Cyber-Physical System (CPS) becomes more and more importance from industrial application (e.g., aircraft control, automation management) to societal challenges (e.g. health caring, environment monitoring). It has traditionally been designed to one specific application domain and to be managed by a single entity, implemented communication between physical world and computational world. However, it still just work within its domain, and not be interoperability. How to make it into scalable? How to make it reusing? These questions become more and more necessary. In this paper, we are trying to developing a common CPS infrastructure, let it be an innovative CPS crossing multiple domains to broad use sensors and actuators. Here, we implement a technique for automatically build a model according to the sensor data in different domains. And based on our approach under continuous situation, it could identify the sensor values right now or estimate next few time step, which we call spatial model or temporal model.

This research is motivated by identified gaps in contemporary planning practices and production processes within firms. Relying solely on experiential knowledge has proven limiting, necessitating a more systematic approach. Previous instances of data anomalies, particularly ongoing challenges in achieving satisfactory delivery reliability, have underlined the need for deeper insights into underlying patterns. The objectives of this study are: • To identify and analyze specific obstacles and challenges affecting load balance and delivery security in Borl.nge's production system. • To explore various methods or strategies aimed at enhancing the process of generating reliable capacity forecasting methods. Both primary and secondary research methods were employed. Primary methods included interviews and the development of a forecast model, while secondary studies encompassed the latest research in the field. The thesis revealed five primary factors hindering capacity attainment: 1. WIP(work in progress)/ slabs material shortages disrupt production flow and escalate costs due to the need for external sourcing of slabs. 2. Transport issues, including incorrect internal deliveries and the weather conditions, pose challenges. 3. Personnel shortages hinder the efficient utilization of production capacity. 4. Machine breakdowns result in production interruptions, leading to capacity loss and inefficiency. 5. Inventory problems, such as insufficient capacity and poor management, impede smooth production operations. Additionally, the second objective was addressed by implementing exponential smoothing for capacity planning forecasts. By updating forecasts every 13 weeks, this study improves the production forecast.

Exponential smoothing algorithms are very attractive for the practical world such as in industry. When considering bivariate exponential smoothing methods, in addition to the properties of univariate methods, additional properties give insight to relationships between the two components of a process, and also to the overall structure of the model. It is important to study these properties, but even with the merits the bivariate exponential smoothing algorithms have, exponential smoothing algorithms are nonstatistical/nonstochastic and to study the properties within exponential smoothing may be worthless. As an alternative approach, the (bivariate) ARIMA and the structural models which are classes of statistical models, are shown to generalize the exponential smoothing algorithms. We study these properties within these classes as they will have implications on exponential smoothing algorithms. Forecast properties are studied using the state space model and the Kalman filter. Comparison of ARIMA and structural model completes the study. / Mathematical Sciences / M. Sc. (Statistics)

Exponential smoothing algorithms are very attractive for the practical world such as in industry. When considering bivariate exponential smoothing methods, in addition to the properties of univariate methods, additional properties give insight to relationships between the two components of a process, and also to the overall structure of the model. It is important to study these properties, but even with the merits the bivariate exponential smoothing algorithms have, exponential smoothing algorithms are nonstatistical/nonstochastic and to study the properties within exponential smoothing may be worthless. As an alternative approach, the (bivariate) ARIMA and the structural models which are classes of statistical models, are shown to generalize the exponential smoothing algorithms. We study these properties within these classes as they will have implications on exponential smoothing algorithms. Forecast properties are studied using the state space model and the Kalman filter. Comparison of ARIMA and structural model completes the study. / Mathematical Sciences / M. Sc. (Statistics)

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State space modeling represents a statistical framework for exponential smoo- thing methods and it is often used in time series modeling. This thesis descri- bes seasonal innovations state space models and focuses on recently suggested TBATS model. This model includes Box-Cox transformation, ARMA model for residuals and trigonometric representation of seasonality and it was designed to handle a broad spectrum of time series with complex types of seasonality inclu- ding multiple seasonality, high frequency of data, non-integer periods of seasonal components, and dual-calendar effects. The estimation of the parameters based on maximum likelihood and trigonometric representation of seasonality greatly reduce computational burden in this model. The universatility of TBATS model is demonstrated by four real data time series.

