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The purpose of this research is to develop an established assortment-planning model identifying procedures and activities for women's wear retail buyers. This research built three assortment-planning models: (a) a conceptual moddel based on a secondary data analysis, (b) a practical-use model based on interviews using questionnaire and a set of activity cards, (c) the suggested model based on the conncetion analysis of the previous two models. Integrated DEFinition (IDEF) Functional modeling method was used to describe procedures and variables of functional activities of assortment planning and to increase the consistency of a model developing process. The variables of functional activities were determined as input, mechanism, constraint, connection, and output based on IDEF0 diagram format. Other research and pilot interviews confirmed the reliability of methodology. Experts and interviewees validated the three models. The abstract level of the suggested assortment-planning model included following concepts: (a) problem recognition, (b) information search, (c) qualitative evaluation, (d) quantitative evaluation, (e) product selection plan, and (f) plan sales. / Ph. D.

Cascades are a popular construct to observe and study information propagation (or diffusion) in social media such as Twitter and are defined using notions of influence, activity, or discourse commonality (e.g., hashtags). While these notions of cascades lead to different perspectives, primarily cascades are modeled as trees. We argue in this thesis an alternative viewpoint of cascades as forests (of trees) which yields a richer vocabulary of features to understand information propagation. We propose to develop a framework to extract forests and analyze their growth by studying their evolution at the tree-level and at the node-level. Furthermore, we outline four different problems that use the forest framework. First, we show that such forests of information cascades can be used to design counter-contagion algorithms to disrupt the spread of negative campaigns or rumors. Secondly, we demonstrate how such forests of information cascades can give us a rich set of features (structural and temporal), which can be used to forecast information flow. Thirdly, we argue that cascades modeled as forests can help us glean social network sensors to detect future contagious outbreaks that occur in the social network. To conclude, we show preliminary results of an approach - a generative model, that can describe information cascades modeled as forests and can generate synthetic cascades with empirical properties mirroring cascades extracted from Twitter. / Ph. D. / How do memes spread on blogs? How and when does a hashtag become popular? Can we predict viral content? This thesis answers such questions by analyzing information dissemination in social media. Only few years ago the goal of modeling large social and technological systems would have been unattainable. However, in less than a decade the world wide web has transformed from a large static library that people only browse into a vast information resource where people interact with each other. Through the emergence of online social networking and social media, daily activities of hundreds of millions of people are migrating to the Web. Today the Web is a “sensor” that captures the pulse of human behavior: what we are thinking, what we are doing, and what we know. Moreover, social media activity has become precursors to several events, particularly disruptive ones like protests, strike, and “occupy” events. Therefore, analyzing and forecasting the emergence of such activity is an important social research problem. This thesis presents analytical and predictive models that can predict and detect bursts of activity in social media like Twitter. We also provide algorithmic tools that can effectively quell the spread of a rumor, predict viral content, and allow scientists to synthetically simulate such events computationally. The achievement of the thesis is to arm social scientists with tools that can assist in understanding some aspects of online social behavior.

In today’s fast-moving world, a company´s ability to align with changes in the market is becoming a major competitive factor. Demand forecasting form the basis of all supply chain planning and is a process that companies often fail to recognize as a key contributor to corporate success. Different contexts and market dynamics creates different challenges for companies to overcome in order to have an efficient forecasting process, matching demand with supply. This master thesis looks at the whole forecasting process, also called forecasting management, at a decentralized global company to identify the main challenges within the process and propose recommendations on how to overcome them. The research is based on a single case study where the forecasting process is investigated using four different dimensions: Functional Integration, Approach, Systems and Performance Measurements. The study identified twelve challenges in the forecasting process where a majority can be connected to issues within information sharing and lack of support in the process. Based on the identified challenges, eight improvement suggestions where developed to target the challenges and improving the process for a decentralized global company. / I dagens snabbt utvecklande och växande landskap så är ett företags förmåga att anpassa sig till marknadens behov en betydande konkurrensfaktor. Säljprognoser utgör grunden för all planering inom försörjningskedjan och är en process som företag ofta inte erkänner som en viktig bidragsgivare till företagets framgång. Olika marknadslandskap och förutsättningar skapar olika utmaningar för företag att bemästra för att kunna bedriva ett effektivt prognosarbete och matcha efterfrågan med utbud. Detta examensarbete tittar på hela prognosprocessen, även kallad prognoshantering, hos ett decentraliserat globalt företag för att identifiera de viktigaste utmaningarna i processen och föreslå rekommendationer om hur man kan övervinna dem. Forskningen bygger på en enda fallstudie där prognosprocessen undersöks utifrån fyra olika dimensioner: Funktionell integration, strategi, system och prestandamätningar. Studien identifierade tolv utmaningar i prognosprocessen där en majoritet kan kopplas till utmaningar inom informationsdelning och brist på stöd i processen. Baserat på de identifierade utmaningarna utvecklades åtta förbättringsåtgärder för att övervinna utmaningarna och förbättra processen för ett decentraliserat globalt företag.

