Application of Fuzzy AHP and TOPSIS methods for a Foreign Direct Investment in the aluminium industry sector

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Master’s Degree

in Economics and Finance

Second Cycle (D.M. 270/2004)

Final Thesis

Application of Fuzzy AHP and TOPSIS

methods for a Foreign Direct Investment

in the aluminium industry sector

Supervisor

Ch. Prof. Marco Corazza

Graduand Elena Dante 853371

Academic Year 2018 / 2019

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Acknowledgement

Vorrei ringraziare inizialmente il professore Marco Corazza per avermi accompagnato nello svolgimento della tesi di laurea. Le sue conoscenze e la disponibilità dimostrata sin da subito sono state preziose durante tutto questo percorso.

Ringrazio anche l’ingegnere Vincenzo Pandolfo, il dottore Giuseppe Gasparini e l’azienda Pandolfo Alluminio S.p.a. tutta per avermi dato la possibilità di lavorare al loro fianco. I mesi trascorsi insieme sono risultati fondamentali per terminare il progetto.

Un ringraziamento va ai miei genitori per essere stati al mio fianco in questi anni. La loro comprensione e il loro supporto sono stati essenziali in momenti di difficoltà.

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Introduction

Multi-Criteria Decision Making (MCDM) is one of the most well-known branch of decision making, representing a starting point for solving important practical issues. In the present thesis, two methods are applied: Fuzzy AHP and TOPSIS.

In order to deal with this problem, the objective of the thesis is to validate or not a future possible investment proposed by a Paduan (IT) aluminium enterprise: Pandolfo Alluminio S.pa. It would like to implement a foreign direct investment (FDI) in a Moroccan firm of the same industry sector. The goal of the thesis is the identification of the best country (Morocco or another one) in the Mediterranean area where apply the FDI for a medium-large aluminium enterprise of Italian North-East, such as Pandolfo Alluminio S.p.a.

The final result will not validate the investment proposed by the Paduan firm and reward Morocco as not the best area for this type of investment, preferring different countries.

The thesis achieves the result through two steps. In the first step, Fuzzy AHP model identifies the most influential factors (cost, labour characteristics, political and economic factors…) for this type of investment. In the second step, using the result obtained from the previous model, TOPSIS method provides the final rank of countries – among those selected previously – from the best one where apply an FDI for an aluminium company of the Italian North-East to the worst one. The link between the methods is fundamental to achieve thesis goal. Fuzzy AHP result (factors ranking for location decision problem) turns out to be the input for the implementation of the TOPSIS method, which selects a suitable country where invest in.

After introducing decision process concept, the first chapter aims to explain in details research objective. It is focused on describing the context in which the thesis is developed and the central problem to solve.

The second chapter treats the basic key points useful for the entire thesis. Firstly, it describes the aluminium economy (market and trade relations between Mediterranean

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INTRODUCTION

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zone countries as regards this material), and then the attention is shifted to Pandolfo Alluminio S.p.a. (history of the firm and the production line). Special attention is given to its trade businesses with other countries to emphasize the relations with foreign markets. The final part of the chapter lists States around the Mediterranean Sea which are assumed to be the possible countries where apply the foreign direct investment described in the previous rows.

The models used to achieve the final goal are described in Chapter III. It starts with the explanation of multi-criteria decision making model useful to solve this type of problem, emphasizing the difference between multi-objective and multi-attribute decision making methods. The chapter continues with the choice of two models to apply in order to achieve thesis goal. Firstly, Fuzzy AHP method will be adopted to analyse which factors influence mostly an Italian North-East MLE (as Pandolfo Alluminio S.p.a.) if it decides to implement an FDI. After it, TOPSIS method will consider the relation between factors identified previously and countries selected in Chapter II.

Chapter IV starts with a description of basic concepts useful for the application of Fuzzy AHP method. Focusing on it, data required to rank factors (costs, labour characteristics, political and economic factors…) are collected through an original questionnaire filled out by managers of Italian North-East aluminium companies similar to Pandolfo Alluminio S.p.a. So, combining the Fuzzy AHP method with a list of location decision factors, it is possible to obtain a rank of chosen location factors based on the preferences of the interviewed sample. This first model ranks factors from the most influential for a decision maker to the least one when foreign direct investment is going to be accomplished.

To complete the chapter, a sensitivity analysis is run to understand possible scenarios at the moment when fuzzy triangle weights in the AHP method are changed.

Chapter V is based on TOPSIS method. Once ranked factors are available, it is possible to combine them with the selected countries in Chapter II to identify the most suitable country to achieve final goal: the implementation of foreign direct investment to an aluminium firm by a medium-large enterprise based on North-East of Italy, as Pandolfo Alluminio S.p.a.

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INTRODUCTION

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The last chapter aims to summarize thesis case study. In particular, the author tries to find some reasons why Morocco will not be the most suitable country where apply an FDI as Pandolfo Alluminio S.p.a. hopes. In addition, other particular scenarios are analysed, assuming to change some initial conditions.

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INTRODUCTION

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1 – THESIS INTRODUCTION

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Chapter I

Thesis Introduction

In economics, decision making problems play a fundamental role in so different fields, for instance, on society, government, stakeholders and customers. Due to its wide use, «decision analysis is recognized as a sound prescriptive theory»1.

In the late 19th century, economy theorists concentrated their attention to develop out political economy with an empirical approach more akin to the physical sciences (Clark, 1998). For this reason, as sustained by Johnson-Laird and Byrne in 1991, rationality becomes a central principle in decision making, considering rational agent as a subject who chooses the action, aiming at the maximizing his expected performance.

The main theoretical paradigm of rational choice theory turns out to be the comparison of costs and benefits for individuals’ choice during different courses of actions. If, on the one hand, many scientists declare that human being acts rationally, on the other hand, it is also true that human action involves both rational and non-rational elements. According to Zavadskas and Turskis (2011), personal emotions, feelings, moral codes, norms and instincts or culturally specific should require not to be included in a good rational choice. However, since it is evident that no human has ever satisfied this requirement, the analysis may be considered irrational. In particular, Weber (2011) describes four different types of actions that influence man’s life:

 affectual (actions which involve affect, feelings or emotions);  traditional actions (actions influenced by customs and habits);

 value-rational actions (actions taken because of intrinsic reasons, for example religious, ethical, aesthetic or other motive, regardless if he/she can achieve success with these choices);

 means-end rational actions (actions that are determined by expectations of the behaviour of objects in the environment and other human beings2).

