Abstract   Purpose - The purpose with this thesis is to investigate how to increase sales forecast accuracy.   Methodology – To fulfil the purpose a case study was conducted. To collect data from the case study the authors performed interviews and gathered documents. The empirical data was then analysed and compared with the theoretical framework.   Result – The result shows that inaccuracies in forecasts are not necessarily because of the forecasting technique but can be a result from an unorganized forecasting process and having an inefficient information flow. The result further shows that it is not only important to review the information flow within the company but in the supply chain as whole to improve a forecast’s accuracy. The result also shows that time series can generate more accurate sales forecasts compared to only using qualitative techniques. It is, however, necessary to use a qualitative technique when creating time series. Time series only take time and sales history into account when forecasting, expertise regarding consumer behaviour, promotion activity, and so on, is therefore needed. It is also crucial to use qualitative techniques when selecting time series technique to achieve higher sales forecast accuracy. Personal expertise and experience are needed to identify if there is enough sales history, how much the sales are fluctuating, and if there will be any seasonality in the forecast. If companies gain knowledge about the benefits from each technique the combination can improve the forecasting process and increase the accuracy of the sales forecast.   Conclusions – This thesis, with support from a case study, shows how time series and qualitative techniques can be combined to achieve higher accuracy. Companies that want to achieve higher accuracy need to know how the different techniques work and what is needed to take into account when creating a sales forecast. It is also important to have knowledge about the benefits of a well-designed forecasting process, and to do that, improving the information flow both within the company and the supply chain is a necessity.      Research limitations – Because there are several different techniques to apply when creating a sales forecast, the authors could have involved more techniques in the investigation. The thesis work could also have used multiple case study objects to increase the external validity of the thesis.

Forecasting is a powerful tool that can enable companies to save millions in revenue every year if the forecast is good enough. The problem lies in the good enough part. Many companies today use Excel topredict their future sales and trends. While this is a start it is far from optimal. Seco Analytics aim to solve this issue by forecasting in an informative and easy manner. The web application uses the ARIMA analysis method to accurately calculate the trend given any country and product area selection. It also features external data that allow the user to compare internal data with relevant external data such as GDP and calculate the correlation given the countries and product areas selected. This thesis describes the developing process of the application Seco Analytics.

The business environment nowadays becomes much more dynamically and tensely than the past driven by the trend of globalization and free trading. Therefore, any enterprise in the world has to face competition from everywhere in the world. Under such complicated business environment, it¡¦s dangerous to make decision based on past experience or instinct. If some key message is missed or not collected, a disaster caused by logical decision, but far away from the reality might just happen. In last couple decades, software providers launched DSS(Decision Support System), BI(Business Intelligence), ¡K, etc. based on current enterprise IT infrastructure like ERP (Enterprise Resource Planning), MRPII(Manufacturing Resource Planning), ¡K, etc. to help enterprise for decision making. However, such systems are not popular in Taiwan, not to mention the successful stories. While I studied the lesson ¡§information technology and competitive advantage¡¨ conducted by Profession Kuo, I concluded from classmates¡¦ discussion that the root causes were as follows. 1. Most Taiwan manufacturers¡¦ strategy is to launch product at lower cost to allow them to win business via price war. Therefore, they are willing to invest tangible hardware, not intangible software. 2. The branches of international companies can¡¦t develop their own information system due to Corporate policy or security concern. Based on above mentioned, I started thinking if we could have a BI system that doesn¡¦t need to spend money, is easy to implement, and no need for Corporate approval. Such BI system could help management to retrieve effective and enough information for precise decision making. After evaluation, I think Microsoft Excel spreadsheet software is the most suitable solution. It¡¦s because almost all enterprises have it, it can contain 1M units of data in a file, and useful tools of macro, pivot table, sorting, filtering, VBA(Visual Basic for Application). Furthermore, the nature of spreadsheet is similar to database structure, so it can be easily integrated with database like SQL database, Microsoft Access. Thanks to Profession Kuo¡¦s coaching, I started doing research, and studied necessary tool like VBA, ¡K, etc. to warm up for this thesis. After months, I finally finish it, and I hope it can contribute to the ones that have similar problem with me.

