Guide Systemic Management for Intelligent Organizations: Concepts, Models-Based Approaches and Applications

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  2. SearchWorks Catalog
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  4. Chapter 13 - Improving the organization and management of extension

Lastly, I will analyze the two cybernetic management approaches with regards to their respective future. Engineers develop heuristic knowledge to build action-oriented solutions for specific situations. This type of knowledge is concrete, contingent, goal-oriented, particular, temporal, contextual, uncertain, value-laden, and task-specific, and as such it challenges the traditional ideals of scientific knowledge, which is typically assumed to be abstract, unconditional, disinterested, universal, timeless, utopian, certain, value-neutral, and theory-bound.

A large part of social-systems engineering produces knowledge through models, with no a priori theories about human action, e.

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For instance, system-dynamics models capture decision rules that define processes driven by actors in concrete situations. Such an epistemology shows a valuable lack of concern for empirically-sourced induced knowledge. This knowledge grows through trial-and-error. This chapter demarcates these epistemological aspects to show how and why a model-based science denotes an engineering attitude that improves action and change in specific settings. This stance is a consistent way of facing the contingency of systems that are formed by free, innovative actors and, furthermore, of developing a science of management.

Well-validated models can improve the management of intelligent organizations Schwaninger ; Kybernetes, —, In the domain of system dynamics and computational modeling, the assurance of model validity is a prominent challenge. A number of contributions concerning validation tests and their epistemological foundations have been developed. Considering the existing literature, however, little has been said about a validation methodology for system-dynamics models. This chapter differentiates two meanings of such methodology. The first meaning denotes a body of methods.

This understanding has been adopted almost exclusively in the field of system dynamics. The second meaning sets forth a comprehensive understanding of the elicitation, description, reflection, and evaluation of issues related to validation, which is currently lacking. That model is used to derive directions for future research as well as actions required to support systemic management for intelligent organizations. The chapter aims at focusing the attention of researchers on validation and at inaugurating a beneficial discussion.


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Despite the strategic relevance of strategic-foresight activities, the strategic-management literature contains only little and fragmented knowledge of how firms successfully manage such activities. This exploratory paper provides insights into the management of strategic-foresight activities based on in-depth case studies of five large multinational companies.

According to our findings, strategic-foresight activities 1 are structurally managed within an organization via one of three models, 2 consist of initiation, modeling, and finalization phases, which occur in circular, iterative loops, 3 use increasingly qualitative and participative methods, 4 support strategic decision-making with a main focus either on innovation and exploration or on planning and exploitation, 5 are subject to substantial change depending on the content-related openness of the corporate strategy diversification vs.

Self-reported data from Switzerland and Germany indicate that top managers vary considerably concerning their awareness of various multiple realities. We explain this phenomenon by drawing on the notion of cognitive style, using the metaphor of hedgehogs and foxes. It is argued that research should move from a focus on moral awareness to value awareness in a very broad sense. The purpose of this paper is to present a conceptual framework, which, rooted in system-oriented management thinking, links the management of innovations with the personal sense of well-being, especially in relation to managers.

Based on the new generation of the St. This logic supposes a shift from the traditional view of coping with resistance to a more positive view of innovations as an imminent psychological resource for personal development and success. Based on an exploratory research study, we are able to present initial empirical findings supporting our conceptual model and to define lines for further research. This paper starts with an inquiry into the ontology of Organizational Intelligence OI , addressing the critical issue of reification and analyzing different conceptions of OI.

The scientific contribution by Markus Schwaninger is presented in its main features and analyzed using the categories derived from a broad literature review on OI. Some methodological suggestions are formulated on how further to study the emerging process of OI. Organizational growth and performance management provide two important research topics for both enterprises and public sector organizations. Wilfried Grossmann University of Vienna, Austria. Christoph Moser University of Vienna, Austria. Looking at the literature and the activities in the two areas shows that process modelling takes a look at the business from a more production and organizational oriented view, whereas business intelligence activities emphasize more the role of the customer in the business process.

In this lecture we want to take a unified view onto these two approaches and show how BPM and BI support each other. For demonstration we use the activities of data understanding and data provisioning which are at the beginning of any BI activity. Due to the abundance of data on the Internet integration of traditional data sources and big data is a challenging task.