1_{Zavadskas, E. K., Turkis, Z., (2001), Multiple criteria decision making (MCDM) methods in}

economics: an overview, Technological and Economic Development of Economy, Vol. 17 (2),

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Individuals in decision making is characterized by bounded rationality, due to limited information owned, cognitive limitations of their minds and the finite limit of time human being has to take decisions (Elster, 1983).

Two important assumptions in rational choice theory involve individuals’ preferences (Zavadskas and Turskis, 2011). The first one is completeness which means that all actions can be ranked based on agents’ preferences while the other assumption is transitivity (if a generic element a1 is preferred to another generic element a2 and the

generic element a2 is preferred to another generic element a3 then a1 is preferred to a3).

With these assumptions and a set of alternatives3 to choose from, individuals are able to rank them in terms of their preference.

1.1 Starting points

In a making-decisions context, decision maker (DM) tries to choose the optimal solution for his problem. However, due to the irrational part described above, DM is not able to find it, except when the criterion is only one. In order to solve this problem, it is possible to adopt a decision process which can be helpful for decision maker in case of conflicts or dissatisfactions.

As defined by Pavan and Todeschi (2009), the aim of the decision process should be two. Considering available data, decision process generates useful information for decision problem and makes the structure problem more understandable too.

Decision process could be described in three phases:

1. Problem identification and structuring: the goal turns out to be the identification of decision purpose, the recognition of the problem to solve, the diagnosis of cause-effect relationships and the judgment of criteria identification.

2 _{Your Article Library,}

http://www.yourarticlelibrary.com/sociology/types-of-social-action-according-to-max-weber/43755.

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2. Model development and use: it involves the development of a decision making formal model based on DM’s preferences, the identification of data values and trade-offs, the presentation of decision goal and alternatives from which the decision maker will choose the best solution.

3. Development of action plans: a plan appears to be necessary to solve decision problem.

A decision process becomes efficaciously optimal at the moment when the solution produces a positive outcome outweighing possible losses.

This thesis finds its foundation on multi-criteria decisions. Multi-criteria decision making (MCDM) definition appears straightforward. It is described by theorists, such as Pavan and Todeschi (2011), as a discipline which deals with human decisions, aiming the choice of the best alternative among different potential candidates, subjected to several criteria4 or attribute5 (concrete or vague).

So, summarizing what has just been explained, the strategy consists of establishing importance or priority to rank proposed alternatives through formal tools.

1.2 Research objective

The project is born after an internship experience in a Paduan (IT) aluminium company. Pandolfo Alluminio S.p.a., as the company is called, has the goal to enlarge its commercial borders, deciding not only to work in Italian areas but also to implement a foreign direct investment (FDI) in a Moroccan aluminium firm. The scope will be to establish some foreign business operations with the most important Moroccan aluminium firm, based in Tangier (MA).

4 _{Criterion is a sort of standards of judgement/rules to test acceptability for which an action}

appears desirable or not.

5 _{Attribute represents the characteristics and qualities of different alternatives (possible}

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The choice of Morocco is the results of research combining economic factors with strong development of domestic market, government incentives for foreign countries that invest in the Kingdom6 and tax facilitations in specific zones (for instance Tangier, Casablanca which are free-trade zones7). Other reasons include the availability of cheaper both workforces, even if qualified, and productivity factors (i.e. electricity, fuel…). Besides that, Tangier is the closest port to Gibraltar Straits, so this represents a strategic position for a company which would export in all Mediterranean zones.

Figure 1.1: Localization of Padua (IT) and Tangier (MA)

Source: Google Maps, https://maps.google.en/

In the previous rows, it has been mentioned the concept of foreign direct investment (FDI). It is defined as an «investment made by a firm or individual in one country into business interests located in another country. Generally, FDI takes place when an investor establishes foreign business operations or acquires foreign business assets»8. This type of investment proves to be different from a direct portfolio investment. In an

6

Officially the Kingdom of Morocco, (Wikipedia: https://en.wikipedia.org/wiki/Morocco).

7 _{«A free-trade zone (FTZ) is a class of special economic zone, It is geographic area where}

goods may be landed, stored, handled, manufactured or reconfigured and re-exported under specific customs regulation and generally not subject to customs duty», (Wikipedia, https://en.m.wikipedia.org/wiki/Free-trade_zone).

8

Investopedia, https://www.investopedia.com/terms/f/fdi.asp.

Padua Tangier

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FDI, investors take both ownership and control positions in the target firm (in this case, the Moroccan firm). Due to it, they become also the managers of the firm in which they invested in, in addition to those already present. The key feature of foreign direct investment is the settlement of the effective control or at least the substantial influence over foreign firm decision making, as happened in this project case. In this way, investors start to collaborate with owners and managers of target foreign firm.

On the other hand, in a foreign portfolio investment (FPI), investors acquire only equities of foreign-based companies without controlling target firms. They delegate decisions to managers, even if this provokes a limit to the freedom to make decisions, since managers and owners' agenda may not always coincide (Goldstein and Razin, 2006).

To be more detailed, the thesis case study could be defined as a horizontal direct foreign investment, referring to the establishment of the same type of Italian firm business in the target foreign one (Morocco).

Due to the fact that Pandolfo Alluminio S.p.a. (PA) managers would like to implement an FDI in Tangier (MA), the analysis aims to validate or not this choice using a mathematical approach and so to analyze if Morocco could be the best country for this type of project. If the result of this thesis coincides with PA intent, the intuition of Paduan managers is the right one.

Starting from a general point of view, the analysis considers several alternative countries in a limited area in which there will be the possibility to develop a foreign direct investment. This investment aims to replicate the same business line developed by Pandolfo Alluminio S.p.a. in the Italian area. So, through the formalization of multi-criteria decision making problem, PA will be able to identify a country where it is possible to find a medium-large enterprise (MLE) of the same sector (aluminium field) in which invest in; it could be Morocco or another one. So, the questions that are under this thesis may be: “Where should an aluminium firm be located for which an Italian MLE, such as Pandolfo Alluminio S.p.a., could be attracted to invest in it? Which factors could influence this decision?”.

Multi-criteria decision making will be the tool through the thesis problem will be solved.