O segmento de fast-food tornou-se um mercado muito concorrido e com empresas bem conhecidas, tais como: Subway, McDonalds, Burguer King, Bobs e Habibs. Técnicas de inteligência artificial e ciência de dados podem oferecer inúmeros benefícios para este mercado, como por exemplo, permitir o desenvolvimento de modelos computacionais para tomada de decisões. No contexto de finanças onde envolvam a comercialização de determinados produtos, é muito comum deparar-se com cenários que envolvam incerteza, principalmente quando se deseja realizar projeções financeiras, avaliar riscos e estimativas. O objetivo deste trabalho consiste em desenvolver modelos probabilísticos baseados em Redes Bayesianas (RB) para realizar predições em vendas e análise de causalidade entre variáveis que influenciam no processo de comercialização de determinados grupos de produtos no seguimento de fast-food. Nesta análise foram avaliadas Redes Bayesianas com aprendizado de estrutura baseado em restrições, através do algoritmo Grow Shrink (GS), e Redes Bayesianas com aprendizado de estrutura baseado em pontuação, através do algoritmo Hill-Climbing (HC), posteriormente foram comparadas com um modelo de série temporal baseado em Generalized Additive Model (GAM). Os dados para análise foram adquiridos de uma rede de fast-food brasileira que possui cerca de 1100 lojas associadas, destas, foram utilizadas lojas que pertencem ao estado de São Paulo, assim como avaliado variáveis de grupos de vendas no período de 2010 à 2017. Os resultados foram avaliados através da métrica Mean Absolute Percentage Error (MAPE), que considera valores reais alimentados em modelos e valores ajustados a partir do modelo e calcula a diferença absoluta entre os dois como porcentagem do valor real, com base neste cálculo é possível obter a acurácia de cada modelo. A Rede Bayesiana (RB) com aprendizagem de estrutura baseada em pontuação, utilizando o algoritmo Hill Climbing (HC), foi escolhida como o melhor modelo, pois apresentou relações causais mais coerentes entre os vértices que influenciam o processo de venda, bem como combinações de vértices que resultam em combos de produtos, além disso, resultou em 97.60% de acurácia na previsão de vendas das lojas do estado de São Paulo (SP) na amostra de teste avaliada, com base na métrica Mean Absolute Percentage Error (MAPE). / The fast-food segment has become a busy market with well-known companies such as: Subway, McDonalds, Burger King, Bobs and Habibs. Artificial intelligence and data science techniques can offer innumerable benefits to this market, such as allowing the development of computational models for decision making. In the context of finances involving the marketing of certain products, it is very common to come across scenarios where uncertainty is involved, especially when financial projections are desired, to evaluate risks and estimation. The objective of this work is to develop probabilistic models based on Bayesian Networks (BN) to make sales predictions and causality analysis among variables that influence the commercialization process of certain product groups in the fast-food segment. In this analysis we evaluated Bayesian networks with learning of structure based on constraints, through the algorithm Grow Shrink (GS), and Bayesian Networks with learning of structure based on score, through the algorithm Hill-Climbing (HC), later were compared with a model time series based on Generalized Additive Model (GAM). The data for analysis were acquired from a Brazilian fast-food chain with approximately 1100 associated stores, of which stores were used that belong to the state of São Paulo, as well as evaluated variables of sales groups in the period from 2010 to 2017. The results were evaluated by using the Mean Absolute Percentage Error (MAPE), which considers real values fed in models and values adjusted from the model and calculates the absolute difference between the two as a percentage of the real value, based on this calculation it is possible to obtain the accuracy of each model. The Bayesian Network (BN) with scoring based structure learning, using the Hill Climbing (HC) algorithm, was chosen as the best model because it presented more coherent causal relationships between vertices that influence the sales process, as well as combinations of vertices that result in product combos, in addition, achieved a 97.60% accuracy in the sales forecast of stores in the state of Sao Paulo (SP) in the test sample evaluated, based on the Mean Absolute Percentage Error (MAPE) metric.

Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2018-06-19T12:25:43Z No. of bitstreams: 1 GRAZIELE_MARQUES_MAZUCO_DOS_SANTOS_DIS.pdf: 3857481 bytes, checksum: 9c3c88f01e8e5d920ba3bc8989d2cfbf (MD5) / Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-06-27T13:05:50Z (GMT) No. of bitstreams: 1 GRAZIELE_MARQUES_MAZUCO_DOS_SANTOS_DIS.pdf: 3857481 bytes, checksum: 9c3c88f01e8e5d920ba3bc8989d2cfbf (MD5) / Made available in DSpace on 2018-06-27T13:21:15Z (GMT). No. of bitstreams: 1 GRAZIELE_MARQUES_MAZUCO_DOS_SANTOS_DIS.pdf: 3857481 bytes, checksum: 9c3c88f01e8e5d920ba3bc8989d2cfbf (MD5) Previous issue date: 2018-04-26 / Demand forecasting is one of the most essential components of supply chain management. Forecasts are used both for long-term and for short-term. Long-term forecasts are important because it is difficult in terms of production to face the demand deviation in a short time, so the anticipation of prediction helps to increase the responsiveness of the supply chain. Short term forecasts are important for the demand monitoring aiming to keep healthy inventory levels. In the fashion industry, the high change of products, the short life cycle and the lack of historical data makes difficult accurate predictions. To deal with this problem, the literature presents three approaches: statistical, artificial intelligence and hybrid that combines statistical and artificial intelligence. This research presents a two-phased method: (1) long-term prediction, identifies the different life cycles in the products, allowing the identification of sales prototypes for each cluster and (2) short-term prediction, classifies new products in the clusters labeled in the long-term phase and adjusts the sales curve considering optimistic and pessimist factors. As a differential, the method is based in dynamic time warping, distance measure for time series. The method is tested in a real dataset with real data from fashion retailers that demonstrates the quality of the contribution. / A previs?o de vendas no varejo da moda ? um problema complexo e um dos componentes essenciais da cadeia de suprimento, sendo utilizada tanto para previs?o de longo prazo quanto para a previs?o de curto prazo. A previs?o de longo prazo ? importante pois ? dif?cil, em termos de produ??o, enfrentar o desvio da demanda em um curto espa?o de tempo, ent?o a previs?o antecipada permite aumentar a capacidade de resposta da cadeia de suprimento. A previs?o de curto prazo ? importante para o acompanhamento da demanda, visando a adequa??o do n?vel de estoque. No varejo da moda a alta rotatividade, o curto ciclo de vida dos produtos e a consequente aus?ncia de dados hist?ricos dificulta a gera??o de previs?es precisas. Para lidar com esse problema, h? na literatura tr?s principais abordagens: estat?stica, baseada em intelig?ncia artificial e h?brida, que combina estat?stica e intelig?ncia artificial. Esta pesquisa prop?e um m?todo de previs?o de vendas em duas etapas: (1) previs?o de longo prazo, que pretende detectar diferentes grupos de produtos com ciclos de vida semelhantes, permitindo assim a identifica??o do comportamento m?dio de cada um dos grupos e (2) previs?o de curto prazo que busca associar os produtos novos nos grupos identificados na etapa de longo prazo e ajustar a curva de vendas levando em considera??o fatores conservadores, otimistas ou pessimistas. Al?m disso, nesta etapa ? poss?vel realizar a previs?o de reposi??o de itens. Como diferencial, o m?todo proposto utiliza a medida de dist?ncia Dynamic Time Warping, identificada na literatura como adequada para lidar com s?ries temporais. O m?todo ? testado utilizando dois conjuntos de dados reais de varejistas da moda, foram realizados dois experimentos, que demonstram a qualidade da contribui??o.

