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Demand Forecasting : A study at Alfa Laval in LundLobban, Stacey, Klimsova, Hana January 2008 (has links)
Accurate forecasting is a real problem at many companies and that includes Alfa Laval in Lund. Alfa Laval experiences problems forecasting for future raw material demand. Management is aware that the forecasting methods used today can be improved or replaced by others. A change could lead to better forecasting accuracy and lower errors which means less inventory, shorter cycle times and better customer service at lower costs. The purpose of this study is to analyze Alfa Laval’s current forecasting models for demand of raw material used for pressed plates, and then determine if other models are better suited for taking into consideration trends and seasonal variation.
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The Application And Evaluation Of Functional Link Net Techniques In Forecasting Electricity DemandYilmaz Ozturk, Isik Ekin 01 December 2008 (has links) (PDF)
This thesis analyzes the application of functional link-net (FLN) method in forecasting electricity demand in Turkey. Current official forecasting model (MAED), which is employed by Turkish Electricity Transmission Company (TEiAS) and other methods are discussed. An emprical investigation and evaluation of using functional link nets is provided.
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Air Passenger Demand Forecasting For Planned Airports, Case Study: Zafer And Or-gi Airports In TurkeyYazici, Riza Onur 01 February 2011 (has links) (PDF)
The economic evaluation of a new airport investment requires the use of estimated future air
passenger demand.Today it is well known that air passenger demand is basicly dependent on
various socioeconomic factors of the country and the region where the planned airport would
serve. This study is focused on estimating the future air passenger demand for planned
airports in Turkey where the historical air passsenger data is not available.For these
purposses, neural networks and multi-linear regression were used to develop forecasting
models.
As independent variables,twelve socioeconomic parameters are found to be significant and
used in models. The available data for the selected indicators are statistically analysed and it
is observed that most of the data is highly volatile, heteroscedastic and show no definite
patterns. In order to develop more reliable models, various methods like data transformation,
outlier elimination and categorization are applied to the data.Only seven of total twelve
indicators are used as the most significant in the regression model whereas in neural network
approach the best model is achieved when all the twelve indicators are included. Both
models can be used to predict air passenger demand for any future year for Or-Gi and Zafer
Airports and future air passenger demand for similar airports.
Regression and neural models are tested by using various statistical test methods and it is
found that neural network model is superior to regression model for the data used in this
study.
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Demand Forecasting : A study at Alfa Laval in LundLobban, Stacey, Klimsova, Hana January 2008 (has links)
<p>Accurate forecasting is a real problem at many companies and that includes Alfa Laval in Lund. Alfa Laval experiences problems forecasting for future raw material demand. Management is aware that the forecasting methods used today can be improved or replaced by others. A change could lead to better forecasting accuracy and lower errors which means less inventory, shorter cycle times and better customer service at lower costs.</p><p>The purpose of this study is to analyze Alfa Laval’s current forecasting models for demand of raw material used for pressed plates, and then determine if other models are better suited for taking into consideration trends and seasonal variation.</p>
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A study of forecasting procedures and the use of methods of future research in determining the demand for and supply of teachers in Indian schools in South Africa from 1975 to 2000.Nair, Ganesh Kitoony. January 1975 (has links)
No abstract available. / Thesis (M.Ed.)-University of Durban-Westville, 1975.
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European Integration: Strategic Market Research and Industry StructuresCukrowski, Jacek, Fischer, Manfred M. January 2000 (has links) (PDF)
The paper is concerned with the impact of market research prior to integration, on
the structures of noncompetitive industries in integrated economy. The analysis focuses
on separated, single commodity, monopolistic markets with stochastic demand.
