W15-ABM-Developing the BCWS Recovery Curve using IEAC-Part 2

  1. Problem Definition

Following my Week 14 assessment of IEAC, 4 methods were used to calculate the EAC.

After reviewing each method, IEAC 3 was determined to be the most suitable however  this still implied that the Estimated Cost at completion would be some 183% above original Budget. This is unrealistic and would appear to be an over estimate of remaining cost.

Another method is now required to determine the IEAC and this weeks blog will assess further alternatives.

2. Feasible Alternatives

Last week we considered IEAC1-4 which can be described as;

  • IEAC1 = ACWP + ((BAC – BCWP) / CPI)
  • IEAC2 = ACWP + ((BAC – BCWP) / SPI)
  • IEAC3 = ACWP + ((BAC – BCWP) / CPI * SPI)
  • IEAC4 = ACWP + ((BAC – BCWP) / ((0.2 * SPI) + (0.8 * CPI))

This week we will assess the IEAC using pproductivity  and unit cost referred to as IEAC 5.

3. Development of the Alternatives

IEAC 5 method considers the Actual productivity or unit cost of work completed to date as the basis for predicting the cost of balance works.

As noted in my week 13 blog, BCWS,BCWP and ACWP figures during Week 0 were calculated in line with the nominated split and weightages provided in notes. This has created some inconsistencies in the reporting figures where by earned values are limited by an pre determined arbitrary weighting which is misaligned with BCWS and ACWP figures – wrongly indicating that productivity is low.

As such, inclusion of week 0 figures in any productivity assessment would not provide an accurate indication of how many hours (or cost) have  been spent to date on actual tasks to establish a correct unit cost.

As such we will exclude week 0 costs and earned value and make an assessment of Productivity based on week 1 to 13 only.

Table 1 below summarises budget and estimate productivity based on the total work units required to completed the course;

CPI is based upon comparison of Budget and Actual unit cost of for works completed from weeks 1 to 13 and indicates that 3 out 5 are operating under budget.

Unit costs associated with weekly blogs and reports are however significantly over budget when considered on a unit cost basis.

Table 2 further outlines the IEAC for method 5.

4. Selection Criteria

The same selection criteria is to be applied as per week 13 blog.

  1. Realistic
  2. Reduce chance of further increases / changes

5. Comparison of the Alternatives

Table 3 outlines comparisons between all 5 methods

IEAC 5 provides the lowest IEAC ($27,717) compared to the previously preferred methods IEAC 1 & 4 ($33,233) which was also determined using CPI figures without any allowance for week 0.

6. Selection of Alternatives

IEAC 5 is considered more appropriate as a method for assessing my own IEAC. It ignores the week 0 anomalies and uses the actual unit cost of works completed so far to estimate . Unit costs do however still include learning curve inefficiencies during early phases and as such, productivity is expected to improve and assist with final cost

7. Performance Monitoring

Current Dashboard to include weekly assessment of IEAC based on unit productivity and cost – excluding week 0 figures.

References

  1. W09_SJP_Forecasts retrieved 5 November 2107 from https://js-pag-cert-2017.com/w09_sjp_forecasts
  2. Chapter  9.5 – Performance Monitoring Progress – Guild of project controls compendium and reference (CaR) | Project Controls – planning, scheduling, cost management and forensic analysis (Planning Planet).  Retrieved from http://www.planningplanet.com
  3. National Defence Industrial Association. (2014). A Guide to managing programs using predictive measures.
 

W12_TH_Contract Risk Mitigation for Tug Boat Rental

1. Problem Definition

Author has been conducting bidding for tug boat rental as part of LNG supply chain to PLN power plant on Kupang area. Duration for the contract is one year period, start from January-December 2018. This contract is very vulnerable to weather conditions because if the weather is bad then the tug boat could not be used. So that, during negotiation meeting, the prospective winner bidder proposed 2 options for its offer. First option is IDR 7,800,000,000 without condition; or second option is IDR 7,500,000,000 + IDR 22,000,000/day stand by rate if tug boat could not be used due to a bad weather.

