V
Virginia L. M. Spiegler
Researcher at University of Kent
Publications - 26
Citations - 1009
Virginia L. M. Spiegler is an academic researcher from University of Kent. The author has contributed to research in topics: Supply chain & Computer science. The author has an hindex of 11, co-authored 22 publications receiving 697 citations. Previous affiliations of Virginia L. M. Spiegler include Cardiff University & Brunel University London.
Papers
More filters
Journal ArticleDOI
Big data analytics in supply chain management: A state-of-the-art literature review
TL;DR: A novel classification framework is proposed that provides a full picture of current literature on where and how BDA has been applied within the SCM context and reveals a number of research gaps, which leads to future research directions.
Journal ArticleDOI
A control engineering approach to the assessment of supply chain resilience
TL;DR: In this paper, the Integral of the Time Absolute Error (ITAE) is used to evaluate the resilience of a make-to-stock supply chain with three decision parameters, i.e., inventory levels and shipment rates.
Journal ArticleDOI
Developing a resilient supply chain strategy during ‘boom’ and ‘bust’
TL;DR: In this article, a framework for the development and implementation of a resilient supply chain strategy is proposed, which illustrates the relevance of various management paradigms (robustness, agility, leanness and flexibility) in increasing a company's ability to deal with disturbances emerging from its supply chain.
Journal ArticleDOI
The value of nonlinear control theory in investigating the underlying dynamics and resilience of a grocery supply chain
TL;DR: In this article, a method to use nonlinear control theory in the dynamic analysis of supply chain resilience is developed and tested using block diagram development, transfer function formulation, describing function representation of nonlinearities and simulation.
Journal ArticleDOI
A technique to develop simplified and linearised models of complex dynamic supply chain systems
TL;DR: Simplification methods to reduce model complexity and to assist in gaining system dynamics insights are suggested and an outcome is the development of more accurate simplified linear representations of complex nonlinear supply chain models.