Floods cannot be prevented, but their devastating effects can be minimized if advance warning of the event is available. The South African Disaster Management Act (Act 57 of 2002) advocates a paradigm shift from the current "bucket and blanket brigade" response-based mind set to one where disaster prevention or mitigation are the preferred options. It is in the context of mitigating the effects of floods that the development and implementation of a reli able flood forecasting system has major significance. In the case of flash floods, a few hours lead time can afford disaster managers the opportunity to take steps which may significantly reduce loss of life and damage to property. The engineering challenges in developing and implementing such a system are numerous. In this thesis, the design and implement at ion of a flash flood forecasting system in South Africa is critically examined. The technical aspect s relating to spatio-temporal rainfall estimation and now casting are a key area in which new contributions are made. In particular, field and optical flow advection algorithms are adapted and refined to help pred ict future path s of storms; fast and pragmatic algorithms for combining rain gauge and remote sensing (rada r and satellite) estimates are re fi ned and validated; a two-dimensional adaptation of Empirical Mode Decomposition is devised to extract the temporally persistent structure embedded in rainfall fields. A second area of significant contribution relates to real-time fore cast updates, made in response to the most recent observed information. A number of techniques embedded in the rich Kalm an and adaptive filtering literature are adopted for this purpose. The work captures the current "state of play" in the South African context and hopes to provide a blueprint for future development of an essential tool for disaster management. There are a number of natural spin-offs from this work for related field s in water resources management. / Thesis (Ph.D.Eng.)-University of KwaZulu-Natal, Durban, 2007.

M.Comm. / The fundamental objective of this paper is to effectively analise and forecast currency option volatility in the South African derivative market. The study of Dollar/Rand volatility is based in the domain of quantitative and international economics. It focuses on the monetary aspect of international finance, where currency volatility is of critical significance in the hedging of open currency option positions used in investment strategies as well as in active currency risk management. Topics covered in this study include firstly a theoretical discussion of option pricing and volatility to provide the necessary financial and statistical background: Advanced volatility issues are secondly addressed to define the volatility matrix and to explain the appearance of volatility smiles and cones as well as the characteristics of the time structure of volatility. The use of volatility as an important risk management tool is also depicted. Various time-series techniques such as the Box Jenkins methodology and decomposition of Dollar/Rand historical and implied volatility are assessed and used to forecast volatility. Univariate and multivariate regression analysis is in addition described and used to find the best estimate for subsequent Dollar/Rand volatility. Finally, the paper is concluded by an analysis of time varying stochastic volatility models such as the models for autoregressive conditional heteroscedasticity. The techniques apply a regression on the variance and include a function to allow for the asymmetric nature of movements in Dollar/Rand volatility. Up to date, no formal in-depth academical research on high frequency currency volatility has been conducted in the South African derivative market. It is therefor crucial to research the unique characteristics of Dollar/Rand option volatility. If the study concludes that Dollar/Rand volatility is predictable, it will have important implications for currency option pricing and portfolio management. Investors seeking to avoid risk, may choose to adjust their portfolios by reducing their commitments to assets whose volatilities are predicted to increase, or by using dynamic diversification approaches to hedge predicted volatility increases. This is particularly true of currency derivative markets where the volatility of the underlying asset has a profound effect on the value of the derivative.

