A previsão com antecedência de curto e médio prazo da vazão em diferentes locais de bacias hidrográficas geralmente é benéfica ao permitir uma resposta antecipada a eventos hidrológicos como cheias, e a operação mais eficiente de obras hidráulicas como barragens. Atualmente, cada vez mais se tem reconhecida a importância da inclusão das incertezas na geração de previsões hidrológicas, feita através de previsões por conjunto (ou ensemble). Neste tipo de previsão são feitas inferências sobre cenários possíveis futuros através da consideração de, por exemplo, múltiplas trajetórias possíveis dos estados da atmosfera, que ao serem aplicadas em um modelo hidrológico resultam em distribuições de trajetórias de vazões. Várias aplicações recentes tem sugerido a possibilidade da tomada de melhores decisões para o futuro quando fundamentadas neste conhecimento das incertezas. No Brasil, um uso predominante de previsões hidrológicas é na operação de reservatórios de usinas hidroelétricas, que constituem a maior fonte de energia do País. As previsões nestes casos são utilizadas tanto para a operação normal do sistema nacional, feita de forma centralizada, como para a operação local das usinas em casos de cheia, onde é necessário velar pela segurança da barragem e pela atenuação de impactos a jusante e/ou a montante dos barramentos. Contudo, a forma como as previsões de vazão são geradas e usadas no cenário nacional não são baseadas em técnicas de previsão por conjunto, onde a própria pesquisa local sobre os potenciais benefícios destas formas de geração de previsões pode ser classificada como incipiente. Assim, o objetivo principal deste estudo é investigar benefícios em termos de qualidade e persistência do uso de previsões de afluência por conjunto para reservatórios em grandes bacias hidrográficas brasileiras em curto e médio prazo. Para cumprir com estes objetivos foram propostos ensaios de previsão de vazão por conjunto para três bacias hidrográficas brasileiras: Alto São Francisco, Doce, e Tocantins. O modelo hidrológico MGBIPH foi aplicado para a execução de previsões retroativas (hindcastings) alimentado por dados de chuva provindos de três diferentes sistemas de previsão meteorológica por conjunto (ECMWF-pf, GEFS, e CPTEC-pf) e mais uma previsão determinística de referência (ECMWF-fc), todos disponíveis na base de dados denominada TIGGE. De uma forma geral, as previsões por conjunto, principalmente dos modelos ECMWF-pf e GEFS, se mostraram superiores em termos de qualidade e persistência na comparação com a previsão determinística. E o uso do Super Ensemble, formado pela combinação dos três modelos mostrou-se uma alternativa entre as melhores testadas, principalmente por ser também uma estratégia robusta. Para uma estratégia de defesa contra cheias, as análises indicam benefícios para a consideração dos membros superiores dos conjuntos, e já para uma estratégia de operação de reservatórios essa visão pode ser mais focada em vazões médias, as quais podem conter algum viés. Já a comparação entre as bacias mostrou que resultados não podem ser transportados de um local para outro, apesar de estarem no mesmo clima. Em relação às incertezas, notou-se que a modelagem hidrológica amplifica as incertezas na previsão na medida em que os estados do modelo da grande bacia evoluem. De qualquer forma, acreditase que os resultados mostram que mais investimentos em técnicas de previsão por conjunto e suas aplicações são um caminho a ser seguido para ampliar os benefícios do uso de previsões hidrológicas. / Short to medium-term streamflow forecasts at different locations in a watershed are generally beneficial to allow an early response to hydrological events such as floods, and more efficient operation of hydraulic structures such as dams. Currently, an increasingly recognition has been given to the including of uncertainties in the generation of hydrological forecasts, what is usually made producting the so called Ensemble Forecast. In this kind of forecast inferences about possible future scenarios are made by considering, for example, multiple possible trajectories of the atmospheric states, which when applied to a hydrological model results in streamflow trajectories distributions. Several recent applications suggested the possibility of better decisions making based on this uncertainties knowledge. In Brazil, a predominant use of hydrological forecasts is for hydropower reservoirs operation, which are the largest source of energy for the country. Future inflows estimates in these cases are used either for normal operation of the national system, done