2. Identify the Possible Alternative

Facing to this case, we have to decide which proposal option is accepted, IDR 7,800,000,000 without condition (option 1); or IDR 7,500,000,000 + IDR 22,000,000/day stand by rate (option 2).

3. Development of The Outcome for Alternative

It is clearly that if we accept first option, then contract price will be IDR 7,800,000,000.

But, for the second option, we must to ensure the stand cost that might be happened. For calculating the standby cost, we need to know the number of bad weather days during period of work. This number may be estimated by using historical weather data. The following table contains weather data for past five years from Indonesian Agency for Meteorological, Climatological and Geophysics:

Table 1. Occurrences of Bad Weather (In Days)

By using Monte Carlo simulation, it is forecasted the total bad weather days for each month in 2018, at P70 as follows:

Table 2. Occurrences of Bad Weather in 2018

Therefore, stand by cost is estimated as 20 days * IDR 22,000,000 = IDR 440,000,000,

so that the price for second option is IDR 7,500,000,000 + IDR 440,000,000 = IDR 7,940,000,000.

4. Selection Criteria

Of course, the main criterion is the lower cost. Another criteria is comes from our bidding procedure, namely the price should be lower than our owner’s estimation (OE) of IDR 8,000,000,000.

5. Analysis & Comparison of Alternative

Below table contains total cost for both options:

Table 3. Total Cost for Both Options

From the table 3, option 1 is cheaper IDR 140,000,000 than option 2

6. Selection of the Preferred Alternative

Based on comparison table above, we decided to proceed with option 1, IDR 7,800,000,000

7. Performance Monitoring and The Post Evaluation of Result

Monitoring and supervision should be conducted strictly during the execution of the work, especially in relation to the determination of whether a day is bad weather or not.

References:

  1. Sullivan, G. W. (2014). Engineering Economy 16th Chapter 12 – Probabilistic Risk Analysis, pp. 526-562. Pearson. Sixteenth Edition.
  2. Monte Carlo Simulation. Retrieved from http://www.palisade.com/risk/monte_carlo_simulation.asp
  3. Asro, Yoseph. (2014). W4_YAW_Contract Risk Mitigation|Kristal AACE 2018. Retrieved from https://kristalaace2014.wordpress.com/2014/03/17/w4_yaw_contract-risk-mitigation/
  4. Fakhri, Muhammad. (2017). W4_MFO_Contract Risk Mitigation|Emerald AACE 2018. Retrieved from http://emeraldaace2017.com/2017/08/22/w4_mfo_contract-risk-mitigation-for-topographic-survey/
  5. Weather data. Retrieved from http://dataonline.bmkg.go.id/home
 

W11_TH_Pareto Priority Index for Gas Station Project

1. Problem Definition

Author’s company has cost reduction campaign on gas station project. Author will define the alternatives for project cost reduction and specify the priority of project that will be execute.

2. Identify the Possible Alternative

Feasible alternatives project to cost reduction are:

  • Electrical Equipment
  • Civil Specification
  • Instrument Equipment
 3. Development of The Outcome for Alternative

Generate estimate cost, estimate saving and probability of success.

 

  • Electrical Equipment

Value engineering evaluation: using compressor with smaller power consumption, using trafo 400/220 V, using UPS only on critical equipment and improvement on lighting specification.

Cost to implement = 120 M IDR

Cost saving = 4,500 M IDR

Probability to success = 0.7

Time to completion = 0.5 year

 

  • Civil Specification

Value engineering evaluation: backfilling specification adjusted by civil site survey

Cost to implement = 30 M IDR

Cost saving = 1,600 M IDR

Probability to success = 0.8

Time to completion = 0.25 year

 

  • Instrument Equipment

Value engineering evaluation: CCTV re-position by hazardous area classification, minimize the use of gas detector, minimize the use of control valve (no redundancy)

Cost to implement = 10 M IDR

Cost saving = 170 M IDR

Probability to success = 0.9

Time to completion = 0.25 year

 

Calculate Pareto Priority Index (PPI), Equation that will be use is as follow

Table 1: PPI Calculation Result

4. Selection Criteria

Cost reducing project that have biggest PPI will be selected

5. Analysis & Comparison of Alternative

Civil Specification has the biggest PPI value among other, according table 2 the project ranking as follow:

Table 2: Project Priority

6. Selection of the Preferred Alternative

Based on above analysis, Civil Specification project is selected due to has biggest PPI (102.40)

 7. Performance Monitoring and The Post Evaluation of Result

To select project more accurately, if it is available historical information can be use to estimate standard times and resources. Whenever update data is available, calculation should be re-run to validate selection.

