with directions for future development of visual EM languages and Enterprise Visual Scenes. .... Being able to collapse life-cycle stow-piping, i.e. play with ...
CE: The Vision for the Future Generation in Research and Applications, R. Jardim-Gonçalves et al. (eds) © 2003 Swets & Zeitlinger, Lisse, ISBN 90 5809 622 X
From enterprise modelling to enterprise visual scenes Frank Lillehagen Computas AS, Norway
J. Krogstie SINTEF, Norway
S. Tinella, H.G. Solheim & D. Karlsen Computas AS, Norway
ABSTRACT: This paper extends the definition and role of Enterprise Modelling (EM), and describes recent research results that have lead to the definition of the next generation of what the authors call Visual Enterprise Scenes (EVS). The focus is on the transition from EM to EVS and what the implications will be for industry, developers of solutions, system providers, future users and user communities, and the information society at large. To explain the need and the way to transition from EM to EVS the authors pose these four questions: 1. 2. 3. 4.
Which enterprise knowledge must be externalized and why – what properties are gained? How to represent enterprise knowledge – what types and kinds of views must be supported? How to support and integrate the many enterprise architectures – what is the glue? Who owns the knowledge and should therefore do the modelling – who learns from modelling?
Answers are given with reference to the prevailing EM definition and reported state-of-the-art. Enhancements to state-of-the-art are discussed and proposed, as is a new EM definition. The paper concludes with directions for future development of visual EM languages and Enterprise Visual Scenes.
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INTRODUCTION
After fifteen years of industrial enterprise modelling, visual models are still just consultancy tools for understanding and resolving complexity. Currently its industrial application is threefold: Enterprise Architecture development, Business Process Modelling and Enterprise Performance Analysis. The largest market segment is Enterprise Architecture, which is created by the inadequacies of the current approaches of Systems Engineering, attempting to integrate and provide solutions to all industrial challenges. The characteristics of the EM models, modelling approaches and usage of models by industry are: – The enterprise knowledge represented is predetermined by vendor proprietary templates, – The modelling approach and what roles to engage is also predetermined, – Modelling is not an integral part of engineering work, but performed in isolation by specialists,
– The user interface is systems engineer oriented, and supports just one style of modelling, – Limited support for knowledge externalization, sharing, and management, – Most models are collections of diagrams and functional views and give no support for adaptation and extension of meta-models, – Models and modelling environments are detached from solutions and execution platforms. In short it is fair to state that EM is just another technology island in the non-interoperable industrial tools and systems landscape. Now this situation is about to change. Enterprise Modelling is widely accepted as the art of “externalising” enterprise knowledge, i.e. representing the core knowledge of the enterprise. The goal is to make explicit knowledge that add value to the enterprise and can be shared by business applications and users for improving the performance of the enterprise. The authors propose that this is best achieved in what they
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define as Enterprise Visual Scenes (EVS). Visual scenes, where workers and players cooperate and interact, have properties that would enhance the values of knowledge sharing and modelling, and could pave the way for a completely new approach to industrial computing. The Active Knowledge Modelling (AKM) technology is offering a new role for Enterprise Modelling: developing visual scenes and modelling actions to capture context and creating contextual descriptions of work. These descriptions will support self-organising solutions, and enable a new approach to Solutions Engineering. The remainder of this paper is organised as follows: Section 2 analyses the industrial challenges and lists new requirements to EM. Section 3 defines EVS and discusses their properties and benefits. Section 4 looks at enhancements to state-of-the-art, while section 5 describes the answers to the crucial questions raised in the abstract. Section 6 defines the new POPS methodologies that must be implemented, and section 7 describes their implementation and use as core metamodels and templates. Section 8 describes some new approaches to enterprise modelling, and section 9 concludes the paper with a summary, and recommendations for future work on EM, EVS and supporting technologies.
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j. Automate or semi-automate information and knowledge management, k. Supporting learning-by-doing and achieve independence of human experts, l. Harmonizing user environments and designing personalized workplaces. Continuous, on-demand industrial computing solutions are urgently needed in order to meet the business demands and opportunities of the new global economy and industries. These solutions must offer qualities, capabilities and services that dramatically reduce the costs of developing, deploying, operating and managing customer solutions. A holistic approach, taking all important aspects into account, when designing processes, organizations, products and systems is needed. The AKM technology has since the early 90’s (Lillehagen 1993) had the goal of providing new approaches to solutions engineering and simplifying industrial computing. The latest discoveries of enterprise visual scenes are perhaps the most significant from a user point of view. Visual scenes will provide users with modelling approaches, user environments and solutions for knowledge creation, sharing, engineering and management. EVS will transform the WWW into the multi-medium that will augment the capabilities and powers of our senses and minds. This will have great impact on the future of computing, and on human sciences.
