The diploma thesis deals with the increasing of elasticity level in supply chain in automotive industry, especially with the import of new cars Peugeot for Czech customers. It solves discrepancy between production and consumption from point of capacity, sentimental, position and time view. It uses a method for demand forecasting by multiple temporal regression and method Vendor Managed Inventory.

Machine learning is a central technology in data-driven decision making. In this study, machine learning in the context of demand forecasting in the motion picture industry from film exhibitors’ perspective is investigated. More specifically, it is investigated to what extent the technology can assist estimation of public interest in terms of revenue levels of unreleased movies. Three machine learning models are implemented with the aim to forecast cumulative revenue levels during the opening weekend of various movies which were released in 2010-2017 in Sweden. The forecast is based on ten attributes which range from public online user-generated data to specific movie characteristics such as production budget and cast. The results indicate that the choice of attributes as well as models in this study were not optimal on the Swedish market as the retrieved values from relevant precision metrics were inadequate, however with valid underlying reasons. / Maskininlärning är en central teknik i datadrivet beslutsfattande. I den här rapporten utreds maskininlärning isammanhanget av efterfrågeprediktion i filmbranschen från biografers perspektiv. Närmare bestämt undersöks det i vilken utsträckningtekniken kan bistå uppskattning av publikintresse i termer av intäkter vad gäller osläppta filmer hos biografer. Tremaskininlärningsmodeller implementeras i syfte att göra en prognos på kumulativa intäktsnivåer under premiärhelgen för filmer vilkahade premiär 2010-2017 i Sverige. Prognostiseringen baseras på varierande attribut som sträcker sig från publik användargenererad data på nätet till filmspecifika variabler så som produktionsbudget och uppsättning av skådespelare. De erhållna resultaten visar att valen av attribut och modeller inte var optimala på den svenska marknaden då erhållna precisionsmått från modellerna antog låga värden, med relevanta underliggande skäl.

The purpose of this study is to provide the case company Nudient with a recommendation of what demand forecasting methods and strategies they should use. To be able to make a tailored recommendation, a literature study is conducted to explore what demand forecasting methods are commonly used on applications similar to the case being studied. The forecasting methods and the strategy regarding when and how to use them are thereafter explored in a main literature review. Empirical data is gathered from the case company in the form of interviews and demand data. The empirical data is then used to evaluate which of the methods found in the literature review are a good fit for Nudient, thereafter the demand forecasting strategy is laid out. The final recommendation is divided into two categories, forecasting the demand for new products and forecasting the demand for mature products. For new products, the recommendation is for Nudient to make use of associative modeling, expert consensus, the Delphi method, and market research. For mature products, the recommendation is for Nudient to make use of the moving average method, double exponential smoothing, regression analysis, associative modeling, expert consensus, and the Delphi method. / Syftet med detta examensarbete är att ge fallstudieföretaget Nudient en rekommendation angående vilka metoder och strategier för efterfrågeprognoser de bör använda. För att kunna ge en skräddarsydd rekommendation genomförs en litteraturstudie med syfte att undersöka vilka efter frågeprognoser som vanligtvis används i applikationer som liknar det fall som studeras. Prognosmetoderna och strategin för när och hur metoderna ska användas utforskas därefter i en huvudlitteraturöversikt. Empirisk data samlas in från fallstudieföretaget i form av intervjuer och efterfrågedata. Den empiriska datan används sedan för att utvärdera vilka av metoderna som identifierades i litteraturöversikten som är passande för Nudient, därefter tas strategin fram för efterfrågeprognoser. Den slutliga rekommendationen är uppdelad i två kategorier, efterfrågeprognoser på nya produkter och efterfrågeprognoser på mogna produkter. För nya produkter är rekommendationen att Nudient bör använda associativ modellering, expertkonsensus, Delphi-metoden och marknadsundersökningar. För mogna produkter är rekommendationen att Nudient bör använda sig av glidande medelvärde, dubbel exponentiell utjämning, regressionsanalys, associativ modellering, expertkonsensus och Delphi-metoden.

