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2018 | Buch

Supply Chain Management and Structural Dynamics Control Kapitel lesen Erstes Kapitel lesen

Structural Dynamics and Resilience in Supply Chain Risk Management

verfasst von: Dmitry Ivanov

Verlag: Springer International Publishing

Buchreihe : International Series in Operations Research & Management Science

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

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SUCHEN

Über dieses Buch

This book offers an introduction to structural dynamics, ripple effect and resilience in supply chain disruption risk management for larger audiences. In the management section, without relying heavily on mathematical derivations, the book offers state-of-the-art concepts and methods to tackle supply chain disruption risks and designing resilient supply chains in a simple, predictable format to make it easy to understand for students and professionals with both management and engineering background.

In the technical section, the book constitutes structural dynamics control methods for supply chain management. Real-life problems are modelled and solved with the help of mathematical programming, discrete-event simulation, optimal control theory, and fuzzy logic.

The book derives practical recommendations for management decision-making with disruption risk in the following areas:

How to estimate the impact of possible disruptions on performance in the pro-active stage?

How to generate efficient and effective stabilization and recovery policies?

When does one failure trigger an adjacent set of failures?

Which supply chain structures are particular sensitive to ripple effect?

How to measure the disruption risks in the supply chain?

Inhaltsverzeichnis

Frontmatter
Chapter 1. Supply Chain Management and Structural Dynamics Control
Abstract
Structural dynamics is a theory that originated in the field of earthquake engineering and computational mechanics (Paz 1990; Clough and Penzien 1993; Chopra 2011; Humar 2012). The major principles of structural dynamics theory relate to discrete and continuous systems subject to mechanical system response to dynamic loads. Another industry stream of structural dynamics theory has been developed in the aerospace control in the area of complex structure coordination (Kalinin and Sokolov 1996; Okhtilev et al. 2006). Generally speaking, engineering approaches to structural dynamics control dealt with coordination of complex networks, the behaviour of which may be affected by internal and external disturbances.
Dmitry Ivanov
Chapter 2. Supply Chain Risk Management: Bullwhip Effect and Ripple Effect
Abstract
Uncertainty is a system property characterizing the incompleteness of our knowledge about the system and the conditions of its development. Uncertainty is a polysemic term (poly – many, sema – a sign). Historically, the first terms related to uncertainty were accident, probability and possibility, which we relate to Aristotle. Up to the twentieth century, the mathematical basics of uncertainty factor description were founded on probability–frequency interpretation and are related to Pascal, Ferma, Bernoulli and Laplace. Modern probability theory is based on the research of Kolmogorov, who introduced an axiomatic definition of probability as a measure related to a system of axioms of a so-called probability space.
Dmitry Ivanov
Chapter 3. Supply Chain Resilience: Modelling, Management, and Control
Abstract
Supply chain resilience is a multi-facet property that comprises a number of components in both internal supply chain processes and in interaction with the environment (Pettit et al. 2010; Fahimnia et al. 2015; Gupta et al. 2016) (Fig. 3.1).
Dmitry Ivanov
Chapter 4. Principles and Methods of Model-Based Decision-Making in the Supply Chain
Abstract
Decision-making is the major activity of supply chain managers. Each decision implies analytical and empirical components. Analytical methods play, therefore, a crucial role in supply chain decision-making methodologies (Kotzab et al. 2005; Dolgui and Proth 2010; Schonberger 2011; Render et al. 2012; Yalaoui et al. 2012; Stadtler et al. 2015). In this Chapter, we describe principles and methods of model-based decision-making in the supply chain.
Dmitry Ivanov
Chapter 5. OR/MS Methods for Structural Dynamics in Supply Chain Risk Management
Abstract
In this Chapter, we analyze state-of-the-art research streams on managing operational and disruption risks in supply chain design and planning. It structures and classifies existing research and practical applications of different quantitative methods subject to recently derived empirical frameworks. We identify gaps in current research and delineate future research avenues. The results of this literature analysis are twofold. Supply chain managers can observe which quantitative tools are available for different applications. On the other hand, from the point of view of operational research, limitations and future research needs can be identified for decision-supporting methods in supply chain risk management domains.
