & reuse compromises </li></ul><ul><li>Example: 5000 parameters, 10 000 tables in SAP R/3 </li></ul><ul><li>Architecture is designed when most users are not known </li></ul><ul><li>Shortage of proper methodologies to evaluate functional size, effort, productivity, schedule. </li></ul><ul><li>No historical data sets </li></ul><ul><li>Even when earlier project data exists, effort and duration for similar ERP projects have been noted to vary widely. </li></ul>Estimating ERP Projects: Some Challenges
    4. 4. The Solution <ul><li>Key idea: integrate the use of COCOMO II, Monte Carlo simulation and portfolio management </li></ul><ul><li>The targeted effects are to systematically cope with: </li></ul><ul><ul><li>Uncertainty of cost drivers </li></ul></ul><ul><ul><li>Strong bias by vendors/consultants in effort estimation. </li></ul></ul>
    5. 5. The Solution: A High-level View
    6. 6. The Steps
    7. 7. The Case Study: Planning <ul><li>Site: Telus Corporation </li></ul><ul><li>Canada-wide roll-out of 8 ERP modules in 13 projects (1997-2003), 67 subprojects </li></ul><ul><li>Size Measure: unadjusted Function Points (IFPUG) </li></ul><ul><li>Reuse levels: based on reuse percentage ratio </li></ul><ul><li>No knowledge of uncertainty of cost drivers </li></ul><ul><li>Used default levels proposed by other authors </li></ul><ul><li>Monte Carlo simulation runs: 10 000 </li></ul>
    8. 8. The Case Study: Execution Example: Probability distribution of project effort in person/months
    9. 9. The Case Study: Execution Example: Probability distribution of time in months
    10. 10. The Results (I) <ul><li>When adjusting cost drivers, we can increase the probability of success under effort constraints and under time constraints. </li></ul><ul><ul><li>For each cost driver, two portfolios are constructed, with either VERY HIGH ratings or LOW ratings </li></ul></ul><ul><ul><li>For 13 out of the 17 drivers, we observed that success could be maximized, when drivers are adjusted. </li></ul></ul>98.88% 96.87% Very high 76.52% 68.78% Very low Under time constraints Under effort constraints Probability of success REUSE rating
    11. 11. The Results (II): Are projects more successful when managed as a portfolio? Context: Under effort constraint 1.16 97.76% 84.31% High uncertainty 1.05 98.81% 93.78% Low uncertainty Portfolio (b) Individual projects (a) Ratio of increase (a)/(b) Probability of success Uncertainty level of cost drivers
    12. 12. The Results (III): Are projects more successful when managed as a portfolio? Context: Under time constraint 9.13 75.91% 8.31% High uncertainty 5.55 87.52% 15.76% Low uncertainty Portfolio (b) Individual projects (a) Ratio of increase (a)/(b) Probability of success Uncertainty level of cost drivers
    13. 13. Validity Concerns <ul><li>External validity: use of ASUG </li></ul><ul><li>Choice of techniques: why these three techniques and not others? </li></ul><ul><li>Replication plans </li></ul>
    14. 14. Conclusions <ul><li>Made a solution proposal with respect to two ‘targeted effects’. </li></ul><ul><li>Early results looks promising </li></ul><ul><li>Observations partly converge with experiences by other authors </li></ul>
    15. 15. Thank you !

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