centrally, as for local operation of the dams in cases of floods, where it is necessary to ensure the dam safety and the mitigation of impacts downstream and/or upstream. However, the currently technique used to generate the operational forecasts is not based on ensembles, and the Brazilian local research on the potential benefits of these forms of forecasts production can be classified as incipient. Thus, the aim of this Thesis was to investigate benefits in terms of quality and persistence of using short to medium-term ensemble inflow forecast for reservoirs located on large Brazilian river basins. To fulfill these objectives streamflow forecast tests have been proposed for three Brazilian river basins: Alto San Francisco, Doce, and Tocantins. The hydrological model MGB-IPH was applied to perform retroactive forecasts (hindcastings) within a period of tests forced by rainfall data from three different ensemble weather forecasting systems (ECMWFpf, GEFs, and CPTEC-pf) and a deterministic prediction reference (ECMWF-fc), all available in the TIGGE archive. In general, the ensemble predictions, especially from ECMWF-pf and GEFs models, were superior in quality and persistence in comparison to the deterministic reference. And the use of the Super ensemble composed by the combination of the three ensemble models was shown to be among the results, and also a robust strategy. For a flood protection strategy, the analyzes indicate benefits in the consideration of the upper bounds of the ensembles, and for a reservoir operation strategy that vision could be more focused on average flow rates, which may contain some verified bias. The comparison between the basins results showed that one can not transport results and considerations from one location to another, despite being in the same climate region. Regarding uncertainties, it was noted that hydrological modeling amplifies the uncertainty in the forecasts, in some extent due to the large basin evolution of state variables. Anyway, it is believed that more investments in ensemble forecasting techniques and its applications shown to be a good way to make better use of forecasts.

South Africa is rich in platinum group metals (PGMs) and these metals are important in providing jobs as well as investments some of which have been seen in the Johannesburg Securities Exchange (JSE). In this country this sector has experienced some setbacks in recent times. The most notable ones are the 2008/2009 global nancial crisis and the 2012 major nationwide labour unrest. Worrisomely, these setbacks keep simmering. These events usually introduce jumps and breaks in data which changes the structure of the underlying information thereby inducing spurious long memory (long range dependence). Thus it is recommended that these two phenomena must be addressed together. Further, it is well-known that nancial returns are dominated by stylized facts. In this thesis we carried out an investigation on distributional properties of platinum returns, structural changes, long memory and stylized facts in platinum returns and volatility series. To understand the distributional properties of the returns, we used two classes of heavy tailed distributions namely the alpha-Stable distributions and generalized hyperbolic distributions. We then investigated structural changes in the platinum return series and changes in long range dependence and volatility. Using Akaike information criterion, the ARFIMA-FIAPARCH under the Student distribution was selected as the best model for platinum although the ARCH e ects were slightly signi cant, while using the Schwarz information criteria the ARFIMA-FIAPARCH under the Normal distribution. Further, ARFIMA-FIEGARCH under the skewed Student distribution and ARFIMA-HYGARCH under the Normal distribution models were able to capture the ARCH effects. The best models with respect to prediction excluded the ARFIMA-FIGARCH model and were dominated by ARFIMA-FIAPARCH model with non-Normal error distributions which indicates the importance of asymmetry and heavy tailed error distributions. / Statistics / M. Sc. (Statistics)

