Submitted by Cristhiane Guerra (cristhiane.guerra@gmail.com) on 2017-01-26T14:55:17Z No. of bitstreams: 1 arquivototal.pdf: 2586746 bytes, checksum: 18b7b08875fbe9dc7bcecd5595b19734 (MD5) / Made available in DSpace on 2017-01-26T14:55:17Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 2586746 bytes, checksum: 18b7b08875fbe9dc7bcecd5595b19734 (MD5) Previous issue date: 2016-07-29 / The demand forecasting studies are of great importance for electricity companies, because there is a need to allocate their resources well in advance, requiring a medium and long- term p lanning. These resources can be the purchase of new equipment, the transmission line acquisition or construction, scheduled maintenance and the purchase and sale of energy. I n this work, a support tool has been developed for experts in strategic planning i n power distribution systems using artificial neural networks to demand forecasting. For the proposed method, it implemented a demand forecasting procedure in the medium term of the region fueled by three substations belonging to the power distribution sys tem managed by EnergisaPB, using a computer model based on Multilayer Perceptron (MLP) artificial neural networks with the assistance of Matlab ® environment. The database was structured by the measurements of active power from 2008 to 2014, provided by En ergisa/PB and the forecast achieved one year ahead (52 weeks) compared with the real data of 2014. In addition, it was possible to evaluate the performance of RNA and estimate the demand growth in the region supplied by each substation, which can assist th e distribution system expansion planning. / Os estudo s de previsão de demanda têm grande importância para empresa s da área de energia elétrica , pois, existe a necessidade de alocar seus recursos com uma certa antecedência , exigindo um planejamento a médio e longo prazo. D entre estes recursos , estão a compra de equipamentos, a aquisição e construção de linhas de transmissão, manutenções programadas e a compra e venda de energia. Nesta premissa, foi desenvolvida uma ferramenta de apoio aos especialistas na área de planejamento estratégico em sistemas de distrib uição de energia elétrica, utilizando redes neurais artificiais para previsão de demanda. Para o método proposto, foi implementado um procedimento de previsão de demanda no médio prazo da região alimentada por três subestações reais pertencentes ao sistema de distribuição de energia gerido pela concessionária Energisa- PB, utilizando um modelo computacional baseado em redes neurais artificiais (RNA) do tipo Multilayer Perceptron (MLP) com o auxílio do ambiente Matlab ® . Foram consideradas as informações reais (banco de dados) da potência ativa, para o período de 2008 até 2014, fornecidas pela própria concessionária e a previsão alcançou o horizonte de um ano a frente (52 semanas). A RNA foi treinada com os dados de 2008 a 2013, e o resultado, comparado com dad os do ano de 2014. Além disso, foi possível avaliar o desempenho da RNA sob diferentes aspectos (volume de treinamento, parâmetros, configurações, camadas ocultas, etc.) e estimar o crescimento de demanda da região alimentada por cada subestação, o que pod e auxiliar o planejamento de expansão do sistema de distribuição.

La presente investigación consiste en Proponer una Solución para Reducir los Reclamos en el Abastecimiento de Repuestos de Productos de Línea Blanca. Para ello, se aplicó principalmente Métodos de Clasificación ABC, Diagramas de Análisis de Actividades, Distribución por Mezcla de Familias, Métodos de Pronósticos de la Demanda, entre otras herramientas de la Ingeniería Industrial. Finalmente, se concluyó que al mejorar la Productividad del “Picking” (Sacado) y del Embalaje, al mejorar la Identificación y Reconocimiento Visual de los Repuestos y de los Espacios y al realizar una mejor Planificación de la Demanda, un adecuado Control del Inventario, una mejor Planificación del Abastecimiento, se reducirán los Reclamos en el Abastecimiento de Repuestos de productos de línea blanca. The present research is to propose a solution to Reduce Claims in supply of spare parts Products Appliances. To do this, we will mainly apply ABC classification methods, diagrams Analysis Activities, Distribution mix of families Methods demand forecast and other tools of industrial engineering. Finally, it was concluded that by improving the productivity of the "Picking" (Taken) and packaging, improving the identification and Visual Recognition of parts and spaces and improving planning Demand with an adequate control of inventory and with a better supply planning, Claims will be reduced in the Supply of white goods´ spare parts.

