Saturday, March 17, 2012
Thursday, March 1, 2012
Tutorial: A practice of Monte Carlo Simulation Model
I would like to teach the Monte Carlo Simulation Model as the risk
management analysis tool in the projects. The practice will be done on a real
project.
The
example of the case study is: “A Financial Analysis on Nord Stream Gas Pipeline
project”
All
data have been collected from below references:
-The European Union of the Natural Gas Industry
(THE EUROGAS ECONOMIC STUDY TASK FORCE)
-NATURAL GAS PRICING AND ITS FUTURE EUROPE
AS THE BATTLEGROUND (2010 Carnegie
Endowment for International Peace)
-European Environment Agency (EN31
Energy prices)
- British Petroleum (BP-AMOCO)
- International Energy Agency (IEA)
- Eurostat
-International Gas Union
- Cedigaz
- Energy Information Administration,
Official Energy Statistics from the U.S. Government
- World Energy Council
- European
Gas Advocacy Forum (The Future Role of Natural Gas)
- EUROPE’S ENERGY
PORTAL
-MIT CEEPR (MIT Centre for Energy and Environmental Policy
Research)
- The Oil Drum: The European Gas Market
-Nord
Stream (The Project & the Environment – The Natural Gas Pipeline through
the Baltic Sea)
- Europe and
natural gas - Are tough choices ahead? By Rune Likvern
-Wikipedia
In this package, I will tell you:
-How we can analyze the initial investment of
the project in the different states of economic (different outcomes) to obtain NPV>
0 and IRR > WACC?
-What could the strategies be behind
a very large profit margin in NG supply in Europe?
-If high profit margin move toward
low profit margin, how will it be affecting on financial costs?
The professional individuals, who
are interested in learning this simulation model, don’t hesitate to send their request
by email to me for further information.
My email is:
soleimani_gh@hotmail.com
Monday, February 20, 2012
The Combination of MS-Project and Excel to analyze Risk Management during the Period of the Project Lifecycle
I think this article will be useful for all project managers and project controllers.
In this article, I am willing to tell you that we can apply the combination of MS- Project and Excel instead of Primavera for small and medium size projects because the price of Primavera software is very expensive. Let me bring you an example as follows:
In this article, I am willing to tell you that we can apply the combination of MS- Project and Excel instead of Primavera for small and medium size projects because the price of Primavera software is very expensive. Let me bring you an example as follows:
We can track and monitor the cost and time of the projects by using of
MS –Project where this software is able to calculate so many factors related to
the cost and the time such as:
BCWS: The budgeted cost of
work scheduled
ACWP: The actual cost of work performed
BCWP: The budgeted
cost of work performed
SV: The schedule
variance
CV: Cost variance
TCPI: The complete performance
index
But we cannot forecast
any risk in related to the time and the cost which are caused the failure of
the project, by using of MS – Project . There are so many tricks in excel to
predict and analyze the risk of the cost and the time where we firstly obtain
the data by MS-Project (such as above parameters), then we will be able to
manage the risk of the projects by using of these tricks. Of course, at the
first, we should be as well as familiar with some concepts and methodologies
such as Monte Carlo Simulation Model or Fuzzy Logic.
To be continued..........
Monday, February 6, 2012
Fuzzy Delphi Method to Design a Strategic Plan (CON). Is This a New Inequality Theorem in Fuzzy Set Theory?
As I mentioned
in my previous article of “Fuzzy Delphi Method to Design a Strategic Plan”,
I would like to continue the debate on Distance Method.
But before going to the distance method, let me explain my story as
follows:
While I was working on discrepancy between basic and distance method on
driving forces, I encountered to a phenomena. Now, let me depict this
phenomenon in the framework of a theorem below cited:
Inequality Theorem in Fuzzy Logic
Assume,
there is the fuzzy subset A of X where X is a universal set. Then, we define
the fuzzy set of A by its membership function (MF=Membership Function) as
follows:
It
means that a real number MFA (x) in the interval [0, 1] is assigned to each
element x where x is a member of X and also the value of MFA (x) at x presents
the grade of membership of x in A.
We
consider below conditions for the fuzzy set A:
-Fuzzy
set A is a convex and normalized fuzzy set in which we can say
the fuzzy set A is a fuzzy number.
