Multivariate control charts based on Bayesian state space models

This paper develops a new multivariate control charting method for vector autocorrelated and serially correlated processes. The main idea is to propose a Bayesian multivariate local level model, which is a generalization of the Shewhart-Deming model for autocorrelated processes, in order to provide the predictive error distribution of the process and then to apply a univariate modified EWMA control chart to the logarithm of the Bayes' factors of the predictive error density versus the target error density. The resulting chart is proposed as capable to deal with both the non-normality and the autocorrelation structure of the log Bayes' factors. The new control charting scheme is general in application and it has the advantage to control simultaneously not only the process mean vector and the dispersion covariance matrix, but also the entire target distribution of the process. Two examples of London metal exchange data and of production time series data illustrate the capabilities of the new control chart.Comment: 19 pages, 6 figure

Identification of Dynamic Systems Using Bayesian Networks

Cílem této práce je vytvoření spojení mezi Bayesovskými sítěmi a parametrickou identifikací dynamických systémů. Nejprvé byl zpracován průzkum dostupné literatury a byly zformulovány důležité teoretické základy. Poté jsou uvedeny modely dynamických systémů na bázi Bayesovských sítí. Těžištěm práce je návrh a ověření metodologie identifikace dynamických systémů pomocí Bayesovských sítí. Součástí metodologie je nový přístup k volbě řádu výsledného modelu. Na závěr, byla ověřena navržená metoda identifikace dynamických systémů pomocí Bayesovských sítí na fyzikálních modelech dynamických systémů.Obecně je možno konstatovat, že je disertační práce zaměřena na návrh nového přístupu k identifikaci dynamických systémů ovlivněných šumem. Uvedené modely dynamických systémů na bázi Bayesovských sítí mohou být také využité k estimaci stavu, sledování a řízení dynamických systémů.The aim of this thesis is to provide the bridging between Bayesian networks and system identification. Firstly, the literature review and necessary theoretical prerequisites are provided. Secondly, Bayesian network based models of dynamic systems are introduced. Next, the methodology of Bayesian network based system identification is proposed and explored on simulated datasets. In addition, a new approach to the order selection for a resulting model is proposed and verified. Finally, the proposed Bayesian network based system identification approach is verified on real dynamic systems.Overally, the thesis proposes a new approach to system identification of dynamic systems influenced by noisy signals. In addition, Bayesian network based models proposed in this thesis can be used for state estimation, monitoring and control of dynamic systems

Supply chain design: a conceptual model and tactical simulations

In current research literature, supply chain management (SCM) is a hot topic breaching the boundaries of many academic disciplines. SCM-related work can be found in the relevant literature for many disciplines. Supply chain management can be defined as effectively and efficiently managing the flows (information, financial and physical) in all stages of the supply chain to add value to end customers and gain profit for all firms in the chain. Supply chains involve multiple partners with the common goal to satisfy customer demand at a profit. While supply chains are not new, the way academics and practitioners view the need for and the means to manage these chains is relatively new. Very little literature can be found on designing supply chains from the ground up or what dimensions of supply chain management should be considered when designing a supply chain. Additionally, we have found that very few tools exist to help during the design phase of a supply chain. Moreover, very few tools exist that allow for comparing supply chain designs. We contribute to the current literature by determining which supply chain management dimensions should be considered during the design process. We employ text mining to create a supply chain design conceptual model and compare this model to existing supply chain models and reference frameworks. We continue to contribute to the current SCM literature by applying a creative application of concepts and results in the field of Stochastic Processes to build a custom simulator capable of comparing different supply chain designs and providing insights into how the different designs affect the supply chain’s total inventory cost. The simulator provides a mechanism for testing when real-time demand information is more beneficial than using first-come, first-serve (FCFS) order processing when the distributional form of lead-time demand is derived from the supply chain operating characteristics instead of using the assumption that lead-time demand distributions are known. We find that in many instances FCFS out-performs the use of real-time information in providing the lowest total inventory cost