Hoy en día, las MYPES tienen un rol muy importante en todas las sociedades del mundo debido a su enorme potencial para desarrollarse, sin embargo, este sector presenta algunas limitaciones estructurales que limitan su sostenibilidad reduciendo su tasa de supervivencia. Esta situación las obliga a ser más flexibles y adaptarse rápida y eficientemente a los cambios del mercado. En este sentido, la presente investigación tiene como propósito analizar y demostrar la importancia de mantener los niveles de servicios óptimos dentro de una MYPE comercializadora de superalimentos que terceriza su proceso de producción, con el fin de evitar impactos económicos negativos que se encuentren vinculados a su proceso logístico. De la misma manera, se presenta una propuesta de mejora considerando los componentes de Gestión por Proceso y Políticas de Inventarios, las cuales son desarrolladas mediante las herramientas de BPM (Business Process Management), pronóstico de la demanda, stocks de seguridad e inventarios cíclicos. La aplicación integrada de estas herramientas permitirá incrementar la eficiencia de los procesos y el nivel de servicio evitando problemas como la entrega de pedidos incompletos, las cancelaciones por falta de stock y fallas en los productos; por ende, las pérdidas de ventas y pago de penalidades por incumplimiento. / Nowadays, MSEs play a very important role in all societies around the world due to their enormous potential for development; however, this sector has some structural limitations that limit its sustainability and reduce its survival rate. This situation forces them to be more flexible and to adapt quickly and efficiently to market changes. In this sense, the purpose of this research is to analyze and demonstrate the importance of maintaining optimal service levels within a superfood marketing MSE that outsources its production process, in order to avoid negative economic impacts that are linked to its logistics process. In the same way, an improvement proposal is presented considering the components of Process Management and Inventory Policies, which are developed through the tools of BPM (Business Process Management), demand forecasting, safety stocks and cyclic inventories. The integrated application of these tools will increase the efficiency of the processes and the level of service, avoiding problems such as the delivery of incomplete orders, cancellations due to lack of stock and product failures; therefore, the loss of sales and payment of penalties for noncompliance. / Trabajo de Suficiencia Profesional

MSc (Statistics) / Department of Statistics / Short-term load forecasting in South Africa using machine learning and statistical models is discussed in this study. The research is focused on carrying out a comparative analysis in forecasting hourly electricity demand. This study was carried out using South Africa’s aggregated hourly load data from Eskom. The comparison is carried out in this study using support vector regression (SVR), stochastic gradient boosting (SGB), artificial neural networks (NN) with generalized additive model (GAM) as a benchmark model in forecasting hourly electricity demand. In both modelling frameworks, variable selection is done using least absolute shrinkage and selection operator (Lasso). The SGB model yielded the least root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) on testing data. SGB model also yielded the least RMSE, MAE and MAPE on training data. Forecast combination of the models’ forecasts is done using convex combination and quantile regres- sion averaging (QRA). The QRA was found to be the best forecast combination model ibased on the RMSE, MAE and MAPE. / NRF

No description available.

Within demand forecasting, and specifically within the field of e-commerce, the provided data often contains erratic behaviours which are difficult to explain. This induces contradictions to the common assumptions within classical approaches for time series analysis. Yet, classical and naive approaches are still commonly used. Machine learning could be used to alleviate such problems. This thesis evaluates four models together with Swedish fin-tech company QLIRO AB. More specifically, a MLR (Multiple Linear Regression) model, a classic Box-Jenkins model (SARIMAX), an XGBoost model, and a LSTM-network (Long Short-Term Memory). The provided data consists of aggregated total daily reservations by e-merchants within the Nordic market from 2014. Some data pre processing was required and a smoothed version of the data set was created for comparison. Each model was constructed according to their specific requirements but with similar feature engineering. Evaluation was then made on a monthly level with a forecast horizon of 30 days during 2021. The results shows that both the MLR and the XGBoost provides the most consistent results together with perks for being easy to use. After these two, the LSTM-network showed the best results for November and December on the original data set but worst overall. Yet it had good performance on the smoothed data set and was then comparable to the first two. The SARIMAX was the worst performing of all the models considered in this thesis and was not as easy to implement. / Inom efterfrågeprognoser, och specifikt inom området e-handel, innehåller den tillhandahållna informationen ofta oberäkneliga beteenden som är svåra att förklara. Detta motsäger vanliga antaganden inom tidsserier som används för de mer klassiska tillvägagångssätten. Ändå är klassiska och naiva metoder fortfarande vanliga. Maskininlärning skulle kunna användas för att lindra sådana problem. Detta examensarbete utvärderar fyra modeller tillsammans med det svenska fintechföretaget QLIRO AB. Mer specifikt en MLR-modell (Multiple Linear Regression), en klassisk Box-Jenkins-modell (SARIMAX), en XGBoost-modell och ett LSTM-nätverk (Long Short-Term Memory). Den tillhandahållna informationen består av aggregerade dagliga reservationer från e-handlare inom den nordiska marknaden från 2014. Viss dataförbehandling krävdes och en utjämnad version av datamängden skapades för jämförelse. Varje modell konstruerades enligt deras specifika krav men med liknande \textit{feature engineering}. Utvärderingen gjordes sedan på månadsnivå med en prognoshorisont på 30 dagar under 2021. Resultaten visar att både MLR och XGBoost ger de mest pålitliga resultaten tillsammans med fördelar som att vara lätta att använda. Efter dessa visar LSTM-nätverket de bästa resultaten för november och december på den ursprungliga datamängden men sämst totalt sett. Ändå visar den god prestanda på den utjämnade datamängden och var sedan jämförbar med de två första modellerna. SARIMAX var den sämst presterande av alla jämförda modeller och inte lika lätt att implementera.

