Selection of Projects for Project Portfolio Using Fuzzy TOPSIS and Machine Learning

Project portfolio management (PPM) is extremely important nowadays due to the increasing severe competitions, accelerated product developments, project complexity, uncertainty, and challenges from global competitors. Therefore, businesses involved in many (dozens or even hundreds) projects need to formulate tactics and strategies to secure firms’ competencies and, most importantly, increase their productivities. Under this globalization context, PPM is to opti-mize the value provided to the customers while minimizing risks and the resources committed to the projects, while critical success factors (CSFs) is applied to anticipate the project’s risk and financial value by early assessment thus to help from the organizational level to predict the per-formance. Despite its importance, the literature on PPM and CSFs at a project level is rather limited, which demands a more profound knowledge about the assessment, ranking, and prior-itization of projects in the early stage. This study seeks to address the following two research questions: Do CSFs vary according to the project category, and how a supportive method can be established to help portfolio managers to select the project for portfolio. As a result, this re-search focuses on the multi-project context in order to fill the above-mentioned research gaps. As the contributions of this study, this study intends to (1) verify the hypothesis that different project category has different CSFs, and (2) contribute to explore how machine learning technol-ogy can be utilized for project selection. Projektisalkun hallinta (PPM) on nykyään erittäin tärkeää lisääntyvien kovien kilpailujen, nopeutuneen tuotekehityksen, projektien monimutkaisuuden, epävarmuuden ja globaalien kilpailijoiden haasteiden vuoksi. Siksi moniin (kymmeniin tai jopa satoihin) hankkeisiin osallistuvien yritysten on laadittava taktiikat ja strategiat, joilla varmistetaan yritysten osaaminen ja mikä tärkeintä, lisää tuottavuuttaan. Tässä globalisaatiokehyksessä PPM: n on optimoitava asiakkaille tarjottu arvo minimoiden riskit ja hankkeisiin sitoutuvat resurssit, kun taas kriittisiä menestystekijöitä (CSF) käytetään ennakoimaan projektin riski ja taloudellinen arvo varhaisella arvioinnilla, jotta apua organisaatiotasolta suorituskyvyn ennustamiseksi. Tärkeydestään huolimatta kirjallisuus PPM: stä ja CSF: stä projektitasolla on melko rajallinen, mikä vaatii syvällisempää tietoa hankkeiden arvioinnista, luokittelusta ja ennakoinnista varhaisessa vaiheessa. Tässä tutkimuksessa pyritään käsittelemään kahta seuraavaa tutkimuskysymystä: vaihtelevatko CSF: t projektikategorian mukaan ja kuinka voidaan luoda tukeva menetelmä salkunhoitajien auttamiseksi valitsemaan projekti salkkuun. Tämän seurauksena tämä uudelleenhaku keskittyy moniprojektiyhteyteen edellä mainittujen tutkimuksen aukkojen täyttämiseksi. Tämän tutkimuksen myötä tämän tutkimuksen tarkoituksena on (1) tarkistaa hypoteesi, että eri projektikategorioilla on erilaiset CSF: t, ja (2) myötävaikuttaa siihen, kuinka koneoppimisen tekniikkaa voidaan hyödyntää projektin valinnassa

A framework for regime identification and asset allocation

The purpose of this thesis is to examine a regime-based asset allocation strategy and evaluate whether accounting for regime-dependent risk and return of asset classes provides any significant improvement on portfolio performance. The South African market and economy are considered as a proxy for the analysis. Motivation of this thesis stems from the growing body of research by practitioners devoted to models that are reflective of the interdependency between financial assets and the real economy. The asset classes under consideration for the analysis are domestic and foreign cash, domestic and foreign bonds, domestic and foreign equity, inflation linked bonds, property, gold and commodities. In order to evaluate the performance of the regime-based strategy, this thesis proposes a framework based on Principal Component Analysis and Fuzzy Cluster Analysis for regime identification and asset allocation. The performance of the strategy is tested against two strategies that are not cognizant of regime changes. These are an equally weighted portfolio and a buy-and-hold strategy. Furthermore, relative performance analysis was performed by comparing the regime-based strategy proposed in this thesis against the Alexander Forbes Large Manager Watch Index. Due to data limitations, the analysis is done on an in-sample basis without an out-of-sample testing. The results from the analysis showed the extent of outperformance of the proposed regime-based strategy relative to an equally weighted strategy and a buy-and-hold strategy. These results were consistent with existing literature on regime-based strategies. Furthermore, the results provided strong motivation for the use of the regime identification framework together with tactical asset allocation proposed in this thesis

