Multicriteria analysis of technological innovation investments using fuzzy sets

Innovation is the way of transforming the resources of a company through the creativity of people into new resources and wealth. Innovation investments are essential instruments in a company’s competitive productivity and profitability strategy. Evaluation of innovation investments is a multicriteria decision making problem with many conflicting tangible and intangible criteria. Vague nature of this evaluation requires a fuzzy multicriteria methodology. In this paper we propose a fuzzy multicriteria method to evaluate technological innovation investments using eight different criteria. Fuzzy TOPSIS method is used in this evaluation and a sensitivity analysis is given. First published online: 07 Sep 201

A hybrid approach to achieve organizational agility: An empirical study of a food company

Purpose: In today’s intense global competition, agility is advocated as a fundamental characteristic for business survival and competitiveness. The purpose of this paper is to propose a practical methodology to achieve and enhance organizational agility based on strategic objectives. Design/methodology/approach: In the first step, a set of key performance indicators (KPIs) of the organization being studied are recognized and classified under the perspectives of balanced scorecard (BSC). Critical success factors are then identified by ranking the KPIs according to their importance in achieving organizational strategic objectives using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). In the second step, three houses of quality (HOQs) are constructed sequentially to identify and rank the main agile attributes, agile enablers, and improvement paths. In addition, in order to translate linguistics judgments of practitioners into numerical values in building HOQs, fuzzy logic is employed. Findings: The capability of the proposed methodology is demonstrated by applying it to a case of a multi-national food company in Iran. Through the application, the company could find the most suitable improvement paths to improve its organizational agility. Research limitations/implications: A limited number of KPIs were chosen due to computational and visual constraints related to HOQs. Another limitation, similar to other agility studies, which facilitate decision making among agility metrics, was that the metrics were more industry-specific and less inclusive. Practical implications: A strong practical advantage for the application of the methodology over directly choosing agility metrics without linking them is that through the methodology, the right metrics were selected that match organization’s core values and marketing objectives. While metrics may ostensibly seem unrelated or inappropriate, they actually contributed to the right areas where there were gaps between the current and desired level of agility. It would otherwise be impossible to choose the right metrics without a structured methodology. Originality/value: This paper proposes a novel methodology for achieving organizational agility. By utilizing and linking several tools such as BSC, fuzzy TOPSIS, and quality function deployment (QFD), the proposed approach enables organizations to identify the most appropriate agile attributes, agile enablers, and subsequently agile improvement paths

An out-of-sample framework for TOPSIS-based classifiers with application in bankruptcy prediction

This work was conducted while Prof. Pérez-Gladish was a visitant researcher at the Business School of The University of Edinburgh. She would like to thank the Spanish Ministry of Education Culture and Sport for its financial support within the framework of its International Mobility Program for Senior Researchers “Salvador de Madariaga” (Reference PRX16-0169).Since the publication of the seminal paper by Hwang and Yoon (1981) proposing Technique for Order Performance by the Similarity to Ideal Solution (TOPSIS), a substantial number of papers used this technique in a variety of applications requiring a ranking of alternatives. Very few papers use TOPSIS as a classifier (e.g. Wu and Olson, 2006; Abd-El Fattah et al., 2013) and report a good performance as in-sample classifiers. However, in practice, its use in predicting discrete variables such as risk class belonging is limited by the lack of an out-of-sample evaluation framework. In this paper, we fill this gap by proposing an integrated in-sample and out-of-sample framework for TOPSIS classifiers and test its performance on a UK dataset of bankrupt and non-bankrupt firms listed on the London Stock Exchange (LSE) during 2010–2014. Empirical results show an outstanding predictive performance both in-sample and out-of-sample and thus opens a new avenue for research and applications in risk modelling and analysis using TOPSIS as a non-parametric classifier and makes it a real contender in industry applications in banking and investment. In addition, the proposed framework is robust to a variety of implementation decisions.PostprintPeer reviewe

Corporate Credit Rating: A Survey

Corporate credit rating (CCR) plays a very important role in the process of contemporary economic and social development. How to use credit rating methods for enterprises has always been a problem worthy of discussion. Through reading and studying the relevant literature at home and abroad, this paper makes a systematic survey of CCR. This paper combs the context of the development of CCR methods from the three levels: statistical models, machine learning models and neural network models, summarizes the common databases of CCR, and deeply compares the advantages and disadvantages of the models. Finally, this paper summarizes the problems existing in the current research and prospects the future of CCR. Compared with the existing review of CCR, this paper expounds and analyzes the progress of neural network model in this field in recent years.Comment: 11 page

