W9_UDS_ Evaluation in Choosing Best Supply Pattern Part 2

  1. Problem Evaluation

Kediri is a city in east java; My Company has a Fuel Terminal in this city but was closed on 2009. Kediri consumes fuel almost 4% around east java region. And from the fuel consume forecast it will be growing up 3% each year.

Phenomena appear in Indonesia and Kediri also, which non subsidiary fuel consumption rise significantly and lead in the market. It is very different condition than few years ago. In this unpredictable situation, we need to prepare the facility of non-subsidiary fuel to catch the opportunity.

  1. Development of feasible alternatives

There are three alternative Fuel Terminals as supply point to supply Kediri area to catch the opportunity:

  • Existing pattern; Surabaya, Malang, and Madiun Fuel Terminal as supply point to supply Kediri area
  • Shortcut pattern; Tuban Fuel Terminal as supply point to supply Kediri area. Tuban regularly also supply to Fuel Terminal in Surabaya, Malang, and Madiun
  • New pattern; Kediri Fuel Terminal as supply point to supply Kediri area. In this alternative we will reopen the Kediri Fuel Terminal.

Multi Attribute Decision Making Method will use in choosing the best alternative pattern to supply Kediri area not only at economic aspect but also other aspect that influence customer satisfaction such as delivery time, transport loss,  operational flexibility, and etc.

  1. Development the outcome for each alternative

In this part (2st part) I will use method that tension not only on economic criteria but also other criteria that is AHP Method.

AHP is multi-objective decision analysis tool first proposes by Saaty. It is designed when either subjective or objective measures are being evaluated in terms of a set of alternatives based upon multiple criteria, organized in hierarchical structure. At the top level, the criteria are evaluated or weighted, and at the bottom level the alternatives are measured against each criterion. The decision maker assesses their evaluation by making pairwise comparisons in which every pair is subjectively or objectively compared. The subjective method involves a 9 point scale that we present later.

The AHP converts these evaluations to numerical values that can be processed and compared over the entire range of the problem. A numerical weight or priority is derived for each element of the hierarchy, allowing diverse and often incommensurable elements to be compared to one another in a rational and consistent way. This capability distinguishes the AHP from other decision making techniques.

  1. Selection of criteria

Decision rule of AHP method is grading the alternative based on AHP score. The higher score alternative is better alternative. So in this evaluation I will choose the alternative with highest score. Beside that In pairwise comparison step the most important is consistency ratio which should be 10% or less.

  1. Analysis and comparison of the alternative

First we must determine the criteria that can influence customer satisfaction and operational excellent. We determine that criteria using brainstorming technique among our expert to get better result. We got four criteria that are:

  • Delivery Time
  • Transport Loss
  • Operational Flexibility
  • Economical Factor

Economical factor contain two method B-C ratio and ERR. This is the advantage of AHP, we can compare not only on economic factor but also other factor that important to us or our customer.

Figure 1. AHP hierarchy in choosing best pattern in supply Kediri area

We also used brainstorming technique when make pairwise comparison for each criteria again alternative and between all criteria.

Table 1. Pairwise comparison for delivery time

Table 2. Pairwise comparison for transport loss

Table 3. Pairwise comparison for operational flexibility

Table 4. Pairwise comparison for economical factor

Table 5. Pairwise comparison for all criteria

Table 6. AHP result

All pairwise comparison in this model has consistency ratio no more than 10%, so all judgment is consistent and appropriate.

  1. Alternative selection

New Pattern alternative has biggest weighted score than the other so it preferred to be used.

  1. Performance monitoring & Post Evaluation Result

Different form the first part, the second part (AHP method) show new pattern alternative is preferred to be used. In this part we use not only economic but also operational and customer satisfaction, so it is more comprehensive and must be better advise to use.

References

  1. Planning Planet. (2017). Multi-Attribute Decision Making. Retrieved from http://www.planningplanet.com/guild/gpccar/managing-change-the-owners-perspective Figures 8-14
  2. Sullivan, G. W., Wicks, M. E., & Koelling, C. P.(2014). Engineering economy 16th Edition. Chapter 14 – Decision Making Considering Multiattributes., pp.559-608.
  3. Norris, G. A., & Marshall, H. E. (1995). Multiattribute decision analysis method for evaluating buildings and building systems. National Institute of Standards and Technology.
  4. Fox, P. William. (2016). Application and Modeling Using Multi-Attribute Decision Making to Rank Terrorist Threats. Journal of Socialomics. 5:2.
 

