Analyzing the Global Big Data Maturity Model Domains for Better Adoption of Big Data Projects

For many years now, big data has revolutionized the world. Today, companies know that creating the most value from their data is essential for their growth. However, not all big data projects are successful; in fact, it is fundamental for companies to make the correct assessment of their capabilities and identify the potential problems to address before the starting point, and this is through maturity models. In previous work, we proposed a new Maturity Model and its framework to track companies’ progress toward successful big data implementation. We identified and categorized the factors influencing big data projects into six categories: strategy alignment, data, people, governance, technology, and methodology. The model provided a final score representing the readiness level for an organization to start its big data implementation. In this paper, we focus specifically on the Global Big Data Maturity assessment tool results. We analyze the importance of maturity domains and detail the final score calculation method using the AHP technique. For this research, we reached out to nineteen North African companies’ big data experts to give us input about their ongoing projects, and the steps are: (1) Collect nineteen big data expert’s ranks for each maturity domain using online forms; (2) Use these ranks alongside the Analytic Hierarchy Process method to have the domain’s weights, which were [0.173, 0.278, 0.128; 0.190; 0.064; 0.166], respectively for the domains [strategy alignment, data, people, governance, technology, and methodology]; Then (3) use the domain’s weights alongside assessment inputs, to calculate accurate weighted scores. As a result, AHP ranks show that the data dimension has the most impact on big data projects’ success, followed by strategy, methodology, governance, people, and, last but not least, technology. The framework dashboards show that most interviewed North African companies have great big data maturity levels

Hierarchical outranking methods for multi-criteria decision aiding

Els mètodes d’Ajut a la Decisió Multi-Criteri assisteixen en la pressa de decisions implicant múltiples criteris conflictius. Existeixen dos enfocaments principals per resoldre aquest tipus de problemes: els mètodes basats en utilitat i d’outranking, cadascun amb les seves fortaleses i debilitats. Els mètodes outranking estan basats en models d’elecció social combinats amb tècniques d’intel·ligència artificial (com gestió de dades categòriques o d’incertesa). Son eines per una avaluació i comparació realista d’alternatives, basant-se en les necessitats i coneixements del prenedor de la decisió. Una de les debilitats dels mètodes outranking és la no consideració de jerarquies de criteris, que permeten una organització natural del problema, distingint diferents nivells de generalitat que modelen les relacions taxonòmiques implícites entre criteris. En aquesta tesi ens enfoquem en el desenvolupament d’eines d’outranking jeràrquiques i la seva aplicació en casos d’estudi reals per problemes de classificació i rànquing.Los métodos de Ayuda a la Decisión Multi-Criterio asisten en la toma de decisiones involucrando múltiples criterios conflictivos. Existen dos enfoques principales para resolver éste tipo de problemas: los métodos basados en utilidad y de outranking, cada uno con sus fortalezas y debilidades. Los métodos outranking están basados en modelos de elección social combinados con técnicas de Inteligencia Artificial (como gestión de datos categóricos o de incertidumbre). Son herramientas para una evaluación y comparación realista de alternativas, basándose en las necesidades y conocimientos del tomador de decisión. Una de las debilidades de los métodos outranking es la no consideración de jerarquías de criterios, que permiten una organización natural del problema, distinguiendo distintos niveles de generalidad que modelan las relaciones taxonómicas implícitas entre criterios. En ésta tesis nos enfocamos en el desarrollo de herramientas de outranking jerárquicas y su aplicación en casos de estudio reales para problemas de clasificación y ranking.Multi-Criteria Decision Aiding (MCDA) methods support complex decision making involving multiple and conflictive criteria. MCDA distinguishes two main approaches to deal with this type of problems: utility-based and outranking methods, each with its own strengths and weaknesses. Outranking methods are based on social choice models combined with Artificial Intelligence techniques (such as the management of categorical data or uncertainty). They are recognized as providing tools for a realistic assessment and comparison of a set of alternatives, based on the decision maker’s knowledge and needs. One of the main weaknesses of the outranking methods is the lack of consideration of hierarchies of criteria, which enables the decision maker to naturally organize the problem, distinguishing different levels of generality that model the implicit taxonomical relations between the criteria. In this thesis we focus on developing hierarchical outranking tools and their application to real-world case studies for ranking and sorting problems

Green Technologies for Production Processes

This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies

A model for assessing procurement irregularities in decision making process at the tendering stage of construction projects

The public procurement in Malaysia has always been regulated by a comprehensive procedure of tender preparation, evaluation and award. Though these measures are meant to insulate unwarranted behaviors or biased decisions of the procurement officers, the public procurement is still plagued with recurring irregularities. Therefore, this study aimed to review and investigate the factors causing irregularities in the current contractor selection and award process. In addition, a conceptual model for improving the procurement decision making process has been developed based on the notion of bounded rationality. In the context of a procurement committee, the individuals were not only influenced by their cognitive limitation, they are also susceptible to irrational group behavior, namely groupthink. The compound of both influences has substantially undermined the deliberation process and hence resulted irregularities in procurement decisions. This research employed quantitative approach and was participated by 289 procurement officers from Malaysian local authorities. Partial Least Square - Structural Equation Modelling (PLS-SEM) statistical analysis technique was employed to test the model. The model confirmed that three antecedents namely accountability, prior knowledge and work experience directly impact the procedural rationality. Whereas, two antecedents namely group insulation and group cohesiveness were directly related to groupthink. Besides, procedural rationality was confirmed to mitigate groupthink effect, whereas groupthink induced defective decision making. In addition, both procedural rationality and defective decision making were found to be associated with procurement decision irregularities. The model was validated for its capability to detect the likelihood of irregularities decisions in the public procurement context