The Potential of Artificial Intelligence in IT Project Portfolio Selection

The rapid growth of innovative technologies and the complexity of IT projects lead to the change in the tools and competency required for organization management and project management. Also, the scope of an IT product is no longer within a single project and team but requires the collaboration among multiple projects, teams and the alignment with the organization’s strategies. Therefore, project portfolio selection becomes a challenging process due to the complexity and uncertainty of various factors and risks. In the IT industry, the emergence of artificial intelligence (AI) could bring opportunities to organizations to address different challenges including challenges in project portfolio selection. In this paper, we have discussed the current challenges in IT project portfolio selection, the available methods and tools and their limitations. Then an overview of the potential applications of AI in IT project portfolio selection is explored. Finally, we conclude the paper by providing future research directions

A holistic multi-methodology for sustainable renovation

A review of the barriers for building renovation has revealed a lack of methodologies, which can promote sustainability objectives and assist various stakeholders during the design stage of building renovation/retrofitting projects. The purpose of this paper is to develop a Holistic Multi-methodology for Sustainable Renovation, which aims to deal with complexity of renovation projects. It provides a framework through which to involve the different stakeholders in the design process to improve group learning and group decision-making, and hence make the building renovation design process more robust and efficient. Therefore, the paper discusses the essence of multifaceted barriers in building renovation regarding cultural changes and technological/physical changes. The outcome is a proposal for a multi-methodology framework, which is developed by introducing, evaluating and mixing methods from Soft Systems Methodologies (SSM) with Multiple Criteria Decision Making (MCDM). The potential of applying the proposed methodology in renovation projects is demonstrated through a case study

Selecting the most suitable classification algorithm for supporting assistive technology adoption for people with dementia: A multicriteria framework

The number of people with dementia (PwD) is increasing dramatically. PwD exhibit impairments of reasoning, memory, and thought that require some form of self‐management intervention to support the completion of everyday activities while maintaining a level of independence. To address this need, efforts have been directed to the development of assistive technology solutions, which may provide an opportunity to alleviate the burden faced by the PwD and their carers. Nevertheless, uptake of such solutions has been limited. It is therefore necessary to use classifiers to discriminate between adopters and nonadopters of these technologies in order to avoid cost overruns and potential negative effects on quality of life. As multiple classification algorithms have been developed, choosing the most suitable classifier has become a critical step in technology adoption. To select the most appropriate classifier, a set of criteria from various domains need to be taken into account by decision makers. In addition, it is crucial to define the most appropriate multicriteria decision‐making approach for the modelling of technology adoption. Considering the above‐mentioned aspects, this paper presents the integration of a five‐phase methodology based on the Fuzzy Analytic Hierarchy Process and the Technique for Order of Preference by Similarity to Ideal Solution to determine the most suitable classifier for supporting assistive technology adoption studies. Fuzzy Analytic Hierarchy Process is used to determine the relative weights of criteria and subcriteria under uncertainty and Technique for Order of Preference by Similarity to Ideal Solution is applied to rank the classifier alternatives. A case study considering a mobile‐based self‐management and reminding solution for PwD is described to validate the proposed approach. The results revealed that the best classifier was k‐nearest‐neighbour with a closeness coefficient of 0.804, and the most important criterion when selecting classifiers is scalability. The paper also discusses the strengths and weaknesses of each algorithm that should be addressed in future research

An Integrated Fuzzy MCDM Hybrid Methodology to Analyze Agricultural Production

A hybrid model was developed by combining multiple-criteria decision-making (MCDM) with the analytic hierarchy process (AHP) and a fuzzy set to give decision support for choosing sustainable solutions to agricultural problems. Six steps were taken to build the suggested hybrid model: identifying and weighing criteria; normalizing data using fuzzy membership functions; calculating the weighting of the criteria using AHP; and selecting the best alternative for the agricultural problem. The objective of this case study is to demonstrate how agricultural production techniques (APTs) are becoming more complex as agricultural production becomes more complex. Organic agriculture aims to protect both the environment and consumer satisfaction by utilizing organic management practices that do not have the negative effects associated with conventional and genetic engineering production. Meanwhile, products obtained through conventional and genetic engineering techniques are more cost-effective. To present the superiority of the proposed fuzzy MCDM hybrid model, this problem is used as the causative agent’s dataset. Because the challenge involves a large number of competing quantitative and qualitative criteria, the assessment approach should improve the ratio of input data to output data. As a result, agricultural productivity should be controlled holistically. However, because the problem may contain both qualitative and quantitative facts and uncertainties, it is necessary to represent the uncertainty inherent in human thinking. To achieve superior outcomes, fuzzy set theory (FST), which enables the expression of uncertainty in human judgments, can be integrated with). The purpose of this study is to present a novel MCDM approach based on fuzzy numbers for analyzing decision-making scenarios. The proposed methodology, which is based on Buckley’s fuzzy analytic hierarchy process (B-FAHP) and the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS), uses Buckley’s fuzzy analytic hierarchy process (B-FAHP) and fuzzy TOPSIS to determine weights and rank alternatives, respectively. As a result, we attempted to include both the uncertainty and hesitancy of experts in the decision-making process through the use of fuzzy numbers. We have three main criteria in this study: Satisfaction (C1), Economy (C2), and Environment (C3). An important objective of the current research is to build a complete framework for evaluating and grading the suitability of technologies. A real-world case study is used to demonstrate the suggested paradigm’s validity. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Un enfoque de toma de decisiones multicriterio aplicado a la estrategia de transformación digital de las organizaciones por medio de la inteligencia artificial responsable en la nube de las organizaciones. Estudio de caso en el sector de salud

