W11_HI_Learning Curve of My Report and Blog Project


  1.  Problem Definition

In Garuda AACE 2015 Project, on Week 10, the Author and 4 other members, submitted Change Order (CO) to Client for descoping Paper Project. The Client accepted the CO with notes that those members need to do rebaseline on W11. Before rebaseline, the Client requested us to apply Learning Curve on the project , in order to have sufficient input for rebaseline. In this week’s blog, the Author will apply Learning Curve on two running project : Weekly Report and Blog.

  1. Development of Feasible Alternatives

The budget manhours plan and actual recorded (cut of week 11) of Author’s Weekly Report as per below :

Table 1. Manhours Plan vs Actual of Weekly Report (as is)

W11 Table 1

The budget manhours plan and actual recorded of Author’s Blog Project as per below :

Table 2. Manhours Plan vs Actual of Blog Project (as is)

W11 Table 2

 

To gain more accurate data, some outlier data (yellow highlight) are excluded from the data source. Table 1 outliers are come from extra works to revise the Team Report. Table 2 outliers are come from extra learning of first blog and zero deliverable or no blog submission on week 8 and 11. The data now as per table below :

 

Table 3. Manhours Plan vs Actual of Weekly Report (clean)

W11 Table 3

 

Table 4. Manhours Plan vs Actual of Blog Project (clean)

W11 Table 4

 

 

 

  1. Development of the Outcomes for Alternativea

The concept of a Learning Curve is each time the cumulative production doubles, the hours required to produce the most recent unit decreases by approximately the same percentage, known as a slope.

Refer to the power trend line approach (Y=ax-b) as explained in Sullivan’s Engineering Economy:

Zu = K(un)   [1]

where:

u = the output unit number

Zu = the number of input resource units needed to produce output unit u

K = the number of input resource units needed to produce the first output unit

S = the learning curve slope parameter expressed as a decimal (can be calculated S = 2n)

n = log s/log 2 = learning curve exponent

 

 

W11 Fig 1

 

Figure 1: Power Trendline approach on Weekly Report

W11 Table 5

W11 Fig 2

 

Figure 2: Power Trendline approach on Blog

 W11 Table 6

 

 

  1. Selection of the Acceptable Criteria

The purpose of this learning curve analysis is to estimate realistic manhours based on previous actual hours and current learning curve parameter that required to complete the task. Basic expectation is realistic efficiency will required less manhours to produce the assigned tasks. However the less efficiency will also be consider for more manhours if realistic actual data stated that condition.

 

  1. Analysis and Comparison of the Alternatives

W11 Fig 3

Figure 3: Plot Unit Time and Cumulative Average on Weekly Report

W11 Fig 4

 

Figure 4: Plot Unit Time and Cumulative Average on Blog

 

  1. Selection of the Preferred Alternative

Based on the Table 5 above, with the current level of efficiency, total hours is estimated decrease to 21.56 hours with average manhours per report is 0.94 or rounded to 1 hours per report.

And based on Table 6 above, with the current level of efficiency, total hours is estimated increase to 63.57 hours with average manhours per report is 2.69 or rounded to 2.7 hours per blog.

 

  1. Performance Monitoring and Post-Evaluation of Results

Periodically along the actual data grows, similar analysis could be perform (including analysis to the other task). We can also perform the Estimate at Completion (EAC) approach, to check the result of Learning Curve Analysis above, in order to provide more confidence on the estimate. The application of EAC for above cases will be done in next blog

 

Reference:

  1. Sullivan, W.G., Wick, E. M., Koelling, C.P. (2012). Engineering Economy, Fifteenth Edition, Pearson, Chapter 3, 108-111.
  2. Humphreys, G. C. (2002). Lerning Curve. Project Management Using Earned Value (3rd ed.). Orange, CA: Humphreys & Assoc.
  3. Darwito, R. (2015) W09_RD_ Team Weekly Report Learning Curve . Retrieved from : https://garudaaace2015.wordpress.com/2015/05/03/w09_rd_-team-weekly-report-learning-curve/
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7 Responses to W11_HI_Learning Curve of My Report and Blog Project

  1. Pingback: W12_HI_Estimate at Completion (EAC) of My Weekly Report and Blog Project | GARUDA AACE 2015

  2. drpdg says:

    Again, simply AWESOME…..!!! Ideal case study and your analysis was perfect. I was thrilled to see you eliminated the outliers (although it would have been great to see your calculations) but that was the right approach.

    As noted in your W12 posting, the only challenge I would issue you (W13 blog topic?) would be to apply the PERT formula to your blog data and see which value comes closest to being able to stay within +/-5%. Keeping in mind that you don’t want to be too HIGH or too LOW……

    Keep up the great work and looking forward to seeing your blog postings each week!!

    BR,
    Dr. PDG, back home in Jakarta

    Like

  3. Pingback: W13_HI_Exercise for Team Re-baseline | GARUDA AACE 2015

  4. Dear Dr.Paul,
    Noted Pak, thank you for the feedback.
    I just posted my W13 blog, but different topic.Plan to apply PERT on next blog posting then.

    Best regards,
    Harnadi Irawan

    Like

  5. Pingback: W14_HI_ Apply PERT Formula on Estimate at Completion (EAC) Calculation | GARUDA AACE 2015

  6. Pingback: W7.0_TK_Learning Curve of My Weekly Report and Blog Project | Golden AACE 2015

  7. Pingback: W9_RM_My Weekly Report and Blog Project Learning Curves | Golden AACE 2015

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