This dissertation consists of two essays. The first essay focuses on developing a quantitative theory for a small open economy dynamic stochastic general equilibrium (DSGE) model with a housing sector allowing for both contemporaneous and news shocks. The second essay is an empirical study on the macroeconomic forecasting using both structural and non-structural models. In the first essay, we develop a DSGE model with a housing sector, which incorporates both contemporaneous and news shocks to domestic and external fundamentals, to explore the kind of and the extent to which different shocks to economic fundamentals matter for driving housing market dynamics in a small open economy. The model is estimated by the Bayesian method, using data from Hong Kong. The quantitative results show that external shocks and news shocks play a significant role in this market. Contemporaneous shock to foreign housing preference, contemporaneous shock to terms of trade, and news shocks to technology in the consumption goods sector explain one-third each of the variance of housing price. Terms of trade contemporaneous shock and consumption technology news shocks also contribute 36% and 59%, respectively, to the variance in housing investment. The simulation results enable policy makers to identify the key driving forces behind the housing market dynamics and the interaction between housing market and the macroeconomy in Hong Kong. In the second essay, we compare the forecasting performance between structural and non-structural models for a small open economy. The structural model refers to the small open economy DSGE model with the housing sector in the first essay. In addition, we examine various non-structural models including both Bayesian and classical time-series methods in our forecasting exercises. We also include the information from a large-scale quarterly data series in some models using two approaches to capture the influence of fundamentals: extracting common factors by principal component analysis in a dynamic factor model (DFM), factor-augmented vector autoregression (FAVAR), and Bayesian FAVAR (BFAVAR) or Bayesian shrinkage in a large-scale vector autoregression (BVAR). In this study, we forecast five key macroeconomic variables, namely, output, consumption, employment, housing price inflation, and CPI-based inflation using quarterly data. The results, based on mean absolute error (MAE) and root mean squared error (RMSE) of one to eight quarters ahead out-of-sample forecasts, indicate that the non-structural models outperform the structural model for all variables of interest across all horizons. Among the non-structural models, small-scale BVAR performs better with short forecasting horizons, although DFM shows a similar predictive ability. As the forecasting horizon grows, DFM tends to improve over other models and is better suited in forecasting key macroeconomic variables at longer horizons.

A previsão com antecedência de curto e médio prazo da vazão em diferentes locais de bacias hidrográficas geralmente é benéfica ao permitir uma resposta antecipada a eventos hidrológicos como cheias, e a operação mais eficiente de obras hidráulicas como barragens. Atualmente, cada vez mais se tem reconhecida a importância da inclusão das incertezas na geração de previsões hidrológicas, feita através de previsões por conjunto (ou ensemble). Neste tipo de previsão são feitas inferências sobre cenários possíveis futuros através da consideração de, por exemplo, múltiplas trajetórias possíveis dos estados da atmosfera, que ao serem aplicadas em um modelo hidrológico resultam em distribuições de trajetórias de vazões. Várias aplicações recentes tem sugerido a possibilidade da tomada de melhores decisões para o futuro quando fundamentadas neste conhecimento das incertezas. No Brasil, um uso predominante de previsões hidrológicas é na operação de reservatórios de usinas hidroelétricas, que constituem a maior fonte de energia do País. As previsões nestes casos são utilizadas tanto para a operação normal do sistema nacional, feita de forma centralizada, como para a operação local das usinas em casos de cheia, onde é necessário velar pela segurança da barragem e pela atenuação de impactos a jusante e/ou a montante dos barramentos. Contudo, a forma como as previsões de vazão são geradas e usadas no cenário nacional não são baseadas em técnicas de previsão por conjunto, onde a própria pesquisa local sobre os potenciais benefícios destas formas de geração de previsões pode ser classificada como incipiente. Assim, o objetivo principal deste estudo é investigar benefícios em termos de qualidade e persistência do uso de previsões de afluência por conjunto para reservatórios em grandes bacias hidrográficas brasileiras em curto e médio prazo. Para cumprir com estes objetivos foram propostos ensaios de previsão de vazão por conjunto para três bacias hidrográficas brasileiras: Alto São Francisco, Doce, e Tocantins. O modelo hidrológico MGBIPH foi aplicado para a execução de previsões retroativas (hindcastings) alimentado por dados de chuva