W7_MFO_ Price Forecasts using Best Fit Curves On Pipeline Project

  1. Problem Definition

In W6 blog posting comment, Dr Paul asked the author to take same case study for W7 blog posting using “Best Fit” curves to predict the 20 km pipeline project cost. So, the author want try to predict the cost of Polyethylene (PE) pipeline project in this W7 blog posting.

  1. Identify the Possible Alternative

Using last week indicative price, then the cost for PE Pipeline project, as follow:

Table 1. Indicative Price of PE Pipeline

From the table above, then to analyze price forecasts for 20 km pipeline will 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
  1. Development of The Outcome for Alternative

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

Figure 1. Input Data

Using these input data and MS Excel “Best Fit” Linear Regression Analysis Curve, then trendline and trending them out to 20 Km provide in figure 2 below. While the trendline use R2 = 0.9901.

Figure 2. Linear Trendline

Then still using data in table 1, now MS Excel “Best Fit” Polynomial Regression Analysis Curve with R2 = 0.9945 will be used in the second analysis. The result of the polynominal regression analysis can be seen in the figure 3 below.

Figure 3. Polynominal Trendline

The latest, on the third data input in table 1 analysis will use MS Excel “Best Fit” Logarithmic Regression Analysis Curve with R2 = 0.9063. The result of the logarithmic regression analysis can be seen in the figure 4 below.

Figure 4. 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 5. All Trendline (Linear, Polynominal, Logarithmic)

  1. Selection Criteria

Further, value of all treadline for PE Pipeline 20 km length, 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”.

  1. Analysis & Comparison of Alternative

The following is data to be used for PERT calculation

Table 2. Trendline Forecasts of PE Pipeline Project

From the table above, we can see

  1. Best case (optimistic) = $ 1,029.13
  2. Most Likely case = $ 1,417.30
  3. Worst case (pessimistic) = $ 2,069.59

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

Step 1 – PERT weighted Mean

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

= $ ((1,029.13) + (4 x 1,417.30) + (2,069.59))/6

= $ 1,461.32

Step 2 – Standard Deviation

= (Largest Value – Smallest Value)/6

= $ (2,069.59 – 1,029.13)/6

= $ 173.41

Step 3 – Variance

= Sigma/Standard Deviation^2

= $ 173.41^2

= $ 30,070.65

The following figure 6 below shows normal distribution curve:

Figure 6. Normal Distribution Curve

The result from the step 3 reveals that the very large variance means that the number is risky, so a higher P number needs to be considered when selecting one, hence for this blog, author use P90 refer to figure 7.

Figure 7. P(90) Distribution Curve

The following above is P(90) cost estimate 20 Km PE Pipeline project with value $ 1,683.29

  1. Selection of the Preferred Alternative

This blog displays one of method in determining price forecast, on next 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 pipeline project.

  1. 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.

Reference:

  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 Techniques, pp. 113-121.
  3. (2017). W11.1_SJP_Forecasts Part 3. Retrieved from https://js-pag-cert-2017.com/w11-1_sjp_forecasts-part-3/
  4. (2009). Excel Dynamic Chart #11: Dynamic Area Chart with IF Functioin – Normal Distribution Chart Statistics. Retrieved from https://www.youtube.com/watch?v=Fp1JV-ZVDZw
  5. (2017). W6_AI_Price Forecact for Offshore Regasification Facility Project. Retrieved from http://emeraldaace2017.com/2017/09/10/w6_ai_price-forecasts-for-offshore-regasification-facility-project/
  6. (2013). Normal curve using excel 2010. Retrieved from https://www.youtube.com/watch?v=hQHiG_cQiUE
 

W8_UDS_ Evaluation in Choosing Best Supply Pattern Part 1

  1. Problem Evaluation

Kediri is a city in east java; My Company has a Fuel Terminal in this city but was closed on 2009. Kediri consumes fuel almost 4% around east java region. And from the fuel consume forecast it will be growing up 3% each year.

