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/
 

1 thought on “W10_TH_ Price Forecasts for Electric Motor CNG Compressor at Gas Station Project”

  1. Perfect, Pak Tommy!!! It doesn’t get any better than this….

    You clearly have mastered this tool…..

    Keep up the good work and hope that you can get your rebaselining done so I can report to your sponsors and champions on your progress soon?

    BR,
    Dr. PDG, Jakarta

     

Leave a Reply

Your email address will not be published. Required fields are marked *