Supplier Selection and Relationship Management: An Application of Machine Learning Techniques

Managing supply chains is an extremely challenging task due to globalization, short product life cycle, and recent advancements in information technology. These changes result in the increasing importance of managing the relationship with suppliers. However, the supplier selection literature mainly focuses on selecting suppliers based on previous performance, environmental and social criteria and ignores supplier relationship management. Moreover, although the explosion of data and the capabilities of machine learning techniques in handling dynamic and fast changing environment show promising results in customer relationship management, especially in customer lifetime value, this area has been untouched in the upstream side of supply chains. This research is an attempt to address this gap by proposing a framework to predict supplier future value, by incorporating the contract history data, relationship value, and supply network properties. The proposed model is empirically tested for suppliers of public works and government services Canada. Methodology wise, this thesis demonstrates the application of machine learning techniques for supplier selection and developing effective strategies for managing relationships. Practically, the proposed framework equips supply chain managers with a proactive and forward-looking approach for managing supplier relationship

Risky Group Decision Making: A Comparative Analysis of FTF and CMC Teams in Stock-Trak Investment Simulations

Few studies have explicitly focused on risky group decision making in information sharing or examined the manifestation of communication differences in face-to-face (FTF) and computer-mediated communication (CMC) teams. Hypothesizing that information-sharing behaviors could be influenced not only by contextual forces but also by personality and trust, we integrate communication mode, trust and personality into a theoretical framework, and also examine the possible impact of these constructs on risky group decision-making outcomes. Our interdisciplinary study integrates the fields of information system management, investment analysis and financial education by examining both FTF and CMC teamwork in a Stock-Trak portfolio simulation. We find that contrary to the common wisdom, even though FTF team members tend to feel greater levels of trust, affiliation and satisfaction in their team collaboration process, CMC teams eventually outperform their FTF counterparts by having greater portfolio returns and investor utilities

Interactions in IS Project Portfolio Selection - Status Quo and Perspectives

One central and important requirement for IS project portfolio selection is the adequate consideration of project interactions. However, the IS discipline notably lacks a common understanding of the nature of project interactions and their impact on IS project portfolio selection. To remedy this we conduct a systematic and interdisciplinary literature review thereby providing a starting point for a cumulative research tradition. The main contribution of this paper is the development of a taxonomy to summarize the current state-of-the-art. Thereby, we provide a basis enabling researchers to develop integrated approaches. Based on the identified research gaps we formulate a research agenda for the field of IS project portfolio selection considering interactions

Efficiency of the rail sections in Brazilian railway system, using TOPSIS and a genetic algorithm to analyse optimized scenarios

A railway system plays a significant role in countries with large territorial dimensions. The Brazilian rail cargo system (BRCS), however, is focused on solid bulk for export. This paper investigates the extreme performances of BRCS through a new hybrid model that combines TOPSIS with a genetic algorithm for estimating the weights in optimized scenarios. In a second stage, the significance of selected variables was assessed. The transport of any type of cargo, a centralized control of the operation, and sharing the railway track pushing competition, and the diversification of services are significant for high performance. Public strategies are discussed.Indisponível

A Multiobjective Optimization Approach for Market Timing

The introduction of electronic exchanges was a crucial point in history as it heralded the arrival of algorithmic trading. Designers of such systems face a number of issues, one of which is deciding when to buy or sell a given security on a financial market. Although Genetic Algorithms (GA) have been the most widely used to tackle this issue, Particle Swarm Optimization (PSO) has seen much lower adoption within the domain. In two previous works, the authors adapted PSO algorithms to tackle market timing and address the shortcomings of the previous approaches both with GA and PSO. The majority of work done to date on market timing tackled it as a single objective optimization problem, which limits its suitability to live trading as designers of such strategies will realistically pursue multiple objectives such as maximizing profits, minimizing exposure to risk and using the shortest strategies to improve execution speed. In this paper, we adapt both a GA and PSO to tackle market timing as a multiobjective optimization problem and provide an in depth discussion of our results and avenues of future research