A mathematical programming approach to multi-attribute decision making with interval-valued intuitionistic fuzzy assessment information

This article proposes an approach to handle multi-attribute decision making (MADM) problems under the interval-valued intuitionistic fuzzy environment, in which both assessments of alternatives on attributes (hereafter, referred to as attribute values) and attribute weights are provided as interval-valued intuitionistic fuzzy numbers (IVIFNs). The notion of relative closeness is extended to interval values to accommodate IVIFN decision data, and fractional programming models are developed based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine a relative closeness interval where attribute weights are independently determined for each alternative. By employing a series of optimization models, a quadratic program is established for obtaining a unified attribute weight vector, whereby the individual IVIFN attribute values are aggregated into relative closeness intervals to the ideal solution for final ranking. An illustrative supplier selection problem is employed to demonstrate how to apply the proposed procedure

A new evaluation model for corporate financial performance using integrated CCSD and FCM-ARAS approach

The financial performance is an indicator of financial stability, health and condition of any organisation. It could be utilised as a proper measure of the firm’s credibility and its ability to pay off debts. Financial institutions use this measure to determine the lending policy and applicants’credits. This study proposes a model based on the CCSD weighing method and hybrid FCM- ARAS approach for clustering and evaluating the financial performance to enable banks to identify target groups and design appropriate and relevant policies. Based on previous studies and the views of senior financial managers of a public bank in Iran, eight economic criteria were evaluated. The presented method was used to assess the financial performance of 58 manufacturing companies applying for loans from a federal bank in Iran. However, the CCSD method was used to calculate criteria weights, and a hybrid FCM-ARAS approach was developed and applied to financial evaluation and clustering the companies. The use of the CCSD method can eliminate errors caused by subjective models and human judgments, and increase the accuracy of the assessment. In this study, the debt ratio and equity to total assets and ROA were identified as the main criteria to assess financial performanc

A FRAMEWORK FOR STRATEGIC PROJECT ANALYSIS AND PRIORITIZATION

Projects that support the long-term strategic intent and alignment are considered strategic projects. Therefore, these projects must consider their alignment with the organization’s current strategy and focus on the risk, organizational capability, resources availability, political influence, and socio-cultural factors. Quantitative and qualitative methods prioritize the projects; however, they are usually suitable for specific industries. Although prioritization models are used in the private sector, the same in the public sector is not widely seen in the literature. The lack of models in the public sector has happened because of the projects’ social implications, the value perception of different projects in the public sector, and potentially differing value perceptions attached to the types of projects in different decision-making environments in the public sector. The thesis proposes a generic framework to develop a priority list of the available basket of projects and decide on projects for the next undertaking. The focus of the thesis is on public projects. The analysis in the framework considers the critical factors for prioritization obtained from the literature clustered through the agglomerative text clustering technique. In the proposed framework, 13 critical clusters are identified and weighted using the Criteria Importance Through Intercriteria Correlation (CRITIC) method to develop their ranking using the Technique for Order of Preference Similarity Ideal Solution (TOPSIS) method. In addition, the proposed framework uses vector weighting to prioritize projects across industries. The applicability of the framework is demonstrated through Qatar’s real estate and transportation projects. The outcome obtained from the framework is compared with those obtained through the experts using the System Usability Scale (SUS). The comparison shows that the framework provides good predictability of the projects for implementation

Revisión del Uso de la Lógica Difusa Aplicada a Modelos de Puntuación Crediticia

La lógica difusa tiene una gran variedad de aplicaciones, especialmente en la toma de decisiones. Uno de los campos donde se está empezando a aplicar es en el bancario, que en los últimos años sufrió un gran golpe debido a la crisis financiera. El presente estudio analiza los modelos de scoring crediticio los cuales permiten a los bancos pronosticar el comportamiento de pago de un cliente. En este modelo se han realizado trabajos principalmente mediante modelos estadísticos y el uso de redes neuronales, pero últimamente también han incursionado los estudios de lógica difusa en este campo. En el artículo se encuentran estudios diferentes, aplicando lógica difusa con diferentes enfoques, todos con resultados positivos.Palabras clave: Lógica difusa, scoring crediticio, préstamo bancario