 

References

  1. Six sigma daily (2014). The Six Sigma Approach to Project Selection. Retrieved from: http://www.sixsigmadaily.com/implementation/the-six-sigma-approach-to-project-selection
  2. Selecting Projects where Six-Sigma can add value (2014). Retrieved from: http://unpan1.un.org/intradoc/groups/public/documents/arado/unpan020936.pdf
  3. Laksono, Andhy. (2014). W13_AL_Pareto Priority Index|Kristal AACE 2014. Retrieved from https://kristalaace2014.wordpress.com/2014/05/21/w13_al_pareto-priority-index/
  4. Adhi, Oktafianto. (2017). W13_OAN_ Pareto Priority Index|Emerald AACE 2018. Retrieved from http://emeraldaace2017.com/2017/11/05/w13_oan_pareto-priority-index/
 

W14_OAN_Project Management Organization

  1. Problem Definition

A project organization is a structure that facilitates the coordination and implementation of project activities. As function foucus on project, thus the right decisions to form the organizational structure of project management will be prioritized.

  1. Development of Feasible Alternatives

Most organization structure arrangements are structured in 3 ways:

  • Functional
  • Project based
  • Matrix

3. Possible Solution

The principles requirement of forming the EPC organization chart described as bellows:

  • Optimizing the resource
  • Power and authority
  • Unity of command and direction
  • Increase productivity

4. Selection Criteria

The selection criteria will be organization chart with many plusses and mitigated as many negatives as possible, also focus and fit characteristic most of our project which is need a long-term focus and commitment.

  1. Analysis and Comparison of the Alternatives
  • Functional organizational structure

The programmatic focus refers to a traditional structure in which program sector managers have formal authority over most resources.

Figure 1: Functional Organizational Structure

  • Project-based organizational structure

Independent project team that separated from the parent organizations, with their own technical staff and management.

Figure 2: Project-Based Organizational Structure

  • Matrix organizational structure

Matrix organizational structure is a hybrid form, it loads a level of project management structure on the functional hierarchical structure.

Figure 3: Matrix Organizational Structure

  1. Selection and Preferred Alternatives

Table 1 show us Organization Type analysis. Project-based organization type has come out as the appropriate design in forming the incoming EPC project organization chart. It has more advantages and we can mitigate negative aspect. There will be hard time for serving more than two managers at the same time. Team member tend to focus on their main manager, because usually performance evaluation is done by main manager, not by project manager.

Table 1: Organization Type Analysis

  1. Performance Monitoring and the Post Evaluation of Result

All of these, project-based organizational structure is the most suite type considering the complexity of EPC Project. However, further evaluation on the implementation is subjected to review.

Refrences

  1. Benefits & Disadvantages of Functional Organizational Structure.
    Retrieved from: http://smallbusiness.chron.com/benefits-disadvantages-functional-organizational-structure-11944.html
  2. Project-Based Organizational Structure.
    Retrieved from: https://yourbusiness.azcentral.com/projectbased-organizational-structure-17237.html
  3. Challenges and Benefits of Matrix Management in the Workplace from: https://www.thebalance.com/matrix-management-2276122
  4. 1_Shinta_Project Management Organization
    Retrieved from https://kristalaace2014.wordpress.com/2014/03/23/w4-1_shinta_project-management-organization/

 

 

W10_TH_ Price Forecasts for Electric Motor CNG Compressor at Gas Station Project

1. Problem Definition

After using Power Sizing Model and Index Value to estimate the indicative price for 0.5 MMSCFD electric motor CNG compressor on Blog Week 8, this week author will use price forecast method to predict the price within next 5 years. This forecasting still using budgetary quotation data from three different compressors manufactures at 2015. MS Excel will be choosing as tool to help the author.

