Productivity assessment has received increased attention over the past several years. At the same time, the focus has moved from single-factor productivity measures, or attempts to characterize performance in terms of simple ratios, to a multi-factor construct. In this dissertation, I propose a new framework of productivity assessment—using notations of relative, absolute, and comparative performance evaluation—via Data Envelopment Analysis (DEA)-based Malmquist indexes. Performance evaluation of member units of a set, whether companies or other organizational units, with respect to a technology that is determined by the entire set may be termed relative performance evaluation. Alternatively, performance evaluation of units of such a set against a different technology may be termed absolute performance evaluation. Traditional DEA evaluates the relative efficiency of such a set of units with respect to the frontier determined by the entire set, and can be used to determine relative performance change. Absolute performance evaluation can be in the context of a different technology, whether determined by a different industry or technology or determined by a different period of time. The latter context occurs in Malmquist productivity index calculations. The former context has not been considered in the open literature. This context defines what is termed comparative performance evaluation, namely, evaluating the performance of a set of units or observed behaviors with respect to the frontier (or best practice) of another set of units, with a different technology, held to exemplary or held to comprise a benchmark set. To operationalize absolute and comparative performance evaluation, and to allow calculation of DEA-based Malmquist productivity indexes, an extension to DEA models is developed in this dissertation. The extension, namely the Benchmark DEA model and score, is applied, in illustrative empirical studies of the productivity of service and manufacturing industries. The aggregated nature of the constituent calculations of the Malmquist index obscure sources and patterns of productivity change. We introduce a process for the analysis of the components of the Malmquist index, which reveals such patterns and presents a new interpretation along with the managerial implication of each component. The approach that is developed can identify strategy shifts of individual companies in a particular time period. Furthermore, we are able to make judgments on whether or not such strategy shifts are favorable and promising. The developed productivity measurement approach is applied to address productivity trends in the computer and automobile industries using Global Fortune 500 data for the period 1991–1997.

The spatial dispersion of the population of a country is a function of its geography, history, and economic development. Enabling access to services and products for spatially dispersed populations is evermore pertinent in today's fiscally constrained socio-political landscape and relevant to both the public and private sectors. Consumer access is improved when larger segments of the population have access within shorter threshold distances, hence consuming less energy. A higher quality of access to centers that provide a service can be brought about by improving the infrastructure in a manner that facilitates access for larger segments of the population of a country or a region. This dissertation develops models for locating centers that address the enabling of access to centers for distance differentiated segments of the population of a region, whether a continent, country, state, or a city. A network of centers provides better access if the centers are located so as to serve maximal populations within each of multiple threshold distances. The models incorporate the consumer's higher utility for shorter distances by employing concentric discs or concentric rings of multiple distance thresholds to account the percentages of the population that are afforded a specific quality of access. The model, referred to as the Multiple-Concentric-Disc Location Model, uses multiple distance thresholds modeled using concentric discs, to differentiate access that is afforded to segments of the population. The model is applied to examining access to locations of branches of the Registry of Motor Vehicles and access to locations of Walmart stores in the Commonwealth of Massachusetts. Further, the use of the model to reveal the relationship between the geographic dispersion of a population and its access to centers-and thereby allow an examination of the challenges of locating centers in different regions of the world-is demonstrated via an extended application of the model to India, Africa, Europe, and USA. Another model, which is referred to as the Multiple-Concentric-Ring Location Model with Utility Decay, brings visibility to the differences in distance to centers for populations within each of a set of concentric rings by aggregating all populated places that are within each concentric ring. Model incorporates the consumer's utility for distance and frequency of usage and assumes that a distance decay function captures the consumer's utility by ascribing probabilities that a consumer will access a center that is within a specified distance. The use of these models to inform location decisions in a variety of infrastructure and economic development scenarios is also demonstrated. The computational studies in this dissertation require generating instances of the location models for multiple regions of the world. To facilitate these studies, a major component of the research is the ability to generate the data required for instances of each of the models, for any region, whether a continent, a country, a state, or a city. To facilitate solution of thousands of model instances, special purpose software employing efficient computational algorithms and complex data structures has been developed to generate the data files-in metric or imperial systems of measurement-for model instances. Visualization procedures for generating layered maps have been developed to facilitate discussion. Computational studies involving thousands of model instances, each with up to 598,488 variables and 448,526 constraints, reveal insights about possible access to services and products for a country.

