Doctor of Philosophy, University of Akron, 2018, Engineering

The Hunger Relief Food Bank is a non-profit organization collecting, organizing, and channeling food to front-line agencies who have the same mission. Food banks in general act as warehouse depots reliant on donations, that distribute food to achieve their goal of ending hunger. The biggest challenge faced by Food Banks — besides matching the supply of funds and donated food with Demand — is managing and improving operations, while coping with uncertainty in Supply and Demand. Critical processes and logistical issues are the main foci for food bank performance, as integration is yet to be achieved. To address this, the researcher developed several data analytical models (including descriptive, explanatory, and forecasting [predictive] models), to provide deeper insight into critical operations and non-traditional supply chain issues influencing food bank performance; first, to fully understand the system dynamics of food bank operations, then, to overcome the uncertainty associated with the system and finally to manage and improve operations. Through Data-Mining techniques we fully understand the system dynamics of food banks and useful information was generated. Understanding the patterns and availability of donated food and the orders frequency helps food bank organizations effectively plan and manage the storage and equitable distribution of food in a sustainable way. Moreover, we explore several predictive models to estimate the quantity of both in-kind food donation and Demand, to be used to overcome the supply and Demand uncertainty experienced by food banks. In addition, Lean Six Sigma methodology was used as a framework to identify opportunities for improvement, while eliminating waste associated with its processes. Simulation-Based Optimization (prescriptive) Models were implemented to investigate operations and to reengineer processes. Similarly, incorporating uncertainty in the developed system, enabled realistic system analysis and established optimum sc (open full item for complete abstract)

Committee: Shengyong Wang (Advisor); Naw Mimoto (Committee Member); Yilmaz Sozer (Committee Member); Ling Chen (Committee Member) Subjects: Evolution and Development; Industrial Engineering; Mathematics; Mechanical Engineering

Doctor of Philosophy, University of Akron, 2017, Mechanical Engineering

Reducing 30-day readmission for certain chronic diseases has gained healthcare provider's attentions especially when the Center for Medicare and Medicaid Services (CMS) started penalizing hospitals for excess readmissions. Hospital readmission reduction program (HRRP) was established by CMS in 2012 and released in 2013 with 1% penalty on the total CMS reimbursement. This penalty increased in 2014 and 2015 to be at maximum 2% and 3% respectively. This study focuses on Congestive Heart Failure (CHF) which has the highest readmission rate faced with the financial impact of this Program. Our research effort on reducing preventable readmission is divided into three main parts: comparing the effectiveness of intervention strategies, finding the characteristics of patients at high-risk to be readmitted, and combining the outcomes of the first two parts to target the right patient with the right and cost-effective actions. Regarding the effectiveness of the most widely used intervention strategies in reducing preventable early readmission rate, several techniques and approaches have been implemented in this work to investigate, analyze, and compare the role of those interventions including Analytical Hierarchy Process (AHP), descriptive model, visualization, statistical analysis, and Lean Six-Sigma (LSS). More than thirty-five studies were carefully collected and analyzed to get the needed data for this research. The overall results showed that educate patients/caregivers (focusing on “Teach Back”) as prior at discharge strategy and home visit as post-discharge strategy are the most recommended strategies followed by telephone and discharge planning and/or instructions (using clear instruction sheets) intervention strategies. Readmission predictive modeling is one of the main proposed readmission reduction methods that have been extensively researched in the recent years. However, little has been done to systematically synthesize and analyze the results from the existin (open full item for complete abstract)

Committee: Shengyong Wang Dr. (Advisor); Chen Ling Dr. (Committee Member); Gregory Morscher Dr. (Committee Member); Yilmaz Sozer Dr. (Committee Member); Nao Mimoto Dr. (Committee Member) Subjects: Engineering; Health Care; Health Care Management; Industrial Engineering; Management; Statistics

Doctor of Philosophy, Case Western Reserve University, 2012, Management Information and Decision Systems

Digitally-enabled generative organizational processes (such as product design and development) change frequently and vary greatly within and between companies, and over time, making them difficult to understand and manage. These kinds of generative processes can be viewed as sets of organizational routines afforded by technology, which in this thesis are commonly labeled as “sociomaterial routines”. To further complicate sense-making of such processes for scholars and practitioners, digital innovations continue to alter the form of sociomaterial routines through the simultaneous consolidation of tasks and expansion of capabilities, and thus provide means to both increase and decrease complexity and variety in organizations. This complex dynamic of sociomaterial routines offers a tantalizing, yet heretofore elusive, opportunity to explore the effects digitalization and process structure have on process variety. The primary research questions addressed in this thesis are: 1) How are sociomaterial routines structurally composed, 2) what variations (over time and space) can we identify across sociomaterial routines, and 3) what can explain these variations? The theorizing and analysis of routine variation and evolution provides new insights and genuine opportunities for research inquiries—such as finding systematic drivers of variation among routines—that have been hitherto out of reach (Pentland et al. 2009). The substance of the thesis draws primarily upon three research articles my colleagues and I have published. The first introduces the suite of tools and techniques we have developed for exploring the structure of sociomaterial routines and analyzing their variation. The second article examines the way in which routines evolve, and the role embedded digital capabilities play in driving that evolution. The third develops and validates a theory of routine variation over across four world class design organizations . The findings from these studies suggest that soc (open full item for complete abstract)

Committee: Kalle Lyytinen (Advisor); Youngjin Yoo (Committee Member); Brian Pentland (Committee Member); Fred Collopy (Committee Member); Richard Buchanan (Committee Member); Richard Boland Jr. (Other) Subjects: Design; Epistemology; Evolution and Development; Information Systems; Information Technology; Management; Philosophy of Science; Social Research; Social Structure