W11_Afra_Blog Posting Learning Curve

1.     Problem Recognition

We are already in week 10 in PMP preparation course; this period is at the end of the course duration. From the beginning until now only blog posting task that have a real and constant target every week. When we make planning time duration (BCWS) 9 weeks ago, we just use our feeling without any formula or technique. This time I’ll evaluate our blog posting learning curve to predict our next hour spends. It will be on the track or not (between early and late BCWS) also helps in rebase line our target.

Figure 1: PMP OPWP team Blog Posting Productivity

Figure 1 above shows the productivity in blog posting project for our team, the variables (M1, M2, …etc.) indicates each member in the team. 

A learning curve is a concept that graphically depicts the relationship between cost and output over a defined period of time. Besides, it is a process where people develop a skill by learning from their mistakes. A steep learning curve involves learning very quickly. 

2.     Feasible alternatives

They are two learning curve methods that can be adopted. Those two methods are:

1.     The Unit Linear Learning Curves (ULC)

2.     The Cumulative Average Linear Learning Curve (CUMAV)

The mail objective of this blog is to analyses the blog posting in last 9 weeks to predict the blog posting productivity for coming three weeks, it also helps to get some picture of our learning curve in blog posting.

3.     Development of the outcome of Alternative

This blog will use methodology to compare two learning curves formula Unit Linear Learning Curves (ULC) and Cumulative Average Linear Learning Curve (CUMAV) to know which one is more appropriate with our behavior blog posting productivity. Regression analysis will be used as comparison rating index of both learning curve formulas ULC and CUMAV.

4.     Selection Criteria

There are three criteria of the best alternative in this method, there are:

1.      The learning rate (s) < 100%, the lower the better.

2.     Coefficient Determination (R²) the higher the better.

3.     Learning Curve Exponent (n) < 1, the lower the better.

The chosen alternative should have learning curve fits most of the criteria.

5.     Analysis and comparison of the Alternative

In this case study, I have 13 data samples (team productivity for blog posting for each member in the team, and we are 13). First step is to find the made lot unit to determine Lot Mid-Point (LMP). The algebraic Lot Mid-Point is defined as the theoretical unit whose cost is equal to the average unit cost for that long in the learning curve. LMP formula is below:

Equation 1: LMP formula

After identifying the LMP I should determine Average Unit Hour (AUH) of each Lot by dividing the total actual hours spending per lot by the lot size. The equation by default is exponential, will be easier and fit with regression analysis method to transfer it to Logarithm Equation as below:

Equation 2: Logarithm Equation (Learning curve formula)

The historical data of blog posting actual hour are in the table below, this data will be used later on for transformation from exponential to Logarithm Equation:

Table 1: Blog Posting Historical Data

All calculations are in the table below will be used for regression analysis:

Table 2: Regression Analysis Calculation

Next step is to find the values of R² and S for both ULC and CUMAV by using the formula below:

Equation 3: Learning Curve Formula

Table 3 below shows these values (R² and S):

Table 3: ULC and CUMAV Comparison Result

After we get the equation of both method ULC and CUMAV, we can predict of the rest blog posting hour spending, using assumption that the team will produce 13 blogs each week so prediction table result is below:

Table 4: Blog Posting Hour Projection

6.     Selection of the preferred Alternative

The comparison results above shows that The Unit Linear Learning Curves (ULC) Method is considered to be the favorable method to apply, rather than Cumulative Average Linear Learning Curve (CUMAV) Method using Heuristic LMP (Lot Midpoint) since its R² has shown higher rate than the CUMAV. The ULC’s R² value of 0.189 explain that the equation for estimating purpose is best fits with the data being analyzed. While the value of s = 0.725 represented the learning rate of our Team in Blog Writing Project. 

7.     Performance Monitoring and the Post Evaluation of result

Using appropriate Learning curve method of our team (ULC) can give us a projection of our rest blog writing project especially when we want to rebase line our BACS. It is projection can guide us to plot our new BACS and maintain our CPI on the track. We can do the same evaluation for the other project in couple of weeks, when it already enough data, because the other project target not start in the beginning of preparation course.

References: 

1.      Planning Planet. (2017). Acquiring Man Power For The Project. Retrieved from http://www.planningplanet.com/guild/gpccar/acquiring-manpower-for-the-project

2.      Staff, I. (2018). Learning Curve. Investopedia. Retrieved 7 January 2018, from https://www.investopedia.com/terms/l/learning-curve.asp

3.      Learning Curves Chapter Learning Curve Analysis Developed as a tool to estimate the recurring costs in a production process – recurring. - ppt download. (2018). Slideplayer.com. Retrieved 7 January 2018, from http://slideplayer.com/slide/7261227/