provindos de três diferentes sistemas de previsão meteorológica por conjunto (ECMWF-pf, GEFS, e CPTEC-pf) e mais uma previsão determinística de referência (ECMWF-fc), todos disponíveis na base de dados denominada TIGGE. De uma forma geral, as previsões por conjunto, principalmente dos modelos ECMWF-pf e GEFS, se mostraram superiores em termos de qualidade e persistência na comparação com a previsão determinística. E o uso do Super Ensemble, formado pela combinação dos três modelos mostrou-se uma alternativa entre as melhores testadas, principalmente por ser também uma estratégia robusta. Para uma estratégia de defesa contra cheias, as análises indicam benefícios para a consideração dos membros superiores dos conjuntos, e já para uma estratégia de operação de reservatórios essa visão pode ser mais focada em vazões médias, as quais podem conter algum viés. Já a comparação entre as bacias mostrou que resultados não podem ser transportados de um local para outro, apesar de estarem no mesmo clima. Em relação às incertezas, notou-se que a modelagem hidrológica amplifica as incertezas na previsão na medida em que os estados do modelo da grande bacia evoluem. De qualquer forma, acreditase que os resultados mostram que mais investimentos em técnicas de previsão por conjunto e suas aplicações são um caminho a ser seguido para ampliar os benefícios do uso de previsões hidrológicas. / Short to medium-term streamflow forecasts at different locations in a watershed are generally beneficial to allow an early response to hydrological events such as floods, and more efficient operation of hydraulic structures such as dams. Currently, an increasingly recognition has been given to the including of uncertainties in the generation of hydrological forecasts, what is usually made producting the so called Ensemble Forecast. In this kind of forecast inferences about possible future scenarios are made by considering, for example, multiple possible trajectories of the atmospheric states, which when applied to a hydrological model results in streamflow trajectories distributions. Several recent applications suggested the possibility of better decisions making based on this uncertainties knowledge. In Brazil, a predominant use of hydrological forecasts is for hydropower reservoirs operation, which are the largest source of energy for the country. Future inflows estimates in these cases are used either for normal operation of the national system, done centrally, as for local operation of the dams in cases of floods, where it is necessary to ensure the dam safety and the mitigation of impacts downstream and/or upstream. However, the currently technique used to generate the operational forecasts is not based on ensembles, and the Brazilian local research on the potential benefits of these forms of forecasts production can be classified as incipient. Thus, the aim of this Thesis was to investigate benefits in terms of quality and persistence of using short to medium-term ensemble inflow forecast for reservoirs located on large Brazilian river basins. To fulfill these objectives streamflow forecast tests have been proposed for three Brazilian river basins: Alto San Francisco, Doce, and Tocantins. The hydrological model MGB-IPH was applied to perform retroactive forecasts (hindcastings) within a period of tests forced by rainfall data from three different ensemble weather forecasting systems (ECMWFpf, GEFs, and CPTEC-pf) and a deterministic prediction reference (ECMWF-fc), all available in the TIGGE archive. In general, the ensemble predictions, especially from ECMWF-pf and GEFs models, were superior in quality and persistence in comparison to the deterministic reference. And the use of the Super ensemble composed by the combination of the three ensemble models was shown to be among the results, and also a robust strategy. For a flood protection strategy, the analyzes indicate benefits in the consideration of the upper bounds of the ensembles, and for a reservoir operation strategy that vision could be more focused on average flow rates, which may contain some verified bias. The comparison between the basins results showed that one can not transport results and considerations from one location to another, despite being in the same climate region. Regarding uncertainties, it was noted that hydrological modeling amplifies the uncertainty in the forecasts, in some extent due to the large basin evolution of state variables. Anyway, it is believed that more investments in ensemble forecasting techniques and its applications shown to be a good way to make better use of forecasts.