INDUSTRIAL CHALLENGES
The industrial community has not been offered much new in terms of IT approaches and solutions over the last fifteen years. The few exceptions that spring to mind are EM, industrial portals and more recently web services. This has left industry with a long list of unsolved and partly solved problems, or rather solutions that create new problems. The situation has been described in the IDEAS project (IDEAS 2003). The synthesized challenges are: a. Aligning Business, ICT and KM at all levels, b. Develop Enterprise Architectures and Intelligent Infrastructure, c. Develop the Enterprise Knowledge Architecture (EKA) and enterprise visual scenes, d. Reduce expenses for application portfolio management and applications integration, e. Achieve cheaper and faster solutions development, delivery, deployment and integration, f. Achieve predictability, accountability, adaptability and trust in networked organizations, g. Achieve ease of re-engineering, reuse and management of solutions, h. Supporting concurrency, context-sensitivity and multiple life-cycles, i. Providing self-organizing, re-generating solutions,
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ENTERPRISE VISUAL SCENES
The authors see four major enterprise visual scenes required to continuously innovate, operate, evolve and transform, and govern and manage future enterprises. Then there will be a multitude of smaller, more project and task focusing scenes to support situated project work. The four major scenes are illustrated in Figure 1.
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Figure 1. The four visual scenes of any type of networked enterprise: 1. Innovative, 2. Operation, 3. Governance, and 4. Evolution. The common Intelligent Infrastructure, sustaining dynamic business behavior is denoted 5.
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Each of the scenes extends, uses and manages different parts of the Enterprise Knowledge Architecture, the middle layer of the common Intelligent Infrastructure (Karlsen 2003). Each scene has associated different methodology models, and delivers different solutions and results. The four Visual Scenes for future enterprising are briefly defined as: 1. The Innovative scene (invent, reuse, test, learn and alternate); concept: “Industrial War-room”, implementing the POPS core meta-modelling methodologies. Status: process and task modelling is underway. The other methodologies need reengineering and re-implementation. The innovative scene manages continuous change in product, process and organizational structures of the Enterprise Knowledge Architecture (EKA). 2. The Operations scene (operate, generate, adapt, extend, manage and terminate); concept: Continuous Business Solutions (CBS) solutions generation and Visual Enterprise Computing (VEC) approach, supported by multiple life-cycle management (adapting and extending Int. infrastructure), implementation: PoC – EA – EI/EAI – Solutions Generation, User Deployment and Agility Management (Elvekrok 2003). 3. The Governance scene (govern, plan, decide, assign, measure and strategize); concepts: bubblemodels, aggregation and propagation of parameters, attributes and values, the “real-time enterprise”, implementation: services to explore and investigate, to support decision-making, manage and govern value-creation in parallel life-cycles, need for powerful analysis and interaction techniques beyond balanced score-cards, use of “value-metric charts” and other value matching techniques. 4. The Evolutions scene (design, configure, change, transform, align, and manifest); concept: “Cont. Collaborative Business Management (CBM)”, implementation: supporting many concurrent Collaborative Business Solutions (CBS’s), concurrency is dependent on a collaborative EKA, Networked Organization of extended serviceteams, and agreed Business Model, all to be supported by continuously adapting and extending Intelligent Infrastructure layered services. 3.1
The powers of visual scenes
There is a need to enhance the way people think about computing, and there is a need to extend enterprise modelling or mind mapping from being a tool-based exercise for experts, isolated from operational business solutions, to become visual environments for a new style of computing supported by an Intelligent Infrastructure. Intelligent in computing context should mean extending our human or organic intelligence,
augmented by open extendable capabilities, constructs and services. These evolve as the enterprise knowledge artifacts are modelled and when models and modelgenerated solutions are executed. Visual patterns, scenes and languages, have at least seven properties that natural language and current software methods will never acquire, and these properties are fundamental for solving the challenges facing industry and IT providers. Some of these properties may seem obvious, but Systems Engineering has not yet understood how to support these knowledge worker needs: 1. Being able to collapse life-cycle stow-piping, i.e. play with abstractions of the time-dimension, 2. Providing methods for dynamically evolving concepts, content, context and actions, 3. Correlation of conceptual views (meta-views) and several situated content and functional views, 4. Defining and applying business and working rules that are valid in given contexts and for limited parameter value sets, 5. Performing innovative work and tasks, and being able to create process meta-models by executing tasks, i.e. support design, 6. Supporting the 4th Horizon of knowledge externalization and assimilation, being able to share even tacit knowledge, 7. After-action reflection is no longer the best way to learn from performing work, pro-action learning is possible in enterprise visual scenes. When we can support these properties then maybe we can truly support design, problemsolving and team-learning. 