The subject of this study is sales forecasting of pharmaceutical representative company. The aim of the project described in this document is to create the custom tailored sales forecasting software for pharmaceutical representative company. The document consists of three main parts. In the analytical part the document the literature analysis and overview of existing solutions is performed. Most widely used forecasting techniques, methods and their appliance possibilities are described and classified. Advantages and disadvantages of existing forecasting tools are exposed. In accordance with the performed analysis the type of the system and forecasting methods that should be implemented in the system were selected. Key features of the created software design are described in the design part of the document. The created software has been examined with the real data of a pharmaceutical representative company. Generated sales forecasts were compared with real sales indexes. Results of the performed research were summarized and conclusions for usage of the created system were drawn.

Os avanços na Tecnologia da Informação e a proliferação de itens de consumo, entre outros aspectos, mudaram o cenário e o desempenho das previsões. Os processos de previsão devem ser reexaminados, estabelecendo mecanismos de comunicação formais que compartilhem a informação entre os diferentes níveis hierárquicos dentro da organização, eliminando ou reduzindo o desconforto das previsões paralelas e desconexas oriundas de níveis hierárquicos diferentes. O objetivo deste trabalho é propor um sistema de apoio à decisão baseado em métodos matemáticos e sistemas de informação, capaz de integrar as previsões de vários níveis hierárquicos de uma empresa por um repositório de dados (Data Warehouse ou DW) e um Sistema de Apoio à Decisão (SAD) com sistema Business Intelligence (BI), onde os níveis hierárquicos acessem as informações com o nível de detalhe apropriado dentro do processo de decisão, alinhado às expectativas corporativas de crescimento. Assim, a modelagem realizada neste trabalho teve como foco a geração de cenários para criar um sistema de apoio à decisão, prevendo demandas agregadas e individuais, gerando uma estrutura de integração entre as previsões feitas em diferentes níveis e alinhando valores oriundos de métodos quantitativos e julgamento humano. Uma das maiores preocupações foi verificar qual método (séries temporais, métodos causais) teria destaque em um processo integrado de previsão. Entre os diferentes testes efetuados, pode-se destacar os seguintes resultados: (1) a suavização exponencial tripla proporcionou melhor ajuste (dos dados passados) de séries históricas de demandas mais agregadas e proporcionou previsões mais precisas de representatividades agregadas. Para séries históricas de demanda individual e representatividade individual, os outros métodos comparados apresentaram desempenho muito próximo; (2) a criação de diferentes cenários de previsão, fazendo uso de um repositório de dados e sistema de apoio à decisão, permitiu análise de uma gama de diferentes valores futuros. Uma forma de simulação para apoiar a formulação das expectativas da diretoria foi adaptada da literatura e sugerida; (3) os erros de previsão nas abordagens top-down ou bottom-up são estatisticamente iguais no contexto desta pesquisa. Conclui-se que o método de suavização exponencial tripla traz menos erros às previsões de séries mais agregadas, se comparado com outros métodos abordados no trabalho. Esse fato está de acordo com asserções encontradas na literatura pesquisada de que o método de suavização exponencial é cada vez mais utilizado na previsão, em detrimento dos métodos causais como a regressão múltipla. Conclui-se, principalmente, que os sistemas SAD e BI propostos deram suporte aos vários níveis hierárquicos, proporcionando variedades de estilos de decisão e que podem diminuir o hiato entre o raciocínio qualitativo adotado em nível estratégico e o aspecto quantitativo mais comum em níveis operacionais em qualquer empresa. / Advances in Information Technology (IT), and the increase of consumption items, among other things, changed the performance in the forecasts predictions. It is not uncommon that organizations will perform parallel forecasts within the various hierarchical levels without communicating with each other. The objective of this work is to build an integrated \"infrastructure\" for forecasting through a repository of data (Data Warehouse or DW) and a Decision Support System (DSS) with Business Intelligence (BI) where the hierarchical levels have access to the information with the appropriate level of detail within the process, aligned to the corporate growth expectations. The modeling in this work focused in the generation of scenarios to create a decision support system, predicting individual and aggregate demand, create a structure for integrating and aligning the estimated forecast generated by quantitative and qualitative methods. After a series of experimental tests, main results found were: (1) triple exponential smoothing provided the best fit using historical aggregated demand, and provided a more precise estimate of aggregate representation. For historical series of individual demand and individual representation, the other methods used for comparison performed similarly; (2) the creation of different scenarios for prediction, using data repository and decision support system, allowed for analysis of a range of different future values. The simulation to support management expectations has been adapted from the literature; (3) the prediction errors in the top-down and bottom-up approaches are statistically the same in the context of this research. In conclusion, the method of triple exponential smoothing has fewer errors in the forecasts of aggregated series when compared to other methods discussed in this work. Moreover, the DSS and BI systems provided decision-making support to the various hierarchical levels, reducing the gap between qualitative and quantitative decision processes thus bridging the strategic and operational decision making processes.

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.