A previsão com antecedência de curto e médio prazo da vazão em diferentes locais de bacias hidrográficas geralmente é benéfica ao permitir uma resposta antecipada a eventos hidrológicos como cheias, e a operação mais eficiente de obras hidráulicas como barragens. Atualmente, cada vez mais se tem reconhecida a importância da inclusão das incertezas na geração de previsões hidrológicas, feita através de previsões por conjunto (ou ensemble). Neste tipo de previsão são feitas inferências sobre cenários possíveis futuros através da consideração de, por exemplo, múltiplas trajetórias possíveis dos estados da atmosfera, que ao serem aplicadas em um modelo hidrológico resultam em distribuições de trajetórias de vazões. Várias aplicações recentes tem sugerido a possibilidade da tomada de melhores decisões para o futuro quando fundamentadas neste conhecimento das incertezas. No Brasil, um uso predominante de previsões hidrológicas é na operação de reservatórios de usinas hidroelétricas, que constituem a maior fonte de energia do País. As previsões nestes casos são utilizadas tanto para a operação normal do sistema nacional, feita de forma centralizada, como para a operação local das usinas em casos de cheia, onde é necessário velar pela segurança da barragem e pela atenuação de impactos a jusante e/ou a montante dos barramentos. Contudo, a forma como as previsões de vazão são geradas e usadas no cenário nacional não são baseadas em técnicas de previsão por conjunto, onde a própria pesquisa local sobre os potenciais benefícios destas formas de geração de previsões pode ser classificada como incipiente. Assim, o objetivo principal deste estudo é investigar benefícios em termos de qualidade e persistência do uso de previsões de afluência por conjunto para reservatórios em grandes bacias hidrográficas brasileiras em curto e médio prazo. Para cumprir com estes objetivos foram propostos ensaios de previsão de vazão por conjunto para três bacias hidrográficas brasileiras: Alto São Francisco, Doce, e Tocantins. O modelo hidrológico MGBIPH foi aplicado para a execução de previsões retroativas (hindcastings) alimentado por dados de chuva provindos de três diferentes sistemas de previsão meteorológica por conjunto (ECMWF-pf, GEFS, e CPTEC-pf) e mais uma previsão determinística de referência (ECMWF-fc), todos disponíveis na base de dados denominada TIGGE. De uma forma geral, as previsões por conjunto, principalmente dos modelos ECMWF-pf e GEFS, se mostraram superiores em termos de qualidade e persistência na comparação com a previsão determinística. E o uso do Super Ensemble, formado pela combinação dos três modelos mostrou-se uma alternativa entre as melhores testadas, principalmente por ser também uma estratégia robusta. Para uma estratégia de defesa contra cheias, as análises indicam benefícios para a consideração dos membros superiores dos conjuntos, e já para uma estratégia de operação de reservatórios essa visão pode ser mais focada em vazões médias, as quais podem conter algum viés. Já a comparação entre as bacias mostrou que resultados não podem ser transportados de um local para outro, apesar de estarem no mesmo clima. Em relação às incertezas, notou-se que a modelagem hidrológica amplifica as incertezas na previsão na medida em que os estados do modelo da grande bacia evoluem. De qualquer forma, acreditase que os resultados mostram que mais investimentos em técnicas de previsão por conjunto e suas aplicações são um caminho a ser seguido para ampliar os benefícios do uso de previsões hidrológicas. / Short to medium-term streamflow forecasts at different locations in a watershed are generally beneficial to allow an early response to hydrological events such as floods, and more efficient operation of hydraulic structures such as dams. Currently, an increasingly recognition has been given to the including of uncertainties in the generation of hydrological forecasts, what is usually made producting the so called Ensemble Forecast. In this kind of forecast inferences about possible future scenarios are made by considering, for example, multiple possible trajectories of the atmospheric states, which when applied to a hydrological model results in streamflow trajectories distributions. Several recent applications suggested the possibility of better decisions making based on this uncertainties knowledge. In Brazil, a predominant use of hydrological forecasts is for hydropower reservoirs operation, which are the largest source of energy for the country. Future inflows estimates in these cases are used either for normal operation of the national system, done centrally, as for local operation of the dams in cases of floods, where it is necessary to ensure the dam safety and the mitigation of impacts downstream and/or upstream. However, the currently technique used to generate the operational forecasts is not based on ensembles, and the Brazilian local research on the potential benefits of these forms of forecasts production can be classified as incipient. Thus, the aim of this Thesis was to investigate benefits in terms of quality and persistence of using short to medium-term ensemble inflow forecast for reservoirs located on large Brazilian river basins. To fulfill these objectives streamflow forecast tests have been proposed for three Brazilian river basins: Alto San Francisco, Doce, and Tocantins. The hydrological model MGB-IPH was applied to perform retroactive forecasts (hindcastings) within a period of tests forced by rainfall data from three different ensemble weather forecasting systems (ECMWFpf, GEFs, and CPTEC-pf) and a deterministic prediction reference (ECMWF-fc), all available in the TIGGE archive. In general, the ensemble predictions, especially from ECMWF-pf and GEFs models, were superior in quality and persistence in comparison to the deterministic reference. And the use of the Super ensemble composed by the combination of the three ensemble models was shown to be among the results, and also a robust strategy. For a flood protection strategy, the analyzes indicate benefits in the consideration of the upper bounds of the ensembles, and for a reservoir operation strategy that vision could be more focused on average flow rates, which may contain some verified bias. The comparison between the basins results showed that one can not transport results and considerations from one location to another, despite being in the same climate region. Regarding uncertainties, it was noted that hydrological modeling amplifies the uncertainty in the forecasts, in some extent due to the large basin evolution of state variables. Anyway, it is believed that more investments in ensemble forecasting techniques and its applications shown to be a good way to make better use of forecasts.

