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Improving The Production Forecasts : Developing a Forecasting Model Using Exponential SmoothingAda Fatemeh, Rezai January 2024 (has links)
This research is motivated by identified gaps in contemporary planning practices and production processes within firms. Relying solely on experiential knowledge has proven limiting, necessitating a more systematic approach. Previous instances of data anomalies, particularly ongoing challenges in achieving satisfactory delivery reliability, have underlined the need for deeper insights into underlying patterns. The objectives of this study are: • To identify and analyze specific obstacles and challenges affecting load balance and delivery security in Borl.nge's production system. • To explore various methods or strategies aimed at enhancing the process of generating reliable capacity forecasting methods. Both primary and secondary research methods were employed. Primary methods included interviews and the development of a forecast model, while secondary studies encompassed the latest research in the field. The thesis revealed five primary factors hindering capacity attainment: 1. WIP(work in progress)/ slabs material shortages disrupt production flow and escalate costs due to the need for external sourcing of slabs. 2. Transport issues, including incorrect internal deliveries and the weather conditions, pose challenges. 3. Personnel shortages hinder the efficient utilization of production capacity. 4. Machine breakdowns result in production interruptions, leading to capacity loss and inefficiency. 5. Inventory problems, such as insufficient capacity and poor management, impede smooth production operations. Additionally, the second objective was addressed by implementing exponential smoothing for capacity planning forecasts. By updating forecasts every 13 weeks, this study improves the production forecast.
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Application of Modern Principles to Demand Forecasting for Electronics, Domestic Appliances and AccessoriesNoble, Gregory Daniel 30 June 2009 (has links)
No description available.
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Sezónní stavové modelování / Seasonal state space modelingSuk, Luboš January 2014 (has links)
State space modeling represents a statistical framework for exponential smoo- thing methods and it is often used in time series modeling. This thesis descri- bes seasonal innovations state space models and focuses on recently suggested TBATS model. This model includes Box-Cox transformation, ARMA model for residuals and trigonometric representation of seasonality and it was designed to handle a broad spectrum of time series with complex types of seasonality inclu- ding multiple seasonality, high frequency of data, non-integer periods of seasonal components, and dual-calendar effects. The estimation of the parameters based on maximum likelihood and trigonometric representation of seasonality greatly reduce computational burden in this model. The universatility of TBATS model is demonstrated by four real data time series.
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Econometric Modeling vs Artificial Neural Networks : A Sales Forecasting ComparisonBajracharya, Dinesh January 2011 (has links)
Econometric and predictive modeling techniques are two popular forecasting techniques. Both ofthese techniques have their own advantages and disadvantages. In this thesis some econometricmodels are considered and compared to predictive models using sales data for five products fromICA a Swedish retail wholesaler. The econometric models considered are regression model,exponential smoothing, and ARIMA model. The predictive models considered are artificialneural network (ANN) and ensemble of neural networks. Evaluation metrics used for thecomparison are: MAPE, WMAPE, MAE, RMSE, and linear correlation. The result of this thesisshows that artificial neural network is more accurate in forecasting sales of product. But it doesnot differ too much from linear regression in terms of accuracy. Therefore the linear regressionmodel which has the advantage of being comprehensible can be used as an alternative to artificialneural network. The results also show that the use of several metrics contribute in evaluatingmodels for forecasting sales. / Program: Magisterutbildning i informatik
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[en] FORECASTING OF JUDICIAL CONTINGENCY IN ELECTRIC SECTOR COMPANIES: AN APPROACH VIA DYNAMIC REGRESSION AND EXPONENTIAL SMOOTHING / [pt] PREVISÃO DE CONTINGÊNCIA JUDICIAL EM EMPRESAS DO SETOR ELÉTRICO: UMA ABORDAGEM VIA REGRESSÃO DINÂMICA E AMORTECIMENTO EXPONENCIALBRUNO AGRÉLIO RIBEIRO 03 October 2012 (has links)
[pt] Esta dissertação tem como objetivo principal a proposição de modelos para
previsão, em um curto prazo, do número de processos que são ajuizados em
desfavor de uma empresa do setor elétrico. A metodologia utilizada consiste em,
a partir de uma análise exploratória dos dados, construir modelos usando uma
estratégia bottom-up, ou seja, parte-se de um modelo simples e processa-se seu
refinamento até encontrar um modelo apropriado que mais se adeque à realidade.
Partiu-se então de um modelo auto projetivo indo até uma formulação de um
modelo de regressão dinâmica. Os modelos são então comparados segundo alguns
critérios, basicamente no que tange à sua eficiência preditiva. Conclui-se ao final
sobre a eficiência de se utilizar modelos de regressão dinâmica para este tipo de
previsão tendo em vista a presença de correlação serial dos resíduos, comumente
presentes nas séries econômicas. Propõe-se, ao final, uma ferramenta para, a partir
dos valores estimados, analisar a viabilidade econômica de estimular ou
desestimular as medidas responsáveis pela geração de processos contra a empresa. / [en] The aim of this dissertation is to develop short term models to forecast the
number of judicial process in electric sector companies. From the methodology
point of view, data is analyzed and models using bottom-up strategy is developed.