Monopolistic firms are considered in dynamic multiperiod model, where intertemporal
links are determined by expenditures on market research in a present period and benefits
from this activity (i.e., smaller variance of the prediction error) in the future. Assuming
that each firm maximizes its total discounted expected utility from profit in indefinite
time, we show that the optimal market research strategy is stationary and depends on
market size. Consequently, in the period following integration firms operating prior to
integration in small markets (such as Slovenia, Czech Republic, Hungary or Estonia) are
expected to have much less information about the integrated market than their
competitors operating before integration on European market. This informational
asymmetry may affect the structure of the industry in integrated economy. In the
extreme case, the firm operating before integration in the small market can be ruled out
from the integrated market. (authors' abstract)
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Bayesian Multiregression Dynamic Models with Applications in Finance and BusinessZhao, Yi January 2015 (has links)
<p>This thesis discusses novel developments in Bayesian analytics for high-dimensional multivariate time series. The focus is on the class of multiregression dynamic models (MDMs), which can be decomposed into sets of univariate models processed in parallel yet coupled for forecasting and decision making. Parallel processing greatly speeds up the computations and vastly expands the range of time series to which the analysis can be applied. </p><p>I begin by defining a new sparse representation of the dependence between the components of a multivariate time series. Using this representation, innovations involve sparse dynamic dependence networks, idiosyncrasies in time-varying auto-regressive lag structures, and flexibility of discounting methods for stochastic volatilities.</p><p>For exploration of the model space, I define a variant of the Shotgun Stochastic Search (SSS) algorithm. Under the parallelizable framework, this new SSS algorithm allows the stochastic search to move in each dimension simultaneously at each iteration, and thus it moves much faster to high probability regions of model space than does traditional SSS. </p><p>For the assessment of model uncertainty in MDMs, I propose an innovative method that converts model uncertainties from the multivariate context to the univariate context using Bayesian Model Averaging and power discounting techniques. I show that this approach can succeed in effectively capturing time-varying model uncertainties on various model parameters, while also identifying practically superior predictive and lucrative models in financial studies. </p><p>Finally I introduce common state coupled DLMs/MDMs (CSCDLMs/CSCMDMs), a new class of models for multivariate time series. These models are related to the established class of dynamic linear models, but include both common and series-specific state vectors and incorporate multivariate stochastic volatility. Bayesian analytics are developed including sequential updating, using a novel forward-filtering-backward-sampling scheme. Online and analytic learning of observation variances is achieved by an approximation method using variance discounting. This method results in faster computation for sequential step-ahead forecasting than MCMC, satisfying the requirement of speed for real-world applications. </p><p>A motivating example is the problem of short-term prediction of electricity demand in a "Smart Grid" scenario. Previous models do not enable either time-varying, correlated structure or online learning of the covariance structure of the state and observational evolution noise vectors. I address these issues by using a CSCMDM and applying a variance discounting method for learning correlation structure. Experimental results on a real data set, including comparisons with previous models, validate the effectiveness of the new framework.</p> / Dissertation
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Metodologia estocástica para previsão de demanda de serviços emergenciais em concessionárias de energia elétrica / Statical methodology for demand forecasting emergency services in the electric utilitiesGuimarães, Iochane Garcia 18 February 2016 (has links)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The goal of the electricity distribution companies is to provide consumers with a continuous
supply of energy and quality. This dissertation addresses the Vehicle Routing Problem,
specifically the partially dynamic routing with static entries, where some events that occur
stochastically are dynamically incorporated during the execution of the service. In this sense,
we sought to develop a methodology to provide the emergency service events that arise randomly
during the working day, taking into account attributes of location, time of service and
time of occurrence, to minimize the travel time of vehicles on scheduled routes. For that, a
sequence of steps has been developed and described for the structuring of a demand forecasting
system, which should be able to design patterns and trends analyzed data from past demands.
Intending to meet these assumptions, the study sought support in two forecasting methods: exponential
smoothing and prediction from conditional probabilities. The study also sought to
identify the main variables that influence the way aleatótia the occurrence of emergency orders.