Econometric and predictive modeling techniques are two popular forecasting techniques. Both ofthese techniques have their own advantages and disadvantages. In this thesis some econometricmodels are considered and compared to predictive models using sales data for five products fromICA a Swedish retail wholesaler. The econometric models considered are regression model,exponential smoothing, and ARIMA model. The predictive models considered are artificialneural network (ANN) and ensemble of neural networks. Evaluation metrics used for thecomparison are: MAPE, WMAPE, MAE, RMSE, and linear correlation. The result of this thesisshows that artificial neural network is more accurate in forecasting sales of product. But it doesnot differ too much from linear regression in terms of accuracy. Therefore the linear regressionmodel which has the advantage of being comprehensible can be used as an alternative to artificialneural network. The results also show that the use of several metrics contribute in evaluatingmodels for forecasting sales. / Program: Magisterutbildning i informatik

[pt] Esta dissertação tem como objetivo principal a proposição de modelos para previsão, em um curto prazo, do número de processos que são ajuizados em desfavor de uma empresa do setor elétrico. A metodologia utilizada consiste em, a partir de uma análise exploratória dos dados, construir modelos usando uma estratégia bottom-up, ou seja, parte-se de um modelo simples e processa-se seu refinamento até encontrar um modelo apropriado que mais se adeque à realidade. Partiu-se então de um modelo auto projetivo indo até uma formulação de um modelo de regressão dinâmica. Os modelos são então comparados segundo alguns critérios, basicamente no que tange à sua eficiência preditiva. Conclui-se ao final sobre a eficiência de se utilizar modelos de regressão dinâmica para este tipo de previsão tendo em vista a presença de correlação serial dos resíduos, comumente presentes nas séries econômicas. Propõe-se, ao final, uma ferramenta para, a partir dos valores estimados, analisar a viabilidade econômica de estimular ou desestimular as medidas responsáveis pela geração de processos contra a empresa. / [en] The aim of this dissertation is to develop short term models to forecast the number of judicial process in electric sector companies. From the methodology point of view, data is analyzed and models using bottom-up strategy is developed. In other words, a simple model is improved step by step until a proper model that fits well the reality is found. From a univariate model it ends up in a dynamic regression model. The models obtained in this study are compared according to some criterion, mainly forecast accuracy. In the end the conclusion is about the efficiency of dynamic regression models for this kind of forecast, which one presents data with serial correlation of residues, commonly present in economic series. In the end, from the estimated values, it´s proposed a mechanism to analyze the economic viability, to encourage or not, actions which are responsible for instigating judicial processes against the company.

Accurate forecasting is a real problem at many companies and that includes Alfa Laval in Lund. Alfa Laval experiences problems forecasting for future raw material demand. Management is aware that the forecasting methods used today can be improved or replaced by others. A change could lead to better forecasting accuracy and lower errors which means less inventory, shorter cycle times and better customer service at lower costs. The purpose of this study is to analyze Alfa Laval’s current forecasting models for demand of raw material used for pressed plates, and then determine if other models are better suited for taking into consideration trends and seasonal variation.

<p>Accurate forecasting is a real problem at many companies and that includes Alfa Laval in Lund. Alfa Laval experiences problems forecasting for future raw material demand. Management is aware that the forecasting methods used today can be improved or replaced by others. A change could lead to better forecasting accuracy and lower errors which means less inventory, shorter cycle times and better customer service at lower costs.</p><p>The purpose of this study is to analyze Alfa Laval’s current forecasting models for demand of raw material used for pressed plates, and then determine if other models are better suited for taking into consideration trends and seasonal variation.</p>