3.2 New business opportunities On-demand or opportunistic business is a term that more and more business managers are using to express the kind of generative capabilities, collaborative knowledge sharing, and reactivating behavior that they expect to get from providers, see the paper on Networked Organizations (Haake, 2003). 3.3 New scientific opportunities Many sciences and scientists that are not yet supported and enhanced by IT innovations will find new incentives and opportunities with the advent of EVS solutions. Their research will be using IT more extensively in innovative work. This is particularly true for the soft sciences like pedagogy, psychology and socio-economic studies. 4
ENHANCEMENTS TO STATE-OF-THE ART
An up to date source on EM can be found in the UEML project (UEML, 2003). The state-of-the-art
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reported there needs just a few extensions to represent also the industrial perspective. 4.1
History of industrial EM
Visual Enterprise Modelling of enterprise aspects started back in the late 80’s, and the first commercially supported software tools were in the market around 1990 (Aris, Metis, and a few more). “The industrial war-room” idea was attempted modelled by Volvo Cars and Metis already in 1991, but persistent model management was an unsolved problem and PC’s were still not on every knowledge workers desk. The first enterprise integration efforts involving models are from 1992–93, eg. the MARITIME project, but lack of web standards became the culprit of those attempts The holistic enterprise modelling approach was defined, and its implications explained by Lillehagen in 1993, but the lack of intelligent infrastructures to support distributed simultaneous modelling, model management and model execution were hurdles that at that time proved too hard to surmount. Recursive AKM development and solutions generation was conceived around 1997, but lack of industrial partners to team up with prohibited prototyping development. Enterprise Architecting was put on the agenda by the US authorities around 1996, and the US Congress passed the Cohen-Klinger Act in April 1999, thereby creating a big market pull. From 1995 to 2000 some of the leading thinkers and developers in this field were contained within large consultancy units and did not find opportunity to pursue their visions and prototyping results. 4.2
Industrial use
State-of-the-art in EM has progressed furthest in certain manufacturing industries, and in particular with respect to these three markets:
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4.3 Research state-of-the-art EM contributes to solving interoperability difficulties by increasing the shared understanding of the enterprise structure and behaviour. EM provides methodologies for the identification of connected roles, objects and processes between enterprises from different perspectives. Sets of software applications used in the enterprises and their relationships can be identified with EM, and their degree of interoperability can be analysed. Many languages and tools (more than 350) exist that support some form of EM with partially overlapping approaches. Today, the first attempts to combine languages are recognisable. For example, the Unified Enterprise Modelling Language project (UEML, IST-2001-34229) has prototyped an integrated approach for exchange of enterprise models among EM tools. Enterprise modelling shows various inadequacies in these areas: representing enterprise knowledge, combining enterprise models, maintaining enterprise models, developing manageable structures of metamodels, enabling model generated solutions, supporting dynamic user environments, and creating the link with software execution platforms. EM languages are tool dependent, fragmented, lack descriptiveness, expressiveness, extensiveness, scientific basis, and other critical qualities needed to represent core enterprise knowledge. Consequently the resulting models are fragmented, inconsistent, non-compliant and incomplete. The solution of these fallacies is to develop and share core languages, services, modelling constructs, models and meta-model structures by use of an Intelligent Infrastructure (II). An example of an II is given in Figure 2 Most methodologies for enterprise modelling lack scientific foundations and are heavily influenced by legacy thinking, most views and methods are inherited from working methods evolved using paper as a
Enterprise Architecture is currently the most vivid and fastest growing market particularly in the US. Business Process Modelling looked like a fast growing market around 1998, but new requirements for security and safety have slowed it down, and currently very little business is collected in this market. Enterprise Performance Analyses is another market that has as yet to really take off.