- Fuzzy
set A is a central triangular fuzzy number where we have:
For central triangular fuzzy number A= (a, b,
c): MFA
(x) = 2(x-a)/c-a If a <
x < b
MFA
(x) = 2(x-c)/a-c If b
< x < c
b = (a + c)/2
Now, we assume the set
of S is included all central triangular fuzzy numbers as follows:
S = [Ai], i = 1, 2, 3,…….n
In
fact, we have:
S =
[A1, A2, A3,…..An]
Or
S =
[(a1, b1, c1), (a2, b2, c2),
(a3, b3, c3),……(an, bn, cn)]
We
define the distance (di) between x1
and x2 into each central
triangular fuzzy number A
assigned to each alpha – cut level as follows:
di =
delta (x) If ai
< x < bi
bi
< x < ci, 0
< alpha-cut < 1 , i = 1,2,3,……
Theorem: If there is below inequality:
d1
< d2 < d3
< d4 …….< dn
Above inequality will be always the constant for
all alpha – cuts in the interval [0, 1].
I have two questions:
-Is this a new inequality
theorem in Fuzzy Numbers?
- If the answer is negative, could
you please introduce me the references?
To be continued……
Friday, February 3, 2012
Fuzzy Delphi Method to Design a Strategic Plan
Nowadays, Fuzzy Delphi Method (FDM) is broadly used by researchers in
the various fields of Science, Technology and Management. This method was
stated by Ishikawa et al. (1993) in which it is the integration between the traditional Delphi techniques and fuzzy set
theory.
In this article, I am
willing to employ FDM to design a strategic plan. At the first, I will explain
a brief methodology of Fuzzy Delphi Method then I will depict step by step to make
a strategic plan and I will highlight where we need to utilize FDM. Finally, I
will bring an example for better perception of FDM.
Methodology
Traditional Delphi method is an approach to gathering information from
high qualified experts to develop the predictions about future events. A panel
of experts is chosen. Then, they release their opinions for each feature where
the responses of experts are collected and analyzed statistically. The
processed data will communicate with the experts again to write another response.
This procedure will be repeated rounds of questioning and written responses in
which the outcome will cover the reasonable data to solve a problem or to
forecast an event in the future. This method was developed by the Rand Corporation
at Santa Monica, California in the late 1960s. One of the most important
problems is to solve the fuzziness of the expert consensus within the group
decision making. Murray et al. (1985) first proposed the application of fuzzy
theory to the Delphi method. Then Ishikawa et al. (1993) utilized the
maximum-minimum method together with cumulative frequency distribution and
fuzzy scoring to compile the expert opinions into fuzzy numbers. We can use triangular
fuzzy number, trapezoidal fuzzy number and Gaussian fuzzy number as the
selection of fuzzy membership functions. There are many Fuzzy Delphi methods
such as basic FDM, Fuzzy Analytic Hierarchy Process (FAHP), and the concept of
distance (dij) between two triangular numbers refer to Kaufmann and Gupta
(1988). In this article, my example is applied the triangular membership
functions referred to basic FDM accompanied by the type of alpha –cut method
(Ranking) as threshold. But in the next article, I will
bring an example of Distance method and I will compare these two methods.
(Why?) I will tell you the reason behind of this comparison in the next
article.
Now, let us see a
literature review of basic FDM as follows:
Yu-Lung Hsu et al. (2010)
stated the steps of basic FDM: [1]
“1. Collect opinions of decision group: Find the evaluation
score of each alternate factor’s significance given by each expert by using
linguistic variables in questionnaires.
2. Set up
triangular fuzzy numbers: Calculate the evaluation value of triangular fuzzy
number of each alternate factor given by experts, find out the significance
triangular fuzzy number of the alternate factor.
3.
Defuzzification: Use simple centre of gravity method to defuzzify the fuzzy
weight.
4. Screen evaluation indexes: Finally proper
factors can be screened out from numerous factors by setting the threshold.”
I also used from above
steps for my example in this article.
Designing
Strategic Plan
Here, I follow the steps
which should be taken to design a strategic plan simultaneously I highlight the
items which need to utilize FDM.
