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This book adopts a case study based research approach to examine
the contemporary issues in the fashion industry. It documents
real-world practices in fashion business from production, marketing
to operations. Founded on an extensive review of literature, these
case studies discuss the challenges that are pertinent to the
current business environment in this important industry, provide
benchmarks and generate insights to practitioners as well as
suggest future directions to researchers. The book serves as a
nexus of the theories and the industrial practices that advances
knowledge for both the academia and the private sector in fashion
business.
This book adopts a case study based research approach to examine
the contemporary issues in the fashion industry. It documents
real-world practices in fashion business from production, marketing
to operations. Founded on an extensive review of literature, these
case studies discuss the challenges that are pertinent to the
current business environment in this important industry, provide
benchmarks and generate insights to practitioners as well as
suggest future directions to researchers. The book serves as a
nexus of the theories and the industrial practices that advances
knowledge for both the academia and the private sector in fashion
business.
Risk analysis is crucial in stochastic supply chain models. Over
the past few years, the pace has quickened for research attempting
to explore risk analysis issues in supply chain management
problems, while the majority of recent papers focus on conceptual
framework or computational numerical analysis. Pioneered by Nobel
laureate Markowitz in the 1950s, the mean-risk (MR) formulation
became a fundamental theory for risk management in finance. Despite
the significance and popularity of MR-related approaches in
finance, their applications in studying multi-echelon supply chain
management problems have only been seriously explored in recent
years. While the MR approach has already been shown to be useful in
conducting risk analysis in stochastic supply chain models, there
is no comprehensive reference source that provides the
state-of-the-art findings on this important model for supply chain
management. Thus it is significant to have a book that reviews and
extends the MR related works for supply chain risk analysis. This
book is organized into five chapters. Chapter 1 introduces the
topic, offers a timely review of various related areas, and
explains why the MR approach is important for conducting supply
chain risk analysis. Chapter 2 examines the single period inventory
model with the mean-variance and mean-semi-deviation approaches.
Extensive discussions on the efficient frontiers are also reported.
Chapter 3 explores the infinite horizon multi-period inventory
model with a mean-variance approach. Chapter 4 investigates the
supply chain coordination problem with a versatile target sales
rebate contract and a risk averse retailer possessing the
mean-variance optimization objective. Chapter 5 concludes the book
and discusses various promising future research directions and
extensions. Every chapter can be taken as a self-contained article,
and the notation within each chapter is consistently employed.
Risk analysis is crucial in stochastic supply chain models. Over
the past few years, the pace has quickened for research attempting
to explore risk analysis issues in supply chain management
problems, while the majority of recent papers focus on conceptual
framework or computational numerical analysis. Pioneered by Nobel
laureate Markowitz in the 1950s, the mean-risk (MR) formulation
became a fundamental theory for risk management in finance. Despite
the significance and popularity of MR-related approaches in
finance, their applications in studying multi-echelon supply chain
management problems have only been seriously explored in recent
years. While the MR approach has already been shown to be useful in
conducting risk analysis in stochastic supply chain models, there
is no comprehensive reference source that provides the
state-of-the-art findings on this important model for supply chain
management. Thus it is significant to have a book that reviews and
extends the MR related works for supply chain risk analysis. This
book is organized into five chapters. Chapter 1 introduces the
topic, offers a timely review of various related areas, and
explains why the MR approach is important for conducting supply
chain risk analysis. Chapter 2 examines the single period inventory
model with the mean-variance and mean-semi-deviation approaches.
Extensive discussions on the efficient frontiers are also reported.
Chapter 3 explores the infinite horizon multi-period inventory
model with a mean-variance approach. Chapter 4 investigates the
supply chain coordination problem with a versatile target sales
rebate contract and a risk averse retailer possessing the
mean-variance optimization objective. Chapter 5 concludes the book
and discusses various promising future research directions and
extensions. Every chapter can be taken as a self-contained article,
and the notation within each chapter is consistently employed.
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