The authors believe that the major reason for this slow acceptance and modest market penetration are mainly to be found in the fact that EM is still a tool based effort for experts, lacking scientifically based methodologies and respective visual language definitions.
Figure 2. The user architecture of an Intelligent Infrastructure with key workplaces and services is instrumental in bringing EM to the users.
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medium, and using static diagrams and natural language for knowledge encoding. Many of these tools do not really warrant the use of the term EM, they are truly fixed template diagramming tools with limited meta-modelling support. Finally, EM aims to support the implementation and operation of software execution. However, only few isolated solutions exist that makes the link explicit between the conceptual EM level and the execution level. 4.4
that for quality assurance should be implemented as recursive and repeatable work processes. The tasks of these work processes are themselves part of the Intelligent Infrastructure. Any task can be invoked and executed as need arises, supporting unpredictable situations. Execution of these tasks may vary between automatic and highly interactive depending on the context. This means that self-adaptive, self-organizing solutions are possible, if situated knowledge can be modelled and activated
EM standards needs updating
Current standardisation activities have little effect on industry. Although many such activities are going on, present standards (e.g. ENV 12204 or DIS 19439) are rarely used within industry. With respect to other, de-facto standards (e.g. from BPMI), industry does not perceive a clear distinction between conceptual and execution-oriented standards. To solve the problems explained, we need to progress beyond the current state-of-the-art by improving current approaches for EM-driven solutions generation, bringing EM to the industrial users.
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4.5
The core knowledge of any enterprise are the four inseparable dimensions of knowledge of product, process, organization and system. Reflective views, recursive work processes, repetitive tasks and solutions, and replicable meta-models and templates are intrinsic properties of these dimensions. Business and other aspects and views are derived from these core enterprise knowledge dimensions. This core knowledge integrates and provides the qualities that future solutions depend on. Working with the leading product and process design companies in the world, we used to call this knowledge of enterprise knowledge for the industrial nervous or logic center. This core description is required in order to define, calculate and manage parameters and balance attributes and value sets across disciplines. Any EM language must be a derivation from the core, otherwise it will not produce quality, manageable models and solutions!
Core modelling languages
The UEML project will develop a unified enterprise modelling language to enable the exchange of enterprise models independent of tools. The partners will provide new solutions for open, tool-independent visual languages to model enterprise core knowledge. These visual languages will offer consistent and coherent enterprise descriptions, and will represent a scientific basis for enterprise modelling. 4.6
The Enterprise Knowledge Architecture
The Enterprise Knowledge Architecture (EKA) uses state-of-the-art IT and visual Enterprise Knowledge Management (EKM) technology to build inline active knowledge models and situated meta-models. These enterprise specific meta-models, including metamodels to integrate partner processes and systems, tune the Intelligent Infrastructure (II) to each enterprise. The enterprise specific II supports simultaneous modelling, meta-modelling, model management and work execution, using model-generated solutions. Intelligence is normally attributed to the capacities and the capabilities of the human brain, but just as knowledge is externalized and represented in various forms (encoding and media) so is intelligence. In this context we define intelligence as: “The ability to use knowledge structures to perform actions, and to assimilate and update knowledge structures when performing actions.” The knowledge structures and views are adapted, extended, coordinated and managed by II services,
ANSWERS TO THE CRUCIAL QUESTIONS
The integration of EM tools, Model Repositories and User Environments Portal Servers (UEPS) into Intelligent Infrastructures motivate the transition from Enterprise Modelling to Enterprise Visual Solutions, taking us from personal tool-based handcraft to model generated solution. Now let us try to respond to the critical questions raised in the abstract. 5.1 Which enterprise knowledge?
5.2 How to represent – building the EKA? The EKA is a set of inter-dependent knowledge representations of many domains, that allow us to separately define, de-couple and manage enterprise knowledge structures and constructs. It provides adaptable visual languages across sectors, and supports interoperable solutions. The six major enterprise knowledge representations (UEMLST) are composed of: 1. User enterprise views (types and kinds), some of these views are comparable to many EE or SE framework views,
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and management structures. These are crucial knowledge constructs and structures for enterprise integration at all layers, and for linking to execution engines. None of them are aware of the key capabilities and services provided by the Intelligent Infrastructure, and of the integrating properties of a logically consistent, coherent and complete EKA layer. This layer must be designed for each enterprise, but the design is based of extensive reuse of constructs and structures and re-activation of tasks as design services. 5.4 Who owns the enterprise knowledge?
