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2013 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Fuzzy Hierarchical Model for Risk Assessment

Principles, Concepts, and Practical Applications

verfasst von: Hing Kai Chan, Xiaojun Wang

Verlag: Springer London

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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SUCHEN

Über dieses Buch

Risk management is often complicated by situational uncertainties and the subjective preferences of decision makers. Fuzzy Hierarchical Model for Risk Assessment introduces a fuzzy-based hierarchical approach to solve risk management problems considering both qualitative and quantitative criteria to tackle imprecise information.

This approach is illustrated through number of case studies using examples from the food, fashion and electronics sectors to cover a range of applications including supply chain management, green product design and green initiatives. These practical examples explore how this method can be adapted and fine tuned to fit other industries as well.

Supported by an extensive literature review, Fuzzy Hierarchical Model for Risk Assessment comprehensively introduces a new method for project managers across all industries as well as researchers in risk management.

this area.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
Making decisions is part of human life. Nevertheless, making a good decision is not always easy. This is mainly because there are many contributing factors (i.e. we have multiple criteria) in a problem. Even worse, many of them involve multiple objectives (i.e. multiple input, multiple output). That means the objectives of the problems in question may be conflicting with each other. On the one hand, solving such problems can entertain multiple dimensionalities.
Hing Kai Chan, Xiaojun Wang
Chapter 2. Risk Assessment
Abstract
Risk is defined by ISO 31000 (2009) as the effect of uncertainty on objectives. Generally, risks may result from different circumstances such as uncertainty in financial markets, supply chain disruptions, project failures, security breaches, quality and safety incidents, environmental causes and disasters as well as deliberate attack from an adversary or unpredictable root cause. It is therefore important to identify and assess risks in order to enable them to be understood clearly and managed effectively. According to Flanagan and Norman (1993), risk management is a process which aims to identify and quantify all risks to which the business is exposed, so that a conscious decision can be made to manage the risks. Norman and Jansson (2004) considered risk management as understanding the risks and minimising their impact by addressing, for example, probability and direct impact. Depending on whether the risk management is assessed under the context of supply chain management, engineering, financial portfolios, information technology, project management, or public health and safety, the definitions and methods for risk management can vary widely. Risk management often includes risk identification, risk assessment, risk prioritisation and risk mitigation strategies as displayed in Fig. 2.1.
Hing Kai Chan, Xiaojun Wang
Chapter 3. Hierarchical Model in Decision Making
Abstract
Decision-making problems normally involve multiple criteria. This is already not an easy problem to address.
Hing Kai Chan, Xiaojun Wang
Chapter 4. An Integrated Fuzzy Approach for Aggregative Supplier Risk Assessment
Abstract
Managing supply chain risk has become a key challenge to many organisations. Among the studies on supply chain risk, supplier risk is one important area (Xiao et al. 2012). Supply uncertainty triggered by supplier performance variability and inconsistency often leads to delayed, deficient or defective deliveries (Davis 1993). It is brought by machine breakdowns, downtimes during manufacturing, quality and yield problems, order-entry errors, forecast inaccuracies or logistical malfunctions (Fynes et al. 2004).
Hing Kai Chan, Xiaojun Wang
Chapter 5. Fuzzy AHP Approach for Analysing Risk Rating of Environmentally Friendly Product Designs
Abstract