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In order to solve this problem, two different models will be applied. Thesis will start with Fuzzy AHP method. It will identify the most important location factors in case a company should implement an FDI. Then TOPSIS method will select the most suitable area – among several alternatives – for this type of investment, considering the influence of factors identified with the previous method. The solution of the initial problem will be Morocco as suggested by Pandolfo Alluminio S.p.a. or another country. In this case, the manager’s intuition would not have been suitable.

To reach the final goal (choose the best country – among all the identified alternatives – where apply an FDI), it is necessary to carry out intermediate goals.

Figure 1.2: Thesis intermediate objectives

Figure 1.2 shows the two parallel researches that are conducted in order to achieve the final goal explained previously. The left branch identifies alternative countries where it could be possible to apply a foreign direct investment. Then, the right branch describes the first part of the thesis in which factors (for instance costs, labour characteristics, political and economic factors…) will be ranked if companies similar to Pandolfo Alluminio S.p.a. would like to invest in a similar project.

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So, the TOPSIS method, through the two parallel studies, identifies the most attractive country for the Italian North-East aluminium MLE, Pandolfo Alluminio S.p.a., which wants to perform an FDI; it could be in Morocco as managers suppose or another one.

It could be opportune to remark the link between Fuzzy AHP and TOPSIS methods. As already explained, the factors affecting decision location problems are identified through Fuzzy Analytic Hierarchy Process. These results turn out to be the input to implement the second method. Indeed, TOPSIS method considers factors weights identified from FAHP, with the list of chosen countries, to select the most suitable one in which an Italian aluminium firm should invest in. The two methods are linked together.

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2 - BACKGROUND

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Chapter II

Background

The first part of the chapter will focus on the aluminium market presentation. In particular, considering current economic and political problems, it will treat future perspectives both from a national point of view and internationally. Finally, there will be a hint about 2050 European programs regarding the relationship between aluminium market and environmental pollution.

Pandolfo Alluminio S.p.a. will be presented in the second section. Importance will be given to market segment that the firm satisfies and its behaviour inside an international context, highlighting its history and its future perspectives, especially the project in Moroccan zone.

The chapter will finish with the description of countries involved in the analysis, assuming to delimit the area to the Mediterranean zone.

2.1 Aluminium economy

Aluminium plays a fundamental role in the global economy and, thanks to its large number of properties9, it is used in many different sectors.

Not only its proprieties make aluminium so usable but also it can be considered a key material for Circular Economy10. It is possible to recycle without losing any quality, and

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Low weight, high strength, great malleability, easy machining, corrosion resistance and easy to mill, drill, cut, punch, bend, weld, bond and tape.

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«Circular Economy is a model of production and consumption, which involves sharing, leasing, reusing, repairing, refurbishing and recycling existing materials and products as long as possible. In this way, the life cycle of products is extended», (Europeans Parliament, http://www.europarl.europa.eu/news/en/headlines/economy/20151201STO05603/circular-economy-definition-importance-and-benefits).

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2 - BACKGROUND

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used repeatedly for the same application, requiring an amount of energy equal to 5% of that needed for primary production11.

European aluminium industry counts nowadays about over a thousand of active firms in the primary and secondary production of this metal. Considering also its transformation in semi-finished products, some years ago total revenues and value-added are estimated about 40 and 12 billion euros, respectively. Italy reported one of the best improvements with a value of 607 thousand tons of aluminium produced. These results try to reassure firms about the positive perspective of aluminium production. In particular, 2018 European outcomes were driven by the growth of transport (automotive), industrial sectors and an upswing of the construction sector.

Figure 2.1: European aluminium demand by sector, 2018

Source: European Aluminium,

https://european-aluminium.eu/activity-report-2018-2019/market-overview/.

From a different point of view, as Figure 2.2 shows, the global production of aluminium changes localization in the last 20 years, showing a constant growth of Chinese market, which now counts about 57% of the global primary aluminium production.

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Primary production is the process through new aluminium is made versus secondary production, in which existing aluminium is recycled into pure metal, (The Aluminium Association, https://www.aluminium.org/industries/production/primary-production). ■Transport ■General engineering ■ Electrical engineering ■Building and construction ■Consumers goods ■ Others ■Stockists 24% 4% 39% 4% 5% 14% 10%

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Figure 2.2: Primary aluminium production changes

Source: European Aluminium, https://www.european-aluminium.eu/.

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However, European production of primary aluminium highlights a drop (-30% from 2008) due to external pressures on the aluminium supply chain from global trade issues. The decrement is also caused by excess capacity in global markets, strict EU regulations and challenges in accessing aluminium scrap. These problems imply a boost in imports from foreign countries.

On the other hand, secondary production of aluminium in the EU highlights a decrement of -0,4 million tons respect to pre-crisis level: 3,2 million tons in 2017 while 3,6 million tons in 2007.

Moving from production to consumption, as the following figure describes, the overall consumption of semi-finished aluminium products are driven by China and the Middle East. On the other hand, the share of European consumption has progressively reduced from 38% in 2000 to 15% in 2017.

Figure 2.3: Global consumption of semi-finished aluminium

Source: Conserva M., (2019), Le prospettive degli estrusi in Italia e nel mondo, meeting

organized by GMS Milano, A&L Alluminio e Leghe with METEF and Centroal, Capriolo – BS.

■North America ■Western Europe ■Eastern Europe ■Central & South America ■China

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2 - BACKGROUND

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Lastly, Europe is a net importer of aluminium ingots (50% in 2018), substituting domestic production with one of the other countries, for example, China (30% with an important growth in 2000-2017) and Turkey, but also Russia, United Arab Emirates, as Figure 2.4 proves.

Figure 2.4: European aluminium demand for aluminium ingots (2000-2050)

Source: European Aluminium, https://www.european-aluminium.eu/, based on CRU

2018 datasets.

On the other site, European exports increase in particular during 2018, counting about 80% of material allocated to Asia, principally China, India and Pakistan.

Considering the objectives of European Aluminium, which represents the aluminium industry sector in Europe, primary aluminium global demand is expected to increase to 50% (107.8 million tons) by 205012. Another important goal to reach will be the

12_{European Aluminium, https://european-aluminium.eu/resource-hub/vision-2050/.}

■Primary Production ■Primary imports ■Recycled basis - - -Primary production: baseline

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reduction by around 60-70% by 2050 of total CO2 emissions from primary production13,

due to the increment of recycled aluminium production, rather than importing it from other countries.

2.2 Pandolfo Alluminio S.p.a.

This section aims to describe the firm considered in the current case study. In particular, it represents the point to start for thesis development.

