Evaluation of control strategies for managing supply chain risks using Bayesian belief networks

Supply chains have become complex and vulnerable and therefore, researchers are developing effective techniques in order to capture the complex structure of the supply network and interdependency between supply chain risks. Researchers have recently started using Bayesian Belief Networks for modelling supply chain risks. However, these models are still focused on limited domains of supply chain risk management like supplier selection, supplier performance evaluation and ranking. We have developed a comprehensive risk management process using Bayesian networks that captures all three stages of risk management including risk identification, risk assessment and risk evaluation. Our proposed new risk measures and evaluation scheme of different combinations of control strategies are considered as an important contribution to the literature. We have modelled supply network as a Bayesian Belief Network incorporating the supply network configuration, probabilistic interdependency between risks, resulting losses, risk mitigation control strategies and associated costs. An illustrative example is presented and three different models are solved corresponding to different risk attitudes of the decision maker. Based on our results, it is not always viable to implement control strategy at the most important risk factor because of the consideration of mitigation cost, relative loss and probabilistic interdependency between connected risk factors

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

This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2013 Elsevier B.V. All rights reserved.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

International competitiveness power and human development of countries

Human development should be the ultimate objective of human activity and its aim should be healthier, longer, and fuller lives. It is expected that if the competitiveness of a country is suitably managed, human welfare will be enhanced as a consequence. The research described here seeks to explore the relationship between the competitiveness of a country and its use for human development. For this purpose, 45 countries were evaluated using data envelopment analysis, where the global competitiveness indicators are taken as input variables and the human development index indicators as output variables. A detailed analysis is also conducted for the emerging economies

Analyzing two-way interaction between the competitiveness and logistics performance of countries

Logistics has crucial importance in national and international trade and, hence, in the development and competitiveness of a country. On the other hand, making investments in different pillars of competitiveness, such as infrastructure, higher education, etc., is expected to enhance logistics performance. In this study, this two-way interaction between the competitiveness and logistics performance of countries is investigated using a hybrid methodology. Initially, the causal directions between the competitiveness of countries and their logistics performance are established by using a Bayesian Net (BN). Subsequently, the cause-effect information gathered from the BN is taken as the input in a Partial Least Square (PLS) path model to highlight the competitiveness pillars that are more critical in contributing to countries’ logistics performance. As the last step, an importance performance map analysis (IPMA) is applied to specify the importance of the pillars that have a significant effect on logistics performance. As a result, a roadmap is provided to policymakers that specify which pillars to focus on, thus delivering a significant and immediate improvement in the logistics performance and highlighting which logistics performance indicators will lead to improvements in the competitiveness of the countries. An empirical study is conducted based on two basic indexes, as follows: (1) the Global Competitiveness Index (GCI) and its pillars are used to track the competitiveness performance, and (2) the Logistics performance Index (LPI) is used to analyze the logistics performance. According to the results, the most important GCI pillars that affect the logistics performance of a country are determined to be “Business Sophistication”, “Financial Market Development”, “Infrastructure” and “Good Market Efficiency” and “Higher Education and Training”. On the other hand, the improvement in the logistics performance index, in its turn, will especially influence the Market Size pillar of a country

Are road transportation investments in line with demand projections? A gravity-based analysis for Turkey

This is the post-print version of the article which has been published and is available at the link below.In this research, an integrated gravity-based model was built, and a scenario analysis was conducted to project the demand levels for routes related to the highway projects suggested in TINA-Turkey. The gravity-based model was used to perform a disaggregated analysis to estimate the demand levels that will occur on the routes which are planned to be improved in specific regions of Turkey from now until 2020. During the scenario development phase for these gravity-based models, the growth rate of Turkey's GDP, as estimated by the World Bank from now until 2017, was used as the baseline scenario. Besides, it is assumed that the gross value added (GVA) of the origin and destination regions of the selected routes will show a pattern similar to GDP growth rates. Based on the estimated GDP values, and the projected GVA growth rates, the demand for each selected route was projected and found that the demand level for some of these road projects is expected to be very low, and hence additional measures would be needed to make these investments worthwhile