Figure 3. Managing types and kinds of enterprise views, and being able to model dependencies of views are important visual modelling capabilities!
2. Enterprise models and sub-models, and structures of integrated solution models, 3. Meta-model definitions of various types and for many kinds of models, 4. Language core visual constructs as basis for modelling languages, 5. Structures of meta-model objects and constructs, and finally, 6. Type-hierarchies representing standardized industrial knowledge. These enterprise knowledge model representations are vital for the formation, integration and operation of intelligent enterprises and smart organizations, and must be visually editable and manageable in order to harvest the full benefits of visual scenes. 5.3
How to support and integrate with the rest?
The portal acts as an integrator and as an environment to plug in and perform application and web services. Application services are work processes, single or cascaded tasks, stored in the repository for re-activation and repetitive execution. The services provided in the portal, supported by the intelligent infrastructure, are services to build knowledge models, to cooperate and collaborate, to perform work and project simulation, services to do work management, and finally services to do work execution. Most existing modelling frameworks like Zachmann, Cimosa, and GERAM represent useful methodology views, but all of them are lacking meta-views, support for meta-modelling languages and meta-model design
Most projects do modelling by using professional model builders and consultants, whereas engineering and industrial users are rarely involved. This is partly due to the user interfaces of the EM tools, but also relates to the value contributed by the modelling process. If EM is externalizing and sharing knowledge, then it should be the knowledge of the people possessing the core enterprise knowledge, so this is truly a question about learning and managing core enterprise role competencies and skills. The plethora of complex modelling tools and applications must be integrated and harmonized to support simple user environments and a variety of modelling approaches.
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THE POPS METHODOLOGY
A methodology (a scientific framework of methods, supported by guiding principles) for expressing stateof-the-art in EM is needed. The authors believe they have enough background materials to implement such a methodology, a meta-meta view of what EM should behold and why it will shape the future of computing. Implementing the POPS methodology has to do with the definition of the “grammar” for descriptiveness and expressiveness, for representation, for extensions and adaptations and for lifecycle management across time and space. The partial, complementary languages can be used separately, and there is no demand to use more than one language from any of the four layers. Each layer has complimentary domain languages. These will be identified in the following sections. 6.1 Process and Task modelling and execution Process and Task modelling is the foundation of the POPS methodology, since this provides for the reflective and recursive properties and also the structure building capabilities and services of the Intelligent Infrastructure.
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WFL
Tasklists Tasklists
Work
UE UE design
Flow
Meta IPM
RAD
Evolving II Work Task-trees
Panels
Task Declaration
Program Mngm.
Int. Infrastr. Infrastr. Mngm. Loading diagrams
Knowledge Mngm.
Life-cycle Mngm.
UEcontent
User Mngm. Mngm.
Figure 4. Process and task modelling – the heart of the POPS methodology, providing the key properties and services.
The descriptive Flow Logic language provides the logic structuring support in all structures of the POPS core, and is used to develop and generate ontologies and logistics. Ontologies as we find them today are legacy structures that will have to be redesigned and maintained as part of holistic enterprise models. At the Activity Engineering layer there are four descriptive process languages, each focusing on engineering methods and calculations according to procedure, information and material flow, role control and resource allocation, and finally coordination and synchronization of processes. These languages feed into the Intelligent Infrastructure meta-model repository, and their common purpose is to design and manage the enterprise metaknowledge, creating consistent descriptive knowledge. This is the basis for enterprise visual scenes. Work execution and management is supported by four methods all handling views and structures of tasks, like task assignment for execution, and finally the tenth language domain supports task declarative modelling for task execution. 6.2
Model Mngm.
Resource Mngm.
EEML
New forms of organizations
The Intelligent Infrastructure will also have to address the issue of enterprise service-teams, team roles, and the matching of resources by use of competence and skill profiles (Peterson 1998). The Intelligent Infrastructure is a platform for distributed, model-generated workplaces, concurrent development and management of models and metaconstructs, methodologies and customer solutions. Introducing the competence and skill profiles of roles and resources will enable dynamic assignment of tasks and services among the people filling these roles. Seven core teams are conceived, see Figure 4, and each team has a set of welldefined services, responsibilities and roles to provide these services on an
Figure 5. A new form of formal organization, serviceteams, clearly defining networked service provisioning responsibilities and roles is needed.