Pandolfo Alluminio S.p.a. has been present in the aluminium extrusion business for fifty years. Thanks to its continuous upgrade and products/services expansions, it earns the reputation of a “one-stop supplier”14

, which sets it apart in the European market (Pandolfo Alluminio, https://www.pandolfoalluminio.com/). The results that have characterized the company up to now have been possible due to a high level of expertise and great communication between the company and customers. It also demonstrates to be at the forefront in the field not only of research and development but also of environmental safety.

Notwithstanding, the project will be focused on Pandolfo Alluminio S.p.a., it will be provided a broader view of the entire corporate framework.

Pandolfo family controls two phases of aluminium value chain through two different firms: Fonderie Pandolfo S.p.a. recycles aluminium scraps and turns them into billets, while Pandolfo Alluminio S.p.a. produces aluminium extrusions profiles, semi-finished elements that are combined in the final product for clients.

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European Aluminium, https://european-aluminium.eu/resource-hub/vision-2050/.

14

«A one-stop shop […] is a business or office where multiple services are offered; i.e., customers can get all they need in just "one-stop"», (Wikipedia, https://en.wikipedia.org/wiki/One_stop_shop). For example, Pandolfo Alluminio S.p.a. produces different typologies of aluminium profiles, supplies technical support etc.

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Figure 2.5: Presidium of Aluminium value chain

Source: E. Dante, (2019), Company internal research, Pandolfo Alluminio S.p.a.

internship experience.

More detailed descriptions of firms activities are presented in the following two figures.

Figure 2.6: Fonderie Pandolfo S.p.a.: description

Source: E. Dante, (2019), Company internal research, Pandolfo Alluminio S.p.a.

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Figure 2.7: Pandolfo Alluminio S.p.a.: description

Source: E. Dante, (2019), Company internal research, Pandolfo Alluminio S.p.a.

internship experience.

Focusing attention on Pandolfo Alluminio S.p.a., it is possible to mention some important goals achieved in 2015-201815, as emphasized in Figure 2.8.

In a glance, it is possible to observe that company profitability ratios considered had a continuous enough marked increment.

In detail, the ROE grew during all the analyzed period, meaning that the company had a great gain in terms of profitability, productivity and management efficiency.

Also the ROA, the return on assets, which shows the percentage of how profitable the company’s assets are in generating revenues, followed the same behaviour, describing a +103%.

For the other profitability ratios, the situation was the same: the EBITDA margin variations suggest that the company was able to generate value through operational management during the last years. Also, the EBIT margin increased.

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For what concerning the cash flow-to-revenue ratio, that allows to evaluate the effectiveness of internal cost controls, it is possible to state that the situation had improved in years.

The profit margin ratio, which is one of the commonly used profitability ratios to gauge, the profitability of a business activity still followed the behaviour of the other ratios.

Figure 2.8: Changes of Pandolfo Alluminio S.p.a. profitability ratios

Source: Orbis database.

Looking at the structure ratios, both the current ratio, which is the ratio between current assets and current liabilities and measures whether a firm has enough resources to pay its debt over next 12 months, and the liquidity ratio – (current assets-stock)/current liabilities – decreased slightly. Nevertheless, the differences were minimal; it is not a problem right now.

4,00% 6,00% 8,00% 10,00% 12,00% 14,00% 16,00% 18,00% 12/31/15 12/31/16 12/31/17

ROE using Net Income ROA using Net Income

EBITDA margin EBIT (operating) margin

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Figure 2.9: Changes of Pandolfo Alluminio S.p.a. structure ratios

Source: Orbis database.

Finally, exploring sales location could appear crucial to understand how the company is positioned in the domestic market and, more importantly, in the foreign one. Figure 2.10 describes how Pandolfo Alluminio S.p.a revenues from sales are allocated between Italy, European Union and countries outside UE. The variation across these three segments was quite uneven. Sales in Italy in 2015-2017 grew by only 3%, while European ones decreased to 16%. On the other hand, the increment in extra-UE sales was relevant, which passed from around €1,2 million to over €5 million in 2017.

Figure 2.10: Revenues from sales of Pandolfo Alluminio S.p.a. 2015-2017

Source: Orbis database.

2015 2016 2017 Extra-EU 1.186.677,00 € 2.497.911,00 € 5.382.500,00 € EU 28.032.762,00 € 25.835.116,00 € 23.417.581,00 € Italy 57.640.261,00 € 56.043.560,00 € 59.494.521,00 € 0,00 € 10.000.000,00 € 20.000.000,00 € 30.000.000,00 € 40.000.000,00 € 50.000.000,00 € 60.000.000,00 € 70.000.000,00 € 80.000.000,00 € 90.000.000,00 € 100.000.000,00 € R ev en u es fr o m s ales 1,00 1,20 1,40 1,60 12/31/15 12/31/16 12/31/17

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Taking into consideration A&L16 sectorial magazine data, it is possible to study in which market segment Pandolfo Alluminio S.p.a. products are sold. Most of the sales satisfy sector composed by durables – products that retain their value for a long time. Differently from this, 9% of the graph is occupied by sales concerning the transport sector (for instance, construction of bus and train carriages), while lower percentages satisfy engineering and building industrial segments.

Figure 2.11: Markets of Pandolfo Alluminio S.p.a.

Source: Data collection and processing: A&L with the collaboration Assomet-Centroal,

Statistics and Market Department, 06/2019, pp. 82-83.

Once described how aluminium market is structured, composition of Pandolfo Alluminio S.p.a - the research input, and its sales reports, it is the time to introduce the first step of the thesis case study.

2.3 Facility location alternatives

The research aims to find the most suitable country where implement a foreign direct investment: it could be Morocco as Pandolfo Alluminio S.p.a. hopes – in this way its intuition can be validated by mathematical methods, or another country.

16

A&L – Aluminium Alloys Pressure Diecasting Foundry Techniques, (06/2019), Metef.

81% 4% 9% 6% _■ Durables ■ Engineering ■ Transports ■ Buildings

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An initial step for this type of project is to ask where a specific economic activity should be located (Beckmann, 1968). In order to select the most appropriate location among a set of alternatives, it necessary to select not only a well-performing facility for the current situation but also a profitable facility for the lifetime of the company (Farahani and Hekmatfar, 2009).

Once mentioned the problem from a theoretical point of view but before presenting the complete list of locations, two other points are necessary to be explored: the criteria used and the related consistent assumptions.

