Practitioners and academics have developed numerous maturity models for many domains in order to measure
competency. These initiatives have often been influenced by the Capability Maturity Model. However, an
accumulative effort has not been made to generalize the phases of developing a maturity model in any domain.
This paper proposes such a methodology and outlines the main phases of generic model development. The
proposed methodology is illustrated with the help of examples from two advanced maturity models in the
domains of Business Process Management and Knowledge Management.

Understanding the Main Phases of Developing a Maturity Assessment Model

Spend your time even for only few minutes to read a book. Reading a book will never reduce and waste your time to be useless. Reading, for some people become a need that is to do every day such as spending time for eating. Now, what about you? Do you like to read a book? Now, we will show you a new book enPDFd common knowledge how companies thrive by sharing what they know that can be a new way to explore the knowledge. When reading this book, you can get one thing to always remember in every reading time, even step by step.

Common Knowledge: How Companies Thrive by Sharing What They Know

Contributors 1: Ikujiro Nonaka and Toshihiro Nishiguchi: Introduction: Knowledge Emergence Part I Knowledge, BA, and Care 2: Ikujiro Nonaka, Noboru Konno, and Ryoko Toyama: Emergence of "Ba": A Conceptual Framework for the Continuous and Self-transcending Process of Knowledge Development of Business Organizations 3: Georg Von Krogh, Kazuo Ichijo, and Ikujiro Nonaka: Bringing Care into Knowledge Development of Business Organizations Part II Technology and Cooperation 4: Kentaro Nobeoka and Yasunori Baba: The Influence of New 3-D CAD Systems on Knowledge Creation in product Development 5: Stefan H. Thomke: The Impact of Technology on Knowledge Creation: A Study of Experimentation in Integrated Circuit Design 6: The Temporal Dynamics of Knowledge Creation in the Information Society 7: Michael A. Cusumano: Focusing Creativity: Microsoft's "Synch-and-Stabalize" Approach to Software Product Development 8: Giorgio De Michelis: Cooperation and Knowledge Creation Part III Transnational Knowledge Creation 9: D. Eleanor Westney: Multinational Enterprises and Cross-Border Knowledge Creation 10: Kenichi Yasumuro and D. Eleanor Westney: Knowledge Creation and the Internationalization of Japanese Companies: Front-Line Management Across Borders Part IV Interfirm Relations 11: Toshihiro Nishiguchi: Coevolution of Interorganzational Relations 12: Sigrun Caspary and Tishihiro Nishiguchi: "Co-opetition" in the Japanese Aircraft Industry 13: James R. Lincoln and Christina Ahmadjian: Shukko (Employee Transfers) and Tacit Knowledge Exchange in Japanese Supply Networks: The Electronics Industry Case 14: Linsu Kim: Absorptive Capacity, Co-opetition, and Knowledge Creation: Samsung's Leapfrogging in Semiconductors 15: Ikujiro Nonaka and Toshihiro Nishiguchi: Conclusion: Social, Technical, and Evolutionary Dimensions of Knowledge Creation Index

Knowledge emergence : social, technical, and evolutionary dimensions of knowledge creation

A case is made for further developing a branch of systems engineering that focuses on problems and issues which arise in the service sector. We promulgate this special focus not only because of the size and importance of the service sector but also because of the unique opportunities that systems engineering can exploit in the design and joint production and delivery of services. We begin by considering the economic, technological and demographic contexts within which the service sector has flourished; we then address both services, especially emerging services, and systems engineering, followed by a discussion of how to advance the field of service systems engineering, and concluding with several remarks. In particular, a number of service systems engineering methods are identified to enhance the design and production/delivery of services, especially taking advantage of the unique features that characterize services — namely, services, especially emerging services, are information-driven, customer-centric, e-oriented, and productivity-focused.