With Industry 4.0, companies are faced with the challenge of managing an ever-increasing amount of data and re-evaluating and innovating their production planning methods. An important aspect of demand forecasting is the accuracy of forecasts compared to outcomes. Research has shown that more complex models perform better in demand forecasting, however, this research has focused on demand forecasting in the IT, finance and e-commerce sectors.   This thesis investigates the application of predictive modelling in demand forecasting in the context of production planning for a medium-sized manufacturing company. The study mainly compares the performance of two predictive models: Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, with the aim of assessing its usefulness in improving the accuracy of demand forecasts. Based on historical sales data, this quantitative case study investigates how these models can improve operational efficiency that can be applied to production planning processes such as optimal inventory and production schedules.    The study found that the LSTM model, through Automated Machine Learning (AutoML), was significantly better than the ARIMA model in terms of forecast accuracy. This was evidenced by lower Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values, indicating that LSTM's ability to capture long-term dependencies and adapt to non-linear patterns provides a more robust tool for demand forecasting in production planning.   This study contributes to the field of industrial engineering by demonstrating the practical benefits of integrating advanced predictive models into manufacturing companies' production planning processes. It highlights the potential of machine learning techniques to transform traditional production planning systems and thus provides insights into the strategic implementation of AI in industrial operations. Future research could explore and compare more models to get a broader picture of how different models perform against each other in terms of prediction errors. / Med Industri 4.0 står företagen inför utmaningen att hantera en ständigt ökande mängd data och att omvärdera och förnya sina metoder för produktionsplanering. En viktig aspekt av efterfrågeprognoser är prognosernas träffsäkerhet jämfört med utfallet. Forskning har visat att mer komplexa modeller presterar bättre vid efterfrågeprognoser, men denna forskning har fokuserat på efterfrågeprognoser inom IT-, finans- och e-handelssektorerna.   Denna studie undersöker tillämpningen av prediktiv modellering vid efterfrågeprognoser i samband med produktionsplanering för ett medelstort tillverkningsföretag. Studien jämför främst prestandan hos två prediktiva modeller: Autoregressive Integrated Moving Average (ARIMA) och Long Short-Term Memory (LSTM) nätverk, i syfte att bedöma hur användbara de är för att förbättra precisionen i efterfrågeprognoser. Baserat på historiska försäljningsdata undersöker denna kvantitativa fallstudie hur dessa modeller kan förbättra den operativa effektiviteten som kan tillämpas på produktionsplaneringsprocesser, såsom lagerhållning och produktionsscheman.    Studien visade att LSTM-modellen, genom automatiserad maskininlärning (AutoML), var betydligt bättre än ARIMA-modellen när det gäller prognosprecision. Detta framgick av lägre RMSE-värden (Root Mean Squared Error) och MAE-värden (Mean Absolute Error), vilket tyder på att LSTM:s förmåga att fånga upp långsiktiga beroenden och anpassa sig till icke-linjära mönster ger ett mer robust verktyg för efterfrågeprognoser inom produktionsplanering.   Denna studie bidrar till området industriell ekonomi genom att visa på de praktiska fördelarna med att integrera avancerade prediktiva modeller i tillverkningsföretagens produktionsplaneringsprocesser. Den belyser maskininlärningsteknikernas potential att omvandla traditionella produktionsplaneringssystem och ger därmed insikter i den strategiska implementeringen av AI i industriell verksamhet. Framtida forskning skulle kunna utforska och jämföra fler modeller för att få en bredare bild av hur olika modeller presterar mot varandra när det gäller prediktionsfel.