It is not easy to localize a new foreign direct investment, due to the great quantity of alternatives and factors involved. Thus, to decrease choice complexity, it is necessary to decrease the number of countries alternatives. To do so, some criteria are needed to consider:

 select hypothetic country close to markets/customers and suppliers to reduce delivery costs and time;

 select hypothetic country close to partner company to increase collaboration (Pandolfo Alluminio S.p.a. and future company to do business with);

 select hypothetic country close to a harbour (for example, Tangier in Morocco case). In most of the case, aluminium is transported through shiploads.

These criteria are followed by consistent assumptions, tailored for this specific research.

It is assumed to select only countries near Mediterranean zone. This area not only includes both Italy and Morocco, which are at the basis of the thesis case study, but it also represents a very good route of commercial.

The selected countries, listed in the following rows, have access to sea, essential since in most of the cases aluminium is transported by shiploads. Besides, all of them are relatively close to Italy, reaching it easily. Therefore, it is assumed to analyze a circumscribed zone around Italy, in particular, around the Mediterranean Sea, as it is possible to observe in Figure 2.12.

The zone assumed to analyze, beyond to consider some European nations, is mainly constituted from developing countries or at least countries that could increase their industrial potentiality.

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The list provides it more in details.

 Albania;  Algeria;  Croatia;  Cyprus;  Egypt;  France;  Greece;  Israel/Palestine;  Italy;  Lebanon;  Libya;  Malta;  Montenegro;  Morocco;  Slovenia;  Spain;  Syria;  Tunisia;  Turkey.

Figure 2.12: Mediterranean zone considered in the thesis

Source: Google Images, https://images.google.en/.

Previously, it is listed all the countries facing the Mediterranean Sea, from a geographical point of view. However, at this precise history moment, some of them have wars going on. It seems reasonable to think that a manager would never invest in a foreign company which is located in an area where social and political conditions are unstable and dangerous.

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Due to these assumptions, it is considered a new updated list of ongoing wars, excluding some countries. Egypt, for instance, is excluded from the analysis because of Sinai insurgency between Islamist militants and Egyptian security forces. Also Israel/Palestine is not listed due to the war began in the mid-20th century. Strikes and protests in Beirut have turned Lebanon into one of the countries with the most austere draft budgets. Moreover, Libya is excluded from the list because of ongoing conflict among rival factions seeking control of the territory and oil. In the end, other two countries do not take part in the thesis: Syria and Turkey. Syrian Civil War is currently the 2nd deadliest war of the 21st century while Turkey has characterized by an armed conflict between Turkey Republic and some Kurdish insurgent groups17.

Figure 2.13: Ongoing wars in the Mediterranean zone

Source: Wikipedia, https://en.wikipedia.org/wiki/Outline_of_war.

Updating the original list, there are no more 19 countries as before but 12 now, excluding countries with ongoing wars and domestic country (Italy), since the goal is to invest in a foreign country:

17

Wikipedia, https://en.wikipedia.org/wiki/Outline_of_war#Wars.

■ Major wars, 10.000+ deaths in the current or past calendar year;

■ Wars, 1.000-9.999 death in current or past calendar year;

■ Minor conflicts, 100-999 deaths in current or past calendar year;

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2 - BACKGROUND 27  Albania;  Algeria;  Croatia;  Cyprus;  France;  Greece;  Malta;  Montenegro;  Morocco;  Slovenia;  Spain;  Tunisia. Other two points about probable results may be interesting to notice.

The first one regards France and Spain. As reported Aluminium 2020 website (https://www.aluminium-messe.com/en/), these two countries are the second and the fifth most important aluminium markets in Europe, stating that their presence in the market is presumably stable. For this reason, they may not be the first countries in which to apply the FDI, if Pandolfo Alluminio S.p.a. aims to invest in foreign firms where country economic conditions are still developing and to boost aluminium industry. Indeed the author expects that France and Spain will cover last positions of the ranking that will be formed, based on the countries where Italian entrepreneurs of aluminium market would prefer to invest. It should be pointed out that this is only a hypothesis; it is possible that the final result does not confirm this statement.

The second conjecture considers countries which have the lowest GDP by industry percentage. The following list excludes those countries eliminated until now (countries with wars going on) or assumed to be among the last positions (France and Spain). The percentage in brackets indicates the GDP by industry18. This index represents how industry sector varies and how it contributes to the country’s economy19.

Albania (24,2%); Malta (10,2%); Algeria (39,3%); Morocco (29,5%); Croatia (26,2%); Montenegro (15,9%); Cyprus (12,5%); Slovenia (32,2%); Greece (16,9%); Tunisia (26,2%). 18

CIA World Factbook 2019, https://photius.com/rankings/2019/economy/gdp_composition_ by_sector_of_origin_industry_2019_image.html.

19

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2 - BACKGROUND

28

A low GDP by industry percentage could prove that the industrial sector in these countries appears not so relevant as the other ones (for example, agriculture and services). Applying an FDI in a country where industry is not so predominant in the economy might not be a good idea. Due to it, the highlighted countries may be at the end of the ranking, based on the countries where Italian aluminium market entrepreneurs would prefer to invest.

It is necessary to precise that the two conjectures are author’s forecasts. Only at the moment when the research will be computed, it will be possible to accept or reject the considerations done.

The following table reports what till now it is declared.

Table 2.1: Explanation summary

Country from which the research starts: • Italy

Countries with wars going on: • Egypt • Israel/Palestine • Lebanon • Libya • Syria • Turkey

Countries with well-structured aluminium market: • France

• Spain

Countries with the lowest GDP by industry percentages: • Cyprus

• Malta • Montenegro

Countries which should be optimal solution where apply an FDI: • Albania • Algeria • Croatia • Greece • Morocco • Slovenia • Tunisia

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2 - BACKGROUND

29

Summarizing, after the description of aluminium economy and Pandolfo Alluminio S.p.a. company, the attention is focused on facility location alternatives. This represents the first step for thesis analysis. It is provided the complete list of countries among which, in the end, it will be possible to identify the most suitable one where implement an FDI. This happens in the case an Italian North-East aluminium MLE would invest in a foreign target firm of the same industry sector.

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2 - BACKGROUND

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3 - THEORETICAL STRUCTURE

31

Chapter III

Theoretical Structure

Since the goal of the thesis is to find the best countries – among those selected before (Chapter II) – to apply a foreign direct investment, it is necessary to select one or more models to perform the choice.

