Distributed Model Management Systems: A Proposal for an Ontology-Based Approach

While managers in today’s global business environments are thrusting for swifter, more flexible, and scalable decision support, technical challenges pertaining to model sharing arise due to the limited capabilities of current model representation techniques. In this research, we propose that ontologies can improve model representation and thus support sophisticated model management capabilities such as model integration and composition in distributive collaborative environments. The proposed solution extends SMML and builds upon previous literature in the semantic web to provide a model representation language that is capable of capturing model structure as well as semantics

Context-aware Intelligent Model Selection System

After more than 40 years of research in computational decision support, model selection and management is still one of the most crucial problems. With organisations facing turbulences in an environment that include constant changes in social, political, technical and economic challenges, the selection of appropriate models for decision support has become even more difficult. Most research efforts do not consider model selection itself as a major aspect of research nor do they reflect on context awareness. The paper explores the early use of Artificial Intelligence (AI) techniques to improve model selection and reviews modern Intelligent Decision Support Systems (IDSS). Since model selection is a central problem for decision makers we specifically analyse research on model selection and identify important characteristics. Based on this analysis we suggest a framework and architecture for a Context-aware Intelligent Model Selection System (CIMSS). The paper concludes with further suggestions for future research

Servitized Enterprises for Distributed Collaborative Commerce

Servitized Enterprises for Distributed Collaborative Commerce: 10.4018/jssmet.2010010105: Agility and innovation are essential for survival in today’s business world. Mergers and acquisitions, new regulations, rapidly changing technolog

An ontology-based model management architecture for service innovation

Organizations have indicated renewed interest in service innovation, design and management, given the growth of service sector. Decision support systems (DSS) play an important role in supporting this endeavor, through management of organizational resources such as data and models. Given the global nature of service value chains, there have been ever increasing demands on managing, sharing, and reusing these heterogeneous and distributed resources, both within and across organizational boundaries, through DSS consisting of database management systems (DBMS) and model management systems (MMS). Analogous to DBMS, model management systems focus on the management of decision models, dealing with representation, storage, and retrieval of models as well as a variety of applications such as analysis, reuse, sharing, and composition of models. Recent developments in the areas of semantic web and ontologies have provided a rich tool set for computational reasoning about these resources in an intelligent manner. In this chapter, we leverage these advances and apply service-oriented design principles to propose an ontology-based model management architecture supporting service innovation. The architecture is illustrated with case study scenarios and current state of implementation. The role of potential information technologies in supporting the architecture is also discussed. We then provide a roadmap to make advancements in research in this direction

Toward an Adaptive Enterprise Modelling Platform

For the past three decades, enterprise modelling (EM) has been emerging as a significant yet complex paradigm to tackle holistic systematic enterprise analysis and design. With a high fluctuation in the global economy, industrial stability and technology shift, the necessity of such paradigms becomes crucial in determining the decisions that an enterprise can make for surviving in such a highly dynamic business ecosystem. EM practices have focused for a long time, on the design-time of enterprise systems. Recently, there has been a rapid development in data analytics, machine learning and intelligent systems from which an EM platform can benefit. EM needs to cope with the new changes in both business and technology; it should also help architects to determine optimum decisions and reduce complexity in technical infrastructure. In this paper, the author discusses several challenges facing enterprise modelling practices and offers an architectural notion for future development focusing on the requirements of a platform that can be called intelligent and adaptive

A practical framework for assessing business intelligence competencies of enterprise systems using fuzzy ANP approach

As traditional concept in management, decision support had a remarkable role in competitiveness or survival of organisations and following, as modern impression, nowadays business intelligence (BI) has various applications in achieving desirable decision supports. Consequently, assessing BI competencies of enterprise systems can enable decision support in firms. This paper presents a practical framework for assessing the business intelligence capabilities of enterprise systems based on a set of novel factors and utilising fuzzy analytic network process (FANP). Through this, the construct of BI competency is decomposed into three main competency parts including ‘managerial’, ‘technical’ and ‘system enabler’ sub-goals, five main factors and 26 criteria. Using this framework, the BI competency level of enterprise systems can be determined which can help the decision makers to select the enterprise system that best suits organisations’ intelligence decision support needs. In order to validate the proposed model, it is applied to a real Iranian international offshore engineering and construction company in the oil industry to select and acquire ERP system. This research provides a complete frame (factors, criteria and procedures) for firms to assess their proposed software and systems in the field of BI competencies and functions

More applicable environmental scanning systems leveraging "modern” information systems

With Ansoff's article about weak signals as a flagship example, a substantial body of knowledge about environmental scanning systems exists. However, these concepts often go unused in practice. The 2008/2009 economic crisis provided a strong, ongoing impulse for redesigning such information systems (IS). This article develops six guidelines for the conceptual design of environmental scanning systems that are more applicable than those specified by previous research. We start with literature research, which reveals three gaps in existing approaches. Then we develop design guidelines to fill these gaps with the help of "modern” IS. To address the lack of sound requirements analysis, our first design principle proposes 360-degree environmental scanning systems for executives and suggests how to select the most important scanning areas. Three further findings cover weaknesses in the IS model perspective, focusing on more effective implications of weak signals. In terms of method, we propose incorporating scanning results more closely into executives' decision-making processes. Applying the design guidelines at a raw materials and engineering company, we arrive at a prototype we call the "corporate radar.” It includes an IS-based tree with economic value added at risk on top. The resulting lessons learned help to evaluate our findings and the research method presented here, as well provide concrete starting points for future researc

Determining factors driving sustainable performance through the application of lean management practices in horticultural primary production

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