on-demand basis. Most of these services will be work process tasks. Some of these tasks will occur in many process descriptions, some modelled top-down and others created bottom up by task execution. Services implemented and managed as work process tasks are very flexible and powerful tools that are mandatory for building and managing the EKA for dynamic networked organizations. 6.3 Product design and engineering Product design processes are created as the product is conceived. Most design disciplines do, however, have fairly fixed tasks and methods. So product design processes are processes where no descriptive views are predefined, and where the descriptive process language constructs, flows and views are created as the design evolves. This flexibility gives full support for recursion, repetition and replication, for rule associations, and for value propagation and aggregation, capabilities vital for design work. Also parameter definition, value balancing and determination are most easily performed in the tasks and processes and not in the many disjoint and different types of product structures that industry has developed over the years. It is our firm belief that product design, involving problem solving and learning will be simplified and supported once the POPS methodologies are fully implemented. The area of product and process design and engineering is a candidate for further research that must be conducted in a service-team environment with the best industrial designers and practitioners involved. 6.4 Systems Engineering The Systems Engineering structures and views will be derived from the EA area, where we currently have
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a selection of meta-models (Computas ITM, 2003) and experiences, so we expect to deliver full solution generation, reuse and management capabilities and services in this dimension. 7
THE EIS META-MODELS AND TEMPLATES
The POPS methodologies will be implemented as a series of complementary logically integrated, but decoupled set of language templates that can be extended, adapted and combined in any purposeful way to yield the model required by the users. The resulting models will have qualities to allow future model splitting and merging, re-engineering and control with versions and configurations. Enterprise qualities, such as interoperability and reusability, will be more easily achieved when enterprise solutions and integration are performed using the EIS templates. Only consistent and coherent meta-models can truly integrate an enterprise. These meta-models are the core of the future visual languages for modelling and operating enterprises, and for managing enterprise knowledge. Their adaptation and extension to specific solutions will be performed by repeatable services implemented as work process tasks, managed by the II. The EIS templates are the templates of all templates, that is all other models and meta-models are extensions and adaptations of sets of EIS templates, and the relations and structures are easily recovered from the II meta-model repository. 8
NEW MODELLING APPROACHES
Most EM tools provides modelling interactions very similar to system modelling tools. Users drag and drop single objects and relationships from menus or meta-model structures, and the granularity of modelling stays constant. The current user environments are a serious hindrance for the acceptance of EM in design and engineering, the user groups with the greatest needs for EM. Modelling dialogues will be simpler, more intuitive and benefit from level of context and content generated from the EKA. The intelligent infrastructure and the contents of its architectures will appear to users as a portal with task specific workplaces. Each workplace provides a set of services and views allowing users to develop, deploy and manage model driven solutions. The services should be implemented in a way that hides complexity from end users. The use of the User Environment Portal Server (UEPS) (Tinella, 2003) is the best approach to create harmonized and integrated user interfaces and new approaches to modelling suited to different contexts and disciplines.
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SUMMARY AND CONCLUSIONS
To summarize the responses to the critical questions, these are the capabilities we must prioritize in future EM and EVS development: – Introduce Intelligent Infrastructure with a process and task repository, providing modelling and other services and supporting re-activation, – Implement the EIS templates, enabling any submodel irrespective of purpose and provider to be consistent and compliant with other sub-models, – Implement the UEPS to support context and adaptable user environments and workplace solutions generation and re-activation, – Support new ways of building and using models making modelling a natural and integral part of knowledge work. Enterprise Modelling (EM) is redefined (last definition by Vernadat and Lillehagen, Verdal 1997), and enhanced by the influence of II services, the EIS templates, and to be consistent with AKM technology and thinking: “EM is externalizing, sharing and managing enterprise knowledge, developing Enterprise Visual Scenes, managing the Enterprise Knowledge Architecture (EKA), enabling solutions generation and knowledge management.” The POPS methodology represents the reflective, recursive, repetitive and replicable core knowledge of an enterprise, the basis for visual language definition. Correctly implemented it provides unique modelling properties, and guarantees model consistency, coherence, compliance and completeness. This means that sub-models can be combined and reused in model structures with control of meta-data and content. Implementing the POPS as the EIS templates will give us the basis for simple, high-quality Enterprise Knowledge Management. ACKNOWLEDGEMENTS The authors would like to thank their colleagues at Computas and the partners in the EXTERNAL, IST1999-10091, and the UEML, IST-2001-34229, projects for their contributions. Many of these have made significant contributions to the results described in this paper.
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