A considerable segment of the business literature has espoused the importance of appropriately using process or manufacturing technology to support competitive strategy. This literature implicitly and explicitly suggests the importance of "fit" between a firm's business level strategy and its process technology. Three gaps remain with respect to the "fit" assertion: (1) The nature of fit is insufficiently specified. (2) No empirical research has attempted to statistically validate the existence of fit within an industry. (3) No empirical research has attempted to statistically link fit to firm performance. To address these issues, this dissertation surveys firms in the U.S. tooling and machining industry to test hypotheses on the nature, existence, and impact on performance of fit. Strategy is assessed as membership in one of six strategic groups derived from clustering eight strategy factors. Factor analysis results in four technology factors, Dedicated Automation, Non-Dedicated Automation, Range of Capabilities, and Computer Aided Design. Performance comprises ROS and average annual sales growth. Findings regarding the nature of fit suggest: (1) Dedicated and non-dedicated automation relate positively to new and existing product stability. Broad product range (products very different from each other) relates negatively to dedicated automation, but does not relate to non-dedicated automation. (2) Linkages may be obscured because multiple capabilities are often bundled in a given technology so that different strategies use the same technology for different reasons. (3) Process technology appears to relate primarily to strategic dimensions concerning physical product characteristics, and very little to service dimensions. The existence of fit is demonstrated by highly significant differences in technology between groups, combined with the qualitative plausibility with which these differences appear to correspond to each strategic group. Although insufficient support was found for fit linked to performance (technology moderating strategic group membership's impact on performance), results suggest that performance advantage from a technology is gained not in the group where it is most appropriate or a given, but in a group where it is also appropriate, but less widespread.

The increasing implementation of teams in organizations has led to much research attention around team processes and performance. Uncertainty exists, however, in how team processes impact collaborative activities and, ultimately, team performance. Recent research has focused on team cognition as a potential means of explaining this uncertainty. Extending this line of inquiry, my dissertation research focuses on the interplay between teams' cognitive and communicative processes that have been implicitly linked in past team research. Specifically, I examine mental model convergence among team members as a specific type of team cognition. By integrating cognition and communication explicitly, the process of mental model convergence as it unfolds during collaborative activities may be analyzed via the verbal exchange of mental model content. Herein, I compare baseline, intervention, and optimal team communication processes to understand how the communication patterns evoking the underlying mental model convergence process of baseline teams may be changed by team interventions and how the process differs among them. Baseline team data comes from 60 student teams working in a laboratory setting. These data are also used to create a model of team communication processes, which is then implemented to simulate the communication processes of teams receiving interventions. The two types of team intervention conditions investigated include initiating collaborative activities with a specific topic discussion and delaying the start of task activities. The teams with optimal communication processes are obtained using genetic algorithm optimization procedures for combinatorial problems with multiple objectives. Specifically, the genetic algorithm evolves generations of team communication processes, beginning with the baseline data, toward optimal cost and time performance. In addition to examining the mental model convergence process, the performance of intervention teams, analyzed on a neural network generated performance assessment model, is compared to baseline teams receiving no interventions and optimal teams. Results indicate that team interventions do not improve team performance equally. Furthermore, event history analysis indicates a temporal shift in the timing of communication patterns between baseline teams and top intervention teams (i.e., the best performing teams receiving interventions). Moreover, top intervention teams have mental model convergence processes that emulate those of optimal teams.

