W16 – ABM- Follow up on Learning Curve for Trestle Barrier Wall Construction at SPJ – 12B

 
  1. PROBLEM DEFINITION

Further to Week 5 & 7 blogs  on the construction of trench RC barrier for SPJ – 12B project and the effects of Learning curve on production we would now like to review production results observed over the past 12 weeks and assess the actual learning curve parameters and make an assessment of the total predicted man hours to complete the entire 1330 Lm of barrier wall – based on actual productivity figures

2. FEASIBLE ALTERNATIVES

Two types of learning curves will be assessed for the purpose of assessing the actual learning curve parameters

  • Unit Learning and;
  • Cumulative average

3. DEVELOPMENT OF OUTCOMES

Selecting the correct learing curve will be critical in estimating the total man hours to complete the works. The total hours (or cost) determined using the Unit Linear method will be the sum of the unit costs however the Cumulative Average linear, the total hours (or costs) will be the unit values times the total number of units up to that point.

Productivities for planned as well as actual cumulative figures are outlined in table 1 below

Contractor has performance from 12th August to the 3rd November is further broken down as per table 2 below.

Note that the Unit Linear figures are based on the weekly average.

To determine which curve can be used to estimate the total man hours at completion we must first evaluate which is most reliable. To achieve this we will perform a regression analysis on both sets of data and determine the R^2 or coefficient of determination.

4. SELECTION CRITERIA

The option which provides a R^2 value  closer to 1.0 will be regarded as representative or best fit of the data. An R^2 of 0.9 or better is considered a good fit however a result of less than 0.9 may mean the resultant equation doe not adequately represent the data that are being analysed.

 5. ANALYSIS OF THE ALTERNATIVES  

Both the Unit linear and Cumulative average are plotted on the graph below including the corresponding R^2 values and equations for each set of data and best fit curves

The Blue line indicates the cumulative average whilst the red dashed line indicates the Unit Linear and whilst the  R^2 for the cumulative average is closer to 1.0 than the unit linear, both options are below 0.9 indicating that the line of best fit or equation may not accurately represent the data.

6. Selection of the Preferred alternative

Whilst neither curve satisfies the requirement of an R^2 value <0.9, the cumulative average may provide the best chance of estimating total man hours at completion

Based on the figure 1 above, the learning rate of the cumulative average-time model is the anit-log of 10^-0.246*log(2) = 84.32%

The cumulative average is determined by =464*67^(log(0.8432)/log(2) = 165 mnhrs /unit with a total number of direct labour hours estimated as 11,047 (165*67 units)

6. Performance Monitoring

The range of the figures obtained from the field data would indicate that there is too much variance in the data to allow for the development of an equation that allows accurate forecasting.

The labour figures and productivities above are based on weekly averages and these will include non productive periods and man hours associated with delays from weather, concrete supply, etc.

It may be advisable to eliminate some of the data points which include excessive delay periods or exclude non productive man hours from the total figures.

Also consideration needs to be given to any changes in actual crew members  and the impact that this may have on the crews overall productivity.

Further assessments will be over the next 2-3 weeks with a closer assessment of man hours spent during disruption or delay periods.

References

  1. Sullivan, G. W., Wicks, M. E., & Koelling, C. P.(2014). Engineering economy 16th Edition. Chapter 3 – Learning and Improvement., pp.110-112.
  2. Humphreys, G.C 2011 Project Management Using Earned Value Humphreys associates, Management Consultants. Second Edition, pp 435-440
  3. Dennis F. Togo, Curivlinear Analysis of Learning for Cost Estimation.  Retrieved from http://http://www.swdsi.org

W12_MFO_Analyzing Cost-Only Alternative Using Equivalent Worth For Selecting Fire Water Pump

 
  1. Problem Definition.

Same case like in W11 blog posting, we have plan to install electric fire water pump for the gas plant and we have received a complete offer with the specifications of the three brands of pumps. In this blog, the author wants to analyze cost-only alternatives of the three brands of pumps using equivalent worth. Which pump should be preferred base on equivalent worth?

  1. Identify the Feasible Alternative.

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

Table 1. The pumps data

This pumps will be used for 10 years and the company has a MARR of 14%

  1. Development of the Outcome for Alternative.

Using table 1 data, we will calculate the PW (Present Worth), AW (Annual Worth), and FW (Future Worth). The result as seen as table below.

Table 2. The result of Equivalent Worth Values

  1. Selection of Criteria.

The pump that will minimize the equivalent worth of total costs over the ten-year analysis period will be used as selection of criteria.

  1. Analysis and Comparison of the Alternative.

The comparison of three brand pump using the PW, AW, and FW methods to minimize total cost as seen as table 3 below.

