Case Analysis of GAINESBORO MACHINE TOOLS CORPORATION: The Dividend Policy

This case is about the impact of an environmental factor (External issue) on dividend policy of the firm (Internal issue). The environmental disaster was Hurricane Katrina which was caused the huge destruction across the south-eastern United States. Because of the storm, the stock market notably fell down. Since it is possible that the price of the shares once more increase even more than before in the near future, Ashley Swenson, chief financial officer (CFO) of Gainesboro Machine Tools Corporation has the dilemma to buy back stock or to spend the money as dividend the shareholders. In fact, the question is: How can she forecast the fortune of the stock market? In the other word, what are the driving forces (as the external factors) which are affecting on internal factors such as dividend policy? Definitely, the best way is to use from Fuzzy Delphi Method (FDM). To perceive FDM, please review my article of “Fuzzy Delphi Method to Design a Strategic Plan” on link” http://emfps.blogspot.com/2012/02/fuzzy-delphi-method-to-design-strategic.html”.

At the first, she can design a strategic plan including current and future BCG matrix. I think that one of the best reference books which has established a logical relationship between BCG matrix and Dividend policy is: “Corporate Financial Strategy” by Ruth Bender and Keith Ward (Elsevier Butterworth-Heinemann)”. I would like to refer you page 34 (please review STEADY STATE), page 59 (Balancing business and financial risk), page 75 (Figure 4.14), page 226 (Dividends and buybacks) on 3^rd edition (2009). Where is the location of the firm in BCG matrix? Stars (Growth), Question marks (Launch), Cash cows (Maturity) or Dogs (Decline).

Accordingto this reference book, we have below conditions for each area of BCG matrix:

Stars (Growth)                                                              Question marks (Launch)

Business risk high                                                                 Business risk very high 

Financial risk low                                                                Financial risk very low

Funding equity                                                                     Funding equity

Nominal dividend payout ratio                                   Nil dividend payout ratio

Cash cows (Maturity)                                                       Dogs (Decline)       

 Business risk medium                                                         Business risk low

Financial risk medium                                                        Financial risk high

Funding debt                                                                      Funding debt

High dividend payout ratio                                             Total dividend payout ratio  

Let me specify the situation of this company on BCG matrix by using of its market share in industry and industry revenue growth rate as follows:

-Referring to Exhibit 6, Gainesboro’s market cap is $ 504,000,000 in 2005 in which we can calculate its share market approximately 1.43% (Please see my spreadsheet).

-Referring to Exhibit 2, the economic indicators show us the high growth rate of macroeconomic environment in USA from 2001 to 2004 while the projected data present us a steady and slow economic growth rate. On the other hand, if we see Exhibit 7, we will find that the expected growth rate of sales (next 3-5 years) for high dividend payout companies is going down whereas the zero-payout companies will have the high growth rate of sales.

We can observe this fact on consolidated Income Statement of Gainesboro (Exhibit 1) where the negative growth rate from 2002 to 2004 accompanied by dividend payout has pushed the current situation of this company on Quadrant IV of BCG matrix which is named Dogs. It means that the current strategies of Gainesboro could be Retrenchment, Divestiture, and Liquidation.

Referring to the case, if Gainesboro diversifies its business units and products such as the Artificial Workforce products, the expected growth rate of sales will go up 15% annually.

In this case, the new situation of company will be on Quadrant I of the BCG matrix (Question Marks) where the dividend policy of this company should be Zero – dividend payout.

Here, I would like to bring you so many logical reasons which approve the Zero – dividend payout as the best option for dividend policy of Gainesboro as follows:

1) Gainesboro has Negative Net Cash Flow. I calculated them in accordance with Exhibit 2 (please see my spreadsheet) below cited:

-Net Cash Flow in 2004 = -78376 (dollars in thousands)

-Net Cash Flow in 2005 (projected) = -36438 (dollars in thousands)

If you see Figure (4.10) on above reference book, you will find that the best dividend policy for Gainesboro is nil dividend payout ratios.

2) Please compare Exhibit 1 with Exhibit 5 just like below table:

Year                                Net income ($000)            Ave. Stock Price     

 2002                                    -$61,322                          $26.45

2003                                      $12,993                          $61.33 

2004                                     -$140,784                        $29.15

2005 (Projected)                   $18,018                              ?