Le marché des véhicules électriques est animé par une dynamique très positive. Il s'agit cependant essentiellement d'un marché de niche. Il est donc légitime de s’interroger quant à son avenir.D'une part, cette dynamique est portée par de fortes préoccupations environnementales et bénéficie d'un large soutien des autorités publiques. Les constructeurs automobiles ont ces dernières années fortement investi dans cette technologie, les progrès technologiques sont rapides et offrent des perspectives intéressantes.D'autre part, des subventions conséquentes sont aujourd’hui nécessaires pour permettre aux véhicules électriques d’être compétitifs. Il est inévitable que ces subventions diminuent si le marché grandit. Deux mécanismes opposés sont donc en jeu et rendent incertain le développement du marché des véhicules électriques pour les années à venir.Notre recherche propose d'analyser ces mécanismes pour les véhicules utilitaires légers, et plus particulièrement pour le transport urbain de marchandises. Les besoins des entreprises de transport de marchandises sont évalués à travers une quarantaine d'entretiens, menés dans quatre pays européens et analysés à la lumière de la théorie de la diffusion de l'innovation. Ces entretiens mettent en évidence les obstacles opérationnels et économiques à l'utilisation de véhicules électriques, qui sont liés à la technologie elle-même mais aussi à sa nouveauté.Une approche quantitative complète cette étude. Elle s’appuie sur un modèle de prédiction de parts de marché, qui quantifie la façon dont les contraintes économiques et opérationnelles évoluent avec les développements technologiques. Ces contraintes sont mesurées par deux indicateurs: l'adéquation de l'autonomie du véhicule avec son usage et les comparaisons de coûts totaux de possession (TCO). Une originalité du modèle est qu’il traite le montant des subventions à l’achat d’un véhicule électrique comme une variable endogène, qui s’adapte dynamiquement aux évolutions du marché.Afin de compenser le manque de données disponibles sur les usages des véhicules utilitaires, un modèle statistique a été développé. Ce modèle permet d’exploiter au mieux les données d'une enquête sur les véhicules utilitaires légers en France, menée par le service de la donnée et des études statistiques (SDES) du Ministère de la Transition Écologique et Solidaire / Freight transport. The needs of freight transport companies are assessed through some forty interviews conducted in four European countries and analyzed in the light of innovation diffusion theory. These interviews highlight the operational and economic obstacles to the use of electric vehicles, which are linked to the technology itself but also to its novelty.A quantitative approach completes this study. It is based on a market share prediction model, which quantifies how economic and operational constraints evolve with technological developments. These constraints are measured by two indicators: the vehicle's range adequacy given its use and total cost of ownership (TCO) comparisons. An original feature of the model is that it treats the amount of subsidies for the purchase of an electric vehicle as an endogenous variable that dynamically adapts to market developments.In order to compensate for the lack of available data on commercial vehicle uses, a statistical model has been developed. This model makes the best use of data from a survey on light commercial vehicles in France, conducted by the statistical department of the Ministry of the Environment (SDES).These analyses confirm that the development of the electric vehicle market is not certain and that it is currently extremely dependent on public support. Even in scenarios of continued financial support from public administrations, exponential market growth is unlikely. Rather, the market will grow slowly for many years to come, the time for technology to overcome its8dependence on public financial support. For example, our reference scenario forecasts a 13% market share for electric vans in 2032

Made available in DSpace on 2016-08-10T10:40:42Z (GMT). No. of bitstreams: 1 RAFAEL GOMES LOIOLA.pdf: 2193685 bytes, checksum: 77fe8f4c3a881108732f58c6013d52b5 (MD5) Previous issue date: 2016-03-09 / The demand forecasting is of essential importance for business environments, in a way to serve as a decision making supporting tool during the development of companies strategic planning. This work strived to compare statistics with artificial intelligence methods applied to provisioning on demand issues using temporal series through Box-Jenkins and Artificial Neural Networks Multilayer Perceptron (MLP) methods. Studies were performed to identify and define the main demand forecasting methods. Subsequently, the selected prediction methods for the analysis of the three most relevant products of a metalworking industry were applied in the period 2012 to 2014. The four last periods were used only for performance validation of both methods, through the analysis of forecast errors. Softwares R, Matlab and SPSS supported the data deployment, modeling and analysis. From those models, a step ahead provisioning of sales of a metal mechanic industry was performed, followed by the comparison of the errors of each method based on root mean squared error, RMSE, and mean absolute percentage error, MAPE, to identify the most satisfactory and adequate provisioning method. The results indicated that the performance of the forecasts using the statistical method of Box-Jenkins in Products 1 and 3 were higher than the application of the MLP neural network models. While, for Product 2 the method of neural networks achieved better results. In the statistics analysis, one could verify that the series present some behavior patterns associated to seasonality and oscillations, being possible to observe that both methods show satisfactory results for each data characteristics of the temporal series. / A previsão de demanda é de essencial importância em ambientes organizacionais, de forma a servir como ferramenta de apoio a tomada de decisão durante o desenvolvimento do planejamento estratégico das empresas. Este trabalho teve como principal objetivo comparar modelos estatísticos e de inteligência artificial para problemas de previsão de demanda utilizando séries temporais por meio dos métodos de Box-Jenkins e rede neural artificial Multilayer Perceptron (MLP). Realizou-se o estudo para identificação e definição dos principais métodos de previsão de demanda. Posteriormente, aplicaram-se os métodos de previsão selecionados para a análise dos três produtos mais relevantes de uma indústria metal mecânica, no período de 2012 até 2014. Os quatro últimos períodos da série foram utilizados apenas para validação de desempenho de ambos os métodos propostos através das análises dos erros de previsão. Os softwares R, Matlab e SPSS apoiaram a aplicação, modelagem e análise dos dados. A partir dos modelos, realizou-se a previsão um passo a frente das vendas de uma indústria metal mecânica e posteriormente fez-se o comparativo de seus resultados através das medidas de erros referentes à raiz quadrada do erro quadrático médio, RMSE, e o erro percentual absoluto médio, MAPE, para identificar o modelo mais satisfatório e adequado para a predição. Os resultados indicaram que o desempenho das previsões utilizando o método estatístico de Box-Jenkins nos Produtos 1 e 3 foram superiores à aplicação dos modelos de rede neural MLP. Enquanto que para o Produto 2, o método de redes neurais alcançou melhores resultados. Nas análises estatísticas verificou-se que as séries apresentam padrões de comportamento referente à sazonalidade e oscilações, sendo possível observar que ambos os métodos apresentam resultados satisfatórios para cada característica de dados das séries temporais estudadas.