Thesis (Naval Engineer and M.S.)--Massachusette Institute of Technology, 2004. / Available from National Technical Information Service, Springfield, Va., 2004. Includes bibliographical references (p. 92). Also available online.

<p> Over the last decade, healthcare supply chain (SC) costs have increased by 40 percent in the United States. A typical hospital&rsquo;s SC costs account for 38 percent of the total, compared to less than 10 percent for most industries. Supply chain costs are the healthcare organizations&rsquo; second biggest expense. Healthcare centers are, therefore, becoming supply chain-sensitive organizations, leading to inefficiency and limited access to quality patient healthcare. This problem is exacerbated by the fact that healthcare SC cost drivers are almost unknown, which makes the work of healthcare SC managers more difficult. This study focuses on uncovering SC cost drivers and provides appropriate cost-reduction strategies tailored to confront each identified driver. Primary data were collected from health centers and secondary data was collected from databases such as Health Care Cost Institute (HCCI), Data Resources Agency for Healthcare Research &amp; Quality, National Health Expenditure Data, and Centers for Medicare &amp; Medicaid. The study looked at the attributes that explain the most variation in each contributing factor. A multiple regression was developed to predict the costs along with F tests and Student t-tests to determine the model goodness-of-fit and each factor&rsquo;s contribution significance. The results of the study might lead to improved efficiency in healthcare organizations and increased access to quality healthcare for the population.</p><p>

<p> The United States (U.S.) healthcare system is the most expensive in the world. To improve the quality and safety of care, health information technology (HIT) is broadly adopted in hospitals. While EHR systems form a critical data backbone for the facility, we need improved 'work-flow' coordination tools and platforms that can enhance real-time situational awareness and facilitate effective management of resources for enhanced and efficient care. Especially, these IT systems are mostly applied for reactive management of care services and are lacking when they come to improving the real-time "operational intelligence" of service networks that promote efficiency and quality of operations in a proactive manner. In particular, we leverage operations research and predictive analytics techniques to develop proactive coordination mechanisms and decision methods to improve the operational efficiency of bed management service in the network spanning the emergency department (ED) to inpatient units (IUs) in a hospital, a key component of healthcare in most hospitals. The purpose of this study is to deepen our knowledge on proactive coordination empowered by predictive analytics in dynamic healthcare environments populated by clinically heterogeneous patients with individual information changing throughout ED caregiving processes. To enable proactive coordination for improved resource allocation and patient flow in the ED-IU network, we address two components of modeling/analysis tasks, i.e., the design of coordination mechanisms and the generation of future state information for ED patients. </p><p> First, we explore the benefits of early task initiation for the service network spanning the emergency department (ED) and inpatient units (IUs) within a hospital. In particular, we investigate the value of proactive inpatient bed request signals from the ED to reduce ED patient boarding. Using data from a major healthcare system, we show that the EDs suffer from severe crowding and boarding not necessarily due to high IU bed occupancy but due to poor coordination of IU bed management activity. The proposed proactive IU bed allocation scheme addresses this coordination requirement without requiring additional staff resources. While the modeling framework is designed based on the inclusion of two analytical requirements, i.e., ED disposition decision prediction and remaining ED length of stay (LoS) estimation, the framework also accounts for imperfect patient disposition predictions and multiple patient sources (besides ED) to IUs. The ED-IU network setting is modeled as a fork-join queueing system. Unlike typical fork-join queue structures that respond identically to a transition, the proposed system exhibits state-dependent transition behaviors as a function of the types of entities being processed in servers. We characterize the state sets and sequences to facilitate analytical tractability. The proposed proactive bed allocation strategy can lead to significant reductions in bed allocation delay for ED patients (up to ~50%), while not increasing delays for other IU admission sources. We also demonstrate that benefits of proactive coordination can be attained even in the absence of highly accurate models for predicting ED patient dispositions. The insights from our models should give confidence to hospital managers in embracing proactive coordination and adaptive work flow technologies enabled by modern health IT systems. </p><p> Second, we investigate the quantitative modeling that analyzes the patterns of decreasing uncertainty in ED patient disposition decision making throughout the course of ED caregiving processes. The classification task of ED disposition decision prediction can be evaluated as a hierarchical classification problem, while dealing with temporal evolution and buildup of clinical information throughout the ED caregiving processes. Four different time stages within the ED course (registration, triage, first lab/imaging orders, and first lab/imaging results) are identified as the main milestone care stages. The study took place at an academic urban level 1 trauma center with an annual census of 100,000. Data for the modeling was extracted from all ED visits between May 2014 and April 2016. Both a hierarchical disposition class structure and a progressive prediction modeling approach are introduced and combined to fully facilitate the operationalization of prediction results. Multinomial logistic regression models are built for carrying out the predictions under three different classification group structures: (1) discharge vs. admission, (2) discharge vs. observation unit vs. inpatient unit, and (3) discharge vs. observation unit vs. general practice unit vs. telemetry unit vs. intensive care unit. We characterize how the accumulation of clinical information for ED patients throughout the ED caregiving processes can help improve prediction results for the three-different class groups. Each class group can enable and contribute to unique proactive coordination strategies according to the obtained future state information and prediction quality, to enhance the quality of care and operational efficiency around the ED. We also reveal that for different disposition classes, the prediction quality evolution behaves in its own unique way according to the gain of relevant information. (Abstract shortened by ProQuest.) </p><p>