This chapter presents a model that integrates fuzzy logic and analytic hierarchy process (AHP) for the selection of green product designs. Life cycle assessment (LCA) is a methodology commonly utilised to analyse the environmental impacts of a product from its origin (i.e. raw materials) to its end-of-life. LCA is a popular and comprehensive tool to accomplish the objective. Please refer to Appendix 1 for an introduction of LCA. Two common critiques of LCA lie in its non-consideration of “uncertainty” when evaluating alternative designs and its time-consuming data collection process as well as in its subsequent analysis. The former limitation is particularly important in the design stage as the final options are not well defined, whereas the latter requires substantial resources and expertise. This chapter proposes an approach that blends structured LCA with fuzzy AHP (FAHP). In doing so, some of the disadvantages of LCA can be remedied, and this provides a practical tool for performing LCA. The result is a tool that is easy to use by practitioners to obtain valuable information for evaluating various product designs and particularly useful in the early stages of design when different options can be evaluated and be screened out.
Hing Kai Chan, Xiaojun Wang
Chapter 6. Fuzzy Extent Analysis for Food Risk Assessment
Abstract
The analytical hierarchy process (AHP) provides an effective way to deal with complex decision making. However, AHP requires decision makers to determine the relative importance of each criterion/factor by means of pairwise comparisons between the relevant criteria/factors included in the analysis. The decision maker may feel uncertain about the pairwise comparison or may consider that it is not a method capable of reflecting a human being’s vague thoughts (Kahraman et al. 2003). Often, the uncertainty inherent in some situations and in some problems cannot be expressed simply by using crisp values from the nine-point scale. To address the limitations of AHP, some scholars have made use of fuzzy set theory, as introduced by Zadeh (1965), to create the fuzzy AHP approach. The main benefit introduced by fuzzy AHP is that it enables a more accurate description of the decision-making process that takes place in real applications where ill-defined uncertainties are not uncommon (Huang et al. 2008).
Hing Kai Chan, Xiaojun Wang
Chapter 7. A Hierarchical Fuzzy TOPSIS Approach for the Risk Assessment of Green Supply Chain Implementation
Abstract
Green supply chain management (GSCM) has emerged as an organisational philosophy in recent years. GSCM helps organisation and their business to improve competitive advantages and profits. Nevertheless, introducing new green initiatives might require the use of new technologies in supply and production processes, as well as the development of new quality systems. Purchasing-wise, it might need the procurement of new raw materials and affect the supplier selection process. Logistics-wise, it might require new inbound and outbound logistics along with new packaging.
Hing Kai Chan, Xiaojun Wang
Chapter 8. Fuzzy-ANP Approach for Environmental Risk Assessment of Product Designs
Abstract
Analytical hierarchy process (AHP) has been widely used to solve many complicated multiple criteria decision (MCDM) problems, as discussed in the previous chapters. However, one limitation of AHP is the assumption of independence among various factors. The dynamic nature of many MCDM problems determines that factors considered in the decision problem are often not independent. The decision will mostly affect the performance of not just one, but other factors. The dynamic characteristics and complexity of the problem environment would require intensive and robust analysis in the decision making process. To address this issue, the ANP is used to take critical factors and their interdependencies into consideration. Nevertheless, ANP does not allow for uncertainty among factors. In fact, the uncertainty associated with the problem, or the lack of environmental data, is the main challenge to many MCDM. Thereby, fuzzy logic, which can be employed to deal with uncertain parameters and information, is introduced in the pairwise comparison of ANP to make up for this deficiency in conventional ANP.
Hing Kai Chan, Xiaojun Wang
Chapter 9. Conclusions and Future Research Directions
Abstract
In this book, the authors have made use of several real-life case studies to demonstrate how hierarchical approach (more specifically AHP and ANP) can be coupled with fuzzy logic in multi-criteria decision-making (MCDM) risk assessment–related problems. In summary, they are supplier selection of a manufacturing organisation, selection of eco-design options, risk management of a food supply chain and risk evaluation of green supply chain implementation. This is summarised in Table 9.1.
Hing Kai Chan, Xiaojun Wang
Backmatter
Metadaten
Titel
Fuzzy Hierarchical Model for Risk Assessment
verfasst von
Hing Kai Chan
Xiaojun Wang
Copyright-Jahr
2013
Verlag
Springer London
Electronic ISBN
978-1-4471-5043-5
Print ISBN
978-1-4471-5042-8
DOI
https://doi.org/10.1007/978-1-4471-5043-5

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