Dmitry Ivanov
Chapter 6. Hybrid Multi-objective Mathematical Optimization: Optimal Control Model for Proactive Supply Chain Recovery Planning
Abstract
We investigate a multi-stage supply chain that displays the following characteristics in line with recent studies (Amiri 2006; Manzini and Bindi 2009; Ivanov et al. 2014, 2015, 2016, 2017; Sawik 2016): (i) system performance depends on its ability to execute despite perturbations; (ii) some system elements may become unavailable because of disruptions in the supply chain, and (iii) the system experiences performance degradation if some of its elements fail.
Dmitry Ivanov
Chapter 7. Control-Theoretic Models and Algorithms for Supply Chain Scheduling with Capacity Disruption and Recovery Considerations
Abstract
Coordinated decision-making distinguishes the supply chain scheduling problem as a specific research topic (Ivanov et al. 2017). In supply chains, after processing at the production plants (i.e., the suppliers), finished products can be delivered to the next production stage in the supply chain or to the customers. In terms of scheduling theory, we have a flow shop process (Johnson 1954; Gonzalez and Sahni 1978; Gupta et al. 2001). At the same time, at each stage in this multistage environment, alternative executors (e.g., production plants and transportation modes) exist which are unequal their processing intensities. Once a job is assigned to a supplier, the processing of the operations of this job can be done either in job shop or flow shop modes. Thus, the supply chain is a hybrid flow shop (Ribas et al. 2010). This requires both machine assignment and sequencing tasks.
Dmitry Ivanov
Chapter 8. Simulation Applications to Structural Dynamics in Service and Manufacturing Supply Chain Risk Management
Abstract
Facility disruption impact on supply chain performance is studied using the example of outsourced academic journal publishing services affected by recent floods in Chennai. A discrete event simulation model is used to identify the performance impact of facility disruptions for the primary vendor. Eighteen scenarios are analyzed in terms of different disruption durations, sourcing strategies and demand patterns. Sensitivity analysis is performed for several input parameters to illustrate the model’s behavior. The analysis allows identification of the optimal sourcing strategy depending on a combination of factors: duration of disruptions, demand patterns and sourcing costs. The results indicate that higher performance can be observed by increasing the dual sourcing component as disruption durations increase. The results have some major implications. First, the analysis can be used to identify the patterns “disruption duration – sourcing strategy” with the lowest performance decrease in order to employ the most efficient reactive sourcing strategy. Second, it becomes possible to identify the most preferable (in terms of sales or efficiency) proactive and reactive sourcing strategies and compare the impacts of different patterns “demand – disruption duration – sourcing strategy” according to multiple performance dimensions.
Dmitry Ivanov
Chapter 9. Entropy-Based Supply Chain Structural Complexity Analysis
Abstract
Although supply chains are often referred to as complex systems, a discussion on why they are is rarely given. The problem of complexity has various aspects and applications (Simon 1962; Bertalanffy 1968; Mesarovic and Takahara 1975; Casti 1979). The literature on complexity shows that the viewpoints regarding the concept of “complexity” tend to be as richly varied as complexity itself. Although no unified definition of a complex system exists, a number of views on complexity may be distinguished.
Dmitry Ivanov
Chapter 10. New Drivers for Supply Chain Structural Dynamics and Resilience: Sustainability, Industry 4.0, Self-Adaptation
Abstract
Nissan Motor Co Ltd. was established in the 1930s in Japan and is dedicated to the automotive business. The natural disasters in Japan in March 2011 badly affected Nissan’s supply chain. The Iwaki and Tochigi plants were almost ruined. Almost all production operations were stopped for many days. The Yokohama plant was able to recover its operation on 17 March, while the Iwaki and Tochigi plants were unable to relaunch production until 18 April.
Dmitry Ivanov
Backmatter
Metadaten
Titel
Structural Dynamics and Resilience in Supply Chain Risk Management
verfasst von
Dmitry Ivanov
Copyright-Jahr
2018
Electronic ISBN
978-3-319-69305-7
Print ISBN
978-3-319-69304-0
DOI
https://doi.org/10.1007/978-3-319-69305-7

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