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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

Thesis (M.S.)--University of Texas at El Paso, 2008. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

This PhD thesis focuses on using time series models for counts in modelling and forecasting a special type of count series called intermittent series. An intermittent series is a series of non-negative integer values with some zero values. Such series occur in many areas including inventory control of spare parts. Various methods have been developed for intermittent demand forecasting with Croston’s method being the most widely used. Some studies focus on finding a model underlying Croston’s method. With none of these studies being successful in demonstrating an underlying model for which Croston’s method is optimal, the focus should now shift towards stationary models for intermittent demand forecasting. This thesis explores the application of a class of models for count data called the Integer Autoregressive Moving Average (INARMA) models. INARMA models have had applications in different areas such as medical science and economics, but this is the first attempt to use such a model-based method to forecast intermittent demand. In this PhD research, we first fill some gaps in the INARMA literature by finding the unconditional variance and the autocorrelation function of the general INARMA(p,q) model. The conditional expected value of the aggregated process over lead time is also obtained to be used as a lead time forecast. The accuracy of h-step-ahead and lead time INARMA forecasts are then compared to those obtained by benchmark methods of Croston, Syntetos-Boylan Approximation (SBA) and Shale-Boylan-Johnston (SBJ). The results of the simulation suggest that in the presence of a high autocorrelation in data, INARMA yields much more accurate one-step ahead forecasts than benchmark methods. The degree of improvement increases for longer data histories. It has been shown that instead of identification of the autoregressive and moving average order of the INARMA model, the most general model among the possible models can be used for forecasting. This is especially useful for short history and high autocorrelation in data. The findings of the thesis have been tested on two real data sets: (i) Royal Air Force (RAF) demand history of 16,000 SKUs and (ii) 3,000 series of intermittent demand from the automotive industry. The results show that for sparse data with long history, there is a substantial improvement in using INARMA over the benchmarks in terms of Mean Square Error (MSE) and Mean Absolute Scaled Error (MASE) for the one-step ahead forecasts. However, for series with short history the improvement is narrower. The improvement is greater for h-step ahead forecasts. The results also confirm the superiority of INARMA over the benchmark methods for lead time forecasts.

CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior / This work proposes a methodology for feasibility study of the distribution of natural gas to remote areas without access through a backbone pipeline. In recent years, one can observe a strong increase in the participation of natural gas as input in energy supply all around the world, including Brazil. The State of CearÃ, in the Northeastern Brazil, shows nowadays a natural gas supply superavit of about four million cubic meters per day. Present natural gas distribution in Cearà State occurs only in Fortaleza Metropolitan area. Although there are in the State many important urban development poles with significant potential to consume natural gas they cannot count yet with necessary supply equipments of that power input as gas pipeline. This is an important problem because wood fuel is largely used in the countryside notwithstanding its damage to the environment. All over the world the attendance of secondary markets with natural gas has been supported by trucks or trains lines as a first step before implementing a pipeline. This work aims to propose and apply a methodology to find the economic and logistics feasibility to distribute natural gas to remote regions. Such a methodology makes use of discrete choice demand forecasting technique using both revealed and stated preference data as well as the capacity facility location problem modelling and conventional indicators of economic feasibility. A case study is discussed involving the CRAJUBAR region of Cearà State. The work aims to contribute in identification of scenarios in which one can have feasible situations of energy input substitution. / Esta dissertaÃÃo propÃe uma metodologia para estudo de viabilidade da distribuiÃÃo secundÃria de gÃs natural em regiÃes afastadas de redes primÃrias de gasodutos. Diante da seguranÃa de fornecimento do gÃs natural apresentada atualmente no paÃs e no Mundo, a sua participaÃÃo na matriz energÃtica vem se intensificando nos Ãltimos anos. O Estado do Cearà apresenta superavit na oferta equivalente a quatro milhÃes de metros cÃbicos por dia de gÃs. Atualmente, a distribuiÃÃo do gÃs natural, nesse Estado, à realizada apenas na RegiÃo Metropolitana de Fortaleza, sendo que no interior se encontram importantes polos de desenvolvimento, como a RegiÃo do CRAJUBAR com uma base industrial com potencial de consumo de gÃs natural, que poderia levar à substituiÃÃo do uso principalmente de lenha no processo produtivo das empresas e, tambÃm, poderia propiciar a interiorizaÃÃo do uso do energÃtico em regiÃes ainda nÃo atendida por gasodutos. O atendimento aos consumidores de gÃs natural tem ocorrido por meio da utilizaÃÃo de distribuiÃÃo secundÃria (gasoduto virtual) indutora de mercado. Assim o objetivo deste estudo reside em propor e aplicar uma metodologia de determinaÃÃo da viabilidade da distribuiÃÃo secundÃria do gÃs natural para regiÃes nÃo atendidas por gasodutos, instrumentada pelo uso de tÃcnicas de previsÃo de demanda, de otimizaÃÃo de custos e de planilha eletrÃnica na determinaÃÃo da viabilidade econÃmica. O trabalho busca contribuir na identificaÃÃo de cenÃrios viÃveis de substituiÃÃo energÃtica para o uso do gÃs natural na regiÃo em estudo.