Table 3. The comparison of the pumps

From the table 3, alternative brand C minimizes all three equivalent-worth values of total costs and is the preferred alternative. The preference ranking (Brand C > Brand B > Brand A) resulting from the analysis is the same for all three methods.

  1. Selection of the Preferred Alternative.

Base from above calculation, brand C minimizes all three equivalent-worth values of total costs and is the preferred alternative for the gas plant.

  1. Performance Monitoring and the Post Evaluation of Result.

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

References:

  1. Sullivan, W.G., Wicks, E. M., Koelling, C. P. (2014). Engineering Economy. Pearson. Sixteenth Edition.
  2. Electric fire water pump specification & quotation Brand A
  3. Electric fire water pump specification & quotation Brand B
  4. Electric fire water pump specification & quotation Brand C
  5. What is the formula for calculating net present value (NPV) in excel. Retrieved from https://www.investopedia.com/ask/answers/021115/what-formula-calculating-net-present-value-npv-excel.asp
  6. How to calculate net present value (Npv) in excel. Retrieved from https://www.youtube.com/watch?v=hG68UMupJzs

 

 

W5.3_MFO_ OmniClass on Gas Metering System Installation

 

1. Problem Recognition, Definition and Evaluation

Our company will install Ultrasonic Gas Metering Station for our project. Is the use of OmniClass can be applied to this project?

This blog is made to revise the W5.2 blog post before.

2. Development of the Feasible Alternatives

OmniClass consists of 15 hierarchical tables, each of which represents a different facet of construction information or entries on it can be combined with entries on other tables to classify more complex subjects.

Fig 1. Inter-related OmniClass

Author will be chosen what kind of tables on OmniClass that applicable for Gas Metering Project WBS.

3. Development of the Outcomes for Each Alternative

To choose top four most applicable tables from the 15 tables, the most appropriate technique will be the multi attributes approach. Compensatory models, the additive weighting technique has been chosen to help performing the comparison and selection. In all compensatory models, which involve a single dimension, the values of all attributes must be converted to a common measureable scale. By using determined criteria related to the Gas Metering Project, the additive weighting technique will rank the 15 tables, shows high to low applicability and relevance to the project.

4. Selection of a Criterion

There are six criteria related to gas metering project that have been determined for the OmniClass tables selection:

  1. WBS location that related to Gas Metering Project (1-3, 3 being highest relationship)
  2. WBS deliverables related to Gas Metering Project (1-3, 3 being highest relationship)
  3. WBS activity related to Gas Metering Project (1-3, 3 being highest relationship)
  4. WBS organization that related to Gas Metering Project (1-3, 3 being highest relationship)
  5. WBS level detail completeness (1-4, 4 being preferable)
  6. Tables applicability/uses for oil & gas project (low to high)

5. Analysis and Comparison of the Alternatives

The comparison result of OmniClass Tables is shown in the following table:

Table 1. OmniClass Tables Comparison Based on Six Selection Criteria

All attributes in table 1, be ranked in order of importance by doing paired comparison between each possible attribute combination. Result as shown on table 2:

Table 2. Ordinal Ranking of OmniClass Tables attributes

Based on Table 2, relative rank = ordinal rank + 1. A rank of 5 is best, the relative ranking will become as follows:

Table 3. OmniClass Tables Selection – Attribute Weight

The attributes values on Table 1 have to be converted in to non-dimensional form. The procedure for converting the original data on table 1 for a particular attribute to its dimensionless rating is:

The non-dimensional (dimensionless) values of the attributes are shown on Table 4:

Table 4. OmniClass Tables Selection – Dimensionless Value

Finally, for each OmniClass table, the normalized weight of the attribute (Table 3) is multiplied the non-dimensional attribute value (Table 4) to obtain a weighted score for the attribute. These weighted score are then summed to arrive at an overall score for each OmniClass table. The result is shown on Table 5.

Table 5. OmniClass Tables Selection – Weighted Score

6. Selection of the Preferred Alternative

Based on calculation and Table 5 comparison, the top three most applicable and relevant WBS from OmniClass for the Gas Metering Project is:

  1. Table 14-Spaces by Form (score 0.310)
  2. Table 23-Products (score 0.357)
  3. Table 31-Phases (score 0.270)

7. Performance Monitoring and Post Evaluation of Results

Monitoring and supervision should be conducted strictly during project to keep the project inline with the WBS.

