As information systems play a more active role in the management and operations of an enterprise, the demands on these systems have also increased. Departing from their traditional role as simple repositories of data, information systems must now provide more sophisticated support to manual and automated decision making; they must not only answer queries with what is explicitly represented in their Enterprise Model, but must be able to answer queries with what is implied by the model. The goal of the TOVE Enterprise Modelling project is to create the next generation Enterprise Model, a Common Sense Enterprise Model. By common sense we mean that an Enterprise Model has the ability to deduce answers to queries that require relatively shallow knowledge of the domain.

An enterprise must be viewed from several perspectives if it is to be fully described and understood (Barnett 1994; ESPIRIT Consortium AMICE 1991). Previous work in the development of architectures by the Automation & Robotics Research Institute (Presley et al. 1993) describes a five view approach. The Business Rule (or Information) View defines the entities managed by the enterprise and the rules governing their relationships and interactions. The Activity View defines the functions performed by the enterprise (what is done) while the Business Process View defines a time sequenced set of processes (how it is done). The resources and capabilities managed by the enterprise are defined in a Resource View. Finally, the Organization View is used to define how the enterprise organizes itself and the set of constraints and rules governing how it manages itself and its processes.

The is about those methods, models and tools which are needed to build the integrated enterprise. The architecture is generic because it applies to most, potentially all types of enterprise. The coverage of the framework spans Products, Enterprises, Enterprise Integration and Strategic Enterprise Management, with the emphasis being on the middle two. The proposal for the architecture follows the architecture itself improving the quality of the presentation and of the outcome. De nitions of Generic Enterprise Reference Architecture, Enterprise Engineering/ Integration Methodology, Enterprise Modelling Languages, Enterprise Models, and Enterprise Modules are given. It is proposed how the above could be developed on the basis of previously analysed architectures (and other results too), such as the , the GRAI Integrated Methodology, , and TOVIE.

A model is a qualitative or quantitative representation of a process or endeavor that shows the effects of those factors which are significant for the purposes being considered. A model may be pictorial, descriptive, qualitative, or generally approximate in nature; or it may be mathematical and quantitative in nature and reasonably precise. It is important that effective means for modeling be understood such as analog, stochastic, procedural, scheduling, flow chart, schematic, and block diagrams.

Reference models can be quite comprehensive, consisting of hundreds or thousands of model objects. This is why various levels of aggregation are used. Reference models provide enterprises with an initial process engineering solution, letting them determine the degree of detail of the model and the business content.

The model will provide consistent value to your customers, employees, suppliers, and lenders, beyond what they expect. 2. The model will be operated by people with the lowest possible level of skill. 3. The model will stand out as a place of impeccable order. 4. All work in the model will be documented in Operations Manuals. 5. The model will provide a uniformly predictable service to the customer. 6. The model will utilize a uniform color, dress, and facilities code.

If we are to use effectively these abstract models and this descriptive material, we must have a comparable exploration of the criteria for determining what abstract model it is best to use for particular kinds of problems, what entities in the abstract model are to be identified with what observable entities, and what features of the problem or of the circumstances have the greatest effect on the accuracy of the predictions yielded by a particular model or theory.

is a style of enterprise application development and integration, based on using automated tools to build system independent models and transform them into efficient implementations.

Various perspectives exist in an enterprise, such as efficiency, quality, and cost. Any system for enterprise engineering must be capable of representing and managing these different perspectives in a well-defined way.

#The availability of high-quality Model Compilers and Virtual Execution Environments (VEEs) that provide "out of the box" platforms upon which Executable UML models can execute. These VEEs, which exist today in a somewhat incipient stage, will someday soon reduce low-level system architectures to near-commodity status.

understand that there are rules to follow if you are to win: 1. The model will provide consistent value to your customers, employees, suppliers, and lenders, beyond what they expect. 2. The model will be operated by people with the lowest possible level of skill. 3. The model will stand out as a place of impeccable order. 4. All work in the model will be documented in Operations Manuals. 5. The model will provide a uniformly predictable service to the customer. 6. The model will utilize a uniform color, dress, and facilities code.

As is used in connection with systems engineering, a model is a qualitative or quantitative representation of a process or endeavor that shows the effects of those factors which are significant for the purposes being considered. Modeling is the process of making a model. Although the model may not represent the actual phenomenon in all respects, it does describe the essential inputs, outputs, and internal characteristics, as well as provide an indication of environmental conditions similar to those of actual equipment.

We build models to increase productivity, under the justified assumption that it's cheaper to manipulate the model than the real thing. Models then enable cheaper exploration and reasoning about some universe of discourse . One important application of models is to understand a real, abstract, or hypothetical problem domain that a computer system will reflect. This is done by abstraction, classification, and generalization of subject-matter entities into an appropriate set of classes and their behavior.

Knowledge management often generates theories that are too general or abstract to be easily testable. In some cases, simulation modeling can help. [WE have developed] an agent-based simulation model derived from a conceptual framework, the Information Space or I-Space and use it to explore the differences between a neoclassical and a Schumpeterian information environment.