Kuan Yew Wong

Bio: Kuan Yew Wong is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Supply chain & Performance measurement. The author has an hindex of 39, co-authored 187 publications receiving 6756 citations. Previous affiliations of Kuan Yew Wong include University of Birmingham & UCSI University.

Critical success factors for implementing knowledge management in small and medium enterprises

Abstract: Purpose – To date, critical success factors (CSFs) for implementing knowledge management (KM) in small and medium enterprises (SMEs) have not been systematically investigated. Existing studies have derived their CSFs from large companies' perspectives and have not considered the needs of smaller businesses. This paper is aimed to bridge this gap.Design/methodology/approach – Existing studies on CSFs were reviewed and their limitations were identified. By integrating insights drawn from these studies as well as adding some new factors, the author proposed a set of 11 CSFs which is believed to be more suitable for SMEs. The importance of the proposed CSFs was theoretically discussed and justified. In addition, an empirical assessment was conducted to evaluate the extent of success of this proposition.Findings – The overall results from the empirical assessment were positive, thus reflecting the appropriateness of the proposed CSFs.Practical implications – The set of CSFs can act as a list of items for SMEs ...

An empirical study of the important factors for knowledge-management adoption in the SME sector

Abstract: Purpose – To investigate the critical success factors (CSFs) for adopting knowledge management (KM) in small and medium‐sized enterprises (SMEs) – an area that has, to date, received very little attention in the literature.Design/methodology/approach – A survey instrument comprising 11 factors and 66 elements was developed. Through a postal survey, data were sought from SMEs in the UK. A parallel one was also administered to a group of academics, consultants and practitioners in the KM field in order to provide a more holistic view of the CSFs.Findings – The survey instrument was shown to be both reliable and valid. Pertinent statistical analyses were then performed. By integrating the results from both groups of respondents, a prioritised list of CSFs, in order of importance for implementing KM, was generated.Research limitations/implications – The number of responses received was rather small since KM is a new and emerging discipline, and not many SMEs have formally implemented it.Practical implications...

Characterizing knowledge management in the small business environment

Abstract: Most of the literature on knowledge management (KM) and its application has, until recently, been centered on large organizations. Pertinent issues in small businesses have to a large extent been neglected. However, small businesses do not necessarily share the same characteristics and ideals as large ones. There are certain unique features of small businesses that need to be understood before KM is implemented in their environment. This paper aims to redress some of this imbalance in the literature by putting KM into the context of small businesses. It looks at their characteristics, their advantages and disadvantages, their strengths and weaknesses, and their key problems and issues, all associated with KM. Recognition of all these elements is crucial in order to provide a well‐suited KM approach for small businesses. The paper culminates with recommendations that will provide important insights to help them accomplish this.

Sustainable supplier selection and order lot-sizing: an integrated multi-objective decision-making process

Abstract: Within supply chains activities, selecting appropriate suppliers based on the sustainability criteria (economic, environmental and social) can help companies move toward sustainable development. Although several studies have recently been accomplished to incorporate sustainability criteria into supplier selection problem, much less attention has been devoted to developing a comprehensive mathematical model that allocates the optimal quantities of orders to suppliers considering lot-sizing problems. In this research, we propose an integrated approach of rule-based weighted fuzzy method, fuzzy analytical hierarchy process and multi-objective mathematical programming for sustainable supplier selection and order allocation combined with multi-period multi-product lot-sizing problem. The mathematical programming model consists of four objective functions which are minimising total cost, maximising total social score, maximising total environmental score and maximising total economic qualitative score. The prop...

Development of key performance measures for the automobile green supply chain

Abstract: The main purpose of this study was to develop a set of measures for evaluating the performance of the automobile green supply chain This study reviewed various literatures on green supply chain performance measurement, environmental management, traditional supply chain performance measurement, and automobile supply chain management In order to comprehensively and effectively establish the relevant measures, a suitable framework which considered the automobile green supply chain as a two-in-one chain was adopted This two-in-one chain comprised a forward and backward chain for the automobile industry Consequently, 10 measures with 49 metrics and 6 measures with 23 metrics were identified and developed for the forward and backward chains, respectively Sequel to the development of these measures, a survey was conducted using a four-page questionnaire distributed to experts (including academics and practitioners) to establish their importance and applicability The findings of this study suggested that the importance and applicability of all the developed measures have been substantiated For the forward chain, the most crucial measure was customer perspective while the most applicable one was traditional supply chain cost The reverse chain measures were topped by management commitment in terms of both importance and applicability This study contributed to the advancement of knowledge by pioneering the development of a set of holistic measures for evaluating the performance of the automobile green supply chain The study was wrapped up with the proposition of directions for further studies

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

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

Abstract: 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

Abstract: 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.

Competing for the Future

Abstract: Everybody knows that digital technology has revolutionized our economy and our lifestyles. But how many of us really understand the drivers behind the technology – the significance of going digital; the miniaturization of electronic devices; the role of venture capital in financing the revolution; the importance of research and development? How many of us understand what it takes to make money from innovative technologies? Should we worry about manufacturing going offshore? What is the role of India and China in the digital economy? Drawing on a lifetime’s experience in the industry, as an engineer, a senior manager, and as a partner in a global venture capital firm, Henry Kressel offers an expert personalized answer to all these questions. He explains how the technology works, why it matters, how it is financed, and what the key lessons are for public policy.