Sales forecasting is an essential part of supply chain management. In retail business, accurate sales forecasts lead to significant cost reductions. Statistical methods that are commonly used for sales forecasting often overlook important aspects unique for the sales time series, which lowers the forecast accuracy. In this thesis we explore whether it is possible to improve short-term sales forecasting by employing deep neural networks. This thesis analyzes performance of various traditional deep neural network designs and proposes a novel architecture. It also explores several data preprocessing methods, both traditional and non-traditional, which turns out to be a crucial part of sales forecasting using deep neural networks. The best methods of deep neural network approach that we found are then compared to other forecasting methods such as traditional neural networks or exponential smoothing. Powered by TCPDF (www.tcpdf.org)

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Predicting sales is an important step for many business processes. Several forecasting methods have been applied to uncountable different problems, however with no present research found in the area of renewable energy certificates. Thus, this study aims to examine the possibility of developing a model based on traded volumes of certificates, where a comparison between simpler and more complex models explores the general increased interest in machine learning models. Therefore, five different models are tested with monthly sales data: the statistical model Seasonal Autoregressive Integrated Moving Average, the machine learning models Support Vector Regression and Extreme Gradient Boosting and further the neural networks Long Short-Term Memory and Bidirectional Long Short-Term Memory. Extensive data preparation is operated by taking into account seasonality and trends where data transformations are applied in addition to feature engineering. To evaluate the models, non-aggregated monthly forecasts as well as aggregated predictions of two and three months are examined. The results show that it is feasible to model the sales volumes of renewable energy certificates. As expected, the models generally perform better when evaluated based on aggregated monthly predictions. Also, when considering both evaluation strategies, the Seasonal Autoregressive Integrated Moving Average, Support Vector Regression and Extreme Gradient Boosting are the only models showing better performance compared to a baseline model. The proposed solution to enable smarter and more efficient trading decisions today is a combination of the aggregated two months and quarterly predictions of the Seasonal Autoregressive Integrated Moving Average and Support Vector Regression models. Considering an expected expansion of relevant available data for the company, the recommendation for the future is to specifically further develop the machine learning models with an anticipation of improved performance and valuable feature importance insights.

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Yes / The rapid rise of many e-commerce platforms for individual consumers has generated a large amount of text-based data, and thus researchers have begun to experiment with text mining techniques to extract information from the large amount of textual data to assist in sales forecasting. The existing literature focuses textual data on product reviews; however, consumer reviews are not something that companies can directly control, here we argue that textual product descriptions are also important determinants of consumer choice. We construct an artificial intelligence (AI) framework that combines text mining, WaveNet neural networks, multiple regression, and SHAP model to explain the impact of product descriptions on sales forecasting. Using data from nearly 200,000 sales records obtained from a cross-border e-commerce firm, an empirical study showed that the product description presented to customers can influence sales forecasting, and about 44% of the key phrases greatly affect sales forecasting results, the sales forecasting models that added key product description phrases had improved forecasting accuracy. This paper provides explainable results of sales forecasting, which can provide guidance for firms to design product descriptions with reference to the market demand reflected by these phrases, and adding these phrases to product descriptions can help win more customers. / The full-text of this article will be released for public view at the end of the publisher embargo on 24 Feb 2025.

本篇目的---對於精實企業來說資訊預測的能力扮演舉足輕重的角色，如汽車製造商須要有可靠的資訊來完成各項重要的決策以保持企業競爭力，市場以及消費者的活動提供了新型態的資料可以透過現代科技來處理分，本篇論文希望從2008年至2016年整合的Google 搜尋趨勢資料來建構預測模型。 設計/方法論/方法---基於五階段消費者購買行為，此研究檢視整個過程中合適的Google關鍵字，並利用滯後變數模型和Google搜尋趨勢來驗證銷售和各種經濟變數之間的關係，預測的銷售會更進一步檢視其正確性。 結論與發現---用來檢視預測正確性的兩種最常見的方法指出Google搜尋趨勢可以作為有效的銷售預測依據，研究發現總體經濟變數和時間序列在預測上相較於Google搜尋趨勢在短期相對有效性小。 研究貢獻---僅有少許在汽車銷量預測上的研究將Google搜尋趨勢和合適的時間滯留列入考量，本篇研究提供消費者行為和銷售資料關係的新視角。 / Purpose – The role of forecasting in a lean enterprise is immense. It is crucial for car manufacturers to have reliable information about the future to make important decisions and stay competitive. Developing markets and consumers provide new types of data that demand modern approaches to be handled. This paper aims to create reliable forecasting models through integration of Google Trends data from 2008 to 2016. Design/methodology/approach – Building on the 5-stage-model of consumer buying behavior, the study identifies suitable Google keywords for this process. Autoregressive distributed lag models are used to examine the relationship between sales and macro-economic variables as well as Google Trends. Predicted sales are used to test for accuracy. Findings – Two most common evaluation measurements for forecasting accuracy suggest the use of Google Trends, as predictors for future sales, is outstanding. The finding concludes that macro-economic variables and seasonality are not as valuable as Google Trends in short-term, up to one year, forecasting. Value – Only little research on car sales forecasting takes Google Trends and their appropriate time lags into account. This analysis provides new insights into the linkage of consumer behavior and sales data.