Previsões probabilísticas de precipitação foram obtidas a partir de um conjunto de simulações pelo modelo WRF e utilizadas como condição de contorno no modelo hidrológico TopModel para previsão hidrometeorológica na bacia do Rio Iguaçu, no estado do Paraná. Nas simulações de cheias, durante o período de elevação do volume de precipitação, o erro médio aritmético do conjunto de previsões foi menor que cada um dos membros utilizados nesse conjunto, indicando melhor destreza do conjunto médio em relação a qualquer previsão determinística. Na dissipação dos sistemas precipitantes, alguns membros obtiveram resultados melhores que o conjunto médio e, em geral, as previsões são confluentes. As melhores previsões de precipitação com o WRF foram obtidas com as combinações de microfísica Lin e convecção de Kain Fritsch, microfísica WSM 5 e convecção de Kain Fritsch e simulações defasadas em 6 horas. As simulações inicializadas em horários mais próximos da ocorrência do fenômeno não garantiram uma melhoria na distribuição de precipitação na bacia. A avaliação do sistema de previsão por conjuntos pelo índice de Brier (IB) e seus termos demonstrou níveis suficientes de confiabilidade e destreza para ser utilizada na maioria dos eventos de precipitação sobre a bacia do rio Iguaçu. Os valores do IB estiveram entre 0,15 e 0,3 com picos isolados. Os valores obtidos para o termo de incerteza estiveram entre 0,1 e 0,25 indicando bons resultados visto que o desejável é o mais próximo de zero. Nos eventos de chuva, o termo de confiabilidade apresentou valores próximos a 0,2 no período da manhã e valores entre 0,3 e 0,4 no período da tarde, com um acréscimo no final da integração. O índice de acerto foi de 60 % a 90 % durante o período de integração (48 horas) para o conjunto médio de previsões e entre 50 a 80% para a previsão determinística. Em todos os horários de simulação o erro de fase foi maior que o erro de amplitude, possivelmente devido aos atrasos da propagação dos sistemas precipitantes e aos efeitos de ajuste das condições físicas iniciais da atmosfera. Os erros de fase e amplitude foram menores na previsão probabilística em todo o período de integração. Assim como na previsão de precipitação, nas simulações de vazão o erro de fase foi maior que o erro de amplitude, indicando que o atraso nas previsões de variação da vazão ainda é o um desafio na previsão hidrometeorológica. Observou-se que o modelo hidrológico é bastante sensível a previsão de precipitação e, portanto, a melhoria das previsões de vazão é diretamente proporcional a diminuição dos erros nas previsões de precipitação. / Probabilistic forecast of precipitation from WRF model simulations was used as input in hydrological TopModel for streamlines forecast in Iguaçu Basin, Parana, southern Brazil. The arithmetic error of precipitation ensemble forecast was smaller than each individual member forecast error in the streamflow increase stage. It means the use of ensemble forecast was better than any deterministic forecast. But when the streamflow decreases, the results are confluent and some individual member forecast was better than ensemble. Simulations using Lin microphysical parameterization and Kain Fritsch, WSM 5 and Kain Fritsch and 6h lagged obtained the better results of precipitation over the basin. The use of runs with initial conditions near the precipitation time did not guarantee better results in the distribution of precipitation on the basin. The Brier Score (BS) of the ensemble system demonstrated that the system is very skillful with values between 0.15 and 0.3. Both uncertainty and reliability terms of BS, 0.1 0.25 and 0.2- 0.4, respectively, were encouraging for use hourly ensemble forecast of precipitation on the watershed. Ensemble forecast provide high values of hit scores (0.6 to 0.9) than deterministic forecast (0.5 to 0.8) at all period of integration. Due the delay in the forecasts of the precipitation systems, the phase error is predominant over amplitude during all time. Both errors were reduced using the ensemble forecasts. The phase errors in hydrological were greater than amplitude such as precipitation forecasts. Thus, for increase streamflow forecast it should reduced the errors in QPF forecasts.