A previsão de demanda desempenha um papel de fundamental importância dentro das organizações, pois através dela é possível obter uma declaração antecipada do volume demandado no futuro, permitindo aos gestores a tomarem decisões mais consistentes e alocarem os recursos de modo eficaz para atender esta demanda. Entretanto, a eficiência na tomada de decisões e alocação dos recursos requer previsões cada vez mais acuradas. Diante deste contexto, a combinação de previsões tem sido amplamente utilizada com o intuito de melhorar a acurácia e, consequentemente, a precisão das previsões. Este estudo tem por objetivo fazer a adaptação de um modelo de previsão para estimar a demanda de produtos destinados à suplementação animal através da combinação de previsões, considerando as variáveis que possam impactar na demanda e a opinião de especialistas. O trabalho está estruturado em dois artigos, sendo que no primeiro buscou-se priorizar e selecionar, através do Processo Hierárquico Analítico (AHP), variáveis que possam impactar na demanda para que estas pudessem ser avaliadas na modelagem via regressão do artigo 2. Por sua vez, no segundo artigo, realizou-se a adaptação do modelo composto de previsão idealizado por Werner (2004), buscando uma previsão final mais acurada. Os resultados obtidos reforçam que as previsões, quando combinadas, apresentam desempenhos superiores para as medidas de acurácia MAPE, MAE e MSE, em relação às previsões individuais. / The demand prediction has a role of fundamental importance inside the organizations, because trough it is possible to obtain a previous declaration of the demanded amount in the future, allowing the managers to take more consistent decisions and to allocate the resources in an efficient manner in order to satisfy this demand. However, the efficiency in the support decision and resource allocation demands accurated predictions. So, the combination of predictions have been used with the aim of improving the accuracy and, consequently, the precision of the prediction. This study has as objective to do an adaptation of a prediction model to estimate the demand of products designated to animal supplementation through the combination of prediction, considering the variables that can impact in the demand and in the expert opinion. The work is structured in two papers, considering that the first searches to priorize and select through the Analitic Hierarch Process (AHP), variables that can impact in the demand, so they could be evalute in the regression modelling of the paper 2. By the way, in the second paper, it was done an adaptation of the composed prediction model proposed by Werner (2004), searching for a more accurated final prediction. The obtained results reinforce that the prediction, when combined, present superior performance to the accuracy metrics MAPE, MAE and MSE, in relation to the individual predictions.

Increasing competition and regulation are raising the pressure on manufacturing organisations to innovate their products. Innovation is fraught by significant uncertainty of whole product life cycle costs and this can lead to hesitance in investing which may result in a loss of competitive advantage. Innovative products exist when the minimum information for creating accurate cost models through contemporary forecasting methods does not exist. The scientific research challenge is that there are no forecasting methods available where cost data from only one time period suffices for their application. The aim of this research study was to develop a framework for forecasting cost uncertainty using cost data from only one time period. The developed framework consists of components that prepare minimum information for conversion into a future uncertainty range, forecast a future uncertainty range, and propagate the uncertainty range over time. The uncertainty range is represented as a vector space representing the state space of actual cost variance for 3 to n reasons, the dimensionality of that space is reduced through vector addition and a series of basic operators is applied to the aggregated vector in order to create a future state space of probable cost variance. The framework was validated through three case studies drawn from the United States Department of Defense. The novelty of the framework is found in the use of geometry to increase the amount of insights drawn from the cost data from only one time period and the propagation of cost uncertainty based on the geometric shape of uncertainty ranges. In order to demonstrate its benefits to industry, the framework was implemented at an aerospace manufacturing company for identifying potentially inaccurate cost estimates in early stages of the whole product life cycle.