This study is concerned with the analyses of modern electric power-grids designed to support large supply-demand considerations in metro areas of large cities. Hence proposed are methods to determine optimal performance of the associated distribution networks vis-á-vis power availability from multiple resources (such as hydroelectric, thermal, wind-mill, solar-cell etc.) and varying load-demands posed by distinct set of consumers of domestic, industrial and commercial sectors. Hence, developing the analytics on optimal power-distribution across pertinent power-grids are verified with the models proposed. Forecast algorithms and computational outcomes on supply-demand performance are indicated and illustratively explained using real-world data sets. This study on electric utility takes duly into considerations of both deterministic (technological factors) as well as stochastic variables associated with the available resource-capacity and demand-profile details. Thus, towards forecasting exercise as above, a representative load-curve (RLC) is defined; and, it is optimally determined using an Artificial Neural Network (ANN) method using the data availed on supply-demand characteristics of a practical power-grid. This RLC is subsequently considered as an input parametric profile on tariff policies associated with electric power product-cost. This research further focuses on developing an optimal/suboptimal electric-power distribution scheme across power-grids deployed between multiple resources and different sets of user demands. Again, the optimal/suboptimal decisions are enabled using ANN-based simulations performed on load sharing details. The underlying supply-demand forecasting on distribution service profile is essential to support predictive designs on the amount of power required (or to be generated from single and/or multiple resources) versus distributable shares to different consumers demanding distinct loads. Another topic addressed refers to a business model on a cost reflective tariff levied in an electric power service in terms of the associated hedonic heuristics of customers versus service products offered by the utility operators. This model is based on hedonic considerations and technoeconomic heuristics of incumbent systems In the ANN simulations as above, bootstrapping technique is adopted to generate pseudo-replicates of the available data set and they are used to train the ANN net towards convergence. A traditional, multilayer ANN architecture (implemented with feed-forward and backpropagation techniques) is designed and modified to support a fast convergence algorithm, used for forecasting and in load-sharing computations. Underlying simulations are carried out using case-study details on electric utility gathered from the literature. In all, ANN-based prediction of a representative load-curve to assess power-consumption and tariff details in electrical power systems supporting a smart-grid, analysis of load-sharing and distribution of electric power on smart grids using an ANN and evaluation of electric power system infrastructure in terms of tariff worthiness deduced via hedonic heuristics, constitute the major thematic efforts addressed in this research study. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

published_or_final_version / Statistics / Master / Master of Social Sciences

Discrete choice models (DCMs) are used to forecast demand in a variety of engineering, marketing, and policy contexts, and understanding the uncertainty associated with model forecasts is crucial to inform decision-making. This thesis evaluates the suitability of DCMs for forecasting automotive demand. The entire scope of this investigation is too broad to be covered here, but I explore several elements with a focus on three themes: defining how to measure forecast accuracy, comparing model specifications and forecasting methods in terms of prediction accuracy, and comparing the implications of model specifications and forecasting methods on vehicle design. Specifically I address several questions regarding the accuracy and uncertainty of market share predictions resulting from choice of utility function and structural specification, estimation method, and data structure assumptions. I1 compare more than 9,000 models based on those used in peer-reviewed literature and academic and government studies. Firstly, I find that including more model covariates generally improves predictive accuracy, but that the form those covariates take in the utility function is less important. Secondly, better model fit correlates well with better predictive accuracy; however, the models I construct— representative of those in extant literature— exhibit substantial prediction error stemming largely from limited model fit due to unobserved attributes. Lastly, accuracy of predictions in existing markets is neither a necessary nor sufficient condition for use in design. Much of the econometrics literature on vehicle market modeling has presumed that biased coefficients make for bad models. For purely predictive purposes, the drawbacks of potentially mitigating bias using generalized method of moments estimation coupled with instrumental variables outweigh the expected benefits in the experiments conducted in this dissertation. The risk of specifying invalid instruments is high, and my results suggest that the instruments frequently used in the automotive demand literature are likely invalid. Furthermore, biased coefficients are not necessarily bad for maximizing the predictive power of the model. Bias can even aid predictions by implicitly capturing persistent unobserved effects in some circumstances. Including alternative specific constants (ASCs) in DCM utility functions improves model fit but not necessarily forecast accuracy. For frequentist estimated models all tested methods of forecasting ASCs improved share predictions of the whole midsize sedan market over excluding ASC in predictions, but only one method results in improved long term new vehicle, or entrant, forecasts. As seen in a synthetic data study, assuming an incorrect relationship between observed attributes and the ASC for forecasting risks making worse forecasts than would be made by a model that excludes ASCs entirely. Treating the ASCs as model parameters with full distributions of uncertainty via Bayesian estimation is more robust to selection of ASC forecasting method and less reliant on persistent market structures, however it comes at increased computational cost. Additionally, the best long term forecasts are made by the frequentist model that treats ASCs as calibration constants fit to the model post estimation of other parameters.