Supply chains for time-sensitive products, and, in particular, for perishable products, pose specific and unique challenges. By definition, a perishable product has a limited lifetime during which it can be used, after which it should be discarded (Federgruen, Prastacos, and Zipkin (1986)). In this dissertation, I contribute to the analysis, design, and management of supply chain networks for perishable products with applications to healthcare. Specifically, I construct generalized network frameworks to capture perishable product supply chains in healthcare operating under either centralized or decentralized decision-making behavior. The dissertation is motivated by applications ranging from blood supply chains to pharmaceuticals, such as vaccines and medicines. The novelty of the modeling and computational framework includes the use of arc multipliers to capture the perishability of the healthcare product(s), along with waste management costs, and risk. The first part of the dissertation consists of a literature review of perishable product supply chains with a focus on healthcare along with an overview of the relevant methodologies. The second part of the dissertation formulates supply chains in healthcare operating under centralized decision-making behavior. In this part, I focus on both the operations management of and the sustainable design of blood supply chains and construct models for regionalized blood banking systems as belonging to the Red Cross. The third part of the dissertation considers competitive behavior, with a focus on the pharmaceutical industry. I construct an oligopoly supply chain network model, with differentiated brands to capture the competition among producers of substitutable drugs using game theory and variational inequality theory. Furthermore, using a case study based on real-world scenarios of a highly popular cholesterol-reducing branded drug, the impact of patent rights expiration of that brand is explored which coincides the time when its equivalent generic emerges into the markets. The calculated results are then compared to the observations from the real-word problem. Finally, the projected dynamical system formulation of the pharmaceutical network oligopoly model is derived. This dissertation is based on the following papers: Nagurney, Masoumi, and Yu (2012), Nagurney and Masoumi (2012), and Masoumi, Yu, and Nagurney (2012) as well as additional results and conclusions.

Thesis (Ph.D.)--Indiana University, Kelley School of Business, 2008. / Title from PDF t.p. (viewed on Jul 23, 2009). Source: Dissertation Abstracts International, Volume: 69-11, Section: A, page: 4414. Advisers: Kurt M. Bretthauer; Munirpallam A. Venkataramanan.

Thesis (Ph.D.) -- University of Western Sydney, [2002]. / "A thesis presented to the University of Western Sydney in partial fulfillment of the requirements for the degree of Doctor of Philosophy " Bibliography: leaves 254-258.