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Complementing Approaches in ERP Effort Estimation Practice: an Industrial Study

Maya Daneva

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Table of Contents

1. Why ERP effort estimation is difficult?

2. The solution proposal

3. The case study

4. Validity threats

5. Future activities

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• 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

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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.

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The Solution: A High-level View

Monte Carlo simulation

Probability distribution of

cost factor values

COCOMO IIProbability

distribution of effort/duration

portfolio management

method

Probability of success

under deadline/effort constraints

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Estimate size

Formulate condition for

portfilo management

Adjust cost drivers to increase portfilo

success

Obtain probability distribution of effort & duration

Run 10000 trials using probabiliy

distribution of cost factor

values

Ascribe distribiution types to cost

drivers

Construct portfolios

Obtain ratio of increase of success probability

Step 1:

Step 2:

Step 3:Step 4:

Step 5:

Step 6:

Step 7: Step 8:

The Steps

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The Case Study: Planning

1. Site: Telus Corporation2. Canada-wide roll-out of 8 ERP modules in 13

projects (1997-2003), 67 subprojects3. Size Measure: unadjusted Function Points (IFPUG) 4. Reuse levels: based on reuse percentage ratio5. No knowledge of uncertainty of cost drivers6. Used default levels proposed by other authors7. Monte Carlo simulation runs: 10 000

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The Case Study: Execution

Effort: Frequency Chart

0

100

200

300

400

500

600

17,9 18,9 19,9 20,9 21,9 22,9

Example: Probability distribution of project effort in person/months

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The Case Study: Execution

Example: Probability distribution of time in months

Time: Frequency Chart

0

100

200

300

400

500

600

3,8 4,8 5,8 6,8 7,8 8,8 9,8

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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.

REUSE rating Probability of success

Under effort constraints Under time constraints

Very low 68.78% 76.52%

Very high 96.87% 98.88%

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The Results (II): Are projects more successful when managed as a portfolio?

Uncertainty level of cost drivers

Probability of success Ratio of increase(a)/(b)Individual

projects(a)

Portfolio(b)

Low uncertainty 93.78% 98.81% 1.05

High uncertainty 84.31% 97.76% 1.16

Context: Under effort constraint

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The Results (III): Are projects more successful when managed as a portfolio?

Uncertainty levelof cost drivers

Probability of success Ratio of increase(a)/(b)Individual

projects(a)

Portfolio(b)

Low uncertainty 15.76% 87.52% 5.55

High uncertainty 8.31% 75.91% 9.13

Context: Under time constraint

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Validity Concerns

1. External validity: use of ASUG

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

3. Replication plans

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Conclusions

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

• Early results looks promising

• Observations partly converge with experiences by other authors

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Thank you !