<p> Decision making under demand uncertainty, a top priority task, has remained as the most challenging problem to many manufacturing leaders due to lack of sufficient information to establish supply chain management (SCM) standard policies. The problem was that business performance could be impeded because optimization models of existing SCM systems lacked appropriate control mechanisms to optimize inventory levels and reduce the bullwhip effect. The purpose of this quantitative correlational study was to investigate the extent to which SCM control mechanisms predict optimized inventory levels (OPT) and reduced bullwhip effect (BWE) based on the perceptions of supply chain (SC) senior-level managers of medium-size and large manufacturing firms in the United States. Model predictive control-based inventory optimization (MPC), internal model control-based inventory optimization (IMC), postponement (POS), and collaboration (COL) were used as predictor variables, and SCM performance was the criterion variables as measured by OPT and BWE. A survey was used to collect data from SC senior-level managers. Regression analysis resulted in two significant regression models for OPT and BWE that explained 61% and 49.7 % of the variance respectively for OPT (<i>p</i> &lt; .05) and BWE (<i>p</i> &lt; .05). As a result, both null hypotheses 1 and 2 were rejected, and support existed for the alternative hypotheses 1 and 2. Practical recommendations included use of MPC to optimize inventory levels, use of POS and COL strategies to reduce the bullwhip effect and optimize inventory levels, and to combine IMC, MPC, POS, and COL to synergistically reduce the bullwhip effect and optimize inventory levels. Recommendations for future research included a replicate quantitative correlation study with expansion to international manufacturing firms, a quantitative structural equation modeling study to examine relative strength and causal relationships among variables, a quantitative meta-analysis study to critically examine the findings of the study across other studies, a quantitative experimental study to further scrutinize the significant relationships between OPT and BWE, and a quantitative experimental study of archival data to reduce self-selection and self-reporting sampling biases.</p>

Thesis (M.S.)--California Polytechnic State University, 2009. / Title from PDF title page; viewed on June 24, 2009. "June 2009." "In partial fulfillment of the requirements for the degree [of] Master of Science in Computer Science." "Presented to the faculty of California Polytechnic State University, San Luis Obispo." Major professor: Alexander Dekhtyar, Ph.D. Includes bibliographical references (p. 160-193).

Thesis (PH.D.) -- Syracuse University, 2006 / "Publication number AAT 3251780."

Model management is a framework for supporting meta-data related applications where models and mappings are manipulated as first class objects using operations such as Match, Merge, ApplyFunction, and Compose. To demonstrate the approach, we show how to use model management in two scenarios related to loading data warehouses. The case study illustrates the value of model management as a methodology for approaching meta-data related problems. It also helps clarify the required semantics of key operations. These detailed scenarios provide evidence that generic model management is useful and, very likely, implementable.

Thesis (Ph.D.)--Indiana University, School of Business, 2006. / Source: Dissertation Abstracts International, Volume: 67-01, Section: A, page: 0267. Adviser: Iris Vessey. "Title of dissertation from home page (viewed Jan. 8, 2007)."

Thesis (Ph.D.)--Andrews University, 2006. / (UnM)AAI3209766. Adviser: Jimmy Kijai. Source: Dissertation Abstracts International, Volume: 67-03, Section: A, page: 1005.

Thèse (M.Sc.A.)--Université de Sherbrooke (Canada), 2007. / Titre de l'écran-titre (visionné le 1 février 2007). In ProQuest dissertations and theses. Publié aussi en version papier.

Thesis (M. S.)--Computing, Georgia Institute of Technology, 2009. / Committee Chair: Loh, Gabriel H.; Committee Member: Clark, Nathan; Committee Member: Jaleel, Aamer; Committee Member: Kim, Hyesoon; Committee Member: Lee, Hsien-Hsin S.; Committee Member: Prvulovic, Milos.