The chapter will be focused on the explanation of multi-criteria decision making (MCDM) problem, representing the group to which models applied in this thesis belong. Involving different actors (people, institutions, state…), multi-criteria decision making is a discipline with recent history since it starts to interest scholars from 1950s-1960s, developing new MCDM models and techniques. Despite its exponential growth, the methodological choices and framework are still discussed (Zavaskas et al., 2014). Moreover than this, the following pages aim to provide a hint about this extended subject. After an initial introduction, attention will be given to the division on different sub-groups of MCDM which theorists generally consider, in order to provide a briefly theoretical structure reference of the thesis.

Finally, the last section presents the procedure for selecting the suitable models to be applied in order to obtain the final result.

3.1 Basic knowledge

Part of Operations Research (OR)20 aims to develop approaches for optimal decision making.

20

Operations Research is the discipline which applies scientific method to managerial and administrative problems, such as systems of industrial production or defence, social and governments programs. It can be defined as an interdiscipline in which developed models use tools of «statistics, optimization, probability theory, […] decision analysis, mathematical modelling and simulation» to describe decision maker behaviour (Zavadskas, E. K., Turkis, Z., (2011), Multiple criteria decision making (MCDM) methods in economics: an overview, Technological and Economic Development of Economy, Vol. 17(2), pp. 397-427).

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A class of them is defined as multi-criteria decision making (MCDM), dealing with the evaluation of a set of alternatives, considering a set of criteria (Triantaphyllou et al., 1998).

It has just been mentioned the concepts “alternatives” and “criteria”, so to avoid confusion with terms used later, the following list provides definitions of basic concepts useful for all the remaining part of the thesis (Zavadskas et al., 2014, Triantaphyllou et

al., 1998 and Hwang et al., 1992).

 Alternative: Alternative represents for decision makers possible choices of actions available. In most of the cases, the set of alternatives is assumed to be finite, screened, prioritized and eventually ranked.

 Attributes: Attributes are the characteristics and qualities of different alternatives. MCDM problems have the aim to select the “best” alternative from a pool of preselected alternatives, considered in terms of different attributes.

 Criterion: Criterion can be defined as standards of judgement/rules to test acceptability or not. Through it, a particular choice or course of action could be considered desirable or not than another.

 Goal: Decision maker reflects his desires through goals in terms of space and time. On the one hand, objectives indicate the desired direction and on the other goals indicate a desired (or target) level to achieve. However, the distinction in literature appears sometimes blurred.

 Decision matrix: In most of the cases MCDM problems can be easily expressed by matrix format. Following Zimmermann (1991), a decision matrix A is an (m×n) matrix, composed by aij elements, which indicates performance of Ai

alternative when it is evaluated in terms of decision criterion Cj, with i = 1,…, M

and j = 1,…,N. It is assumed that Wj, for j = 1,…,N, weights of relative

performance of the decision criteria, is determined by decision maker. An example of decision matrix is the following one:

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Table 3.1: Decision matrix

Criteria C1 C2 … CN Alternatives W1 W2 … WN A1 a11 a12 … a1N A2 a21 a22 … a2N … … … … … AM aM1 aM2 … aMN

Source: Triantaphyllou, E., Shu, B., Nieto Sanchez, S. and Ray, T., (1998), Multi

– Criteria Decision Making: An Operations Research Approach, Encyclopedia

of Electrical and Electronics Engineering, J. G. Webster, Ed., John Wiley & Sons, New York, NY, Vol. 15, pp. 175-186.

 Decision weight: Some MCDM methods require assigning weights of importance to attributes. Normally these weights are normalized.

 Objective: Decision maker (DM) has the objective “to do better”. More precisely, objectives can be considered the reflections of decision maker’s wishes and they represent the direction in which his work should be organized. In other words, objective represents something that DM would like to get by addressing his decisions.

3.2 Multi-criteria decision making (MCDM) problem

The class of models called multi-criteria decision making (MCDM) is considered by scholars (for instance, Triantaphyllou E. and others) the «most well-known branch of decision making»21. As mentioned before, MCDM is a branch of Operations Research (OR) models which treats decision problems under the presence of decision criteria.

21

Triantaphyllou, E., Shu, B., Nieto Sanchez, S. and Ray, T., (1998), Multi-Criteria Decision

Making: An Operations Research Approach, Encyclopedia of Electrical and Electronics

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MCDM, being an advanced field of OR, can provide to decision makers/analysts with a consistent number of different methodologies to solve decision problems (Hwang and Yoon, 1981, Zopounidis and Doumpos, 2002, Figueira et al., 2005). It is also considered one of the fastest-growing problem areas in many disciplines as Zavadskas

et al. affirmed in 2014.

3.2.1 Definition of MCDM and proceeding steps

According to Kumar (2013) definition, MCDM is a process of evaluating real-world situations, depending on qualitative/quantitative criteria in environment dimension which could be certain, uncertain or risky with the objective to find the most suitable strategy/course of actions/choice/policy among several available options. In addition, thanks to MCDM methods, the problem of decision maker can be solved through the evaluation of a set of alternatives in order to identify the most desirable one among them. It is also possible to rank these alternatives from the best to the worst one or to group them in predetermined classes.

Multi-criteria decision making methods find a solution that is good among a set of alternatives which is generally not possible using traditional methods (for instance, standard optimization techniques).

To sum up, they model a set of alternatives using more than one criterion in order to consider all the possible impacts/consequences/effects on the result. Examples of MCDM applications are constantly present in everyday life. There are numerous examples of choices in which a group of alternatives must be judged based on many characteristics. Think, for example, when a customer has to buy a car. He does not just think about how fast the car is, but he considers also price, fuel consumption, engine power, gearbox type, vehicle dimensions and so on.

Another example happens when a company director has to hire new employees, the alternatives are the candidates and the criteria can be the educational degree, professional experience and so on.

Proceeding with the description of MCDM, Zavadskas and Turkis (2014) identify the main steps of multi-criteria decision making, as follows:

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2. assumption of main objectives/criteria by which alternatives are judged; 3. generation of alternatives to consider to reach final goals;

4. evaluation of an impact of each criterion on the decision making function, meaning that the decision maker should express his preferences about criteria based on his personal importance.