2. Identify the Possible Alternative

Using last week indicative price, then capex value for 0.5 MMSCFD electric motor CNG compressor, as follow:

Figure 1. CEPCI Annual Index

 

Table 1. CEPCI Index Value Result to 3 Quotation

Table 2. Indicative Price 2015-2017 use P50

From the table above, author will analyze price forecasts for next 5 years use:

  1. MS Excel “Best Fit” Linear Regression Analysis Curve
  2. MS Excel “Best Fit” Polynomial Regression Analysis Curve
  3. MS Excel “Best Fit” Logarithmic Regression Analysis Curve

3. Development of The Outcome for Alternative

These are the following an initial data plotting in determining price forecast:

Figure 2. Input Data

Using these input data (indicative price 2015-2017) and MS Excel “Best Fit” Linear Regression Analysis Curve, then trendline and trending them out to 5 years provide in picture (2) below. While the trendline use R2 = 0.9482.

Figure 3. Linear Trendline

Then still using data input in 2015-2017, now MS Excel “Best Fit” Polynomial Regression Analysis Curve with R2 = 1 will be used in the second analysis. The result of the polynominal regression analysis can be seen in the picture (3) below.

Figure 4. Polynominal Trendline

The latest, on the third data input in 2015-2017 analysis will use MS Excel “Best Fit” Logarithmic Regression Analysis Curve with R2 = 0.9483. The result of the logarithmic regression analysis can be seen in the picture (4) below.

Figure 5. Logarithmic Trendline

With the purpose to make it simple to see the results of the analysis, then bellow will be displayed plotting all three trendline in one chart.

Figure 6. All Trendline (Linear, Polynominal, Logarithmic)

4. Selection Criteria

Further, value of all treadline for the fifth year, which is 2022, will be used, ranked and analyzed using PERT calculation. As for the smallest value represents “best case”, middle value represents “most likely” and the highest value represents “worst case”.

5. Analysis & Comparison of Alternative

The following is data to be used for PERT calculation

Table 3. Trendline Forecasts of 0.5 MMSCFD electric motor CNG compressor

From the table above, we can see

  1. Best case (optimistic) = $ 280
  2. Most Likely case = $ 285
  3. Worst case (pessimistic) = $ 372

Using PERT calculation, then the Mean, Sd, and variance:

Step 1 – PERT weighted Mean

= ((Optimistic)+(4 x Most Likely)+(pessimistic))/6

= $ ((280) + (4 x 285) + (372))/6

= $ 1792/6

= $ 298.67

Step 2 – Standard Deviation

= (Largest Value – Smallest Value)/6

= $ (372 – 280)/6

= $ 92/6

= $ 15.33

Step 3 – Variance

= Sigma/Standard Deviation^2

= $ 15.33^2

= $ 235.1

The following picture (6) below shows normal distribution curve:

Figure 7. Normal Distribution Curve

From the step 3, there is big variance means that the risk was big, so need high contingency to cover the risk. Hence, P(75) will be considered to being calculate for the indicative price.

Figure 8. P(75) Distribution Curve

The following above is P(75) cost estimate 0.5 MMSCFD electric motor CNG compressor in 2022 with value $ 310.93.

6. Selection of the Preferred Alternative

This blog displays one of method in determining price forecast, on next week blog another price forecast method will be applied. So in the last price forecast series, the best and optimum forecast method will be chosen to be applied in part of financial economic model for 0.5 MMSCFD electric motor CNG compressor.

7. Performance Monitoring and The Post Evaluation of Result

Forecasting method very dependent on the amount of data used, so it will be better and optimal if forecasting calculations using updated and valid data. Therefore project character are dynamic and unique, preferably input data for price forecast is updated periodically as a continual process of checking, reviewing and monitoring.