How to improve the innovation level of a country? A Bayesian net approach

This study aims to provide strategic guidelines to policy makers who are developing strategies to improve their country’s innovativeness. In this paper, we claim that innovation cannot be related only to some factors inherent in the environment of a country, nor is it a single entity to be managed without any linkages to the rest of the actors comprising the competitiveness of a country. Hence, a comprehensive study on innovation should cover the interaction between competitiveness indicators and innovation. For this purpose, the innovation performance of 148 countries is analyzed using an integrated cluster analysis and a Bayesian network framework. These countries are first clustered based on the average values of their competitiveness indicators representing 12 pillars and several sub-pillars adopted from the Global Competitiveness Reports of World Economic Forum for the 2009-2012 period. As a result, five appropriate clusters emerge: Leaders, Followers, Runners Up, Developing Ones, and Laggers. A factor analysis is then conducted to reveal the main characteristics of each cluster in terms of competitiveness indicators. Subsequently, a Bayesian network is constructed and sensitivity analyses are performed to reveal important policies for each cluster

A decision support system to evaluate the competitiveness of nations

The measurement of competitiveness and strategy development is an important issue for policy makers. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices as well as to conduct a detailed analysis on the ongoing performance of nations’ competitiveness. For this purpose, a methodology composed of three steps is used. To start, a combined clustering analysis methodology is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient to define the stage of competitiveness a country belongs. In the proposed methodology, 135 criteria are used for a proper classification of the countries. Relationships between the criteria and classification of the countries are determined using Artificial Neural Networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in the third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one. As a final analysis, the dynamic change of the rank of the countries over years has also been investigated

Effects of quotas on Turkish foreign trade: a gravity model

As stated by a European Union Commission Report (2009), Turkey's role as a world trade participant has grown in recent years, particularly as the country has been capitalizing more on its unique geopolitical position. Given the substantial trade volume and deep-rooted relations between Turkey and the EU, due attention should be paid to their trade and economic relations, and steps should be taken to improve these relations. Turkey is the biggest economy that is in a Customs Union (CU) with the EU, but not a member of the EU, along with Andorra, Monaco, and San Marino. When it joined the CU in 1996, Turkey removed all customs duties and equivalent charges as well as quantitative restrictions. However, some EU countries impose quota limits on Turkish road transporters that may indirectly restrict trade between Turkey and the country in question. This study has investigated the effect of road-transport quotas on Turkish foreign trade with EU countries. A gravity model estimated using panel data from 18 selected EU countries between 2005 and 2012 was used for this purpose. Furthermore, as one of the leading sectors using road transportation for Turkish exports to EU countries, the textile sector was analyzed as a case study. The results indicated that quotas have significant effects on total Turkish exports by road transport as well as Turkish textile exports to EU countries. The estimated loss of Turkish exports to the selected countries in the time period analyzed was 10.6 billion dollars of Turkey's total exports by road transport and 5.65 billion dollars of Turkey's total textile exports. Therefore, it can be concluded that the quota limitations are against CU regulations because they limit not only road transportation, but also trade between parties

The basic competitiveness factors shaping the innovation performance of countries

The goal of this research is to use Bayesian Networks to discover the relations among the components of competitiveness and the innovation level of countries.For this purpose, initially the competitiveness performance of 148 countries is analyzed using an integrated cluster analysis and factor analysis framework. This facilitates the basic areas where each cluster group demonstrates a good performance and those where they need improvements relative to the othergroups in order to increase their competition level. Subsequently; a Bayesian Network is constructed using WinMine software based on competitiveness indicators drawn from WEF pillars and sub-pillars. This analysis, in its turn,investigates whether the competitiveness stage to which a country belongs has an important impact on its innovation performance and highlights, which of the basic competitiveness variables has a significant impact in shaping its innovation level

The competitiveness of nations and implications for human development

This is the post-print version of the final paper published in Socio-Economic Planning Sciences. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.Human development should be the ultimate objective of human activity, its aim being healthier, longer, and fuller lives. Thus, if the competitiveness of a nation is properly managed, enhanced human welfare should be the key expected consequence. The research described here explores the relationship between the competitiveness of a nation and its implications for human development. For this purpose, 45 countries were evaluated initially using data envelopment analysis. In this stage, global competitiveness indicators were taken as input variables with human development index indicators as output variables. Subsequently, an artificial neural network analysis was conducted to identify those factors having the greatest impact on efficiency scores