In other words, a simple model is improved step by step until a proper model that
fits well the reality is found. From a univariate model it ends up in a dynamic
regression model. The models obtained in this study are compared according to
some criterion, mainly forecast accuracy. In the end the conclusion is about the
efficiency of dynamic regression models for this kind of forecast, which one
presents data with serial correlation of residues, commonly present in economic
series. In the end, from the estimated values, it´s proposed a mechanism to
analyze the economic viability, to encourage or not, actions which are responsible
for instigating judicial processes against the company.
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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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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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Die Re-Analyse von Monitor-Schwellenwerten und die Entwicklung ARIMA-basierter Monitore für die exponentielle Glättung /Becker, Claudia. January 2006 (has links) (PDF)
Katholische Universiẗat, Diss.--Eichstätt-Ingolstadt, 2006.
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[en] ANALYSIS AND FORECASTING OF TIME SERIES USING MULTIPLE SEASONAL EXPONENTIAL SMOOTHING AND SIMULATION TECHNIQUES IN THE WIND ENERGY PRODUCTION / [pt] ANÁLISE E PREVISÃO DE SÉRIES TEMPORAIS UTILIZANDO AMORTECIMENTO EXPONENCIAL COM MÚLTIPLOS CICLOS E TÉCNICAS DE SIMULAÇÃO NA PRODUÇÃO DE ENERGIA EÓLICAMATHEUS FERREIRA DE BARROS 17 May 2016 (has links)
[pt] A presente dissertação se insere no contexto da energia eólica, que é a
fonte de energia que mais cresce na matriz elétrica brasileira, segundo dados da
Empresa de Pesquisa de Energia (EPE), com projeções para que esse
crescimento se mantenha. Com isso, a principal motivação do presente trabalho
é o fato de que desenvolver e aplicar métodos de previsão cada vez mais precisos
para as variáveis determinantes na produção de energia eólica em um
aerogerador, como a velocidade do vento, é de crucial importância para o
planejamento da operação do sistema elétrico nacional. Logo, o objetivo
principal do trabalho é adaptar e aplicar uma metodologia de previsão de séries
temporais em um banco de dados formado por medições de velocidade de vento.
A metodologia se constrói a partir da análise exploratória dos dados, onde pode
se observar características importantes, como estacionariedade na média e uma
estrutura sazonal complexa, que envolve um ciclo diário e uma sazonalidade
mensal. Com isso, foi adaptado um modelo de amortecimento exponencial com
múltiplos ciclos que incorpora simulação de Monte Carlo e decomposição da
série através do método TBATS, para realizar as previsões. Como resultados e
conclusões, é possível observar que modelo adaptado se mostrou adequado para
tratar o problema proposto, quando comparado com os modelos de previsão
estabelecidos pela literatura, resultando em um aumento na precisão das
previsões realizadas. / [en] This work is in the context of wind energy, which is the energy source that
grows more in the Brazilian energy matrix, according to the Energy Research
Company (EPE), with projections that this growth will continue. Thus, the main
motivation of this work is the fact that developing and implementing
increasingly precise forecasting methods for the key variables in the production
of wind energy in a wind turbine, such as wind speed, is of crucial importance
for planning of the national electric system operation. Therefore, the main
objective of this work is to adapt and apply a time series forecasting
methodology in a database formed by wind speed measurements. The
methodology is built from the exploratory analysis of data, which can be
observed important features such as stationary mean and a complex seasonal
structure, which involves a daily cycle and monthly seasonality. Thus, it was
adapted an exponential smoothing model that incorporates multiple cycles,
Monte Carlo simulation and decomposition of the series through the TBATS
method, to make forecasts. As results and conclusions, it is possible to observe
that model adapted was adequate to address the proposed issue, compared with
the forecast models established in the literature, resulting in an increase in the
accuracy of forecasts made.
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AnÃlise de modelos de sÃries temporais para a previsÃo mensal do imposto de renda / Analysis of models of secular series for the monthly forecast of the income taxAlan Vasconcelos Santos 03 July 2003 (has links)
Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / O presente trabalho objetiva realizar previsÃes mensais da sÃrie do imposto de renda para o perÃodo de 2002. A metodologia empregada para alcanÃar essa finalidade consiste na utilizaÃÃo da tÃcnica de combinaÃÃo de previsÃes. Especificamente, combinam-se os resultados de previsÃo advindos de trÃs mÃtodos diferentes: tÃcnica do alisamento exponencial, metodologia de Box-Jenkins (modelos ARIMA) e modelos vetoriais de correÃÃo de erro. Obtida a previsÃo final, compara-se este resultado com os valores reais observados da sÃrie do imposto de renda para o ano de 2002 a fim de verificar o desempenho e a acurÃcia do modelo. / The main objective of this work was to generate predictions, at a monthly frequency, from 1990 to 2001, of income tax revenue. The methodology used was the one of forecast combining. Specifically, exponential smoothing, an ARIMA and VAR with error correction models were pooled to obtain final prediction. Ex-post forecast errors were used to test the performance of the model. Results indicated that combining performs better than individual models, and errors are in an acceptable interval for this type of prediction.
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