Risk Management in Environment, Production and Economy

The term "risk" is very often associated with negative meanings. However, in most cases, many opportunities can present themselves to deal with the events and to develop new solutions which can convert a possible danger to an unforeseen, positive event. This book is a structured collection of papers dealing with the subject and stressing the importance of a relevant issue such as risk management. The aim is to present the problem in various fields of application of risk management theories, highlighting the approaches which can be found in literature

An integrated approach to value chain analysis of end of life aircraft treatment

Dans cette thèse, on propose une approche holistique pour l’analyse, la modélisation et l’optimisation des performances de la chaîne de valeur pour le traitement des avions en fin de vie (FdV). Les recherches réalisées ont débouché sur onze importantes contributions. Dans la première contribution, on traite du contexte, de la complexité, de la diversité et des défis du recyclage d’avions en FdV. La seconde contribution traite du problème de la prédiction du nombre de retraits d’avions et propose une approche intégrée pour l’estimation de ce nombre de retraits. Le troisième et le quatrième articles visent à identifier les parties prenantes, les valeurs perçues par chaque partenaire et indiquent comment cette valeur peut affecter les décisions au stade de la conception. Les considérations relatives à la conception et à la fabrication ont donné lieu à quatre contributions importantes. La cinquième contribution traite des défis et opportunités pouvant résulter de l’application des concepts de la chaîne logistique verte, pour les manufacturiers d’avions. Dans la sixième contribution, un outil d’aide à la décision a été développé pour choisir la stratégie verte qui optimise les performances globales de de toute la chaîne de valeur en tenant compte des priorités et contraintes de chaque partenaire. Dans la septième contribution, un modèle mathématique est proposé pour analyser le choix stratégique des manufacturiers en réponse aux directives en matière de FdV de produits comme le résultat des interactions des compétiteurs dans le marché. La huitième contribution porte sur les travaux réalisés dans le cadre d’un stage chez le constructeur d’avions, Bombardier. Cette dernière traite de l’apport de « l’analyse du cycle de vie » au stade de la conception d’avions. La neuvième contribution introduit une méthodologie d’analyse de la chaîne de valeur dans un contexte de développement durable. Finalement, les dixième et onzième contributions proposent une approche holistique pour le traitement des avions en FdV en intégrant les concepts du « lean », du développement durable et des contraintes et opportunités inhérentes à la mondialisation des affaires. Un modèle d’optimisation intégrant les modèles d’affaires, les stratégies de désassemblage et les structures du réseau qui influencent l’efficacité, la stabilité et l’agilité du réseau de récupération est proposé. Les données requises pour exploiter le modèle sont indiquées dans l’article. Mots-clés: Fin de vie des avions, analyse de la chaîne de valeurs, développement durable, intervenants.The number of aircrafts at the end of life (EOL) is continuously increasing. Dealing with retired aircrafts considering the environmental, social and economic impacts is becoming an emerging problem in the aviation industry in near future. This thesis seeks to develop a holistic approach in order to analyze the value chain of EOL aircraft treatment in the context of sustainable development. The performed researches have led to eleven main contributions. In the first contribution, the complexity and diversity of the EOL aircraft recycling including the challenges and problem context are discussed. The second contribution addresses the challenges for estimation of retired aircrafts and proposes an integrated approach for prediction of EOL aircrafts. The third and fourth contributions aim to identify the players involved in EOL recycling context, values perceived by different shareholders and formulate that how such value can affect design decisions. Design stage consideration and manufacture’s issues are discussed and have led to four main contributions. The fifth contribution addresses the opportunities and challenges of applying green supply chain for aircraft manufacturers. In the sixth contribution, a decision tool is developed to aid manufactures in early stage of design for their green strategy choices. In the seventh contribution, a mathematical model is developed in order to analyze the strategic choice of manufacturers in response to EOL directives as the result of the interaction of competitors in the market. An internship project has been also performed in Bombardier and led to the eighth contribution, which addresses life cycle approach and incorporating the sustainability in early stage of design of aircraft. The ninth contribution introduces a methodology for analyzing the value chain in the context of sustainable development. Finally, the tenth and eleventh contributions propose a holistic approach to EOL aircraft treatment considering lean principals, sustainable development, and global business environment. An optimization model is developed to support decision making in both strategic and managerial level. The analytical approaches, decision tools and step by step guidelines proposed in this thesis will aid decision makers to identify appropriate strategies for the EOL aircraft treatment in the sustainable development context. Keywords: End of life aircraft, value chain analysis, sustainable development, stakeholders