Complementing Approaches in ERP Effort Estimation Practice: an Industrial Study Maya Daneva

Table of Contents Why ERP effort estimation is difficult? The solution proposal The case study Validity threats Future activities

Why ERP effort estimation is difficult?

The solution proposal

The case study

Validity threats

Future activities

ERP projects are notorious for delays, budget overruns, cancellations Customization & reuse compromises Example: 5000 parameters, 10 000 tables in SAP R/3 Architecture is designed when most users are not known Shortage of proper methodologies to evaluate functional size, effort, productivity, schedule. No historical data sets Even when earlier project data exists, effort and duration for similar ERP projects have been noted to vary widely. Estimating ERP Projects: Some Challenges

ERP projects are notorious for delays, budget overruns, cancellations

Customization & reuse compromises

Example: 5000 parameters, 10 000 tables in SAP R/3

Architecture is designed when most users are not known

Shortage of proper methodologies to evaluate functional size, effort, productivity, schedule.

No historical data sets

Even when earlier project data exists, effort and duration for similar ERP projects have been noted to vary widely.

The Solution Key idea: integrate the use of COCOMO II, Monte Carlo simulation and portfolio management The targeted effects are to systematically cope with: Uncertainty of cost drivers Strong bias by vendors/consultants in effort estimation.

Key idea: integrate the use of COCOMO II, Monte Carlo simulation and portfolio management

The targeted effects are to systematically cope with:

Uncertainty of cost drivers

Strong bias by vendors/consultants in effort estimation.

The Solution: A High-level View

The Steps

The Case Study: Planning Site: Telus Corporation Canada-wide roll-out of 8 ERP modules in 13 projects (1997-2003), 67 subprojects Size Measure: unadjusted Function Points (IFPUG) Reuse levels: based on reuse percentage ratio No knowledge of uncertainty of cost drivers Used default levels proposed by other authors Monte Carlo simulation runs: 10 000

Site: Telus Corporation

Canada-wide roll-out of 8 ERP modules in 13 projects (1997-2003), 67 subprojects

Size Measure: unadjusted Function Points (IFPUG)

Reuse levels: based on reuse percentage ratio

No knowledge of uncertainty of cost drivers

Used default levels proposed by other authors

Monte Carlo simulation runs: 10 000

The Case Study: Execution Example: Probability distribution of project effort in person/months

The Case Study: Execution Example: Probability distribution of time in months

The Results (I) When adjusting cost drivers, we can increase the probability of success under effort constraints and under time constraints. For each cost driver, two portfolios are constructed, with either VERY HIGH ratings or LOW ratings For 13 out of the 17 drivers, we observed that success could be maximized, when drivers are adjusted. 98.88% 96.87% Very high 76.52% 68.78% Very low Under time constraints Under effort constraints Probability of success REUSE rating

When adjusting cost drivers, we can increase the probability of success under effort constraints and under time constraints.

For each cost driver, two portfolios are constructed, with either VERY HIGH ratings or LOW ratings

For 13 out of the 17 drivers, we observed that success could be maximized, when drivers are adjusted.

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

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

Validity Concerns External validity: use of ASUG Choice of techniques: why these three techniques and not others? Replication plans

External validity: use of ASUG

Choice of techniques: why these three techniques and not others?

Replication plans

Conclusions Made a solution proposal with respect to two ‘targeted effects’. Early results looks promising Observations partly converge with experiences by other authors

Made a solution proposal with respect to two ‘targeted effects’.

Early results looks promising

Observations partly converge with experiences by other authors

Thank you !