Bettina Gleichauf

Enterprise models constitute a valuable basis for enterprise transformation because they usually represent a widely accepted image of an enterprise. Practitioners often put a lot of effort in the creation and maintenance of such models that therefore represent a significant investment. However, so far the information contained in enterprise models is to a large extend dateless which means it is hardly used to describe the transformation itself consistently. Therefore we propose a method to systematically derive an enterprise transformation model based on existing models representing enterprise structures at different points in time. The result of the method application is a set of project outlines derived from enterprise models. In order to generalize our approach to a multi-period transformation model capable of coping with dynamic changes and plan deviations we propose a respective conceptual system. Our research artefact (the method) is finally demonstrated in a case study.

Enterprises are subject to continuous change driven by top-down planned transformation projects as well as by bottom-up initiatives realizing what is called the evolution of the enterprise. Enterprise architecture (EA) planning presents itself as a means for facilitating and controlling this change. Nevertheless, the methods and models of EA planning developed in recent years either have a strong focus on planned (proactive) transformations or on guided (reactive) evolution. In this paper, we outline an EA planning method that accounts for both types of enterprise change by illustrating the interplay of EA planning, requirements, release and synchronization management. Specifically we focus on the coordination of design activities modeled as intermeshed closed-loop control systems and on an integrated information model describing EA transformation planning.

Enterprise architecture management (EAM) is expected to provide business value by guiding the continuous development and transformation of an enterprise. Based on the approach we strive for constructing useful artifacts that guide the successful and situational design of EAM. In order to do so we argue for a thorough analysis of the design problem in advance. This is realized by a two-step survey conducted on EAM practices. The empirical analysis reveals eight determining design factors of EAM, a delineation of three different types of EAM design in the form of clusters as well as insight about the successfulness of the different types.

Enterprise architecture (EA) describes the fundamental structure of an organization from business to IT. EA as a practice as well as a research topic has been around for several years. However, existing methods largely neglect the existence of time which is essential in order to systematically approach EA planning. The article at hand builds a process model for EA planning as a de-sign research artifact. We therefore use another more general design research artifact a method for process engineering in order to systematically build our proposed planning process. From a design science research (DSR) perspec-tive we demonstrate, how elements of the DSR knowledge base can be applied to create new DSR artifacts and how DSR might build a toolbox as it is availa-ble in other mature engineering disciplines.

The transparency created by enterprise models is a valuable support for organizational engineering and especially enterprise transformation. However, current approaches are hardly suitable to also create transparency of the enterprise transformation process itself. The paper at hand contributes a systematic approach for capturing dynamics of enterprise transformation in enterprise models. Therefore we differentiate dimensions of time (modeling time, valid time) of enterprise models on a macro level as well as a set of model transformation operations on a micro level. The proposed model transformation operations on a micro level are suitable for describing the differences of as-is and to-be models in an enterprise transformation program.

Modern enterprises face the challenge to survive in an ever changing envi­ronment. One commonly accepted means to address this challenge and further enhance survivability is enterprise architecture (EA) management, which provides a holistic model-based approach to business/IT alignment. Thereby, the decisions taken in the context of EA management are based on accurate documentation of IT systems and business processes. The maintenance of such documentation causes high investments for enterprises, especially in the absence of information on the change rates of different systems and processes. In this paper we propose a method for gathering and analyzing such information. The method is used to analyze the life spans of the application portfolio of three companies from different industry sectors. Based on the results of the three case studies implications and limitations of the method are discussed.

Enterprise Architecture (EA) models provide information on the fundamental as-is structure of a company or governmental agency and thus serve as an informational basis for informed decisions in enterprise transformation projects. At the same time EA models provide a means to develop and visualize to-be states in the EA planning process. Results of a literature review and implications from industry practices show that existing EA planning processes do not sufficiently cover dynamic aspects in EA planning. This paper conceptualizes seven levels of complexity for structuring EA planning dynamics by a system of interrelated as-is and to-be models. While level 1 represents the lowest complexity with non-connected as-is and to-be models, level 7 covers a multi-period planning process also taking plan deviations during transformation phases into account. Based on these complexity levels, a multi-stage evolution of EA planning processes is proposed which develops non-dynamic as-is EA modeling into full-scale EA planning.

Enterprise Architecture (EA) models capture the fundamental elements of organizations and their relationships to servedocumentation, analysis and planning purposes. As the elements and their relationships change over time, EA planningbecomes increasingly complex. An analysis of existing methods shows that the complexity of dynamics is not sufficientlyaddressed. We argue that a sophisticated understanding of the complexity matter is prerequisite for EA planning methodconstruction. As Chaos Theory (CT) is deployed in natural and social sciences-as well as in different contexts of ISresearch-to describe and understand the behavior of complex systems over time, we use properties of CT to assess thecomplexity of dynamics in EA planning and to derive requirements for EA planning methods. Our findings emphasize theimportance of initial conditions of the architecture for EA planning and the need to harmonize planning granularities in orderto achieve predictable results.