In fact, by designing a
strategic plan, we look at the image of the company or industry including
current vision and mission then we will make new vision and vision as the
outlooks of the company or industry. Let’s go the steps:
1) Overview of the
company
2) History of the company
3) Current vision &
mission
4) Strategic goals
5) Current strategies
6) External or Internal consideration
(Is the company Industry base or Resource base?)
7) Financial performance
8) Financial and Strategic
Objectives
4) Strategy - Making Hierarchy
including corporate strategy, business strategy, functional area strategies
within each business, operating strategies within each business.
5) PEST Analysis (The
components of a company’s Microenvironment) including:
5-1) The impact factors of
political issues
5-2) Dominant Economic Factors
5 -3) Dominant Socio – Cultural Factors
5-3) Dominant Technology Factors
We need to
use from FDM to rank and to find priorities.
6) Porter’s
forces
According to Porter’s forces, we have below forces:
-Power of Suppliers
-Power of Buyers
-Threat of New Entrants
-Threat of Substitutions
-Rivalry
To determine the ranking and priorities
for each force, we need to use from FDM.
7) Strategic Map
Application
By using of Strategic Group Maps, we will be able to assess the market
positions of key competitors. We should identify the competitive
characteristics and choose pairs of these differentiating characteristics then
we should plot the firms on a two – variable map (pair characteristics).
Therefore, we need to utilize FDM to rank a pair characteristic for all
firms.
7) Driving Forces
We will use FDM to rank the
most important driving forces that can affect an industry.
8) Industry structure
9) Key Success Factors (KSF)
These Key Success Factors can be referred to Driving forces. Which are
the drivers of change unique?
10) The External Factor Evaluation (EFE) Matrix and the Competitive
Profile Matrix (CPM)
By using of Driving Forces and KSF, we should find the most important
Opportunities and Threats which are affecting on industry and company. Then we
should rank all weights and ratings for each Opportunities and Threats.
Therefore, we need to approach FDM.
11) Traditional Porter’s Value Chain
12) Appraising the Resources and Capabilities
What are the Resources and Capabilities important to industry? What are
the rank of them for the industry and the company?
We can use FDM to rank the Resources and Capabilities for the industry
and the company.
13) To derive all key Strengths and key Weaknesses of the company
referred to Appraising the Resources and Capabilities and Value Chain. For
instance, we can use the value chain in cost analysis in which we should
analyze three sections of Sequence of Analysis, Value Chain, and cost drivers.
14) Assessment of internal factors for strategic advantage
15) The Internal Factor Evaluation (IFE) Matrix
By using of above items (11, 12, 13, 14), we are expected to find the
most important Strengths and weaknesses which are affecting on the company.
Then we should rank all weights and ratings for each Strengths and weaknesses.
Therefore, we need to approach
FDM.
16) Impact Analysis
This is a matrix where Strengths and weaknesses are on column and the
drivers of change (Driving Forces) are on row. We should give the score numbers
between minus (x) to plus (x) for each member of this matrix.
As you can see, to solve fuzziness among this matrix, we need to use
FDM.
17) Competitor Analysis
This matrix is another type of Impact Analysis in which the company and
major competitors are on column of matrix and the drivers of change are on row.
We can also utilize FDM to solve fuzziness among this matrix.
17) SWOT Matrix
18) The Space Matrix
In this matrix, we have two internal dimensions (financial position, and
competitive position) and two external dimensions (stability position, and
industry position) on vertical and horizontal axis of a four – quadrant
framework. We should give the score
numbers between minus (x,y) to plus (x,y) for each dimension.
The best method to prevent the fuzziness is to use FDM.
19) The Boston Consulting Group (BCG) Matrix
20) The Internal – External (IE) matrix
When we need to use FDM for EFE and IFE, it means that we can apply FDM
for this matrix.
21) The Grand Strategy Matrix
22) Ansoff Matrix
23) GE / Mc
Kinsey Matrix
We should find
the external factors which are affecting on industry or market attractiveness
and so we should extract the internal factors which are affecting on
competitive strength of a strategic business unit (Business unit strength).
Then, we should give the weight and rating to each factors in which we will
have a rank for market attractiveness and a rank for Business unit strength. On
column of Mc Kinsey matrix, we have market attractiveness and on row, we have
Business unit strength. Finally, we can evaluate the business unit in
linguistic values of low, medium, high.