We present a process model for data integration and show how this model can be realized using the ADOxx platform. The basic idea of the model is simultaneous processing of the data workflow and the associated workflow of the metadata which describe the data processing activities. Such a model supports better understanding of the data and extends traditional methods for accessing data quality. Yoshinori Hara Kyoto University, Japan. Hisashi Masuda Kyoto University, Japan. We define Japanese creative services and discuss how they have been sustained successfully and its application to global service enhancement.

Several Japanese creative services are expanding their activities toward global markets. We explain the mechanisms of the sustainability and scalability of advanced cases of Japanese Creative Services. We developed a meta-modeling platform for handling the combined analysis of the Japanese Creative Services. We believe that this kind of approach will contribute to creating new values within the field of service science and for value-added global services.

Decision makers use models to understand and analyze a situation, to compare alternatives and to find solutions. Additionally, there are systems that support decision makers through data analysis, calculation or simulation. While humans prefer graphical or textual models, machine-interpretable models have to be represented in a formal language.

This lecture describes a meta-modelling approach, which combines human-interpretable graphical enterprise architecture models with machine-interpretable enterprise ontologies.

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A metamodel which is represented as a formal ontology determines the semantics of the modeling language. To create a graphical modelling language, a graphical notation can be added for each class of the ontology. Every time a new modelling element is created during modelling, an instance for the corresponding class is created in the ontology. Thus, models for humans and machines are based on the same internal representation.

The approach has been applied in the CloudSocket project for selection of cloud services in order to achieve Business Process as a Service. Florian Johannsen Hochschule Schmalkalden, Germany. However, considering the rapidly changing customer requirements in times of high market transparency and the increasing collaboration between organizations, the conduction of BPI projects has become very challenging.

Implicit process knowledge from diverse process participants needs to be elicited and transformed into improvement opportunities. In this context, the results achieved need to be properly documented, communicated and processed throughout a company. Dimitris Karagiannis University of Vienna, Austria. As the paradigm of enterprise modelling originally envisioned, a hybridization of modelling approaches is needed in order to cover the multiple facets of a business view, its context and requirements for different types of resources — including IT services and infrastructure.

This novel approach makes use of semantic networks to extend model-awareness towards arbitrary types of models that are developed for specialized communities aiming for domain-specificity or even case-specificity in their modelling language, therefore favoring productivity at the expense of reusability across domains. The technological space for capturing and bridging knowledge through model semantics is primarily based on diagrammatic models. Two categories of models are employed in this context: 1 Models of Concepts for describing a common understanding of a domain through its concepts and relations; 2 Models that use Concepts are typically domain-specific models based on some already established understanding of the domain.

The main assumption is that a modelling method may evolve iteratively based on changing modelling requirements and feedback loops. Evangelia Kavakli University of the Aegean, Greece. The conceptual modelling framework applied in CORE employs a set of complimentary and intertwined modelling paradigms based on enterprise capabilities, goals, actors, and information objects. The lecture will define the foundational concepts of CORE as well as the way of working from capturing textual descriptions from stakeholders, progressing to formally defining models of early requirements, based on the CORE meta-model, and in a stepwise refinement define functional and non-functional requirements of desired systems.

In the era of global economy and frequent changes, the information systems development also faces the need for continuous realignment with the business processes and systems. Continuous development, continuous delivery, and continuous engineering have become common notions in contemporary systems development language.

In this context, right requirements still are the key of project success; however the methods for their engineering must adhere to challenges and possibilities of enterprise digitalization levels. One of the opportunities that are provided by enterprise modeling tools is the possibility to utilize enterprise models in requirements engineering. In general, process algebra can be the most suitable formal method to specify IoT systems due to the equivalent notion of processes as things.

However there are some limitations to predict smart IoT systems with the properties of distribution, mobility and real-time.

Concepts, Models-Based Approaches and Applications

For example, Timed pi-Calculus has capability of specifying time property, but is lack of direct specifying both execution time of action and mobility of process at the same time. And d-Calculus has capability of specifying mobility of process itself, but is lack of specifying various time properties of both action and process, such as, ready time, timeout, execution time, deadline, as well as priority and repetition. In order to overcome the limitations, this lecture presents a process algebra, called, dTp-Calculus, extended from d-Calculus, by providing with capability of specifying probabilistic transitions with the set of time properties, as well as priority and repetition.