大陸由於幅員廣大人口眾多，尤其在經濟改革開放後，市場規模急速成長，是目前世界各國汽車市場的新興地，故為每一汽車廠家必爭之地。本研究試圖找出大陸地區汽車需求的主要影響因素，並建立汽車需求的實證模式，以提供政府制定政策與汽車業赴大陸投資之參考。本研究根據過去相關文獻及學術研究機構對汽車需求之影響因素及預測方法之探討，歸納出影響汽車需求之十大因素及常用來預測汽車需求量之多元迴歸分析法，並根據大陸官方公佈或統計之次級資料加以預估。 　　預測結果發現影響大陸地區大客車的因素為國民生產毛額及人口數，而人口因素帶來的影響變化比國民生產毛額為大。而營業用小客車之影響因素為國民所得；大、小貨車的影響因素為國民生產毛額。綜合言之，大陸地區在1992年至2000年間各類型商用車均呈現成長之趨勢。基於大陸汽車市場相對於其他國家的高度成長，故政府應適度開放台灣地區汽車廠商赴大陸投資以免錯失良機。但仍應確保台商在大陸投資環境之安定，並成立大陸商情中心及時提供汽車商情，而業者也應對大陸政策之變化等相關因素加以注意才是。

As transportation policies are changing to encourage alternative modes of transportation to reduce congestion problems and air quality impacts, more planning organizations are considering or implementing activity-based travel demand models to forecast future travel patterns. The proclivity towards operating activity-based models is the capability to model disaggregate travel data to better understand the model results that are generated with respect to the latest transportation policy implementations. This thesis first examines the differences between the two major modeling techniques used in the United States and then describes the assessment tool that was developed to recommend whether a region should convert to the advanced modeling procedures. This tool consists of parameters that were decided upon based on their known linkages to the advantages of activity-based models.

Accurate demand forecasting is a key capability for a manufacturing organization, more so, a semiconductor manufacturer. Many crucial decisions are based on demand forecasts. The semiconductor industry is characterized by very short product lifecycles (10 to 24 months) and extremely uncertain demand. The pace at which both the manufacturing technology and the product design changes, induce change in manufacturing throughput and potential demand. Well known methods like exponential smoothing, moving average, weighted moving average, ARMA, ARIMA, econometric methods and neural networks have been used in industry with varying degrees of success. We propose a novel forecasting technique which is based on Support Vector Regression (SVR). Specifically, we formulate ν-SVR models for semiconductor product demand data. We propose a 3-phased input vector modeling approach to comprehend demand characteristics learnt while building a standard ARIMA model on the data. Forecasting Experimentations are done for different semiconductor product demand data like 32 & 64 bit CPU products, 32bit Micro controller units, DSP for cellular products, NAND and NOR Flash Products. Demand data was provided by SRC(Semiconductor Research Consortium) Member Companies. Demand data was actual sales recorded at every month. Model performance is judged based on different performance metrics used in extant literature. Results of experimentation show that compared to other demand forecasting techniques ν-SVR can significantly reduce both mean absolute percentage errors and normalized mean-squared errors of forecasts. ν-SVR with our 3-phased input vector modeling approach performs better than standard ARIMA and simple ν-SVR models in most of the cases.

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