3.2.2 MCDM applications and historical background

Multi-criteria decision making methods become increasingly popular in decision making due to their ability to solve complex socio-economic, environment and government problems. «It is not an exaggeration to argue that almost local or federal government, industry, or business activity involves, in one way or the other, the evaluation of a set of alternatives in terms of decision criteria»22. The following list exemplifies MCDM applications.

 Industrial engineering applications of multi-criteria decision making include the application of decision analysis in integrated manufacturing (Putrus, 1990).  Evaluation of technology investment decisions (Boucher and McStravic, 1991).  Flexible manufacturing systems (Wabalickis, 1988).

 Layout design (Cambron and Evans, 1991).

 Other engineering problems (Wang and Raz, 1991). MCDM huge utilization is due to its numerous advantages:

 it helps the agent to make decisions through the examination of the problem’s structure in a complex way and considering more than one criteria;

 it gives the same importance to qualitative criteria as well as quantitative criteria;

 it considers the decision maker preferences;

 the methodological approach is both rigorous and realistic.

22

Triantaphyllou, E., Shu, B., Nieto Sanchez, S. and Ray, T., (1998), Multi-Criteria Decision

Making: An Operations Research Approach, Encyclopedia of Electrical and Electronics

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At the beginning of 1960s, mostly in Europe, multi-criteria analysis23, and in particular MCDM, were developed, even if they start to take form only in 1970s. It was emphasized the difficult of agent in decision process due to incomplete information, limited resources or conflicting interests. Simon (1957), Koopmans (1951), Kuhn and Tucker (1951) and Charnes et al. (1955) discovered that decision maker does not prefer optimal solution but the most satisfied one, he prefers to achieve targets rather than maximize or minimize goals.

The basic task of multi-criteria decision making methods is to study how the decision maker (DM) should take decisions. It is rare that there is only a single objective/criterion/point of view to make a real-world decision. So MCDM tries to help DM when multiple conflicting decision factors have to be considered simultaneously.

Then, during 1980s and 1990s, there were the development and the consolidation of a large number of MCDM methods.

3.2.3 MADM vs MODM

According to Hwang et al. (1981), multi-criteria decision making can be divided into two categories: multi-objective decision making (MODM) and multi-attribute decision making (MADM).

Multiple-objective decision making (MODM) is associated with the problems in which the alternatives are not determined. The models belonging to this category design the “best” alternative. It is the results by considering the interactions between the designed constraints which best satisfy decision maker and some acceptable levels of quantifiable objectives.

MODM is characterized by:

1. a quantifiable objectives set; 2. a well-defined constraints set;

23

Multi-criteria analysis is defined as «a decision-aid and a mathematical tool allowing the comparison of different alternatives or scenarios according to many criteria, often conflicting, in order to guide the decision maker towards a judicious choice», (Roy, B., (1996), Multi criteria

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3. a process in order to obtain some trade-off information between the stated quantifiable objectives and also between stated/unstated no-quantifiable objectives.

MODM problems are associated to design problems.

Differently, multi-attribute decision making (MADM) is used for selection problems. The feature that distinguishes MADM is a limited (in some cases, also small) number of predetermined alternatives. A level of attributes achievement (not necessarily quantifiable) is associated to alternatives, based on which the final decision is made. The alternative final selection is done helping by inter- and intra-attribute comparisons.

Table 3.2: MADM vs MODM

MADM MODM

Criteria (defined by) Attributes Objectives

Objective Implicit Explicit

Attribute Explicit Implicit

Constraint Inactive (incorporated into attributes)

Active

Alternative Finite number, discrete Infinite number, continuous

Interaction with DM Not much Mostly

Usage Selection / Evaluation Design

Source: Hwang, C. L., Yoon, K., (1981), Multiple Attribute Decision Making, Methods

and Applications, A State-of-the-Art Survey, Lecture Notes in Economics and

Mathematical Systems, Springer-Verlag Berlin Heidelberg New York.

With the passing of time the necessity to compare and classify MCDM increased, starting by MacCrimmon, which was the first one who recognized the importance of the selection problems and proposes a taxonomy of MCDM methods.

During these years the ways to classify MCDM methods are a lot (Hwang and Yoo, 1981, Larichev, 2000, Figueira et al., 2005). For instance, Zavadskas and Turkis (2011),

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considering Larichev paper (2000), proposed the following classification according to the types of information available:

 methods based on quantitative measurements24

, in particular on multiple criteria utility theory25 (TOPSIS, LINMAP, MOORA, COPRAS, COPRAS-G);

 methods based on qualitative initial measurements, such as AHP and fuzzy set theory methods. Fuzzy numbers approximately express linguistic variables dealing with situations which are too complex or not so well-defined to be described by quantitative expressions (Larichev and Moshkovich, 1997, Larichev and Brown, 2000, Ustinovichius et al., 2009, 2010, 2011);

 comparative methods based on pair-wise comparison of alternatives, proposing methods as ELECTRE, PROMETHEE, TACTIC, ORESTE and others (Turkis, 2008);

 methods based on qualitative measurements not converted into quantitative variables, including methods of verbal decision analyzing with a high level of uncertainty (Berkeley et al., 1991);

 methods categorized as continuous or discrete, depending on alternatives domains.

Multi-criteria decision making methods can consider a single decision maker (DM) or a group of them. In particular, Chen and Hwang (1991), taking into account single DM, provided taxonomy of MCDM methods as the following figure shows. There are scenarios in which information are available or not. Moreover, in the case in which there is information on attributes, these methods can be classified in standard level, ordinal or cardinal way.

24

Quantitative versus qualitative measurement. The former is based on numerical data, while the later includes human experience or judgement as a factor in the calculation.

25

«In decision theory, utility is a measure of the desirability of consequences of the courses pf action that applies to decision making under the risk», (Zavadskas, E. K., Turkis, Z., (2011),

Multiple criteria decision making (MCDM) methods in economics: an overview, Technological

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Figure 3.1: Chen and Hwang MCDM classification

Source: Triantaphyllou, E., (2000), Multi-criteria Decision Making Methods: A

Comparative Study, Springer Science + Business Media, B. V.

All the methods presented in the figure are not described since this is not the scope of the thesis.

To sum up, it is possible to affirm that MCDM is one of the most used decision making methodologies not only in science but also business, governmental world and so on. Thanks to its properties, it is really helpful to improve the quality of decisions, in addition to making the decision making process more efficient and easier to understand. In order to find the best models for the thesis aim, the procedure to follow is described in the following paragraph.