The results obtained with these methods, assisted in the capture of the stochasticity of the order
process emergency orders, as well as in forecasting service demand. The work seeks to identify
the input variables for routing, providing subsidies for the analyzed company that does not have
this information. / A meta das empresas de distribuição de energia elétrica é proporcionar ao consumidor
um fornecimento de energia contínuo e com qualidade. Esta dissertação aborda o Problema do
Roteamento de Veículos, mais especificamente o roteamento parcialmente dinâmico, com entradas
estáticas, onde alguns eventos que ocorrem de forma estocástica são incorporados dinamicamente
durante a execução do serviço. Neste sentido, buscou-se elaborar uma metodologia
capaz de prever as ocorrências de serviços emergenciais, que surgem aleatoriamente durante
a jornada de trabalho, levando em consideração atributos de localização, tempo de serviço e
horário de ocorrência, visando minimizar o tempo de deslocamento dos veículos nas rotas programadas.
Para isso, foi desenvolvida e descrita uma sequência de etapas para estruturação de
um sistema de previsão de demanda, o qual deve ser capaz de projetar padrões e tendências dos
dados analisados a partir de demandas passadas. Pretendendo atender a estes pressupostos, o
estudo buscou suporte em dois métodos de previsão: suavização exponencial e previsão a partir
de probabilidades condicionais. O estudo ainda buscou, identificar as principais variáveis que
influenciam de maneira aleatótia a ocorrência de ordens emergenciais. Os resultados obtidos
com estes métodos, auxiliaram na captura da estocasticidade do processo de despacho de ordens
emergências, bem como, na previsão de demanda de serviço. O trabalho busca identificar as
variáveis de entrada para o roteamento, proporcionando subsídios para a empresa analisada que
não dispõe destas informações.
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Gerencimento da demanda: um survey na cadeia de suprimentos automotiva brasileira / Demand management: a survey in the brazilian automotive supply chainEsteves, Mario Augusto Matos Simon [UNESP] 21 October 2016 (has links)
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Previous issue date: 2016-10-21 / Com o contínuo crescimento da competitividade global, o grande desafio é trabalhar de forma enxuta, mas sem prejudicar o nível de serviço ao cliente. Para isso, busca-se uma rápida e adequada integração das necessidades do mercado na direção dos fornecedores, de modo a balancear e alinhar estrategicamente a demanda com a capacidade operacional ao longo de toda a cadeia de suprimentos. Para a presente pesquisa, utilizou-se levantamento do tipo survey, e o objetivo geral é verificar o panorama atual das práticas de Gestão de Demanda e Previsão de Demanda nas indústrias da Cadeia de Suprimentos Automotiva Brasileira, identificando as principais práticas utilizadas e as principais dificuldades relacionadas à execução dos processos de gestão e previsão de demanda, bem como as consequências causadas pelas variações e incertezas de demanda. Para tanto, com base na revisão da literatura e no método hipotético dedutivo de Popper, foi elaborado um questionário que foi respondido por 37 empresas da cadeia de suprimento automotiva dos mais diversos setores. Os resultados mostram que as empresas da cadeira de suprimento automotiva fazem uso com predominância de técnicas mais simples como opiniões de executivos e da equipe de vendas e utilização de médias móveis. A falta de disponibilidade de dados, a necessidade de capacitação e treinamento da equipe e a deficiencia no conhecimento dos modelos e ferramentas de previsão de demanda aparecem como as maiores barreiras para elaboração das previsões de demanda. / With the continued growth of global competitiveness, the challenge is to work lean way, but without affecting the level of customer service. As a result, a quick and proper integration of the market requirements towards suppliers should be sought, in order to balance and strategically align the demand with the operational capacity along the entire supply chain. This research use the survey method and the overall objective is to find what the current situation of Demand Management and Demand Forecasting practices in the industries of Brazilian Automotive Supply Chain, identifying the main practices and the difficulties related to the implementation of the management and demand forecasting processes, as well as those caused consequences as a result of variations and demand uncertainties. Therefore, based on the literature review and popper´s hypothetico-deductive method, it has been designed a questionnaire that was answered by 37 companies in the automotive supply chain in various sectors. The results show that companies in the automotive supply chair make use predominantly of the simplest techniques as executive and sales force opinion methods and use of moving averages. The lack of availability of data, the need of professional training and deficiency of knowledge of the models and demand forecasting tools appear as major barriers to development of demand forecasts.