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Estudios Estadísticos, leída el 08-02-2023Organisations are committed to understanding both the needs of their customers and the capabilities and plans of their competitors and partners, through the processes of acquiring and evaluating market information in a systematic and anticipatory manner. On the other hand, most organisations in the last few years have defined that one of their main strategic objectives for the next few years is to become a truly data-driven organisation in the current Big Data and Artificial Intelligence (AI) context (Moreno et al., 2019). They are willing to invest heavily in Data and AI Strategy and build enterprise data and AI platforms that will enable this Market-Oriented vision (Moreno et al., 2019). In this thesis, it is presented a Multicriteria Decision Making (MCDM) model (Saaty, 1988), an AI Digital Cloud Transformation Strategy and a cloud conceptual architecture to help AI leaders and organisations with their Responsible AI journey, capable of helping global organisations to move from the use of data from descriptive to prescriptive and leveraging existing cloud services to deliver true Market-Oriented in a much shorter time (compared with traditional approaches)...Las organizaciones se comprometen a comprender tanto las necesidades de sus clientes como las capacidades y planes de sus competidores y socios, a través de procesos de adquisición y evaluación de información de mercado de manera sistemática y anticipatoria. Por otro lado, la mayoría de las organizaciones en los últimos años han definido que uno de sus principales objetivos estratégicos para los próximos años es convertirse en una organización verdaderamente orientada a los datos (data-driven) en el contexto actual de Big Data e Inteligencia Artificial (IA) (Moreno et al. al., 2019). Están dispuestos a invertir fuertemente en datos y estrategia de inteligencia artificial y construir plataformas de datos empresariales e inteligencia artificial que permitan esta visión orientada al mercado (Moreno et al., 2019). En esta tesis, se presenta un modelo de toma de decisiones multicriterio (MCDM) (Saaty, 1988), una estrategia de transformación digital de IA de la nube y una arquitectura conceptual de nube para ayudar a los líderes y organizaciones de IA en su viaje de IA responsable, capaz de ayudar a las organizaciones globales a pasar del uso de datos descriptivos a prescriptivos y aprovechar los servicios en la nube existentes para ofrecer una verdadera orientación al mercado en un tiempo mucho más corto (en comparación con los enfoques tradicionales)...Fac. de Estudios EstadísticosTRUEunpu

A Review of Failure Mode and Effects Analysis (FMEA) for Sustainable Manufacturing and Improvement in Electrostatic Chuck Manufacture and Operation

Failure modes and effect analysis (FMEA) is widely used in industry to quantify, mitigate, and eliminate risk for products and processes. It has the potential to be an important technique in supporting sustainable manufacturing by reducing the risks associated with transitioning to more sustainable processes. Whilst traditional FMEA does quantify risk by calculating a risk priority number (RPN), there are limitations to the usefulness of this due to the lack of objectiveness inherent in the method. In this paper improvements to the traditional FMEA approach are reviewed and their appropriateness in the specific case of the manufacture of electrostatic chucks (ESC) is considered.</p

Expert Weighting Based Dynamic Eco-efficiency Assessment of World Consumption

Optimizing the consumption of natural resources and ensuring the availability of resources for both current and future generations has been the target for sustainability research. This paper aims to assess the eco-efficiency of global resource consumption through the environmental footprint perspective. The study effectively utilized EXIOBASE 3.41, a multi-region input-output (MRIO) database, for collecting data and Multi-criteria decision making (MCDM) approach for eco-efficiency assessment. Besides, the present paper utilizes expert weighting strategies such as EPP, SAB, Harvard, and EQUAL for assigning relative significance to various environmental indicators. Primarily, the data sample represents the influence of environmental stressors like GHG emission, land use, energy use, material consumption, water consumption. The study expands through three major scenarios in terms of importance to the economic and environmental outcomes. As such, with three scenarios and four weighting strategies, twelve situations are considered for the purpose of the study. The study findings indicate that the eco-efficiency score for given weighting strategies concerning economic and environmental impact demonstrates a significant statistical difference. The countries like China, India, Russia, Mexico, and Turkey are worst performing while Switzerland, Japan, UK, Germany, and France are best performing in terms of eco-efficiency score. Finally, k-mean clustering algorithm has applied to rank the countries centered on eco-efficiency score and weighing strategie