 

References:

  1. Planning Planet (2017). Creating The Owners Cost Estimate (Top Down). Retrieved from http://www.planningplanet.com/guild/gpccar/creating-the-owners-cost-estimate
  2. Sullivan, G. W. (2014). Engineering Economy 16th Chapter 3 – Cost-Estimation Techique, pp. 113-121
  3. Irene, Audray. (2017). W6_AI_Price Forecasts for Offshore|Emerald AACE 2018. Retrieved from http://emeraldaace2017.com/2017/09/10/w6_ai_price-forecasts-for-offshore-regasification-facility-project/
 

W9_TH_Present Economy Study for Selecting CNG Compressor with Electric Motor Prime Mover

1. Problem Definition

New gas station will be built on 2018, author need to compare the three brands of CNG compressor using present economy study. Which CNG compressor with electric motor prime mover has the most efficient cost?

2. Identify the Possible Alternative

The following table contains data of three brand of CNG compressor that will be selected.

Table 1. The CNG Compressor Data

The CNG compressor will be operated 10 hours per day or 3,650 hours per year. 5 hours on PLN peak hours (waktu beban puncak/WBP) and 5 hours on not PLN peak hours (luar waktu beban puncak/LWBP). Peak hours and not peak hours have different electricity cost.

3. Development Of the Outcome For Alternative

Before calculate the electricity cost expense of the CNG compressor, we must know the electric power costs per kWh. From the PLN website the electric power costs per kWh for B-3 group is 1,035.78 IDR for not peak hours (LWBP) and K x 1,035.78 IDR for peak hours (WBP) as shown as table below. We assume that PLN use maximum K value which mean 2.

Table 2. The Electric Power Costs per kWh

The electricity cost expense for the Brand A CNG compressor is

((75 kW / 0.85)*(1,035.78 IDR /kWh)*(1,825 hours / year)) + ((75 kW / 0.85)*(1,035.78 IDR/kWh*2)*(1,825 hours / year)) = 361,519,588 IDR

The electricity cost expense for the Brand B CNG compressor is

((90 kW / 0.80)*(1,035.78 IDR /kWh)*(1,825 hours / year)) + ((90 kW / 0.80)*(1,035.78 IDR/kWh*2)*(1,825 hours / year)) = 408,304,476 IDR

The electricity cost expense for the Brand C CNG compressor is

((85 kW / 0.72)*(1,035.78 IDR /kWh)*(1,825 hours / year)) + ((85 kW / 0.72)*(1,035.78 IDR/kWh*2)*(1,825 hours / year)) = 347,058,805 IDR

4. Selection of criteria

CNG compressor selection criteria is CNG compressor that have the most efficient total cost of owning and operating

5. Analysis and Comparison of Alternatives

The total cost of owning and operating the all CNG compressors as shown as table below.

Table 3. The total cost of owning and operating the all CNG compressors

From the table 3, Brand B have the most efficient total cost of owning and operating.

6. Select of the preferred alternative

Base from above calculation, Brand B have the most efficient total cost of owning and operating. So, author will recommend the brand B for the gas station project.

7. Performance Monitoring and Post Evaluation of Result

Monitoring should be conducted during execution of the project to ensure that all requirements are met.

References:

  1. Sullivan, G. W. (2014). Engineering Economy 16th Chapter 2 – Present Economy Studies, pp. 67-73
  2. Fakhri, Muhammad. (2017). W11_MFO_Present Economy Studies|Emerald AACE 2018. Retrieved from http://emeraldaace2017.com/2017/10/27/w11_mfo_present-economy-study-for-selecting-fire-water-pump/
  3. Tarif Dasar Listrik PLN Juli-September 2017. Retrieved from http://listrik.org/pln/tarif-dasar-listrik-pln/
 

W13_OAN_Pareto Priority Index

  1. Problem Definition

In this week Blog Posting, Author will analyze alternative method of tank repair. As we now, Storage tank is our critical facilities.  As Oil and Gas price now struggling, company has cost reduction campaign. Define the alternatives for project cost reduction and specify the priority of project that will be execute. Therefore, we must find the best alternative method to maintain reliability of our storage tank, especially old storage tank.

  1. Development of Feasible Alternatives

Feasible alternative project to maintain old storage tanks are:

Glass Fiber Reinforced Polymer (GFRP) Lining

Re-bottoming include cathodic protection

Plate doubling include cathodic protection

3.Possible Solution

Generate estimate cost, estimate saving and probability of success.

