Thesis (Ph. D.)--University of Texas at Austin, 2002. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis (Ph. D.)--Oregon State University, 2002. / Typescript (photocopy). Includes bibliographical references (leaves 126-133). Also available via the World Wide Web.

Thesis (M.B.A.)--University of Hong Kong, 1985.

Thesis (M.A.)--York University, 1998. Graduate Programme in Social Anthropology. / Typescript. Includes bibliographical references (leaves 167-172). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL:http://wwwlib.umi.com/cr/yorku/fullcit?pMQ33504.

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: developing countries; drinking water treatment; solar disinfection. Includes bibliographical references (p. 63-65).

Demand forecasting is an important step of a company’s supply chain management process, allowing companies to project their needs for different components that are used in the final product. This is even more important in emerging industries with job order (or project-based) products where historical demands do not exist and components may not be readily available or may involve a long lead time. Developing a demand forecasting model which accurately projects the needs of components for a company can decrease costs while decreasing overall lead times of final products. This demand forecast model takes into account projected component needs along with the likelihood of successfully winning a project bid. The model is extended to four different demand forecasting formulas incorporating different use of the winning probabilities. Historical results are then used to compare the methods and their advantages and disadvantages are discussed. / text

Implementing Build to Order (BTO) strategies instead of usual Build to Stock have the potential of sorting out some individual problems of the automotive industry (e.g. high stock of finished goods). BTO reconnects the customer to the Supply Chain by focusing on him/her and making him/her to the starting point of the production process. Consequently some objectives like stock reduction as well as an increase of market shares and profits are linked with BTO. However, some requirements and preconditions have to be fulfilled to realize BTO strategies. First of all flexibility across processes, products and volume have to be assured. Therefore actions and strategies like Direct Order Booking, integration of suppliers and 3rd party logistics, platforms and modular design could be adopted. Furthermore, it's necessary to take measures to reduce the Order to Delivery (OTD) time generally and the time of information flow, which currently accounts for more then 85% of the delay in the production and planning process, specially. Promoting IT along the whole Supply Chain, might be the key to success in this case. Finally it should be noted that BTO could not be the overall solution for the whole automotive industry. It will only be applicable if some circumstances are satisfied. Therefore the premium segment (customers going for individuality and prestige) as well as the European market (customers with relatively high willingness to wait) are the possible fields of implementing BTO in the near future. (author's abstract) / Series: Schriftenreihe des Instituts für Transportwirtschaft und Logistik - Supply Chain Management

Information Technology infrastructures continue to be dynamic, evolving, and business critical investments for companies of all sizes. Even with moves to virtualize end user computing functions, the evolution of network architectures, mobile computing devices and corporate security requirements will continue to necessitate technology upgrades requiring, at their core, the rudimentary act of placing hardware at specific physical locations on a prescribed timeline. In distributed corporate environments, deploying a range of devices sourced from multiple suppliers into geographically dispersed locations can be a challenge in material management and logistics planning. This Multi-Source, Multi-Site style of deployment is a complex balance of competing timelines where failures to meet delivery targets can have costly impacts that cascade throughout the project. Perturbations in global supply chains, manufacturing schedules, and local shipping capacities drive fluctuations in a supplier's ability to consistently and predictably execute to delivery timelines so it is the task of a deployment Project Manager to interpret a variety supply chain signals and take action to minimize the negative impacts of supply chain challenges. In that effort, the deployment PM will benefit from a structured approach to defining how available supply chain data will be used to help manage expectations, monitor execution, and effect the overall deployment success. In this paper, I present an approach that breaks deployment planning into 3 primary deliverables; the Site Plan, the Data Plan, and the Monitoring Plan. Executing those three plans will drive a PM to understand the supply chain data available to them, translate that data into information useful and understandable by all stakeholders, and monitor the progress of the supply chain against a deployment schedule. In practical terms, those plans culminate in a data mining and data management methodology that can be supported with spreadsheet based dashboards that provide both a fixed Snapshot of the status of the deployment as well as a rolling Timeline of key material movements over the duration of the deployment. The data management approach described here is specifically designed to avoid complex macro development, database queries, or software purchases that may not be available to all Project Managers. Applying the Multi-Source, Multi-Site approach, a PM can gain useful and relevant information from various streams of supply chain data using straightforward spreadsheet manipulations. With a clearer picture of supply chain execution, a PM tasked with a Multi-Source, Multi-Site deployment can better leverage project change control methods to improve their chances of successfully meeting their schedule and cost targets. / text

