Applying a decision model based on multiple criteria decision making methods to evaluate the influence of digital transformation technologies on enterprise architecture principles
Izabelle Hannemann, Sarah Rodrigues, Eduardo Loures, Fernando Deschamps, Jose Cestari
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引用次数: 2
Abstract
Organisations all over the world are going through the process of digital transformation (DT). Enterprise Architecture (EA) is a method and an organising principle that aligns the business's objectives and strategies with the Information Technology strategy and execution plan. EA provides a guide to direct the evolution and transformation of enterprises with technology. The EA principles are one of the key concepts in the definition of EA; they assist in recognizing the organization vision and validating the outcomes. However, the lack of adequate instruments for assessing the current state and identifying opportunities for EA management procedures improvement often leave organisations unsure of where to begin improving their procedures. The aim of this paper is to help organisations identify these improvement opportunities. To do so, a decision model was developed to evaluate the influence DT technologies have on the EA principles proposed by The Open Group Architecture Framework (TOGAF). A literature review was conducted, and five main DT Technologies applied in the EA scope were identified. With that, a decisional model was created based on two decision-making methods called Decision-Making Trial and Evaluation Laboratory and PROMETHEE. The 21 architecture principles proposed by TOGAF were evaluated and the influence the technologies exercised on the principles were identified. As a result, Big Data and Cloud Computing technologies were indicated as having the greatest effect over the analysed principles, therefore concluding that when applied in the EA scope, these technologies can help organisations improve their EA procedures.
期刊介绍:
IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly.
The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).