Phenomena appear in Indonesia and Kediri also, which non subsidiary fuel consumption rise significantly and lead in the market. It is very different condition than few years ago. In this unpredictable situation, we need to prepare the facility of non-subsidiary fuel to catch the opportunity.

  1. Development of feasible alternatives

There are three alternative Fuel Terminals as supply point to supply Kediri area to catch the opportunity:

  • Existing pattern; Surabaya, Malang, and Madiun Fuel Terminal as supply point to supply Kediri area
  • Shortcut pattern; Tuban Fuel Terminal as supply point to supply Kediri area. Tuban regularly also supply to Fuel Terminal in Surabaya, Malang, and Madiun
  • New pattern; Kediri Fuel Terminal as supply point to supply Kediri area. In this alternative we will reopen the Kediri Fuel Terminal.

Multi Attribute Decision Making Method will use in choosing the best alternative pattern to supply Kediri area not only at economic aspect but also other aspect that influence customer satisfaction such as delivery time, transport loss,  operational flexibility, and etc.

  1. Development the outcome for each alternative

In this part (1st part) I will use method that different from my Company usually used (NPV, IRR, Payback Period and PI) but tension only on economic criteria that are B-C Ratio and ERR.

B-C Ratio This method is very useful to select alternative in economical approach with a simple way, because it compare positive (cash in) and negative (cash out) cash flow of each alternatives. This is the process of quantifying cost and benefit of project over a period time.

External Rate of Return (ERR) is also known as the “Modified Internal Rate of Return”. This method measured not only depends on the cash flow from an investment and also on any assumptions about reinvestment rate.

  1. Selection of criteria

The Rule of thumb in Benefit – Cost ratio method is Alternative will be feasible if B-C ratio greater than one. So in this evaluation we will eliminate alternative with B-C Ratio less than one, because it not economically feasible (their cash out higher than their cash in).

ERR decision rule: If ERR ≥ MARR, the project is economically justified. So in this evaluation I will eliminate alternative with ERR value less than 10.5% (My Company Hurdle Rate), because it not economically feasible.

  1. Analysis and comparison of the alternative

Data of three alternatives that use in B-C ratio calculation are show below:

Table 1. Alternatives data

Figure 1. Profit per year shortcut pattern and new pattern alternatives

Shortcut Pattern and New Pattern have incremental benefit each year because in this alternatives have investment to improve facility to catch increasing demand opportunity. Present Worth of this profit PW (B) is  1,347,925,669,473 IDR.

Calculation result of Conventional and Modified B-C Ratio are in table below:

Table 2. B-C ratio calculation result

There is no B-C Ratio of each alternative that less than 1, so no one will be eliminated. Existing Pattern doesn’t have Modified B-C Ratio (N/A) because this alternative doesn’t have investment cost.

This three alternatives data such as interest, period, investment, benefit and cost to calculate ERR:

Table 3. Alternatives data

Using data in table 3, we get the result of ERR calculation of each alternative:

Table 4. ERR formula and calculation result

Based on ERR calculation table above there is no value less than MARR 10.5% so all alternative economically justified. In existing pattern there is no ERR value because it does not have investment cost so we cannot calculate it.

  1. Alternative selection

All method show shortcut pattern alternative dominating over other alternative in both B-C Ratio and ERR Method. This alternative is recommended to be used.

  1. Performance monitoring & Post Evaluation Result

Even I already have the chosen alternative base on economic aspect, I will evaluate this project base on other aspect such as operational and customer satisfaction. In next evaluation (2nd part) I will use AHP method to capture more aspect not only economic.

References

  1. Sullivan, G. W., Wicks, M. E., & Koelling, C. P.(2014). Engineering economy 16th Edition. Chapter 10 – Evaluating Project with the Benefit – Cost Ratio Method., pp.467-491. Prentice Hall.
  1. Sullivan, G. W., Wicks, M. E., & Koelling, C. P. (2014). Engineering economy 16th Edition. Chapter 5 – Evaluating a Single Project., pp.210-263.
  2. Planning Planet. (2017). Benefit Cost Analysis. Retrieved from http://www.planningplanet.com/guild/gpccar/managing-change-the-owners-perspective
  3. Mind Tools. (2017). Cost-Benefit Analysis. Retrieved from https://www.mindtools.com/pages/article/newTED_08.htm