Reference:

  1. Planning Planet (2017). Creating Work Breakdown Structure. Retrieved from http://www.planningplanet.com/guild/gpccar/creating-work-breakdown-structure
  2. Hendarto, Tommy. (2017). W6.1_TH_Standardized WBS Structures for Gas Station Project-Part 3. Retrieved from http://emeraldaace2017.com/2017/09/22/w6-1_th_-standardized-wbs-structures-for-gas-station-project-part-3/
  3. OmniClass (2017), OmniClass Table 21 – Elements (includes design elements). Retrieved from www.omniclass.org/tables/OmniClass_21_2012-05-16.zip
  4. Gannasonggo, Gustaf. (2012). W3_GGS_OmniClass WBS|Casablanca AACE 2012. Retrieved from https://aacecasablanca.wordpress.com/2012/02/06/w3_ggs_omniclass-wbs-table-selection-using-additive-weighting-technique/

 

 

W13_TH_Selection LNG ISOtank Using Present Worth Method

 
1. Problem Definition

The decline in world oil prices resulted hard competition between gas and oil fuel. As we know that gas is alternative fuel beside oil. Gas is feasible to use when oil price above 50 $/barrel. When oil price is around 50 $ /barrel, gas player need to very efficient on supply chain to make sure gas price is still acceptable for costumer. Indonesia is now developing LNG retail supply chain. One of the critical parts of this supply chain is LNG ISOtank. Author wants to reduce cost by selection on LNG ISOtank Investment.

Figure 1. LNG ISOtank Truck

2. Develop the Feasible Alternative

Manufacturer purpose 2 alternative, among others:

  1. Using LNG ISOtank ASME U stamp
  2. Using LNG ISOtank that follow ASME U stamp

Compare Value from Present Worth Method approaching will be used to select the best option.

3. Development of The Outcome for Alternative

Calculate all variable including Capital Expenditure/Capex, net income, operating cost, maintenance cost, and salvage value.

4. Selection Criteria

The acceptance criteria when the present worth value or PV ≥ 0 or the large value.

5. Analysis & Comparison of Alternative

Regarding to company data, that we summarize on Tabel-1 which represent for using LNG ISOtank ASME U stamp.

Tabel-1 LNG ISOtank ASME U stamp Cash Flow (in IDR)

Refer to Bank Indonesia Rate at 2017 is 7%, now we can drag Future Value/FV from 10th year to zero (initial) to calculate Present Value/PV, a summarize calculation represent on Tabel-2.

Table-2 PV for all cash flow LNG ISOtank ASME U stamp (in IDR)

Regarding to Tabel-1 and Tabel-2 now we can total Cash Flow in and out IDR. 1,425,478,520 + IDR. (1,080,000,000) = IDR. 345,478,520 (LNG ISOtank ASME U stamp is economically justified because PW ≥ 0 ) but we not finished yet, we have to calculate for LNG ISOtank that follow ASME U stamp.

next step we calculate for LNG ISOtank that follow ASME U stamp, the cash flow show on tabel-3.

Tabel-3 LNG ISOtank that follow ASME U stamp Cash Flow (in IDR)

Regarding to tabel-3, now we can calculate PV for all year cash flow, it shown on tabel-4

Tabel-4. PV for all cash flow LNG ISOtank that follow ASME U stamp (in IDR)

Regarding to Tabel-3 and Tabel-4 now we can total Cash Flow in and out IDR. 1,420,395,027 + IDR. (918,000,000) = IDR. 502,395,027 (LNG ISOtank that follow ASME U stamp is economically justified because PW ≥ 0 )

Now we can compare each PW from LNG ISOtank ASME U stamp and follow ASME U stamp, it shown on tabel-5

Tabel-5 Comparison LNG ISOtank ASME U stamp and follow ASME U stamp Present Worth Value (in IDR)

6. Selection of the Preferred Alternative

Regarding to tabel-5 it shown that follow ASME U stamp has PW Value higher than ASME U stamp, gap value between them is IDR. 156,916,507.08, it can conclude that follow ASME U stamp in economically point of view can justified to select.

7. Performance Monitoring and the Post Evaluation of Result

Management must monitor maintenance cost, because maintenance cost has given effect to select better decision.

References:

  1. Sullivan, G. W. (2014). Engineering Economy 16th Chapter 5 – Evaluating a Single Project, pp. 213-215. Pearson. Sixteenth Edition.
  2. BI Rate and Primary Reserve Requirement Lowered Again (February, 2016). Retrieved from http://www.bi.go.id/en/ruang-media/siaran-pers/Pages/sp_181416.aspx
  3. Ardiansyah. (2017). W4_A_Selecting Oil Pump|Emerald AACE 2018. Retrieved from https://emeraldaace2017.com/2017/08/22/w4_a_selection-oil-pump-using-present-worth-method/
  4. Pengiriman LNG Pertama dengan Media (December, 2013). Retrieved from http://www.candraawiguna.id/2013/12/pengiriman-lng-pertama-dengan-media.html

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/