Estimate Saving based on fuel throughput per day, compare to construction for new tank (12 months project duration) and operation live of new tank (20 years).

  1. Glass Fiber Reinforced Polymer (GFRP) Lining (90 days project Duration – 5 years life time)
  • Total Cost                             : Rp. 400 Million
    Tank Cleaning
    Sand Blasting
    Fiber Reinforced
    Lining
  • Estimate Saving : Rp. Rp. 27 Billion
    Project duration
    Operation live

2. Re-bottoming include cathodic protection (180 days project duration – 5 years life time)

  • Total Cost : Rp. 1 Billion
    Tank Cleaning
    Repair all bottom plate
    Sand Blasting
    Cathodic Protection
  • Estimate Saving : Rp. 18 Billion
    Project duration
    Operation live

3. Plate Doubling include cathodic protection (135 days project duration – 2 years life time)

  • Total Cost : Rp. 300 Million
    Tank Cleaning
    Bottom plate assessment
    Plate doubling
    Sand Blasting
    Cathodic Protection
  • Estimate Saving : 16 Billion
    Project duration
    Operation live

Calculate Pareto Priority Index (PPI), Equation that will be use is as follow

Table 1: PPI Calculation Result

  1. Selection Criteria

Cost reducing project that have biggest PPI will be selected

  1. Analysis and Comparison of the Alternatives

Glass Fiber Reinforced Polymer (GFRP) Lining project has the biggest PPI value among other, according table 2 the project ranking as follow:

Table 2: Project Priority

  1. Selection and Preferred Alternatives

Based on above analysis, Glass Fiber Reinforced Polymer (GFRP) Lining project is selected due to has biggest PPI (216)

  1. Performance Monitoring and the Post Evaluation of Result

To select project more accurately, if it is available historical information can be use to estimate standard times and resources. Whenever update data is available, calculation should be re-run to validate selection.

Refrences

  1. Six sigma daily (2014). The Six Sigma Approach to Project Selection. Retrieved from: http://www.sixsigmadaily.com/implementation/the-six-sigma-approach-to-project-selection
  2. Selecting Projects where Six-Sigma can add value (2014). Retrieved from: http://unpan1.un.org/intradoc/groups/public/documents/arado/unpan020936.pdf
  3. W13_AL_Pareto Priority Index
    Retrieved from https://kristalaace2014.wordpress.com/2014/05/21/w13_al_pareto-priority-index/

 

 

W12_OAN_Car Selection using AHP

  1. Problem Definition

In this week blog posting, Author would like analyze choosing a car, using Analytic Hierarchy Process.

  1. Development of Feasible Alternatives

There are 2 alternatives car to choose, using 5 criteria which are Safety, Design, After Sales Service, Fuel Consumption, and Performance. Price will be set aside until the benefits of the alternatives are evaluated.

  1. Car A: Rp. 380 Milion
  2. Car B: Rp. 410 Milion
  3. Car C: Rp. 360 Milion
  4. Car D: Rp. 390 Milion

 

  1. Possible Solution

The information above is arranged in a hierarchical tree as below:

Figure 1: Hierarchical tree

  1. Selection Criteria

Table 1 will be the relative score:

Table 1: Relative Score

Car with the highest benefit – Cost Ratio will be selected.

  1. Analysis and Comparison of the Alternatives

Based on table 2, the relative score will be:

Table 2: Score – for alternative solution

Table 3: Average Value

Table 4: Weighted Matrix Comparison

Table 5: Consistency Vector

Table 6: CI Table

For Matrix 5 is 1.12.

We do same steps for each criteria.

Table 7: Rank based on Criteria

The alternative ranking is the product between alternative ranking matrix and criteria ranking.

Table 8: Alternative vs Criteria Matrix

  1. Selection and Preferred Alternatives

After alternative ranking has been defined, we have to compare between benefits and costs (price) using benefit – cost ratio.

Table 9: Cost Normalized

Table 10: Benefit – Cost Ratio

Based on Benefit – Cost Ratio, Car B has the highest ratio. Therefore, Car B will be Author choice.

