In this thesis, we investigate the potential of improving demand management activities in the global supply chains. In the increasingly global world, commerce is becoming more complex with an incredible amount of internal and external information available for businesses to select, analyze, understand and react. We identify opportunities for companies to convert data and business information into actionable intelligence. We first study the logistics industry with real data. In the Less-than-Truckload (LTL) market, we analyze an extensive historical shipment database to identify important factors to estimate LTL market rates. Quantifying critical expert knowledge, we develop a price estimation model to help shippers reduce their logistics cost and carriers to better manage their demand. In our second study, we analyze a global supply chain in the high tech industry. Using the demand dependency structure of certain products, we identify collaboration opportunities in the ordering practices that results in increased forecast accuracy. In our third study, we focus on using historical product adoption patterns for developing good pre-launch forecasts for new product introductions. Through a normalization approach and algebraic estimation procedures that use intuitive parameters, our models provide opportunities to significantly improve pre-launch forecast accuracy. Finally, in our fourth study, we develop novel approaches for modeling and mitigating the impact of demand seasonality in new product diffusion context. Focusing mainly on practical applications, our research shows that companies can find innovative ways for turning raw data into valuable insights leading to better demand management activities.

Made available in DSpace on 2014-07-29T15:08:23Z (GMT). No. of bitstreams: 1 dissertacaoPriscila.pdf: 645879 bytes, checksum: 1150784f73524c6b5341fd319cc9d608 (MD5) Previous issue date: 2009-01-02 / n the planning of the energetic operation, analysis and forecasts of the flow are very important. A huge difficulty in the forecast of flow is the seasonality presence, due to drought and flood periods in the year. Many scientists, with different methodologies, have been concerned with finding a best model, compared with the utilized by Brazil s system - Markovian Model. The Makovian Model, or selfregressive with order 1, is a Box & Jenkins methodology, and requires data handling to treat non-stationarity, or the use of regular models, requiring a hardly theoretical formulation for the statistical procedures. Therefore, the statistical models, autoregressive model with seasonality and Holt-Winters model, of treatment of temporal series are presented and, carried out the flow s analysis and forecast for three study groups, in two different (historical) horizons. The performance of the models was compared and the results showed that the proposed models presents better adjust than the model adopted by Brazilian system / No planejamento da operação energética, a análise e previsão de vazões são muito importantes. Uma grande dificuldade na previsão de vazões é a presença da sazonalidade, devido aos períodos de seca e cheia no ano. Muitos estudiosos, com metodologias diversas, têm se preocupado em encontrar um modelo de melhor ajuste, em comparação ao utilizado pelo sistema brasileiro, ou seja, o modelo auto-regressivo de ordem 1, que consiste numa metodologia de Box & Jenkins e exige manuseio nos dados para tratar a não-estacionariedade. O presente trabalho analisa e compara os modelo utilizados pelo sistema brasileiro (PAR), com modelo matemático que considera a sazonalidade dos dados (SAR) e o método de Holt-Winters e, modelos amplamente estudados como PARMA e ANFIS. O desempenho dos modelos foi comparado e os resultados mostraram que em muitos estudos os modelos PAR/PARMA e ANFIS apresentam melhor ajuste , no geral, em relação aos demais