How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

How have Japanese companies become world leaders in the automotive and electronics industries, among others? What is the secret of their success? Two leading Japanese business experts, Ikujiro Nonaka and Hirotaka Takeuchi, are the first to tie the success of Japanese companies to their ability to create new knowledge and use it to produce successful products and technologies. In The Knowledge-Creating Company, Nonaka and Takeuchi provide an inside look at how Japanese companies go about creating this new knowledge organizationally. The authors point out that there are two types of knowledge: explicit knowledge, contained in manuals and procedures, and tacit knowledge, learned only by experience, and communicated only indirectly, through metaphor and analogy. U.S. managers focus on explicit knowledge. The Japanese, on the other hand, focus on tacit knowledge. And this, the authors argue, is the key to their success--the Japanese have learned how to transform tacit into explicit knowledge. To explain how this is done--and illuminate Japanese business practices as they do so--the authors range from Greek philosophy to Zen Buddhism, from classical economists to modern management gurus, illustrating the theory of organizational knowledge creation with case studies drawn from such firms as Honda, Canon, Matsushita, NEC, Nissan, 3M, GE, and even the U.S. Marines. For instance, using Matsushita's development of the Home Bakery (the world's first fully automated bread-baking machine for home use), they show how tacit knowledge can be converted to explicit knowledge: when the designers couldn't perfect the dough kneading mechanism, a software programmer apprenticed herself withthe master baker at Osaka International Hotel, gained a tacit understanding of kneading, and then conveyed this information to the engineers. In addition, the authors show that, to create knowledge, the best management style is neither top-down nor bottom-up, but rather what they call "middle-up-down," in which the middle managers form a bridge between the ideals of top management and the chaotic realities of the frontline. As we make the turn into the 21st century, a new society is emerging. Peter Drucker calls it the "knowledge society," one that is drastically different from the "industrial society," and one in which acquiring and applying knowledge will become key competitive factors. Nonaka and Takeuchi go a step further, arguing that creating knowledge will become the key to sustaining a competitive advantage in the future. Because the competitive environment and customer preferences changes constantly, knowledge perishes quickly. With The Knowledge-Creating Company, managers have at their fingertips years of insight from Japanese firms that reveal how to create knowledge continuously, and how to exploit it to make successful new products, services, and systems.

The knowledge-creating company

The knowledge acquisition (KA) process is not ''mining from the expert's head'' and writing rules for building knowledge-based systems (KBS), as it was 20years ago when KA was often confused with knowledge elicitation activity, and modern engineering tools did not exist. The KA process has definitely changed. Today knowledge acquisition is considered a cognitive process that involves both dynamic modeling and knowledge generation activities. KA should be seen as a spiral of epistemological and ontological content that grows upward by transforming tacit knowledge into explicit knowledge, which in turn becomes the basis for a new spiral of knowledge generation. This paper presents some of our attempts to build a new knowledge acquisition methodology that brings together and includes all of these ideas. KAMET II, the evolution of KAMET (Cairo, 1998), represents a modern approach to creating diagnosis-specialized knowledge models that can be run by Protege 2000, the open source ontology editor and knowledge-based framework.

The KAMET II methodology: Knowledge acquisition, knowledge modeling and knowledge generation

Current simulation models are not only represented in the form of traditional data or formula for pure theory analysis but expanded to be simulation modeling assets that are featured with complicated structure, diverse behaviors, and abundant semantics. Semantic composability, therefore, receives a constantly growing attention in recent years. So far, one of the popular solutions to enhance semantic expressiveness is domain specific modeling based on general metamodeling (GMM) facilities. But for some particular domains, researchers identified the need of deeper semantic expressiveness therefore proposed domain specific metamodeling (DSMM). Hence, this paper aims to explore the underlying methodologies of DSMM for supporting deep semantic composability. Compared with several usual alternatives based on GMM, this paper applies the multi-level metamodeling architecture to create a set of metamodeling primitives using an example named SEvent. In fact, SEvent is a novel formalism that slightly extends Petri net to support continuous states transition and continuous event triggering. As a proof of concept, we concentrate on developing the textual syntax of SEvent and using it to represent torpedo’s behaviors.

Domain Specific MetaModeling for Deep Semantic Composability

Model-driven engineering has become popular in the combat effectiveness simulation systems engineering during these last years. It allows to systematically develop a simulation model in a composable way. However, implementing a conceptual model is really a complex and costly job if this is not guided under a well-established framework. Hence this study attempts to explore methodologies for engineering the development of simulation models. For this purpose, we define an ontological metamodelling framework. This framework starts with ontology-aware system conceptual descriptions, and then refines and transforms them toward system models until they reach final executable implementations. As a proof of concept, we identify a set of ontology-aware modelling frameworks in combat systems specification, then an underwater targets search scenario is presented as a motivating example for running simulations and results can be used as a reference for decision-making behaviors.

https://publications.drdo.gov.in/ojs/index.php/dsj/article/download/12777/7203

Model Driven Combat Effectiveness Simulation Systems Engineering

Knowledge management is fast becoming a commercial necessity for many organisations, in order that they manage their intellectual assets and gain competitive advantage. Most knowledge resides in human memories and managing it is seen as a human-oriented process rather than a technology-based solution. Nevertheless, technology can be utilised as a knowledge management enabler with automated tools such as knowledge-based systems that are used to capture and manage knowledge. These systems are designed and developed using knowledge engineering techniques that are similar to those used in software engineering, but have more emphasis on the role of knowledge in the reasoning process. There is no standard modelling language available in this field and most of the techniques used are usually adapted from the software engineering domain. Although these languages are used in a mix of notations for knowledge modelling, the literature shows that the Unified Modeling Language (UML) is much preferred as it is a standardised modelling language widely adopted by industry and academia. However, little research has been done in extending the standardised language for modelling knowledge-based systems. This research has developed, validated and evaluated a knowledge modelling profile, based on the profile extension mechanism of UML, for modelling knowledge-based systems. The research was organised in three stages. The initial stage is a comprehensive study of the role of knowledge-based systems in managing knowledge and how conceptual models are used to design and build these systems. The intermediate stage involves structuring a methodologically sound and systematic process for developing the profile extension. The final stage addresses the validation and evaluation of the profile using tools and case studies, one of which included the design and development of a prototype knowledge-based system. This research has contributed to the standardisation of the knowledge modelling language based on UML which enables knowledge-based systems to be designed coherently and enables the profile to be integrated into the Model Driven Architecture space as a domain specific language. It also bridges the gap between domain analysis and system implementation and contributes to a better understanding of how profiles should be designed.

Mohd Syazwan Abdullah

Papers

A UML profile for conceptual modelling of knowledge-based systems