A case for service systems engineering

A survey on fog computing for the Internet of Things

We present a tutorial on knowledge management (KM) and a roadmap of this special issue around the knowledge life-cycle. Knowledge management is a discipline that provides a strategy, process and technology to share and leverage information and expertise that increases our level of understanding, to more effectively solve problems and make decisions. We address three key views: (1) codification (tacit and explicit knowledge types); (2) various knowledge environments; and (3) cognitive aspects, followed by detailed descriptions of features and technology, and how this capability is being leveraged in forward-looking organizations.

http://erdos.csie.ncnu.edu.tw/~chuang/KMPapers/c2.pdf

Knowledge management tutorial: an editorial overview

Predictive and reactive train scheduling are tactical and operational decision making, respectively, under constraints (e.g. resource capacity, managerial objectives) and under uncertainty (e.g. imprecise data and information, unforeseen events). Predictive scheduling produces timetables taking into account the market demand and resource utilization levels. Reactive scheduling challenges disruptions to timetables and schedules trains and operations with imprecise plans. Expert knowledge is indispensable for finding practical solutions for both predictive and reactive scheduling. Consequently, knowledge management strategies, processes and technologies can improve the decision-making process and outcomes. This paper focuses on the following issues. Five dimensions are introduced to distinguish predictive and reactive train-scheduling activities. The combined use of data and knowledge and the differences in uncertainty levels are used to comparatively position the two scheduling approaches. The intensity of reliance on explicit and tacit knowledge is highlighted via the elaboration and classification of knowledge used in either one or both scheduling environments. The significance of train-scheduling tacit knowledge elicitation is described by, first, presenting a real case analysis which resulted in the elicitation of rich and valuable tacit knowledge (timetabling heuristics) from explicit knowledge (timetable) and, second, generalizing lessons learned from this process. The contributions of the tacit knowledge elicitation process to the enhancement of the train-scheduling system which leads to better resource utilization and customer satisfaction are itemized.

Predictive and reactive approaches to the train-scheduling problem: a knowledge management perspective

We describe a novel class of systems called intelligent tactical decision support systems which enable firms to make superior pricing decisions within a dynamic competitive environment. The paper outlines the unique aspects of such systems in relation to commercially available systems. These aspects are seen to be their ability to process and use knowledge on the one hand and information external to the organization on the other. The systems use nonlinear models, optimization, and learning algorithms to provide decision support capabilities for the generic price-setting problem. The general theme of the paper is to see the systems described within the paper as the support tools for a generic price setting process. The description of the systems start with a generic one for pricing decision support in any consumer market and is then specialized to various markets, each of which could benefit from a specific variant of the generic pricing technology which has been adapted to that particular industry. The adaptations from the generic pricing system to the systems for petrol pricing, retail pricing, and telecommunications taxation, are explained. These three examples show a variety of price-setting situations ranging from near commodity pricing, as in the case of petrol, to pricing of very sophisticated services, as in the case of mobile telephony. Field experiments with these systems show that they perform significantly better than unassisted human decision-makers. This is seen from controlled experiments where these systems have demonstrated significant profit uplift when applied to the experimentation sites as compared to the "control" sites where the systems have not been applied.

http://erdos.csie.ncnu.edu.tw/~chuang/KMPapers/R2_3.pdf

Intelligent decision support for the pricing of products and services in competitive consumer markets

The objective of this paper is to propose an alternative approach that can be directly used by marketers in order to evaluate an offer with or without the involvement of potential customers. In order to deal with such an uncertain multi-criteria evaluation problem, a modification of the analytic hierarchy process (AHP) is proposed, based on a fuzzy preference programming method. This method takes into account customer's uncertain or irrational behavior by dealing with inconsistencies and derives crisp priorities from inconsistent interval pairwise comparison matrices. It is based on interval representations of the comparison judgments, as the prioritization problem is formulated as a fuzzy programming problem for obtaining an optimal crisp priority vector that maximizes the overall degree of satisfaction of the decision-maker.

N.P. Cassaigne

Papers

Knowledge management tutorial: an editorial overview

Predictive and reactive approaches to the train-scheduling problem: a knowledge management perspective

Intelligent decision support for the pricing of products and services in competitive consumer markets

Fuzzy analytical approach to the evaluation of offers in the mobile telecommunication market