  1. Performance Monitoring and the Post Evaluation of Result

In order to have more accurate result, pairwise comparison matrix can be done by surveying the experts or anyone who has good understanding of the problems.

Refrences

  1. Using AHP in Decision Making in Engineering Applications : Some Challenge
    Retrived from http://bit.csc.lsu.edu/trianta/Journal_PAPERS1/AHPapls1.pdf
  2. AHP Tutorial.
    Retrieved from https://pdfs.semanticscholar.org/7e27/b5a124c2e6829e1ff0d3e1279c2dbc9ebe2a.pdf
  3. W5_WW_Analytical Hierarchy Process
    Retrieved from https://garudaaace2015.wordpress.com/2015/03/28/w5_ww_-analytical-hierarchy-process-2/

 

 

W12_UDS_Emerald Blog Posting Learning Curve

  1. Problem Evaluation

We are already in week 13 AACE preparation course; this period is in middle 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) 14 weeks ago, we just use our feeling without any formula or technique. This time we want to 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 help us in rebase line our target.

Figure 1. Emerald AACE Blog Posting productivity

  1. Development of feasible alternatives

Refer to engineering economy book and Humphreys, they are two learning curve methods that we can adopt. Those two methods are:

  1. The Unit Linear Learning Curves (ULC)
  2. The Cumulative Average Linear Learning Curve (CUMAV)

In this small research, we analyzed our blog posting data in last 13 weeks to predict our next blog posting productivity, it also help us to get some picture of our learning curve in blog posting.

  1. Development the outcome for each alternative

This small research methodology is compare two learning curve formula ULC and CUMAV which more appropriate with our behavior blog posting productivity. Regression analysis will use as comparison rating index of both learning curve formulas ULC and CUMAV.

  1. Selection of 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.

  1. Analysis and comparison of the alternative

Because we use group data (each week team productivity) in this research, first we should made lot unit to determine Lot Mid-Point (LMP). LMP formula that we are used is in the below :

Figure 2. LMP Equation

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

Ln (Yx) = Ln (T1) + b x Ln (X)

With definition:

  • Ln(Yx) = natural logarithm of each unit hours spend to produce X unit
  • Ln (T1) = natural logarithm of the first unit produced
  • Ln (X) = natural logarithm of unit X produced
  • b = the exponential value associated with the slope

For transform reason we use historical data of blog posting actual hour, the table is below:

Table 1. Blog Posting Historical Data

All data being calculated to following table for regression analysis pupose:

Table 2. Regression Analysis Calculation

There is no outliers’ data on table above, all data in between LCL and UCL line. In generate regression of each method (ULC and CUMAV) we use excel data analysis and get the summary below:

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 additional assumption team will produce 7 blog each week so prediction table result is below:

Table 4. Blog Posting Hour Projection

  1. Alternative selection

From the comparison result table above has shown 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 0.186 explain that the equation for estimating purpose is best fits with the data being analyzed. While the value of s = 0.72 represented the learning rate of Emerald AACE Team in Blog Writing Project. This number also tells us that Emerald AACE’s skill improvement in doing Blog Writing Project from W1 to W13 have been increased by 28%. The n value, indicate that Emerald AACE Team underspending in this project by (-0.28) and (-0.02) worth.

  1. Performance monitoring & Post Evaluation 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. Sullivan, G. W., Wicks, M. E., & Koelling, C. P.(2014). Engineering economy 16th Edition. Chapter 3 – Cost Estimation Techniques., pp.110-113. Prentice Hall.
  2. Humphreys, G. (2014). Project Management Using Earned Value (Third Edition). Chapter 22 – Learning Curves., pp.435-441.Humphreys & Assoc.
  3. Planning Planet. (2017). Acquiring Man Power For The Project. Retrieved from http://www.planningplanet.com/guild/gpccar/acquiring-manpower-for-the-project
 

W14-ABM-Developing the BCWS recovery curve using IEAC


Problem Definition

Further to my Week 13 blog and the development of new baseline schedule, the development of a revised BCWS (early and late curve is required).

The new BCWS curve must assess the extent of balance works and the forecast cost required to complete. To support this process we will now look at the current Estimate at Completion figures and use these estimates to help establish recovery BCWS curves.