The trend seasonal demand pattern is encountered when both trend and seasonal influences are interactive. The problem of this research is to project the seasonal market sales using ice cream and fresh milk in Taiwan as examples. In order to improve the accuracy of forecast, two different methods are validated and the best forecasting method is selected based on the minimum Mean Square Error. In this study, we present two forecasting models used for evaluation to predict seasonal market sales of ice cream, fresh milk, and air conditioner in Taiwan. It includes Winters multiplicative seasonal trend model and the Decomposition method. Two different methods are validated and the best forecasting method is selected based on the minimum Mean Square Error. After the validation process, Winters multiplicative seasonal trend model is selected based on the minimum MSE, and the monthly sales forecast for the year of 2011 is conducted using the data(60 months). Number Cruncher Statistical System (NCSS) is used for analyzing the data which proves useful and powerful. In summary, the results demonstrate that Winters multiplicative seasonal trend model has the smallest mean square error in this case. Therefore, we conclude that both Winters multiplicative seasonal trend model and the Decomposition model are well fitted for forecasting the seasonal market sales. Yet, Winters multiplicative seasonal trend model is the better method to be used in this study since it generates the smallest mean square error (MSE) during the period of validation.

The thesis is focused on the draft of a financial plan for the engineering company PBS Turbo s.r.o. for the period 2020-2023. The first chapter of this thesis consists of theoretical aspects, which are necessary for making a financial plan. The following part is practical, dealing with the company's introduction and its strategic and financial analysis. Based on the results of the analyses, a financial plan in an optimistic and pessimistic variant is proposed in the final part, including their evaluation.

Tener un acertado pronóstico de ventas es un tema indispensable en empresas productoras y distribuidoras, ya que impacta positivamente en la toma de decisiones relacionadas a la demanda, gestión de inventario, abastecimiento, distribución y surtido de productos, así como otras áreas del negocio como finanzas, marketing, operaciones y servicio al cliente. Sin embargo, actualmente, en la mayoría de los casos, las predicciones de las ventas se basan en análisis individuales de los productos, mas no en técnicas de clustering, las cuales permitirían reducir el error y ser más acertados en sus pronósticos. Por ello, el presente trabajo propone evaluar y comparar el impacto de la precisión de los pronósticos de ventas del método tradicional, regresión lineal simple con series de tiempo, con los métodos de clustering k-means y Ward. El alcance de la investigación será a los cinco principales productos de una importante empresa productora y distribuidora de alimentos procesados en el Perú. Los indicadores que se utilizarán para identificar el método más preciso serán los errores medios absolutos (MAD) y errores cuadráticos medios (MSE) resultantes. / Having a successful sales forecast is an essential issue in manufacturing and distributing companies, since this impacts the decision-making related to demand, inventory management, supply, distribution and product assortment, as well as other areas of the business such as finance, marketing, operations and customer service. However, currently, in most cases, sales forecast are based on individual product analyzes, but not on clustering techniques, which would reduce error and be more accurate in their forecasts. Therefore, the present work proposes to evaluate and compare the impact of the precision of the sales forecasts between the traditional method, simple linear regression with time series, and the clustering methods such as k-means and Ward. The investigation scope will be the five main products of an important manufacturer and distributor of processed foods in Peru. The indicators that will be used to identify the most accurate method will be the mean absolute deviation (MAD) and mean square errors (MSE). / Tesis

