Competitiveness analysis of automotive industry in Turkey using Bayesian networks

Cinicioglu, Esma Nur; Önsel, Şule; ílengin, Füsun

doi:10.1016/j.eswa.2012.03.032

Cited by 18 publications

(19 citation statements)

References 1 publication

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“…A BCN is a directed acyclic graph (Cinicioglu et al, 2012) where, if there is a directed arc from a variable X 1 to a variable X 2 , then we call X 1 the parent of X 2 and X 2 the child of X 1 .…”

Section: Fundamentals Of Bcnsmentioning

confidence: 99%

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

Ülengin

Önsel

Aktaş

et al. 2014

European Journal of Operational Research

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Three levels of competitiveness affect the success of business enterprises in a globally competitive environment: the competitiveness of the company, the competitiveness of the industry in which the company operates and the competitiveness of the country where the business is located. This study analyses the competitiveness of the automotive industry in association with the national competitiveness perspective using a methodology based on Bayesian Causal Networks. First, we structure the competitiveness problem of the automotive industry through a synthesis of expert knowledge in the light of the World Economic Forum's competitiveness indicators. Second, we model the relationships among the variables identified in the problem structuring stage and analyse these relationships using a Bayesian Causal Network. Third, we develop policy suggestions under various scenarios to enhance the national competitive advantages of the automotive industry. We present an analysis of the Turkish automotive industry as a case study. It is possible to generalise the policy suggestions developed for the case of Turkish automotive industry to the automotive industries in other developing countries where country and industry competitiveness levels are similar to those of Turkey.

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Section: Fundamentals Of Bcnsmentioning

confidence: 99%

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

Ülengin

Önsel

Aktaş

et al. 2014

European Journal of Operational Research

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“…El método aplicado es, en general, muy claro y específico, sin embargo, sus resultados son subjetivos. [24] propone un marco en el que se analizan las diferentes variables de la competitividad. Para realizar el análisis de la competitividad, el autor establece el uso de una metodología que define en un marco con 3 etapas, las cuales son detalladas en la Fig.…”

Section: Métodos Para Medir Competitividadunclassified

Modelo basado en Análisis Envolvente de Datos (DEA) para medir la competitividad de las manufactureras peruanas del sector textil

Gamarra-Alván

Díaz-Muñante

2018

Rev. peru. comput. sist.

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La competitividad proporciona mayores niveles de productividad, eficiencia y calidad, de ahí su importancia para medirla. Este trabajo presenta un modelo DEA para medir la competitividad de las manufactureras del sector textil peruano. El modelo propuesto consta de dos fases: la primera fase, mide los niveles de desarrollo de la calidad, innovación y eficiencia de las empresas; la segunda fase la competitividad de las empresas considerando su calidad innovación y eficiencia. Para ilustrar el modelo propuesto se usó el software SIAD aplicado a 16 empresas del sector de manufactura textil, y se muestra el ranking de las empresas por cada factor y por la competitividad.

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“…Ulengin, Kabak, Onsel, et al (2010) developed a BN scenario to analyse transportation-environment relationships. Cinicioglu, Önsel, and Ülengin (2012) In this paper, key factors of portfolio management will be identified and scenarios with different combinations of portfolio management practices will be studied below.…”

Section: Scenario Analysis By Bayesian Networkmentioning

confidence: 99%

A methodology for assessing the effect of portfolio management on NPD performance based on Bayesian network scenarios

Chen

2016

Expert Systems

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Firm growth and profitability come primarily from new product development. Portfolio management has been emphasized in improving new product development (NPD) performance under multiple project environments. However, few researchers have demonstrated the consequence of different combinations of portfolio management practices on NPD performance.In this study, a decision support methodology based on Bayesian network scenarios is used to simulate the effect of portfolio management on NPD performance in uncertain environments. Portfolio management (PM), the extended application of investment portfolio theory to new product development (Cooper, Edgett, & Kleinschmidt, 1999), has been widely recognized as a crucial technique to improve NPD performance, especially for project-based organizations. As a strategic tool, portfolio management is about making important decisions including NPD project selection, resource allocation and balancing NPD projects (Cooper et al., 1999). Therefore, firms need to address the issues about how to conduct PM to realize strategic objectives (Lerch & Spieth, 2013).

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Competitiveness analysis of automotive industry in Turkey using Bayesian networks

Cited by 18 publications

References 1 publication

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

Modelo basado en Análisis Envolvente de Datos (DEA) para medir la competitividad de las manufactureras peruanas del sector textil

A methodology for assessing the effect of portfolio management on NPD performance based on Bayesian network scenarios

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