Calculating the IEAC can be performed using a number of different methods  and this blog will look at each method and its suitability in supporting the development of recovery BCWS figures/curves.

Feasible Alternatives

EAC data will be assessed at both the Programme and Project levels using the following methods;

  • IEAC1 = ACWP + ((BAC – BCWP) / CPI)
  • IEAC2 = ACWP + ((BAC – BCWP) / SPI)
  • IEAC3 = ACWP + ((BAC – BCWP) / CPI * SPI)
  • IEAC4 = ACWP + ((BAC – BCWP) / ((0.2 * SPI) + (0.8 * CPI))

Development of the Alternatives

Table 01 below is taken from the NDIA/GPC and outlines the assumptions and considerations when using each of the 4 forecasting tools.

Table 01

Each of the techniques will be assessed using project data from Week 13 as per table below. i.e An EAC will be assessed for each individual project / deliverable.

Table 01 – Project Performance Data (week 13)

Selection Criteria

The following criteria will be used to assess the preferred method of establishing the EAC and using to establish a revised BCWS curve for the recovery schedule

  • EAC takes account of Scope which was previously “underestimated” for  and is corrected for balance activities
  • EAC is realistic but also reduces the risk of reporting further increases i.e. pessimistic.
  • Forecast costs accurately reflect the remaining scope

Comparison of the Alternatives

EAC results for each method and level are provided under table 2.0

Table 02 – IEAC Results

The original BAC for all 6 projects was estimated at $20,700 however IEAC calculated using the 4 methods above predict a cost overrun in the range of +20,060 to $62,936 above BAC.

Analysis of these results is as follows

  1. Project 6 – Bid Project:Project 6 is yet to commence and appears to be influenced by a imbalance in ACWP & BCWP figures taken from Week 0 milestone split. CPI and SPI figures for this project are not considered to be reflective of the actual remaining works and it may be more suitable to ignore this section from the EAC calculation and continue to rely on the original BAC.
  2. This imbalance from week 0 split is also observed in projects 2 and 5 where hours recorded within week 0 account for the bulk of current ACWP despite relatively low BCWP recorded. This skews the CPI and SPI figures
  3. IEAC3 – A result of $83,636 is not considered realistic and appears to be influenced by the late starts made on cheat sheet and Bid project (See above).
  4. It can be seen that there is a major difference between the EAC figures calculated at the programme level and those which have been calculated using performance data from each individual project.

Selection of Alternative

From the above, IEAC3 will not be considered as it is not considered realistic and appears to be overly pessimistic.

IEAC 2 is not considered appropriate given the impact of skew to SPI figures due to heavy reliance on SPI and limitations within later part of project.

Whilst IEAC 1 and 4 have produced values which are almost identical in value, IEAC 4 is considered the most appropriate given its partial consideration of SPI ensuring that figures are not overly optimistic.

However adjustment of the Total EAC to account for Project 6 (BID) will be made to take account of unrealistic EAC figures generated when using the reported CPI and SPI figures. This adjustment would be relevant to all options regardless. EAC for project 6 will be based on 100% of BAC.

Finally, to ensure that the accuracy of the EAC is maintained in all areas of the programme, the IEAC will be based on the cumulative EAC value for all 6 projects and not based on the programme level CPI and SPI figures. ($35,186)

With respect to the development of revised BCWS curve, the EAC values to be used are as follows;

Table 03 – IEAC FINAL

Performance Monitoring

Whilst adjustment of BCWS figures is not considered a regular event, the  monitoring of IEAC figures is a continual process and must be undertaken regularly. IEAC4 is recommended for use within weekly reporting against the new BCWS recovery curve.

References

  1. W09_SJP_Forecasts retrieved 5 November 2107 from https://js-pag-cert-2017.com/w09_sjp_forecasts
  2. Chapter  9.5 – Performance Monitoring Progress – Guild of project controls compendium and reference (CaR) | Project Controls – planning, scheduling, cost management and forensic analysis (Planning Planet).  Retrieved from http://www.planningplanet.com
  3. National Defense Industrial Association. (2014). A Guide to managing programs using predictive measures.