Dynamic Modeling For Business Management
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Dynamic Modeling for Business Management
Author: Bernard McGarvey
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-05-04
Modelling is a tool used by savvy business managers to understand the processes of their business and to estimate the impact of changes. Dynamic Modelling for Business Management applies dynamic modelling to business management, using accessible modelling techniques that are demonstrated starting with fundamental processes and advancing to more complex business models. Discussions of modelling emphasize its practical use for decision making and implementing change for measurable results. Readers will learn about both manufacturing and service-oriented business processes using hands-on lessons. Then will then be able to manipulate additional models to try out their knowledge and address issues specific to their own businesses and interests. Some of the topics covered include workflow management, supply-chain-management, and strategy.
Business Dynamics: Systems Thinking and Modeling for a Complex World with CD-ROM
Author: John Sterman
language: en
Publisher: McGraw-Hill Education
Release Date: 2000-02-23
Today’s leading authority on the subject of this text is the author, MIT Standish Professor of Management and Director of the System Dynamics Group, John D. Sterman. Sterman’s objective is to explain, in a true textbook format, what system dynamics is, and how it can be successfully applied to solve business and organizational problems. System dynamics is both a currently utilized approach to organizational problem solving at the professional level, and a field of study in business, engineering, and social and physical sciences.
Dynamic Modeling, Predictive Control and Performance Monitoring
Author: Biao Huang
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-04-11
A typical design procedure for model predictive control or control performance monitoring consists of: 1. identification of a parametric or nonparametric model; 2. derivation of the output predictor from the model; 3. design of the control law or calculation of performance indices according to the predictor. Both design problems need an explicit model form and both require this three-step design procedure. Can this design procedure be simplified? Can an explicit model be avoided? With these questions in mind, the authors eliminate the first and second step of the above design procedure, a “data-driven” approach in the sense that no traditional parametric models are used; hence, the intermediate subspace matrices, which are obtained from the process data and otherwise identified as a first step in the subspace identification methods, are used directly for the designs. Without using an explicit model, the design procedure is simplified and the modelling error caused by parameterization is eliminated.