According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

Social Network Analysis

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

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

What Video Games Have to Teach Us about Learning and Literacy

“Industrie 4.0” and Smart Manufacturing - A Review of Research Issues and Application Examples

The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.

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Machine learning in manufacturing: advantages, challenges, and applications

The primary function of any enterprise is to sell its products and/or skills profitably. This aim, compounded by the economic necessity, is often achieved by collaborating with other enterprises. Therefore, manufacturing systems can no longer be seen in isolation; they must be seen in the context of the total business and the associated key linkages of the business: back through the supplier chain and forward into the distribution and customer chain. In reality these chains often take the forms of enterprise networks. There can be almost infinite variations to the range and scope of collaborations within an enterprise network. This paper discusses some recent developments in the nature of bilateral enterprise collaborations and the resulting emergence of new manufacturing paradigms. It attempts to identify the key types of collaborations (supply chain, extended enterprise and virtual enterprise) and analyses their essential attributes and operational characteristics.

Anatomy of enterprise collaborations

Increasing market demand towards higher product and process quality and efficiency forces companies to think of new and innovative ways to optimize their production. In the area of high-tech manufacturing products, even slight variations of the product state during production can lead to costly and time-consuming rework or even scrapage. Describing an individual product's state along the entire manufacturing programme, including all relevant information involved for utilization, e.g., in-process adjustments of process parameters, can be one way to meet the quality requirements and stay competitive. Ideally, the gathered information can be directly analyzed and in case of an identified critical trend or event, adequate action, such as an alarm, can be triggered. Traditional methods based on modelling of cause-effect relations reaches its limits due to the fast increasing complexity and high-dimensionality of modern manufacturing programmes. There is a need for new approaches that are able to cope with this complexity and high-dimensionality which, at the same time, are able to generate applicable results with reasonable effort. Within this paper, the possibility to generate such a system by applying a combination of Cluster Analysis and Supervised Machine Learning on product state data along the manufacturing programme will be presented. After elaborating on the different key aspects of the approach, the applicability on the identified problem in industrial environment will be discussed briefly.

An approach to monitoring quality in manufacturing using supervised machine learning on product state data

To compete in the competitive global market place, manufacturers and suppliers have to come up with novel ways to sell their wares. The observed recent trend is that they need to package their core products with additional services to make their products more attractive. This comprehensive packaging, which we term as Extended Products, is driven not only by the competitive pressures but also by others, such as the legislative pressures to encompass the growing environmental concerns. This paper discusses the drivers to the development of Extended products and why it is necessary for manufacturers and suppliers to form formal alliances in order to reliably supply a comprehensive set of Extended Products. A very first general model of the Extended Product is also developed. The "product" itself has been integrated in many conceptual models and strategies within production engineering. Mostly these models or views, however, are very strongly oriented towards engineering processes. Business aspects have been omitted because the market was partly seen as some type of 'black box' where unknown customers buy the products. This view has considerably altered due to the fact that advanced logistics and transportation systems have brought different national and international markets closer together. Consequently, competition has been increasing substantially, resulting in more dynamic markets. As a result new enterprise strategies based on novel concepts of the products to be delivered need to be developed that can embrace these challenges. Today technical strategies such as e-business, e-commerce, m-commerce or financial strategies such as shareholder-value dominate the discussion of CEO's work and put an enormous pressure on enterprises to be agile, flexible and innovative. The enterprises are being confronted with a more global and more dynamic markets, which creates more competition among the different enterprises. The desktop PC market can be taken as an example of these developments because the sales of new desktop computers went down substantially in the first quarter of 2001 and only innovative companies such as Dell computers which offer a very broad service package to the customer were not hit by this crisis (Wirtschaftswoche 2001, S. 100). The distinguishing features against the competitors were the services offered. Such product extensions can be considered as an opportunity to differentiate one's products from that of competitors' in order to enhance competitiveness. We differentiate these extensions based on the tangible aspects of a product and the intangible product extensions based on integrated services offered with the products. The objective is to offer

Klaus-Dieter Thoben

Papers

“Industrie 4.0” and Smart Manufacturing - A Review of Research Issues and Application Examples

Machine learning in manufacturing: advantages, challenges, and applications

Anatomy of enterprise collaborations

An approach to monitoring quality in manufacturing using supervised machine learning on product state data

Extended Products: Evolving Traditional Product Concepts