Long short-term volume forecasting is essential for companies regarding their logistics service operations. It is crucial for logistic companies to predict the volumes of goods that will be delivered to various centers at any given day, as this will assist in managing the efficiency of their business operations. This research aims to create a forecasting model for outbound logistics volumes by utilizing design science research methodology in building 3 machine-learning models and evaluating the performance of the models . The dataset is provided by Tetra Pak AB, the World's leading food processing and packaging solutions company,. Research methods were mainly quantitative, based on statistical data and numerical calculations. Three algorithms were implemented: which are encoder–decoder networks based on Long Short-Term Memory (LSTM), Convolutional Long Short-Term Memory (ConvLSTM), and Convolutional Neural Network Long ShortTerm Memory (CNN-LSTM). Comparisons are made with the average Root Mean Square Error (RMSE) for six distribution centers (DC) of Tetra Pak. Results obtained from encoder–decoder networks based on LSTM are compared to results obtained by encoder–decoder networks based on ConvLSTM and CNN-LSTM. The three algorithms performed very well, considering the loss of the Train and Test with our multivariate time series dataset. However, based on the average score of the RMSE, there are slight differences between algorithms for all DCs.

Submitted by SANTIAGO BURGADA (sburgada@maxam.net) on 2015-01-25T12:10:08Z No. of bitstreams: 1 DISSERTATION SANTIAGO BURGADA CORPORATE INTERNATIONAL MASTERS SUBMISSION VERSION.pdf: 3588309 bytes, checksum: b70385fd690a43ddea32379f34b4afe9 (MD5) / Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2015-02-04T19:27:15Z (GMT) No. of bitstreams: 1 DISSERTATION SANTIAGO BURGADA CORPORATE INTERNATIONAL MASTERS SUBMISSION VERSION.pdf: 3588309 bytes, checksum: b70385fd690a43ddea32379f34b4afe9 (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2015-02-11T13:27:32Z (GMT) No. of bitstreams: 1 DISSERTATION SANTIAGO BURGADA CORPORATE INTERNATIONAL MASTERS SUBMISSION VERSION.pdf: 3588309 bytes, checksum: b70385fd690a43ddea32379f34b4afe9 (MD5) / Made available in DSpace on 2015-02-11T13:34:18Z (GMT). No. of bitstreams: 1 DISSERTATION SANTIAGO BURGADA CORPORATE INTERNATIONAL MASTERS SUBMISSION VERSION.pdf: 3588309 bytes, checksum: b70385fd690a43ddea32379f34b4afe9 (MD5) Previous issue date: 2014-10-29 / Industrial companies in developing countries are facing rapid growths, and this requires having in place the best organizational processes to cope with the market demand. Sales forecasting, as a tool aligned with the general strategy of the company, needs to be as much accurate as possible, in order to achieve the sales targets by making available the right information for purchasing, planning and control of production areas, and finally attending in time and form the demand generated. The present dissertation uses a single case study from the subsidiary of an international explosives company based in Brazil, Maxam, experiencing high growth in sales, and therefore facing the challenge to adequate its structure and processes properly for the rapid growth expected. Diverse sales forecast techniques have been analyzed to compare the actual monthly sales forecast, based on the sales force representatives’ market knowledge, with forecasts based on the analysis of historical sales data. The dissertation findings show how the combination of both qualitative and quantitative forecasts, by the creation of a combined forecast that considers both client´s demand knowledge from the sales workforce with time series analysis, leads to the improvement on the accuracy of the company´s sales forecast.