In the
result, to prevent fuzziness, we can use FDM for this matrix.
24) Finally, we can find out the suggested vision and mission
by using of above framework.
Example of Fuzzy Delphi Method
As you can see, the driving forces are the most
important factors to design a strategic plan. Therefore, I have chosen the
driving forces as my example of FDM depicted as follows:
According to book of
“Crafting and Executing strategy: The Quest for Competitive Advantage: Concepts and Cases by Thompson, Peteraf, Gamble, Strickland, the most common driving forces have been
introduced below cited:
The Most Common Driving Forces
1. Changes
in the long-term industry growth rate.
2. Increasing globalization.
3. Emerging new
Internet capabilities
4. Changes in who
buys the product and how they use it
5. Production
innovation
6. Technological
change and manufacturing process innovation
7. Marketing innovation.
8. Entry or exit of major firms
9. Diffusion of technical know – how across more companies and more
countries
10. Changes in cost & efficiency
11. Growing buyer preferences for differentiated products instead of
standardized commodity product (or for a more standardized product instead of
strongly differentiated products)
12. Reductions in uncertainty & business risks.
13.
Regulatory influences government policies changes.
14. Changing
societal concerns, attitudes, and life styles.
I write the codes for driving forces as follows:
Codes of Driving Forces
|
|||
1
|
DF1
|
||
2
|
DF2
|
||
3
|
DF3
|
||
4
|
DF4
|
||
5
|
DF5
|
||
6
|
DF6
|
||
7
|
DF7
|
||
8
|
DF8
|
||
9
|
DF9
|
||
10
|
DF10
|
||
11
|
DF11
|
||
12
|
DF12
|
||
13
|
DF13
|
||
14
|
DF14
|
||
I assume that I have chosen 14 high qualified experts to conduct a
Delphi technique and also I have found the weights for each expert in
accordance with their work experiences, academic level and so on. (The weights
are not need for this article. I will reserve them for my next article)
Experts
|
Weights
|
E1
|
0.04
|
E2
|
0.08
|
E3
|
0.09
|
E4
|
0.1
|
E5
|
0.07
|
E6
|
0.05
|
E7
|
0.09
|
E8
|
0.07
|
E9
|
0.06
|
E10
|
0.06
|
E11
|
0.07
|
E12
|
0.09
|
E13
|
0.05
|
E14
|
0.08
|
The linguistic values have been purposed as follows:
Linguistic Values
Pessimistic = P
Most likely = M
Optimistic = O
I collected the significant opinions of the experts
for each driving force. Here, I have brought a sample for DF1. The others (thirteen samples) have been
included in my spreadsheet of excel file.
DF1
|
||||
Experts
|
Weight
|
P
|
M
|
O
|
2
|
6
|
8
|
||
E2
|
0.08
|
1
|
5
|
9
|
E3
|
0.09
|
3
|
7
|
10
|
E4
|
0.1
|
1
|
6
|
9
|
E5
|
0.07
|
2
|
7
|
9
|
E6
|
0.05
|
4
|
5
|
6
|
E7
|
0.09
|
3
|
6
|
9
|
E8
|
0.07
|
5
|
7
|
9
|
E9
|
0.06
|
1
|
6
|
10
|
E10
|
0.06
|
3
|
6
|
8
|
E11
|
0.07
|
2
|
5
|
9
|
E12
|
0.09
|
4
|
6
|
8
|
E13
|
0.05
|
1
|
4
|
7
|
E14
|
0.08
|
2
|
5
|
9
|
I calculated
the evaluation value of triangular fuzzy number of each driving force given by
the experts. In fact, we have three matrixes as follows:
W ij = (Pij, Mij, Oij)
Where:
i = 1, 2 ….14
j = 1, 2 ….14
No. j
driving force given by No. i expert of 14 experts and 14 driving forces (j)
Then the fuzzy weighting Wj
of No. j driving force is: Wj = (Pj, Mj, Oj) and j
= 1, 2 ….14 where:
Pj = Min {Pij}, Mj = Sum {Mij} /14, Oj = Max
{Oij}
The below table shows us the final results:
DF
|
P
|
M
|
O
|
|
DF1
|
1
|
5.79
|
10
|
|
DF2
|
2
|
6.36
|
10
|
|
DF3
|
1
|
6.36
|
10
|
|
DF4
|
1
|
6.43
|
10
|
|
DF5
|
1
|
6.14
|
10
|
|
DF6
|
1
|
6.29
|
10
|
|
DF7
|
2
|
6.14
|
10
|
|
DF8
|
1
|
6.29
|
10
|
|
DF9
|
1
|
6.07
|
10
|
|
DF10
|
1
|
6.29
|
10
|
|
DF11
|
2
|
6.86
|
10
|
|
DF12
|
1
|
6.36
|
10
|
|
DF13
|
2
|
6.05
|
9
|
|
DF14
|
2
|
6.43
|
10
|
The next step is
Defuzzification in which we should defuzzify the fuzzy weight of Wj.
