W3_AFS_ Learning Curve


Problem Recognition, Definition, and Evaluation

We have done work loading histogram in my previous blog to compare each activity load hours with baseline 11 hours. To get work loading histogram more real, we will apply learning curve to our histogram to see the impact/change.

Development of The Feasible Alternatives

A Learning curve is a mathematical model that explains the phenomenon of increased work efficiency and improved organizational performance with repetitive production of a good or service.

Development of The Outcomes For Each Alternative

The basic concept of learning curves is that some input resources decrease, on a per-output-unit basis, as the number of units produced increases. Most learning curves are based on the assumptions that a constant percentage reduction occurs in. Following is equation that be used to compute resource requirements assuming a constant percentage reduction in input resources each time the output quantity is doubled.

        Zu = K (un)

Where     u     = the output unit number;

    Zu    = the number of input resources unit needed to produce output unit u;

    K    = the number of input resources unit needed to produce the first output unit;

    s    = the learning curve slope parameter expressed as a decimal

    n    = = the learning curve exponent

Selection of Criteria

We calculate cumulative average time for blog posting activity using equation above as follows


Fig 1: Table Calculation Blog Posting Activity


Fig 2: Learning Curve Blog Posting Activity

Analysis and Comparison of The Alternatives

We use this equation to calculate all activities in work loading histogram and we get the figure as below


Fig 3: Work loading Histogram using Learning Curve equation

Selection of The Preferred Alternatives

Based on work loading histogram above, we get information that:

  1. Peak time will be at W11 – W14
  2. Only from W11 until W14, refer to the plan, each member will be overloaded (4 weeks less than previous blog)
  3. We should reduce workload from W11-W14 by increasing productivity on the weeks
  4. We have many spare hour per week on W1 – W10 and W15 – W20 to be optimized for reducing workload at the peak time
  5. Total hours is 181 hrs compare with 235 hrs in the previous blog

Performance Monitoring and Post-Evaluation of Results

Applying learning curve is giving our histogram more reasonable due to in reality, by performing repeated activity, it will increase our work efficiency and productivity.

REFERENCE

Giammalvo, PD. (2013). AACE Certification Preparation Course. Retrieved from http://www.build-project-management-competency.com/

Angeli, E., Wagner, J., Lawrick, E., Moore, K., Anderson, M., Soderlund, L., & Brizee, A. (2010, May 5). General format. Retrieved from http://owl.english.purdue.edu/owl/resource/560/01/

Sullivan, WG., Wicks, EM., Koelling, CP. (2012). Engineering Economy Fifteenth Edition P86-89.

Syafri, AF. (2013, September). W2_AFS_Workloading Histogram. Retrieved from https://simatupangaace2014.wordpress.com/category/postings-by-authors/a-fahmi-s/


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2 Comments

Filed under A Fahmi S

2 responses to “W3_AFS_ Learning Curve

  1. AWESOME posting, Pak Fahmi!!! Really well done. I guess my only question is why you didn’t cite Humphrey’s as well? What you will find is Humphrey’s and Engineering Economy offer two slightly different formula to calculate learning curve and what I was hoping was that someone would compare those two differences.

    Why not take this same case study and try it using Humphrey’s approach and see what you come out with?

    BR,
    Dr. PDG, Jakarta

  2. Pingback: WK5_Mahfoodha_ Learning Curve Analysis | PMI-Oman 2014

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