Die vorliegende Arbeit befasst sich mit Innovationsdiffusionsmodellen und deren Anwendung in der Marketingpraxis. Sie hat zwei Ziele: Einen Überblick über existierende Innovationsmodelle zu schaffen und ein neues besseres Modell zu entwickeln. Es wird ein neuer Klassifizierungsansatz vorgeschlagen, mit dessen Hilfe ein strukturierter Überblick über die vorhandenen zahlreichen Innovationsdiffusionsmodelle möglich wird. Die Klassifizierung beruht auf den Annahmen in den Innovationsdiffusionsmodellen. Dies erlaubt im Gegensatz zu den bekannten Klassifizierungen (z.B. von Roberts/Lattin (2000)) die Bildung von disjunkten Modellklassen. Anhand der neuen Klassifizierung werden die prominenten Modelle, wie z.B. Bass-Modell (1969) bzw. Kalish-Modell (1985) eingeordnet und ihre Vor- und Nachteile aufgezeigt. Dieser Ansatz erleichtert eine Entscheidung für das beste zu verwendende Modell, wenn bekannt ist, welche Daten (Absatzdaten, Daten über Konsumenten etc.) zur Verfügung stehen und/oder welches Ziel (Absatzprognose, Preisbestimmung) verfolgt wird. Im zweiten Teil der Arbeit wird ein neues hybrides Innovationsdiffusionsmodell – das Information-Disicion-Evaluation-Modell (IED-Modell) – vorgestellt. Das IED-Modell besitzt zahlreiche Vorteile gegenüber existierenden Innovationsdiffusionsmodellen. Die Struktur des IED-Modells ist sehr allgemein so, dass das IED-Modell als eine Modellklasse bezeichnet werden könnte. Werden die Annahmen des IED-Modells genau definiert (z.B. über die Anzahl der Wettbewerbsprodukte usw.), erhält es eine explizite Form, die prominenten Innovationsdiffusionsmodellen ähnlich oder vollkommen identisch sein kann (für die Erstellung einer expliziten Form des IED-Modells siehe www.ied-modell.de). Ein solcher allgemeiner Modellierungsansatz des IED-Modells ist neu für die Innovationsdiffusionsforschung. Das IED-Modell und dessen Annahmen werden mittels Monte-Carlo-Simulationen analysiert. Beim empirischen Test an realen Daten wird das IED-Modell mit vier renommierten Innovationsdiffusionsmodellen verglichen. Laut diesem Vergleich ist das Anpassungsvermögen des IED-Modells im Durchschnitt besser als das der vier Vergleichsmodelle. Bei drei- und zehnmonatlichen Prognosen zeigte das IED-Modell eine sehr gute Vorhersagefähigkeit. / This work assesses innovation diffusion models and their application in marketing management. Its two principal aims are: (1) to give an overview of existing innovation diffusion models and (2) to develop a new and improved model. A new classification approach is proposed. The classification methodology bases on typical assumptions made in innovation diffusion models. Unlike prior classifications, e.g. Roberts/Lattin (2000), this approach allows for disjunctive classes. By means of this classification renowned models like Bass Model (1969) or Kalish Model (1985) are categorized, and their advantages and disadvantages are analyzed. This helps decide which model should be used depending on data availability (sales data, consumer data etc.) and the overall goal of a model investigation (sales forecast, pricing etc.). In the second part of this work the new hybrid innovation diffusion model – Innovation-Decision-Evaluation model (IED model) – is described. The model has several advantages compared with existing models. The structure of the IED Model is non-specific so that the IED model can be described as a distinct model class. When assumptions of the IED model are specified (e.g. number of competitive products) the model gets an explicit form which can be similar or even identical to other innovation diffusion models (for the design of an explicit model form see also www.ied-modell.de). Such a generalized modeling approach in IED modelling is new in innovation diffusion research. The IED model and its assumptions are analysed with Monte Carlo simulations. Its results are also empirically tested and compared with four renowned innovation diffusion models. The comparison reveals that the IED model has the best average fit and good forecast goodness.