Agronomists are constantly experimenting with improved plot techniques that can enable them to make more precise inferences from field data. This dissertation reports two investigations: (a)evaluation of the yield potentials of some barley genotypes using two non-traditional methods, and (b)comparative assessment of the two methods. Two separate but related experiments were conducted. The nearest neighbor procedure was the first. The use of spaced-plant parameters to predict yield at normal commercial density was the second experiment. Four variations of the nearest neighbor procedure were examined. For each version the plant to be evaluated always occupied the center of the rectangle of nearest neighbors. Evaluation consisted of yield adjustments where the yield of the individual plot was compared with the mean of its nearest-neighbor genotypes. Individuals were ranked according to those deviations. Unadjusted yield data were also ranked. The error mean squares derived from ranks of various configurations were compared inter se and with that from unadjusted yield. Nearest neighbors always showed a smaller error variance than the unadjusted data. Of these the first nearest neighbors produced the smallest mean square for error and, hence, the highest efficiency of genotype ranking. This procedure substantially controlled for the effects of soil heterogeneity. Averages of individual ranks were computed and related to respective genotypes (entries). For each procedure the top 25% which fell in the upper bracket of the yield curve were considered to possess high yield potentials. This method of adjustment, ranking, averaging, and selection was applied to the unadjusted data as well as to each of the nearest neighbor procedures. Unadjusted mean yield and nearest neighbor techniques were contrasted. The rankings generated by the two procedures were similar but not identical. The significantly lower error variance of the nearest neighbor adjustments indicated that those should be used instead of unadjusted mean yield when precision is needed. However, unadjusted mean yield ranking provides broad identification of high yielding genotypes, and is a simpler statistical procedure. The second experiment examined the effectiveness of yield and yield components of spaced plants in predicting yield at normal cultural density. It was conducted for two years using primarily trend analysis. Results for individual years showed that none of the metric components of spaced plants was a satisfactory predictor of crop yield. However, when data were pooled over the whole experimental period, most of the yield components of spaced plants showed highly significant correlations with crop yield. Regression models were developed from the components which demonstrated good prediction of crop yield. Under the conditions of this study, productivity (biological yield or total weight) was revealed by all the analyses as the most important spaced-plant component for predicting yield at higher densities.

This thesis proposes a novel synergy of the classical scenario and robust approaches used for strategic supply chain optimization under uncertainty. Two novel formulations, namely the naïve robust scenario formulation and the affinely adjustable robust scenario formulation, are developed, which can be reformulated into tractable deterministic optimization problems if the uncertainty is bounded by the infinity-norm. The two formulations are applied to a classical farm planning problem and an energy and bioproduct supply chain problem. The case study results demonstrate that, compared to the scenario formulation, the proposed formulations can achieve the optimal expected economic performance with smaller number of scenarios, and they can correctly indicate the feasibility of a problem. The results also show that the affinely adjustable robust scenario formulation can better address uncertainties than the naïve robust scenario formulation. Next, a strategic optimization problem for an industrial chemical supply chain from DuPont was studied. The supply chain involves one materials warehouse, five manufacturing plants, five regional product warehouses and five market locations. Each manufacturing plant produces up to 23 grades of final products from 55 grades of primary raw materials. The goal of the strategic optimization is to determine the capacities of the five plants to maximize the total profits of the supply chain system while satisfying uncertain customer demands at the different market locations. A mathematical model is developed to relate the material and product flows in the supply chain, based on which the classical scenario approach and the affinely adjustable robust scenario formulation were developed to address the uncertainty in the demands. The case study results show the advantages of the affinely robust scenario formulation over the scenario formulation. Using the affinely adjustable robust scenario formulation often results in problems with very large sizes, which cannot be solved by regular optimization solvers efficiently. In order to exploit the decomposable structure of the formulation, Dantzig-Wolfe decomposition is studied in the thesis. Two approaches to implement Dantzig-Wolfe decomposition are developed, and both approaches involve the solution of a sequence of linear programming (LP) and mixed-integer linear programming (MILP) subproblems. The computational study of the industrial chemical supply chain shows that a combination of the two Dantzig-Wolfe approaches can achieve an optimal or a near-optimal solution much more quickly than a state-of-the-art commercial LP/MILP solver, and the computational advantage increases with the increase of number of scenarios involved in the problem. / Thesis (Master, Chemical Engineering) -- Queen's University, 2014-03-24 20:39:42.761