Şule Önsel Ekici

Exploring dependency based probabilistic supply chain risk measures for prioritising interdependent risks and strategies

In this paper, we introduce an integrated supply chain risk management process that is grounded in the theoretical framework of Bayesian Belief Networks capturing interdependency between risks and risk mitigation strategies, and integrating all stages of the risk management process. The proposed process is unique in four different ways: instead of mapping the supply network, it makes use of Failure Modes and Effects Analysis to model the risk network which is feasible for modelling global supply chains; it is driven by new dependency based risk measures that can effectively capture the network wide impact of risks for prioritisation; it utilises the concept of Shapley value from the field of cooperative game theory to determine a fair allocation of resources to the critical risks identified; and the process helps in prioritising potential risk mitigation strategies (both preventive and reactive) subject to budget and resource constraints. We demonstrate its application through a simulation study.

Exploring the interaction between competitiveness of a country and innovation using Bayesian networks

ABSTRACT 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. World Economic Forum (WEF)’s competitiveness model consisting of 12 pillars and 19 sub-pillars is an attempt along these lines. By analysing the interaction between the Innovation pillar and the remaining 11 pillars and their sub-pillars comprising the competitiveness indicators, this paper aims to provide strategic guidelines to policy-makers who search for strategies to improve their country’s innovativeness level. For this purpose, WEF’s Global Competitiveness Index data for the period (2009-2012) is employed. The innovation performance of 148 countries is analysed using an integrated cluster analysis and a Bayesian Network (BN) framework. The use of BNs enables us to discover the probabilistic dependency structure of competitiveness indicators and its innovation performance, which may be analysed in more detail through evidence observation and sensitivity analyses conducted in the network. Thus, with this research, presenting the multidimensional nature between competitiveness indicators and Innovation, a decision support tool for policy-makers is presented, which can be used to form strategy guidelines for enhancing a country’s Innovation level.

Assessment of implication of competitiveness on human development of countries through data envelopment analysis and cluster analysis

Globalization speeds up competition among nations in various sectors. In terms of multinational and transnational phenomena, countries are seen as inescapable from competition, thus the linking of the term global with “competitiveness.” The research described here explores the relationship between the competitiveness of a country and its implications for human development. For this purpose, using data envelopment analysis (DEA) and cluster analysis, 44 selected countries were evaluated. An output-oriented super-efficiency model where global competitiveness indicators are taken as input variables with human development indicators as output variables is utilized. Then cluster analysis depending on the competitiveness and human development indicators is conducted by using self-organizing maps to specify the development levels of the countries. Both analyses are repeated for years between 2005 and 2007. Finally, the relationship between the super efficiency scores and the development levels is analyzed.

Detectability Based Prioritization of Interdependent Supply Chain Risks

Supply chain risks must be assessed in relation to the complex interdependent interaction between these risks. Generally, risk registers are used for assessing the importance of risks that treat risks in silo and fail to capture the systemic relationships. Limited studies have focused on assessing supply chain risks within the interdependent network setting. We adapt the detectability feature from the Failure Modes and Effects Analysis (FMEA) and integrate it within the theoretically grounded framework of Bayesian Belief Networks (BBNs) for prioritizing supply chain risks. Detectability represents the effectiveness of early warning system in detecting a risk before its complete realization. We introduce two new risk measures and a process for prioritizing risks within a probabilistic network of interacting risks. We demonstrate application of our method through a simple example and compare results of different ranking schemes treating risks as independent or interdependent factors. The results clearly reveal importance of considering interdependency between risks and incorporating detectability within the modelling framework.