M.Comm. / As part of supply chain management, the handling of market demand information forms one of the most important concepts in any supply chain. One of the specific goals of supply chain management is to manage and co-ordinate the flow of information from the original source to the final customer. If consumer demand forms the activating element in the supply chain, it becomes clear that the process of demand planning can play an active role in improving the effectiveness of a supply chain. The correct management of information can thus greatly influence the level of integration, the responsiveness, level of customer service and value added to the end product. This is however not a one-sided approach where demand planning can be used as the tool to facilitate supply chain synchronization. The opposite effect can also be found that certain efforts to synchronize the supply chain can greatly improve the demand planning process. The fast moving consumer goods (FMCG) industry relies heavily on forecasted demand figures due to the structure of this industry 5. Developing demand forecasts forms a great part of the demand planning process and the accuracy, timely flow, interpretation and final format of the information is of the utmost importance. A well controlled forecasting process can form a solid foundation to address supply chain problems, reduce the level of wastage, increase the product value to the customer and improve the level of supply chain agility. With this background, the aim of this study will be: To explore the subject of Demand Planning in the synchronization of a FMCG supply chain. It will aim to show how an effective demand planning process can positively influence the supply chain management process and form an active element in supply chain synchronization. To investigate certain supply chain strategies on demand planning to indicate the level of integration between these two processes. In order to do this, a theoretical study needs to be done on Demand Planning and into the elements thereof. Within this structure it will be possible to formulate a structure to evaluate the concept of Demand Planning.

M. Ing. / Undoubtedly, forecasting accuracy presents many advantages to a business, but the opposite is also true for forecasting inaccuracy. This paper is intended to outline the effects of forecasting accuracy on business planning while also investigating factors that affect it. The role of the human element in this regard is also discussed in the report. The study is qualitative in nature with an exploratory approach. A survey and focus group interviews / discussions were conducted so as to achieve the aim of the project. The information obtained from these two methods was used to explore the research questions which in turn were designed to identify the impact of forecasting accuracy and factors that affect this accuracy. The findings of the study indicate that the effect of forecasting accuracy is more significant than commonly perceived. The findings also outline the important factors affecting forecasting accuracy. The basis of this argument is that most of the factors that affect forecasting accuracy can be controlled and are centered on people. Therefore, in order for companies to survive, they must begin improving v their forecasting process and paying more attention to the human element of this process.

Background   By the beginning of the 20th century, the men’s suit entered the menswear market as one the most important fashion garments everdevised. At the same time, fashion became mainly a female engagement, resulting in an under representation of men’s fashion through out the past decade. Relating to the textile and apparel industry, fashion forecasting has become an increasingly important business activity. But the nature of fashion forecasting and the historical neglecting of the men’s suit has created complications when performing this activity. Purpose   The purpose of this thesis is to examine the men’s suit and its development from the given starting point in the 20th century until today, in order to derive a fashion forecasting model suggesting its development by 2029. Design/methodology/approach   This thesis uses an abductive research approach and qualitative multi-methods to answer the research questions. The usage of an intermediate research project answers the first research question. The second research question is answered through the synthesis ofa literature study and semi-structured interviews. The third research question is answered through the derived forecasting model, accomplished through theory matching. Findings   By carrying out a historical investigation of the men’s suit, and then applying this to the derived forecasting model, the men’s suit is expected to be found in both single- and double-breast styles. The suit will have classical features represented through the length, canvas structure, and shoulder construction. Originality/value   This paper carries out a historical investigation of the men’s suit never been done before. It introduces an evaluation framework to categorise and classify the men’s suit, as well as a forecasting model followed by an actual fashion forecast.

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.