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3.3 Selection of suitable methods

Multi-criteria decision making problems can be properly considered suitable for facility location selection problem as Tuzkaya et al. (2008) affirm. Before going on, it could be opportune to provide a brief explanation of facility location concept defining it as the place where company production is carried on. Its selection aims to locate, re-locate or expand firm operations, through the identification, analysis and selection among different alternative locations (Yaşlioğlu et al., 2016).

This paragraph will analyze the characteristics of multi-criteria problem proposed in this thesis and select the most appropriate MCDM method for solving it, considering De Keyser and Springael (2010) classification procedure.

The two authors proposed an efficient procedure to follow in order to find the method which should be more adaptable for the type of decision problem. It consists of four questions. Each of these questions analyzes a dimension (number of alternatives, determinism or not, aggregation and the last one which depends on the outcomes). The answer to each of these questions is related to find a different multi-criteria decision making problem category.

First dimension: number of alternatives

In the first dimension, it is analyzed the characteristics of the set of alternatives. It could be small or large.

 Question: Is the number of alternatives small enough to make two-by-two comparisons26?

Yes: Multi-attribute decision making (MADM) methods. «The term MADM will be used here as a reference to MCDM methods handling

26

As it will be explained in details in Chapter IV, two-by-two comparison (pair-wise comparisons) appears an easier way for the decision maker to express his/her preferences. For example, the location factors that will be analysed by Fuzzy AHP method (cost, labour characteristics, economic factors etc.) will be compared two-by-two.

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multi-criteria problems with a small explicit list of alternatives»27. Pairwise comparison of alternatives is possible.

No: Multi-objective decision making (MODM) methods. «The term MODM will be used here as a reference to MCDM methods handling multi-criteria problems with a large set of alternatives»28.

In this thesis, it is assumed that there are fewer decision makers29 who evaluate the factors30 and make decisions among alternatives. So, in this case, the set of alternatives is small, since it is considered only countries in the Mediterranean zone, as explained in Chapter II. This means that, for the thesis case, it will prefer multi-attribute decision making methods.

Second dimension: determinism or not

Second dimension studies the kind of data more in details. In real situation, for instance, it is possible to happen that data collected to implement a method appear not necessarily deterministic.

 Question: Are the data of the problem deterministic (crisp) or not? Yes: Deterministic.

No: Nondeterministic (e.g. stochastic, fuzzy).

It should be stressed that “data”, in this particular context, refers to the evaluations of the alternatives on the criteria.

27

De Keyser, W. and Springael, J., (2009), Why don’t we KISS? A Contribution to Close the

Gap between real-world Decision Makers and Theoretical Decision-Model Builders, UPA

(University Press Antwerp).

28

De Keyser, W. and Springael, J., (2009), Why don’t we KISS? A Contribution to Close the

Gap between real-world Decision Makers and Theoretical Decision-Model Builders, UPA

(University Press Antwerp).

29

As will be explained in the next chapter, data will be collecting through an interview to five different companies.

30_{As it is specified in previous chapters, the term “factor” indicates the variables that affect}

facility location selection process (costs, labour characteristics, economic and political factors…). In the next chapter will be described more in details.

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In this thesis, it is possible to assume that the criteria of each alternative involve risks and uncertainty. In particular, the data and values used to achieve thesis final goal are collected not only from government documents, official websites, statistics etc. but also from subjective judgements and impressions that the selected companies interviewed will provide (this passage will be explained in detail later). This could imply vagueness. So, nondeterministic data will be also considered.

Third dimension: aggregation

The task of each MCDM must be the aggregation of the existing data across multiple criteria considered.

Two different questions are necessary in this case.

 Question 1: Do you, as decision maker, want to take all factors (criteria) into account?

No: A hierarchy among the factors exists. This means that decision maker prefers to achieve a better value on a factor positioned higher in the hierarchy than achieve a better value situated lower in this hierarchy. Yes: Go to question 2.

 Question 2: Can you express differences in importance in the factors (criteria) on an ordinal scale, on a ratio scale or it is impossible to express this?

Yes: on an ordinal scale

An order amongst the factors. The decision maker (DM) in this case is allowed to rank the factors ranging from little to great importance, for example.

Yes: on a ratio scale

Trade-offs between the factors. The decision maker can give numerical value on a ratio scale as weights for the factors. He considers all the factors and balances each of them against any other factor.

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No: No a priori inter-criteria information. Decision maker is not able to provide a priori useful inter-criteria information31.

In this thesis, it is assumed to express the importance of factors through hierarchical order. More precisely, it is used a pair-wise comparison between factors in order to determine the importance among them.

Fourth dimension

This last dimension depends on the result of the first one. MODM or MADM. Depending on the outcome, a specific question is formulated.

In the case in which MCDM problem belongs to MADM-class, as happen for this thesis, the question is about the type of data used. Factors affecting location selection is characterized by both qualitative and quantitative data (cf. Note 24).

Till now it is discussed about the general procedure to find suitable methods from a theoretical point of view. Now it is selection time. To avoid confusion, it is pointed out that there are many MCDM approaches. However, describing each of them would go beyond the scope of this thesis. For this reason, the author decides to focus only on those used to solve the initial problem

According to answers provided in the above questions, it is possible to identify the most appropriate methods to achieve final results for this thesis. If the aim is to select the most suitable country where apply a foreign direct investment in an aluminium firm, as mentioned before, some factors (for example costs, labour characteristics, economic and political factors…) influence this choice. So, due to their characteristics, it opts for MADM methods with non-deterministic data which are both qualitative and quantitative, as explained before. In cases similar to the one presented by this thesis, De Keyser and Springael suggest models such as SAW, AHP, TOPSIS (these are some

31 «Remark that “no a priori inter-criteria information” does not mean that the decision maker

either wants to or does not want to take all criteria into account in the same way. It is rather an expression of his inability to express the information appropriately», De Keyser, W. and Springael, J., (2009), Why don’t we KISS? A Contribution to Close the Gap between real-world

Application of Fuzzy AHP and TOPSIS methods for a Foreign Direct Investment in the aluminium industry sector

Master’s Degree

in Economics and Finance

Final Thesis

Application of Fuzzy AHP and TOPSIS

methods for a Foreign Direct Investment

in the aluminium industry sector

Acknowledgement

Contents

Introduction

Chapter I

Thesis Introduction

Chapter II

Background

Chapter III

Theoretical Structure