 What can you consider instead of question mark? Definitely Gainesboro’s stock price will significantly increase if the management prediction about the revenue growth rate is true. Therefore, the best strategy is to repurchase Gainesboro’s shares.

3) Firstly, let me have an overview on all theories of dividend policy as follows:

-Dividend Relevance Theories

-Dividend Irrelevance Theories

Dividend Relevance Theory

  A) Traditional Model

B) Walter’s Model

C) Gordon’s Dividend Capitalization Model

D) Bird-in-hand Theory

E) Dividend Signaling Theory

F) Agency Cost Theory

 Dividend Irrelevance Theories

G) Residual Theory

H) Modigliani and Miller (M&M) Model

 I) Dividend Clientele Effect

 J) Rational Expectations Model

In the next article, I will examine each one of above models to find out the best dividend policy for Gainesboro. 


 

Note:  “All spreadsheets and calculation notes are available. The people, who are interested in having my spreadsheets of this case analysis 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.”

 

To be continued …….

                                                                                              

                    

Monte Carlo Simulation Model to Design Heat Exchangers

For further practice of Monte Carlo Simulation Model, you can review below papers which are applying the Monte Carlo Simulation Model to design Heat Exchangers:

-Abdelaziz, O., & Radermac, R. (2010). Modelling heat exchangers under consideration of manufacturing tolerances and uncertain flow distribution. International Journal of Refrigeration, 33 (2010), 815 – 828.

-Li, Min., & Lai, Alvin. C.K. (2012). Parameter Estimation of In-situ Thermal Response Tests for Borehole Ground Heat Exchangers. International Journal of Heat and Mass Transfer, 55 (2012), 2615 – 2624.

-Zhihua, Chen., & Dechao, Chen. (2011). Research on Stochastic Characteristic of Ground Heat Exchanger of Ground Source Heat Pumps with Monte-Carlo Method. IEEE,

978-1-4577-0290-7/11.

-Gupta, Ashutosh., & Mukherjee, Bhaswati., & Upadhyay, S. K. (2008). Weibull Extension Model: A Bayes Study Using Markov Chain Monte Carlo Simulation. Reliability Engineering and System Safety, 93 (2008), 1434–1443.

-Liu, Hongwei., et al. (2007). Monte Carlo Simulations of Gas Flow and Heat Transfer in Vacuum Packaged MEMS Devices. Applied Thermal Engineering, 27 (2007), 323–329

-Kovtanyuk, Andrey. E., & Botkin, Nikolai. D., & Hoffmann, Karl-Heinz. (2012). Numerical Simulations of a Coupled Radiative–Conductive Heat Transfer Model Using a Modified Monte Carlo Method. International Journal of Heat and Mass Transfer, 55 (2012), 649 – 654.

- BADAR, MA., & ZUBAIR, SM., & SHEIKH, AK. (2003). UNCERTAINTY ANALYSIS OF HEAT-EXCHANGER THERMAL DESIGNS USING THE MONTE-CARLO SIMULATION TECHNIQUE. PERGAMON-ELSEVIER SCIENCE LTD, ENERGY, 18, 859-866.

-Ayunov, D. E., & Duchkov, A. D. (2008).  Monte-Carlo Simulation for Estimating Topographic Disturbance to Heat Flow Data. Russian Geology and Geophysics,  49 (2008),  291–296

 

Taylor & Francis Online :: Uncertainty Analysis of Heat Exchanger Thermal-Hydraulic Designs - Heat Transfer Engineering - Volume 8, Issue 2

Taylor & Francis Online :: Uncertainty Analysis of Heat Exchanger Thermal-Hydraulic Designs - Heat Transfer Engineering - Volume 8, Issue 2

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 

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:

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..........

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:

MFA:  X         [0, 1]     

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……

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
E1	0.04	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

 Finally, we should define a threshold as alpha-cut to obtain proper driving forces which can be screened out from numerous driving forces by setting this threshold. The principle of screening is as follows:

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:

http://emfps.blogspot.com/2010/10/blog-post.html

http://emfps.blogspot.com/2010/10/energy-saving-through-efficient.html

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. 

 References

Book

1) Bojadziev, George., & Bojadziev, Maria (2007). FUZZY LOGIC FOR BUSINESS, FINANCE, AND MANAGEMENT (2^nd 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 (18^th 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.