The simple method is to
use below formula:
Sj =
(Pj + Mj+ Oj) / 3 j
= 1, 2 ….14
The results have
been included in below table:
DF
|
Sj
|
DF1
|
5.595238
|
DF2
|
6.119048
|
DF3
|
5.785714
|
DF4
|
5.809524
|
DF5
|
5.714286
|
DF6
|
5.761905
|
DF7
|
6.047619
|
DF8
|
5.761905
|
DF9
|
5.690476
|
DF10
|
5.761905
|
DF11
|
6.190476
|
DF12
|
5.785714
|
DF13
|
5.666667
|
DF14
|
6.142857
|
If Sj > alpha-cut, then No. j driving force
is the evaluation index.
If Sj < alpha-cut, then delete No. j driving
force.
In
this article, I consider alpha-cut = 6. Therefore, the appropriate driving
forces are:
DF2: Increasing globalization
DF7: Marketing innovation
DF11: Growing buyer preferences
for differentiated products instead of standardized commodity product (or for a
more standardized product instead of strongly differentiated products)
DF14: Changing societal concerns, attitudes, and
life styles
I have brought all
above steps of Fuzzy Delphi Method on spreadsheet of excel file.
Note:
“All spreadsheets and calculation notes are available. The people, who are
interested in having my spreadsheets of this method as a template for further
practice, do not hesitate to ask me by sending an email to: soleimani_gh@hotmail.com or call
me on my cellphone: +989109250225. Please be informed these spreadsheets are
not free of charge.”
I made all triangularFuzzy numbers by using of Monte Carlo technique because I had not any expert’s opinion.
This
template can be also utilized for designing in the fields of engineering such
as heat exchangers or Air –Pre Heater
(APH) stated on below links:
As you
know, there are so many business opportunities on the models of heat exchangers
or APH to save energy. Everybody can test her/his creativity and chance to find
out the new models.
Now, we
can design the strategic plan for many topics by using of above template. Here
are two examples of these topics:
-A
strategic plan on Electricity Industry in USA
-A
strategic Plan on Residential property industry in Middle East.
Book
1) Bojadziev,
George., & Bojadziev, Maria (2007). FUZZY LOGIC FOR BUSINESS, FINANCE,
AND MANAGEMENT (2nd ed.). London: World Scientific Publishing Co. Pte. Ltd.
2) Thompson, Arthur., Peteraf, Margaret., Gamble,
John., Strickland, A. J. (2011). Crafting and Executing strategy: The Quest for Competitive Advantage: Concepts and
Cases (18th ed.).
Churchville: Irwin/McGraw-Hill.
Papers
1) Hsu,Yu-Lung.,
Lee, Cheng-Haw., & Kreng, V.B. (2010). The application of Fuzzy Delphi
Method and Fuzzy AHP in lubricant regenerative technology selection. Expert
Systems with Applications, 37, 419–425.
2) Dutta, Palash., Boruah, Hrishikesh., & Ali,Tazid.
(2011). Fuzzy Arithmetic with and without using α-cut method: A Comparative
Study. International Journal of Latest Trends in Computing 2 (1).
3) Glumac,
Brano., Han, Qi., Smeets, Jos., & Schaefer, Wim. (2011). Brownfield
redevelopment features: applying Fuzzy Delphi. Journal of European Real
Estate
Research, 4(2), 145-159.
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