He, Zhengjun. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 125-129). / Abstracts in English and Chinese. / ABSTRACT --- p.i / ACKNOWLEDGEMENTS --- p.iv / TABLE OF CONTENTS --- p.v / LIST OF FIGURES --- p.vii / LIST OF TABLES --- p.xi / NOMENCLATURE --- p.xii / Chapter Chapter 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Issues and problems --- p.2 / Chapter 1.2 --- Objectives --- p.3 / Chapter 1.3 --- Methodology --- p.3 / Chapter 1.4 --- Significance and benefits --- p.5 / Chapter 1.5 --- Organization of the thesis --- p.5 / Chapter Chapter 2 --- BACKGROUND AND LITERATURE --- p.7 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Daylight data measurement --- p.7 / Chapter 2.3 --- Satellite-based models to derive illuminance --- p.9 / Chapter 2.3.1 --- Irradiance derived from satellite pixel values to illuminance (indirect approaches) --- p.9 / Chapter 2.3.1.1 --- Heliosat algorithms --- p.10 / Chapter 2.3.1.2 --- Perez et al. model --- p.20 / Chapter 2.3.1.3 --- Uetani model --- p.24 / Chapter 2.3.1.4 --- Gautier et al. model --- p.25 / Chapter 2.3.1.5 --- Janjai et al. model --- p.27 / Chapter 2.3.1.6 --- Comparison of different models --- p.28 / Chapter 2.3.1.7 --- Irradiance to illuminance using luminous efficacy models --- p.31 / Chapter 2.3.2 --- Satellite pixel values to illuminance (direct approaches) --- p.34 / Chapter 2.4 --- CIE standard skies --- p.37 / Chapter 2.5 --- Sky luminance distribution and sky types prediction using meteorological data --- p.40 / Chapter 2.6 --- Sky types and sky luminance distribution prediction using satellite images --- p.48 / Chapter 2.7 --- The needs for deriving daylight data from satellite images in Subtropical southern China --- p.51 / Chapter 2.8 --- General climate information of Hong Kong --- p.52 / Chapter Chapter 3 --- USING SATELLITE-BASED METHODS TO PREDICT DAYLIGHT ILLUMINANCE --- p.55 / Chapter 3.1 --- Introduction --- p.55 / Chapter 3.2 --- Data --- p.56 / Chapter 3.3 --- Methodology --- p.62 / Chapter 3.3.1 --- Satellite pixel value to cloud index --- p.63 / Chapter 3.3.2 --- Cloud index to global illuminance: indirect approach --- p.69 / Chapter 3.3.2.1 --- Cloud index to global irradiance --- p.69 / Chapter 3.3.2.2 --- Global irradiance to global illuminance --- p.73 / Chapter 3.3.3 --- Cloud index to global illuminance: direct approach --- p.75 / Chapter 3.4 --- Model precision and results --- p.79 / Chapter 3.4.1 --- Irradiance model precision --- p.79 / Chapter 3.4.2 --- Illuminance models precision --- p.80 / Chapter 3.4.3 --- Model performance under different seasons --- p.85 / Chapter 3.5 --- Conclusions --- p.87 / Chapter Chapter 4 --- USING SATELLITE-BASED METHOD TO PREDICT SKY TYPES --- p.89 / Chapter 4.1 --- Introduction --- p.89 / Chapter 4.2 --- Data --- p.90 / Chapter 4.3 --- CIE Standard General Sky --- p.92 / Chapter 4.4 --- Sky type prediction --- p.93 / Chapter 4.4.1 --- Sample data --- p.93 / Chapter 4.4.2 --- Assessment of other approaches --- p.97 / Chapter 4.4.3 --- Formulation of a method to predict sky conditions under subtropical context --- p.101 / Chapter 4.5 --- Model precision and results --- p.105 / Chapter 4.6 --- Conclusions --- p.116 / Chapter Chapter 5 --- CONCLUSION --- p.117 / Chapter 5.1 --- Research summary --- p.117 / Chapter 5.1.1 --- The indirect approach to derive global illuminance --- p.117 / Chapter 5.1.2 --- The direct approach to derive global illuminance --- p.118 / Chapter 5.1.3 --- Sky types prediction --- p.118 / Chapter 5.2 --- Conclusion and discussion --- p.119 / Chapter 5.3 --- Research contributions and limitations --- p.122 / Chapter 5.4 --- Needs for further research --- p.123 / BIBLIOGRAPHY / APPENDIX

This thesis addresses the extratropical transition (ET) of tropical cyclones. ET is the process by which a tropical cyclone, upon encountering a baroclinic environment at higher latitudes, loses its tropical characteristics and transforms into an extratropical cyclone. The three main goals of the thesis are to develop a historical climatology of global ET occurrence, to examine future projections of ET using a global climate model, and to advance the predictive understanding of ET. A global climatology of ET from 1979-2017 is presented, which explores frequency of occurrence, geographical and seasonal patterns, climate variability, and environmental settings associated with different types of ET in global ocean basins. ET is defined objectively by means of tropical cyclones' trajectories through the Cyclone Phase Space (CPS), which is calculated using storm tracks