It can be considered one of the most practical and innovative approaches to model probabilistic behavior of smart IoT systems. It is a process which is guided by a particular methodology the choice of which is based on the contextual setting of the research topic the challenges as well as the motivation for a solution the deliverables.

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In this lecture, we will examine issues involved in research with particular emphasis on research in the field of Enterprise Information Systems EIS. We will examine the issues underpinning the EIS domain, the emergence of research needs from digitisation, to interpretation and more recently to transformation of enterprises. Given this context, we will explore different research methodologies that may be deployed, such as those of Design Science Research, Action Research, Case Study, Survey Design, Mixed Methods and Qualitative Methods, each one of which is suited to different motivational characteristics of the underlying research goals.

We will also explore the social dimension of research, including issues encountered by researchers in settings involving communities within their specific research environment as well as other related communities e. Branching out from a close research environment by exploiting gained experiences, towards collaborative initiatives, competing for research funding and helping the next generation of researchers will also be addressed in this lecture.

For decades, in many, particularly complex organization systems, there is an open issue how to support information management process so as to produce useful knowledge and tangible business values from data being collected. One of the central roles in addressing this issue still play databases and information systems.

In recent years, we are the witnesses of great movements in the area of business information management. Such movements are both of technological and methodological nature. By this, today we have a huge selection of various technologies, tools, and methods in data engineering as a discipline that helps in a support of the whole data life cycle in organization systems, as well as in information system design that supports the software process in data engineering. Despite that, one of the hot issues in practice is still how to effectively transform large amounts of daily collected operational data into the useful knowledge from the perspective of declared company goals, and how to set up the information design process aimed at production of effective software services in companies.

This lecture is intended to address interdisciplinary character of a set of theories, methodologies, processes, architectures, and technologies in disciplines such as Data Engineering, Information System Design, Big Data, NoSQL Systems, and Model Driven Approaches in a development of effective software services. The lecture will give a short overview of the main workshop contributions. Models are the basic human tools for managing complexity and understanding.

As such, they play a key role in all scientific and engineering disciplines as well as in everyday life. Many modeling paradigms have evolved over time in different disciplines, resulting in a wide variety of modeling languages, methods and tools that have come and gone. This is particularly true for informatics, which is a modeling discipline in itself: for long it has systematized the field of modeling, for example by introducing model hierarchies, by ontological foundations, by developing universal modeling languages such as UML, or by specifying domain-specific modeling methods DSMMs for areas of application where universal approaches fail.

In the context of digital transformation, modeling plays a central role in ensuring the functionality, security and quality of complex digital ecosystems. We approach this in the lecture from a rigorously model-centered perspective, which sees a digital ecosystem as a construct of networked model handlers in the sense of model producers and model consumers, whereby these handlers in turn are instances of models. We will illustrate the paradigm of model-centric architecture with the results of projects we have carried out in the areas of assistive systems, mechatronic systems and Quality Aware Software Engineering.

In addition, Martin Paczona will give students in a working group in-depth and practical insight. Among other things, they will learn and apply the principles of a DSMM for the design and development of electric vehicle test benches. Business processes in the age of the internet are typically not restricted to single organizations but cross organizational borders to customers, suppliers and other organizations. The design of business processes for these business communities is a complex collaborative task, which requires special methodological support. This course introduces Horus, which includes a set of modeling methods and languages to support the whole life cycle of business processes within business communities.

Horus is based on high-level Petri Nets for procedure modeling and provides additional modeling support for objects, resources, organizational structures, business goals and business rules. Simulation based concepts are provided to evaluate models. Besides describing the basic concepts of Horus, the course also gives an overview about ongoing research work.

Companies must exploit the potential of new technologies, in order to offer their customers innovative solutions with clearly defined benefits. Thus, IT plays an important role, as the implementation of an innovative and agile system setup is a fundamental success factor. In this lecture, Dr. The modelling of knowledge, action and time is a topic of current research within the broader domain of knowledge representation and reasoning. The course will focus on declarative approaches for modelling and reasoning with change, paying particular attention to the integration of knowledge and action.

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Chapter 13 - Improving the organization and management of extension

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