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

Innovation in a Complex, Uncertain World: Clarifying the Questions, Seeking the Answers

Innovation has at least 40 definitions, many of which can lay claim to being reliable and valid guidelines for organizations to make improvements by doing something new and different. Towards the goal of providing insights to facilitate fruitful pursuit of supply chain, the Third Annual World Class Supply Chain Summit focused on the theme of Innovation in a Complex, Uncertain World. At this invitation-only summit on May 9th, 2018 in Milton, Ontario, executives, scholars, and students discussed a range of innovation topics. The core of those discussions sought clarity on the following: The complexities, uncertainties, and challenges that are prompting the need for innovation in contemporary supply chains Effective ways for tapping into the potential to innovate New ideas from the next generation of researchers and practitioners Questions that demand rigorous research about innovation in supply chains The summit addressed those four issues with two keynote presentations, a panel discussion, and three-minute lightning talk presentations by five students (from the doctoral through to the undergraduate level). In addition to giving voice to the next generation (via the students’ 3-minute presentations), the summit was also designed to uncover perspectives from business disciplines outside of supply chain management (SCM). This was reflected mainly in the inclusion of panelists whose expertise on the subject of innovation was built in the field of entrepreneurship. Incorporating perspectives from the next generation and from beyond the traditional scope of SCM proved useful in generating some insightful conclusions. Among those conclusions, four of the main ones are: Effective usage of supply chain analytics has the potential to yield meaningful returns for transportation services providers The creativity necessary for innovation can be learned so employers should invest in cultivating creativity and its application to challenges of interest, particularly for new and young employees Though seemingly bewildering, the complexity and challenges in modern supply chains represent opportunity for innovation Innovations need not be revolutionary in order to be of real value to an organization firm and its stakeholders This white paper reports on (a) the underlying details of those points (e.g., specific real world examples presented to reinforce those points), (b) some critical unanswered questions that surround those points, and (c) potential research projects to address those questions. These helped to solidify the summit as a valuable contributor to industry-academia deliberations of relevance to the SCM field

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda

Supply chain vulnerability assessment: A network based visualization and clustering analysis approach

Supply chains are large, complex, and often unpredictable. Purchasing and supply managers and supply chain risk managers need methods and tools to enable them to quickly understand how unexpected disruptions in the supply chain start and grow and to what extent will they negatively impact the flow of goods and services. This paper introduces a methodological approach that can be used by both researchers and managers to quickly visualize a supply chain, map out the propagation path of disruptive events from the supply side to the end customer and understand potential weaknesses in the supply chain design; taking into account the structure, connectivity, and dependence within the supply chain. The approach incorporates a Petri net and Triangularization Clustering Algorithm to offer insights into a supply chain network\u27s vulnerabilities and can be used to efficiently assess supply chain disruption mitigation strategies, especially in complex and difficulty to analyze supply chain systems

Systems Statistical Engineering – Hierarchical Fuzzy Constraint Propagation

Driven by a growing requirement during the 21st century for the integration of rigorous statistical analyses in engineering research, there has been a movement within the statistics and quality communities to evolve a unified statistical engineering body of knowledge (Hoerl & Snee, 2010). Systems Statistical Engineering research seeks to integrate causal Bayesian hierarchical modeling (Pearl, 2009) and cybernetic control theory within Beer\u27s Viable System Model (S Beer, 1972; Stafford Beer, 1979, 1985) and the Complex Systems Governance framework (Keating, 2014; Keating & Katina, 2015, 2016) to produce multivariate systemic models for robust dynamic systems mission performance. (Cotter & Quigley, 2018) set forth the Bayesian systemic hierarchical constraint propagation theoretical basis for modeling the amplification and attenuation effects of environmental constraints propagated into systemic variability and variety. In their theoretical development, they simplified the analysis to only deterministic constraints, which models only the effect of statistical risks of failure. Imprecision and uncertainty in the assessment of environmental constraints will induce additional variance components in systemic variability and variety. To make causal Bayesian hierarchical modeling more capable of capturing and representing the imprecise and uncertain nature of environments, we must incorporate rough or fuzzy functions and boundaries to model imprecision and grey boundaries to model uncertainty in constraint propagation at each system level to measure the overall impact on the organization variability and variety. This paper sets forth a proposed research method to incorporate rough, fuzzy, and Grey set theories into Systems Statistical Engineering causal Bayesian hierarchical constraints modeling