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Estudo de técnicas de apoio a definições em contratos de energia elétricaTeixeira, Raphael Francisco Firmiano 30 August 2017 (has links)
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Previous issue date: 2017-08-30 / Frequentemente os valores dos parâmetros exigidos em um Contrato de Fornecimento
de Energia Elétrica para consumidores não industriais são estimados com base na
previsão de demanda utilizando o método “Naive”, por vezes, com algum ajuste
empírico, o que pode gerar um contrato não-ótimo para o consumidor. Exemplo desse
tipo de consumidor são as universidades, principalmente as públicas, por possuírem
dimensões físicas consideráveis. Em consumidores com esse tipo de comportamento, a
elaboração de um perfil de demanda baseado em estudo do funcionamento das
instalações torna-se algo muito complicado. Tendo em vista tratar-se de um consumidor
pertencente ao Serviço Público, há a necessidade de Contratos definidos com critérios
suficientemente claros, haja vista a pressão dos órgãos de controle. Mais ainda quando
se considera o uso responsável e eficaz do dinheiro público. Portanto, métodos com
base na previsão de demanda do consumidor, em função do seu histórico e capazes de
uma aproximação maior com a realidade, seriam importantes para obter contratos com
valores financeiros minimizados. Tendo os dados de Demandas Registradas da
Universidade Federal de Juiz de Fora e dados auxiliares de Temperaturas e Calendário
de Aulas, desenvolvemos um método que testa previsões realizadas por métodos
lineares (Médias Móveis, ARIMA e Holt-Winters), com previsões realizadas por
métodos não- lineares (Redes Neurais). Comparamos estas previsões, e a melhor foi
levada a um processo de otimização utilizando Algoritmos Genéticos. Essa otimização
revelou dados ótimos para o Contrato e os respectivos custos. A previsão com melhor
desempenho foi a obtida utilizando-se Redes Neurais, sem os dados auxiliares. A
otimização levou a escolha da Tarifa Azul, com previsão de ganhos econômicos para a
UFJF. / The values of the parameters required in a Contract of Electric Power Supply are often
estimated with bases on the demand forecasted using the “Naive” method, for nonindustrial
consumers. Sometimes, the method suffers some empirical adjustment, which
can generate a non-optimal contract for the consumer. Universities (in special the public
ones) are examples of these type of consumers since they have considerable physical
dimensions. The elaboration of a demand profile for these type of consumers, based on
a study of the operation of the facilities, is a complicated task. Because the consumer is
part of the Public Service, there is a need for Contracts defined with sufficiently clear
criteria, given the pressure of the control bodies, particularly when the responsible and
effective use of the public money is considered. Therefore, methods based on the
consumer demand forecast in function of consumer’s history, and capable of greater
approximation with reality, would be important to obtain contracts with minimized
financial values. A method was developed based on data of the registered demands of
the Federal University of Juiz de Fora (UFJF), and Temperature and Class Calendar
adjuvant data. The method tests the predictions made by linear (Moving Averages,
ARIMA and Holt-Winters) and by non-linear methods (Neural Networks). The
predictions were compared and the best one was taken to an optimization process using
Genetic Algorithms. The optimization revealed optimal data for the contract and its
costs. The prediction that showed the best performance was the one obtained using
Neural Networks without the adjuvant data. The optimization led to the choice of the
“Tarifa Azul”, with possible economic gains for UFJF.
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