ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: Business Administration
- Creators: Fowler, John
Description
In this dissertation research, I expand the definition of the supply network to include the buying firm’s competitors. Just as one buyer-supplier relationship impacts all other relationships within the network, the presence of competitor-supplier relationships must also impact the focal buying firm. Therefore, the concept of a “competitive network” made up of a focal firm, its competitors and all of their combined suppliers is introduced. Utilizing a unique longitudinal dataset, this research explores how the organic structural changes within the new, many-to-many supply network impact firm performance. The investigation begins by studying the change in number of suppliers used by global auto manufacturers between 2004 and 2013. Following the Great Recession of 2008-09, firms have been growing the number of suppliers at more than twice the rate they had been reducing suppliers just a few years prior. The second phase of research explores the structural changes to the network resulting from this explosive growth in the number of suppliers. The final investigation explores a different flow – financial flow -- and evaluates its association with firm performance. Overall, this dissertation research demonstrates the value of aggregating individual supply networks into a macro-network defined as the competitive network. From this view, no one firm is able to control the structure of the network and the change in structure directly impacts firm performance. A new metric is introduced which addresses the subtle changes in buyer-supplier relationships and relates significantly to firm performance. The analyses expand the body of knowledge through the use of longitudinal datasets and uncovers otherwise overlooked dynamics existing within supply networks over the past decade.
ContributorsHuff, Jerry (Author) / Fowler, John (Thesis advisor) / Rogers, Dale (Committee member) / Carter, Craig (Committee member) / Arizona State University (Publisher)
Created2016
Description
The use of Red Blood Cells (RBCs) is a pillar of modern health care. Annually, the lives of hundreds of thousands of patients are saved through ready access to safe, fresh, blood-type compatible RBCs. Worldwide, hospitals have the common goal to better utilize available blood units by maximizing patients served and reducing blood wastage. Managing blood is challenging because blood is perishable, its supply is stochastic and its demand pattern is highly uncertain. Additionally, RBCs are typed and patient compatibility is required.
This research focuses on improving blood inventory management at the hospital level. It explores the importance of hospital characteristics, such as demand rate and blood-type distribution in supply and demand, for improving RBC inventory management. Available inventory models make simplifying assumptions; they tend to be general and do not utilize available data that could improve blood delivery. This dissertation develops useful and realistic models that incorporate data characterizing the hospital inventory position, distribution of blood types of donors and the population being served.
The dissertation contributions can be grouped into three areas. First, simulations are used to characterize the benefits of demand forecasting. In addition to forecast accuracy, it shows that characteristics such as forecast horizon, the age of replenishment units, and the percentage of demand that is forecastable influence the benefits resulting from demand variability reduction.
Second, it develops Markov decision models for improved allocation policies under emergency conditions, where only the units on the shelf are available for dispensing. In this situation the RBC perishability has no impact due to the short timeline for decision making. Improved location-specific policies are demonstrated via simulation models for two emergency event types: mass casualty events and pandemic influenza.
Third, improved allocation policies under normal conditions are found using Markov decision models that incorporate temporal dynamics. In this case, hospitals receive replenishment and units age and outdate. The models are solved using Approximate Dynamic Programming with model-free approximate policy iteration, using machine learning algorithms to approximate value or policy functions. These are the first stock- and age-dependent allocation policies that engage substitution between blood type groups to improve inventory performance.
This research focuses on improving blood inventory management at the hospital level. It explores the importance of hospital characteristics, such as demand rate and blood-type distribution in supply and demand, for improving RBC inventory management. Available inventory models make simplifying assumptions; they tend to be general and do not utilize available data that could improve blood delivery. This dissertation develops useful and realistic models that incorporate data characterizing the hospital inventory position, distribution of blood types of donors and the population being served.
The dissertation contributions can be grouped into three areas. First, simulations are used to characterize the benefits of demand forecasting. In addition to forecast accuracy, it shows that characteristics such as forecast horizon, the age of replenishment units, and the percentage of demand that is forecastable influence the benefits resulting from demand variability reduction.
Second, it develops Markov decision models for improved allocation policies under emergency conditions, where only the units on the shelf are available for dispensing. In this situation the RBC perishability has no impact due to the short timeline for decision making. Improved location-specific policies are demonstrated via simulation models for two emergency event types: mass casualty events and pandemic influenza.
Third, improved allocation policies under normal conditions are found using Markov decision models that incorporate temporal dynamics. In this case, hospitals receive replenishment and units age and outdate. The models are solved using Approximate Dynamic Programming with model-free approximate policy iteration, using machine learning algorithms to approximate value or policy functions. These are the first stock- and age-dependent allocation policies that engage substitution between blood type groups to improve inventory performance.
ContributorsDumkrieger, Gina (Author) / Mirchandani, Pitu B. (Thesis advisor) / Fowler, John (Committee member) / Wu, Teresa (Committee member) / Ju, Feng (Committee member) / Arizona State University (Publisher)
Created2020