Process modeling

Abstract: "Business model" was one of the great buzz-words of the Internet boom. A company didn't need a strategy, a special competence, or even any customers--all it needed was a Web-based business model that promised wild profits in some distant, ill-defined future. Many people--investors, entrepreneurs, and executives alike--fell for the fantasy and got burned. And as the inevitable counterreaction played out, the concept of the business model fell out of fashion nearly as quickly as the .com appendage itself. That's a shame. As Joan Magretta explains, a good business model remains essential to every successful organization, whether it's a new venture or an established player. To help managers apply the concept successfully, she defines what a business model is and how it complements a smart competitive strategy. Business models are, at heart, stories that explain how enterprises work. Like a good story, a robust business model contains precisely delineated characters, plausible motivations, and a plot that turns on an insight about value. It answers certain questions: Who is the customer? How do we make money? What underlying economic logic explains how we can deliver value to customers at an appropriate cost? Every viable organization is built on a sound business model, but a business model isn't a strategy, even though many people use the terms interchangeably. Business models describe, as a system, how the pieces of a business fit together. But they don't factor in one critical dimension of performance: competition. That's the job of strategy. Illustrated with examples from companies like American Express, EuroDisney, WalMart, and Dell Computer, this article clarifies the concepts of business models and strategy, which are fundamental to every company's performance.

Abstract: OVERVIEW OF SPATIAL DATA PROBLEMS Introduction to Spatial Data and Models Fundamentals of Cartography Exercises BASICS OF POINT-REFERENCED DATA MODELS Elements of Point-Referenced Modeling Spatial Process Models Exploratory Approaches for Point-Referenced Data Classical Spatial Prediction Computer Tutorials Exercises BASICS OF AREAL DATA MODELS Exploratory Approaches for Areal Data Brook's Lemma and Markov Random Fields Conditionally Autoregressive (CAR) Models Simultaneous Autoregressive (SAR) Models Computer Tutorials Exercises BASICS OF BAYESIAN INFERENCE Introduction to Hierarchical Modeling and Bayes Theorem Bayesian Inference Bayesian Computation Computer Tutorials Exercises HIERARCHICAL MODELING FOR UNIVARIATE SPATIAL DATA Stationary Spatial Process Models Generalized Linear Spatial Process Modeling Nonstationary Spatial Process Models Areal Data Models General Linear Areal Data Modeling Exercises SPATIAL MISALIGNMENT Point-Level Modeling Nested Block-Level Modeling Nonnested Block-Level Modeling Misaligned Regression Modeling Exercises MULTIVARIATE SPATIAL MODELING Separable Models Coregionalization Models Other Constructive Approaches Multivariate Models for Areal Data Exercises SPATIOTEMPORAL MODELING General Modeling Formulation Point-Level Modeling with Continuous Time Nonseparable Spatio-Temporal Models Dynamic Spatio-Temporal Models Block-Level Modeling Exercises SPATIAL SURVIVAL MODELS Parametric Models Semiparametric Models Spatio-Temporal Models Multivariate Models Spatial Cure Rate Models Exercises SPECIAL TOPICS IN SPATIAL PROCESS MODELING Process Smoothness Revisited Spatially Varying Coefficient Models Spatial CDFs APPENDICES Matrix Theory and Spatial Computing Methods Answers to Selected Exercises REFERENCES AUTHOR INDEX SUBJECT INDEX Short TOC

Abstract: (2002). Business Dynamics—Systems Thinking and Modeling for a Complex World. Journal of the Operational Research Society: Vol. 53, No. 4, pp. 472-473.

Abstract: The supervision of technical processes is the subject of increased development because of the increasing demands on reliability and safety. The use of process computers and microcomputers permits the application of methods which result in an earlier detection of process faults than is possible by conventional limit and trend checks. With the aid of process models, estimation and decision methods it is possible to also monitor nonmeasurable variables like process states, process parameters and characteristic quantities. This contribution presents a brief summary of some basic fault detection methods. This is followed by a description of suitable parameter estimation methods for continuous-time models. Then two examples are considered, the fault detection of an electrical driven centrifugal pump by parameter monitoring and the leak detection for pipelines by a special correlation method.

Abstract: Fault detection and diagnosis is an important problem in process engineering It is the central component of abnormal event management (AEM) which has attracted a lot of attention recently AEM deals with the timely detection, diagnosis and correction of abnormal conditions of faults in a process Early detection and diagnosis of process faults while the plant is still operating in a controllable region can help avoid abnormal event progression and reduce productivity loss Since the petrochemical industries lose an estimated 20 billion dollars every year, they have rated AEM as their number one problem that needs to be solved Hence, there is considerable interest in this field now from industrial practitioners as well as academic researchers, as opposed to a decade or so ago There is an abundance of literature on process fault diagnosis ranging from analytical methods to artificial intelligence and statistical approaches From a modelling perspective, there are methods that require accurate process models, semi-quantitative models, or qualitative models At the other end of the spectrum, there are methods that do not assume any form of model information and rely only on historic process data In addition, given the process knowledge, there are different search techniques that can be applied to perform diagnosis Such a collection of bewildering array of methodologies and alternatives often poses a difficult challenge to any aspirant who is not a specialist in these techniques Some of these ideas seem so far apart from one another that a non-expert researcher or practitioner is often left wondering about the suitability of a method for his or her diagnostic situation While there have been some excellent reviews in this field in the past, they often focused on a particular branch, such as analytical models, of this broad discipline The basic aim of this three part series of papers is to provide a systematic and comparative study of various diagnostic methods from different perspectives We broadly classify fault diagnosis methods into three general categories and review them in three parts They are quantitative model-based methods, qualitative model-based methods, and process history based methods In the first part of the series, the problem of fault diagnosis is introduced and approaches based on quantitative models are reviewed In the remaining two parts, methods based on qualitative models and process history data are reviewed Furthermore, these disparate methods will be compared and evaluated based on a common set of criteria introduced in the first part of the series We conclude the series with a discussion on the relationship of fault diagnosis to other process operations and on emerging trends such as hybrid blackboard-based frameworks for fault diagnosis