Submitted by Adriana Bezerra Bessa (adrianabbessa@gmail.com) on 2018-05-09T00:07:09Z No. of bitstreams: 1 Tese_AdrianaBessa_versaofinal.pdf: 4846338 bytes, checksum: 5d2e8d52cd770e8fd17a4a9adee180d2 (MD5) / Approved for entry into archive by Thais Oliveira (thais.oliveira@fgv.br) on 2018-05-10T17:26:20Z (GMT) No. of bitstreams: 1 Tese_AdrianaBessa_versaofinal.pdf: 4846338 bytes, checksum: 5d2e8d52cd770e8fd17a4a9adee180d2 (MD5) / Approved for entry into archive by Suzane Guimarães (suzane.guimaraes@fgv.br) on 2018-05-11T12:30:07Z (GMT) No. of bitstreams: 1 Tese_AdrianaBessa_versaofinal.pdf: 4846338 bytes, checksum: 5d2e8d52cd770e8fd17a4a9adee180d2 (MD5) / Made available in DSpace on 2018-05-11T12:30:08Z (GMT). No. of bitstreams: 1 Tese_AdrianaBessa_versaofinal.pdf: 4846338 bytes, checksum: 5d2e8d52cd770e8fd17a4a9adee180d2 (MD5) Previous issue date: 2018-04-24 / A previsão de vendas é um aspecto crítico para maior parte das organizações, já que permite tornar o processo de planejamento mais eficiente, impactando assim nos resultados a serem obtidos pelas empresas. Entre as diversas técnicas de previsão, temos o grupo de métodos estatísticos clássicos e os métodos avançados, que trazem uma contribuição no tratamento das não linearidades. É neste contexto, que surge o problema desta dissertação: Quais são as técnicas que apresentam maior acurácia quando aplicadas para previsão de vendas no varejo de moda? Para responder a esta questão, esse trabalho avaliou dez métodos de previsão: Naive, SARIMA, SARIMA com exógenas, SARIMA GARCH, SARIMA GARCH com exógenas, método atual utilizado pela empresa estudada, rede neural MLP, rede neural MLP com exógenas, rede neural recorrente LSTM e rede neural recorrente LSTM com exógenas para quatro séries de quantidades vendidas de categorias de produtos distintas de uma empresa varejista do setor de moda. É fundamental destacar, que de forma casual, a pesquisa identificou que as quatro séries semanais de vendas dos produtos analisados são estacionárias, considerando um período longo de dez anos, o que por si só já é um resultado relevante. A análise dos diversos métodos de previsão para cada série de produto mostrou que os métodos avançados superaram os métodos estatísticos clássicos e, mais especificamente, a rede neural recorrente LSTM foi a que apresentou a maior precisão. Sendo assim, não há dúvidas que adoção dos métodos avançados para as empresas, que atuam no varejo de moda, pode trazer melhorias significativas em termos de gestão de estoque, de gestão da cadeia de abastecimento e de gestão de caixa, garantindo um aumento de eficiência e dos resultados das mesmas. De forma prática, para a empresa estudada foi obtido um incremento de acuracidade de 54,32%. / The sales forecasting is a critical aspect for most organizations, since it allows to make the planning process more efficient, thus impacting the results to be obtained by the companies. Among the various forecasting techniques, we have the group of classical statistical methods and the advanced methods, which make a contribution in the treatment of nonlinearities. It is in this context, that the problem of this dissertation arises: What are the techniques that present the greatest accuracy when applied to forecast sales in fashion retail? In order to answer this question, this study evaluated ten predictive methods: Naive, SARIMA, SARIMA with exogenous, SARIMA GARCH, SARIMA GARCH with exogenous, current method used by the studied company, MLP neural network, MLP neural network with exogenous, recurrent neural network LSTM and LSTM recurrent neural network with exogenous for four series of quantities sold from product categories distinct from a retailer in the fashion industry. It is important to highlight that, on a casual basis, the research identified that the four weekly series of sales of the analyzed products are stationary, considering a long period of ten years, which in itself is already a relevant result. The analysis of the various prediction methods for each product series showed that the advanced methods overcame the classic statistical methods and, more specifically, the recurrent neural network LSTM was the one that presented the highest precision. Therefore, there is no doubt that adoption of the advanced methods for companies that operate in fashion retail can bring significant improvements in terms of inventory management, supply chain management and cash management, ensuring an increase in efficiency and in its results. In practice, for the company studied, an accuracy increase of 54.32% was obtained.