Interdependency modeling of supply chain risks incorporating game theoretic risks

Most of the current risk quantification techniques being applied in the field of Supply Chain Risk Management consider risk factors to be independent. This research considers risks as interdependent triggers, events and consequences. We propose a risk quantification framework based on Bayesian belief network modeling that is an effective method to capture the mentioned interaction between various risk factors. The conflicting incentives among stakeholders in a supply chain can jeopardize the success of a project and therefore, quantification of this category of risks named as 'Game theoretic risks' needs special consideration. We have assessed game theoretic risks in the development project of Boeing 787 aircraft. The game theoretic analysis captures uncertainty of the Tier-1 suppliers about the cost functions of each other and demonstrates that any uncertainty of information in a supply chain can adversely affect the intended outcome. Finally, we have designed a fair sharing partnership featuring continuous time domain and present value of money concept that aligns the conflicting incentives

PARAMETRIC ANALYSIS OF VARIOUS CONTROL ALGORITHMS FOR SEMI-ACTIVE SUSPENSION SYSTEM

The research paper involves modeling of passive and semi-active suspension systems using quarter car model. Different systems incorporating control strategies of groundhook, skyhook and hybrid are modeled on the basis of fuzzy logic control. A novel optimized control system is also developed that utilizes relative displacement and relative velocity across the masses as inputs to the fuzzy logic controller resulting in the output of modulating damping coefficient. A comparison of all the designed systems is carried out with the passive system for ascertaining ride comfort and road handling characteristics. The optimized system outperforms all other systems in both the parameters based on the rapid stabilizing time and minimum percentage overshoot. The optimized system comprises three membership functions for each of the inputs resulting in only nine rules thus enabling faster processing of the controller besides improved performance. The results manifest successful designing of an optimized fuzzy logic based control strategy for semi-active suspensions

Quantification of supply chain risks : development and application of a novel risk management framework

Purpose: Supply chain risk management is still an active area of research that necessitates application of existing risk quantification techniques in established fields for potential application in the research area. This study is focused on bridging the research gap through development of a novel risk management framework that merges the two quantitative techniques of Bayesian belief network and Game theory. Bayesian belief networks capture the interdependency between various risk factors while Game theory models the risks associated with conflicting incentives of stakeholders in a supply network. The proposed framework segregates risk drivers, events and consequences and captures their interdependency. Development project of Boeing 787 aircraft was a unique venture in terms of its unconventional supply chain and unproven technology. Instead of the realization of anticipated shorter development time and reduced costs, the project turned out to be a fiasco. A new term ‘game theoretic risks’ has been coined to acknowledge the risks associated with conflicting incentives of various stakeholders in a supply network. Research Approach: This study has focused on a review of the literature for establishing the research gap followed by designing of a novel framework (conceptual model) for quantification and management of supply chain risks. Subsequently, the model has been applied on the development project of Boeing 787 aircraft. Findings and Originality: The developed framework is unique in its design merging two quantitative techniques in the field of Supply Chain Risk Management. Application of the novel framework on a case study of Boeing 787 project revealed its merits in terms of capturing the dynamics of interacting risk factors. Bayesian belief network is a useful modelling technique for risk quantification capturing the interdependency between various risks. Game theoretic modelling provides an opportunity to model the risks associated with conflicting incentives of the stakeholders within a supply network. Game theory based modelling captured the behaviour of suppliers in relation to the uncertainty about cost functions and the final response of Boeing to the delays caused by suppliers. The results clearly manifested importance of the game theoretic risks and as there was no policy to align the incentives of suppliers in Boeing 787 project, there was a major delay causing financial penalty to Boeing. Furthermore, lack of management expertise in Supply Chain Risk Management was a major factor for the higher development cost. It has been proved that lack of information in a supply chain affects the desired objectives of a project. To our best knowledge, such a framework has never been developed and applied in the field of Supply Chain Risk Management. Research Impact: The output of this study will help researchers model the supply chain risks in a more realistic way and stimulate further research in this direction. Practical Impact: The application of our developed model on a case study reveals its practical impact. Had the Boeing considered the holistic view of the supply chain risks, the project delay and financial loss would not have materialized. Our model can be utilized in supply networks of other industries

Cost effectiveness and manageability based prioritisation of supply chain risk mitigation strategies

Risk treatment is an important stage of the risk management process involving selection of appropriate strategies for mitigating critical risks. Limited studies have considered evaluating such strategies within a setting of interdependent supply chain risks and risk mitigation strategies. However, the selection of strategies has not been explored from the perspective of manageability- the ease of implementing and managing a strategy. We introduce a new method of prioritising strategies on the basis of associated cost, effectiveness and manageability within a theoretically grounded framework of Bayesian Belief Networks and demonstrate its application through a simulation study. The proposed approach can help managers select an optimal combination of strategies taking into account the effort involved in implementing and managing such strategies. The results clearly reveal importance of considering manageability in addition to the cost effectiveness within a decision problem of ranking supply chain risk mitigation strategies

Performance Analysis of a Semiactive Suspension System with Particle Swarm Optimization and Fuzzy Logic Control

This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control

A NOVEL APPLICATION OF PARTICLE SWARM OPTIMIZATION TECHNIQUE IN SEMI-ACTIVE SUSPENSION SYSTEM CONTROL

This paper is based on the quarter car semi-active suspension system. Semi-active suspension system proves to be a better choice in comparison with the passive and active systems keeping in view the inherent benefits of better performance, light weight and cost effective design. The semi-active system is driven by a fuzzy logic controller that is based on the feedback of relative displacement of the suspension with respect to the road disturbance and relative velocity across the damper. Fuzzy logic control can cope with the non-linearities of system using heuristic rules. Three gains are incorporated in the system corresponding to the inputs and output of the controller. Particle swarm optimization method is utilized for its better convergence and precision. The technique results in evaluation of appropriate gains that result in superior performance of the designed system. The models are compared on the basis of suspension displacement and tire displacement for ascertaining ride comfort and road handling attributes respectively