Resource management is at the heart of many diverse science and engineering research areas. Although the general notion of what constitutes a resource entity seems similar in different research areas, their types, characteristics, and constraints governing their behavior are vastly different depending on the particular domain of research and the nature of the research itself. Often research related to resource modeling and management focus on largely homogeneous resources in a relatively simplified model of the real world. The problem becomes much more challenging to deal with when working with a complex real life domain with many heterogeneous resource types and intricate constraints. In this dissertation, we have looked at the modeling and management of resource instances and tried to develop a better sense of what makes them different from other objects in a system. As part of this work, We formally define the general resource management problem, identify its major sub problem areas and their associated complexities, and look at the problem in the context of a particularly complex and dynamic environment, namely the emergency department (ED) of a hospital. We propose an approach to the problem and some of its complexities by presenting an overall unifying view, as well as tools and methods for dealing with, this pervasive, yet surprisingly under examined, type of entity, i.e. resources. We have discovered that one of the discerning characteristics of resource instances in complex and dynamic environments seem to be their dynamic capability profile that may changes depending on system context. This, in turn, often results in complex substitutability relationship amongst resource instances. We have identified four major sub-problem areas that can provide a holistic view of any resource management service. These separate, yet interconnected, areas of concerns include resource modeling, resource request specification, resource constraint management, and resource allocation. Resource modeling involves capturing of resource characteristics and their potentially dynamic behavior. Request definitions describe how resource users specify requirements for resources in a particular domain. In most domains, there are constraints that need to be satisfied while serving resources to fulfill specific requests. The fourth area of concerns, the allocation of resources, is a complex component with multiple subcomponents that closely interact with each other. In this thesis, we have described an architecture for a exible resource management service based on the above described separation of concerns. We have proposed some simple, yet effective, techniques for modeling resource instances, specifying resource requests, specifying and managing resource constraints, and allocating resource instances to meet a resource demand characterized by a continuous stream of requests. Using our proposed design, we have developed ROMEO, a resource management service and customized it to serve a task coordination framework based on Little-JIL process definition language. Our work then concentrated on evaluating the effectiveness of ROMEO in supporting simulations and executions of complex processes. For this evaluation purpose, we developed a simulation infrastructure named JSim on top of Juliette, Little-JIL's execution environment. We ran a variety of simulations of patient care processes in EDs using our ROMEO-JSim infrastructure. We also used ROMEO to support the actual execution (rather than just the simulation) of a large mediation process. A central premise, hypothesized and explored in this thesis, is a novel way of thinking about resource instances in dynamic domains, namely defining them with a set of guarded capabilities, some of which may be dependent on the execution state of the system. This has led us to think about how to represent execution states of a running system and what types of system state information might be important for representing the guard functions on the capabilities of a resource instance that define the resource instance's ability to satisfy a request at a given execution state of the system. We have also identified a small set of common types of attributes of resource instances that seem able to support specification of a large variety of resource instances in complex domains. We believe that our research supports our hypothesis that specifying resource instances as having sets of guarded capabilities provides a useful abstraction for modeling many of the complex dynamic behaviors of resource instances in such domains as hospital EDs.

Local area networks (LANs) provide links between computing devices, such as workstations, file servers, and printers, over a relatively small geographic area (usually within a single building or organization). While computer networks have existed for almost thirty years, networking technology has only recently become available to small organizations. In the late 1980's the use of LANs has begun to mushroom, and presumably has begun to affect the organizations which use LANs. A review of the literature reveals many technical and product-oriented articles about LANs, but no previous research into the organizational effects of LANs. However, a strong tradition of studying the effects of technology on social structures exists. This dissertation describes a study which builds from this tradition. The research described herein is an exploratory study of the effects of LANs on a small business environment. The particular environment chosen, the auto parts industry, is representative of many inventory-intensive, transaction-oriented small businesses. Forty-six respondents, all auto parts retailers, were asked about their reasons for acquiring their LANs and about their level of satisfaction with their LANs. Growth trends, employment trends, and regional differences in LAN usage were also studied. In brief, the major findings of the research are the following: Acquisition and satisfaction. (1) Three clusters of factors stand out among the reasons why auto parts firms have made the investment in networking technology: financial controls, managerial controls, and external factors impinging on the firm. The financial controls cluster is the most important. (2) A high level of satisfaction with computer networks exists in the auto parts user community. (3) The reasons for the high level of user satisfaction reported in Finding 2 were examined. Users report being most satisfied with financial objectives, followed closely by managerial objectives. As a group they are less satisfied with external objectives. Growth. (4) Auto parts companies which have purchased computer networks have shown significant growth, as reflected in increases in the total number of employees since the purchase. (5) Auto parts companies which have purchased computer networks have not experienced significant changes in the number of office employees since the purchase. Employment trends. (6) In the auto parts industry, the use of unskilled, or lesser-skilled, employees on the auto parts counter is increasing and is likely to continue to increase. (7) In the auto parts industry, the number of women in non-traditional positions (such as working on the parts counter or in management) has increased in the last decade. Regional differences. (8) The proportion of stores in the metropolitan Boston area which have computer networks is significantly lower than the proportion in all other areas contacted during the interview process.