from best track data and geopotential height fields from reanalysis datasets. Two reanalysis datasets are used and compared for this purpose, the Japanese 55-year Reanalysis (JRA-55) and the ECMWF Interim Reanalysis (ERA-Interim). Results show that ET is most common in the western North Pacific and the North Atlantic, where about half of the tropical cyclones transition into extratropical cyclones. Coastal regions in these basins also face the highest rates of landfalling ET storms. In the Southern Hemisphere basins, ET percentages range from about 20% to 40%. Different "ET pathways" through the CPS are linked to different geographical trajectories and environmental settings: A majority of ETs start with the tropical cyclone becoming thermally asymmetric and end with the formation of a cold core. This pathway typically occurs over warmer sea surface temperatures and takes longer than the reverse pathway, in which a tropical cyclone undergoes ET by developing a cold core before becoming asymmetric. The classifications of tropical cyclones into "ET storms" (tropical cyclones that undergo at some point in their lifetimes) and "non-ET storms" (tropical cyclones that do not undergo ET) obtained from JRA-55 and ERA-Interim are evaluated against the classification obtained from the best track records. In contrast to the CPS definition of ET, which is automated and objective, the best track definition of ET is given by the subjective judgment of human forecasters who take into account a wider range of data. According to the F1 score and the Matthews correlation coefficient, two performance metrics that balance classification sensitivity and specificity, the CPS classification agrees most with the best track classification in the western North Pacific and the North Atlantic, and least in the eastern North Pacific. The JRA-55 classification achieves higher performance scores than does the ERA-Interim classification, mostly because ERA-Interim has a bias toward cold-core structures in the representation of tropical cyclones. Future projections of ET are examined using a five-member ensemble of a coupled global climate model, the Flux-Adjusted Forecast-oriented Low Ocean Resolution (FLOR-FA) version of CM2.5 developed at the Geophysical Fluid Dynamics Laboratory. First, CPS is applied to 1979-2005 FLOR-FA output to develop a historical ET climatology, which is compared to the 1979-2005 ET climatology obtained from JRA-55. This comparison shows that FLOR-FA simulates many unrealistic low-latitude ET events, due to strong local maxima in the geopotential height fields used as input to calculate the CPS parameters. These local maxima, which arguably result from strong grid-scale convective updrafts, mislead the CPS to detect an upper-level cold core where one is not present. Three solutions to this problem are examined: changing the algorithm to compute the CPS parameters such that it uses 95th percentile values of geopotential instead of the maxima, a temporal smoothing of the CPS parameters, and a combination of the previous two. All three modifications largely correct the misdiagnosed cases. Future (2071-2100) projections of ET activity under the Representative Concentration Pathway 4.5 are then explored. A number of changes between the future and historical simulations are robust with respect to the different modifications to the CPS described above, though few are statistically significant. A statistical model that predicts ET in the western North Pacific and the North Atlantic is introduced. The model, a logistic regression with elastic net regularization, was developed with a focus on predictive performance as well as physical interpretability and thus resides at the interface between machine learning and traditional statistics. It uses eight predictors that characterize the storm and its environment, the most important ones being latitude and sea surface temperature. The model is shown to have skill in forecasting ET at lead times up to two days, and it can predict the phase evolution of storms that undergo ET as well as of storms that remain tropical throughout their lifetimes. When used as an instantaneous diagnostic of a storm's tropical/extratropical status, the model performs about as well as the CPS in the western North Pacific and better than the CPS in the North Atlantic, and it predicts the timings of the transitions better than the CPS in both basins. The model can be integrated into statistical tropical cyclone risk models, or may be applied to provide baseline guidance for operational forecasts.