Decision analysis under uncertainity for sustainable development

Aplicat embargament des de la data de defensa fins el 31 de desembre de 2019Policy-making for sustainable development becomes more efficient when it is reliably backed by evidence-based decision analysis. Concretely, this is crucial in the planning of public services delivery. By translating "raw" data into information, decision analysis illuminates our judgment, and ultimately the policies we adopt. In the context of public services provision, decision analysis can support the prioritization of policy options and the monitoring of progress. However, most models are deterministic - that is, they do not consider the uncertainty in their evidence. These "incomplete" models, through their impact in policy decisions, can ultimately lead to an inefficient use of resources. The main barriers to a wider incorporation of uncertainty are: (i) the complexity of the approaches currently available, and (ii) the need to develop methods tailored to the specific decision problems faced in public services delivery. To overcome these limitations, this thesis intends to facilitate the incorporation of uncertainty in the evidence into decision analysis for sustainable development. We propose two methods. First, a non-compensatory multi-criteria prioritization under uncertainty model. Given multiple criteria and uncertain evidence, the model identifies the best policy option to improve service provision for sustainable development. The non-compensatory nature of our model makes it an attractive alternative to the widely used composite index approach. Second, a compositional trend analysis under uncertainty model to monitor service coverage. By considering the non-negativity and constant-sum constraints of the data, our model provides better estimates for measuring progress than standard statistical approaches. These two methods are validated in real case studies in the energy, water and health sectors. We apply our prioritization model to the context of strategic renewable energy planning, and the targeting of water, sanitation and hygiene services. Furthermore, we use our trend analysis model to the global monitoring of water and sanitation and child mortality. Our results emphasize the importance of considering and incorporating uncertainty in the evidence into decision analysis, particularly into prioritization and monitoring processes, both central to sustainable development practice.La formulación de políticas para el desarrollo sostenible es más eficiente cuando está respaldada por un análisis de decisiones basado en evidencia. Esto es especialmente crucial en la planificación de la prestación de servicios públicos. Al transformar los datos "brutos" en información, el análisis de decisiones ilumina nuestro juicio y, en última instancia, las políticas que adoptamos. En el contexto de la provisión de servicios públicos, el análisis de decisiones puede apoyar la priorización de las políticas públicas, así como el monitoreo del progreso. Sin embargo, la mayoría de los modelos son deterministas, es decir, no consideran la incertidumbre presente en la evidencia. Estos modelos "incompletos" pueden, a través de su impacto en las decisiones políticas, conducir a un uso ineficiente de los recursos. Las principales barreras para una incorporación más amplia de la incertidumbre son: (i) la complejidad de los enfoques actualmente disponibles, y (ii) la necesidad de desarrollar métodos adaptados a los problemas de decisión específicos a la planificación de los servicios públicos. Para superar estas limitaciones, esta tesis pretende facilitar la incorporación de la incertidumbre presente en la evidencia en el análisis de decisiones para el desarrollo sostenible. Proponemos dos métodos. Primero, un modelo de priorización multicriterio no compensatorio bajo incertidumbre. Dados múltiples criterios y evidencias con incertidumbre, el modelo identifica la mejor política para mejorar la provisión de servicios para el desarrollo sostenible. La naturaleza no compensatoria de nuestro modelo lo convierte en una alternativa atractiva al enfoque de índices compuestos ampliamente utilizado. Segundo, un modelo de análisis de tendencias composicionales bajo incertidumbre para monitorear la cobertura de los servicios. Al considerar las restricciones de no negatividad y de suma constante de los datos, nuestro modelo proporciona mejores estimadores para medir el progreso que los enfoques estadísticos estándar. Estos dos métodos se validan en casos de estudio reales en los sectores de energía, agua y salud. Aplicamos nuestro modelo de priorización al contexto de la planificación estratégica de energías renovables y de los servicios de agua, saneamiento e higiene. Además, utilizamos nuestro modelo de análisis de tendencias para el monitoreo global del accesso a agua y saneamiento, así como de la reducción de la mortalidad infantil. Nuestros resultados enfatizan la importancia de considerar e incorporar la incertidumbre de la evidencia en el análisis de decisiones, particularmente en los procesos de priorización y monitoreo, ambos centrales para la práctica del desarrollo sostenible.Postprint (published version