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

House of Quality

The effectiveness of a sales promotion can be examined by decomposing the sales “bump” during the promotion period into sales increase due to brand switching, purchase time acceleration, and stockp...

Impact of Sales Promotions on when, what, and how Much to Buy:

Quality function deployment: A literature review

Big data analytics in logistics and supply chain management: Certain investigations for research and applications

Artificial neural networks can be seen as nonlinear generalizations of conventional Statistical or econometric models. This paper studies goodness-of-fit and forecasting Performance of neural networks with one hidden layer. The central dependent variables regarded are purchase incidence and brand choice within the context of consumer non-durables. The connectionist models are compared to the well-known NBD and conditional logit models. Models are estimated on the basis of household panel data. On the whole, the neural models achieve better results than their conventional counterparts. Influence of predictors is interpreted using a distribution of elasticities or change rates.

Konnexionistische Kaufakt- und Markenwahlmodelle

A key success factor for a highway freight transport platform is the alignment with the requirements of the local market and its stakeholders. This research firstly analyzes the stakeholders' characteristics and relations in the Chinese highway freight transport market. Based on the different information requirements found and the current market situation, a framework for a Chinese highway freight transport platform that supports the integration of the hithero scattered transport resources is developed. Then, the implementation of the framework is described via a case study. The main idea of this research is that the management model is as important as advanced technology, and that future developments like green transport should be taken into consideration to support sustainable development.

A highway freight transport platform for the Chinese freight market — Requirements analysis and case study

This work surveys decision support systems that use sales and shopping basket data to suggestregular and promotional price changes in order to improve profit and turnover in retailing (so-called retail revenue management [RRM] systems). Starting with a demonstration of the impactof pricing on profit, we critically assess the pricing methods currently used in retailing and fromthis basis show the potential for improvement using retail revenue management systems. Fur-thermore, we classify the different types of pricing problems and describe the methods that areused in retail revenue management. Finally, we discuss the pricing process of retail revenuemanagement systems and report the benefits of such systems typically observed in practice. Keywords Price Optimization, Dynamic Pricing, Revenue Management, Decision Support System Andreas MildInstitute of Production Management, Vienna University of Economics and Business, Vienna, Austria.Martin NatterMarketing Department, University of Frankfurt, Frankfurt am Main, Germany.Thomas ReuttererInstitute of Retailing and Marketing, Vienna University of Economics and Business, Vienna, Austria.Alfred Taudes

Retail Revenue Management

The capability to bring products to market which comply with quality, cost and development time goals is vital to the survival of firms in a competitve environment. New product development comprises knowledge creation and search and can be organized in different ways. In this paper, we study the performance of several alternative organizational models for new product development using a model of distributed, self-adapting (learning) agents. The agents (a marketing and a production agent) are modelled via neural networks. The artificial new product development process analyzed starts with learning on the basis of an initial set of production and marketing data about possible products and their evaluation. Subsequently, in each step of the process, the agents search for a better product with their current models of the environment and, then, refine their representations based on additional prototypes generated (new learning data). Within this framework, we investigate the influence of different types of new product search methods and generating prototypes/learning according to the performance of individual agents and the organization as a whole. In particular, sequential, team-based Trial & Error and House of Quality guided search are combined with prototype sampling methods of different intensity and breadth; also, the complexity of the agents (number of hidden units) is varied. It turns out that both the knowledge base and the search procedure have a significant impact on the agents' generalization ability and success in new product development.

An agent-based investigation into the new product development capability

The knowledge required for decision making in a firm is distributed across various departments. In practice, cross-functional teams are used to integrate this distributed knowledge. Incentive schemes are of crucial importance to encourage departments to share knowledge. The authors study different incentive schemes by means of a two stage model. In the first step departments have to choose between learning and sharing knowledge, in the second stage, they bargain about a new product feature. The outcome of the bargaining process in the second stage depends on the capabilities of the agents and their uncertainty about the opponent. The result of the second stage determines the agents' payoffs which in turn influence the time allocation. In a simulation study, we investigate different incentive systems and show to which extent a firm has to reward the sharing of knowledge in order to reach its overall objectives. Furthermore, we are able to derive an analytical solution for the bargaining process under uncertainty and compute Nash equilibria for a discrete set of possible actions.

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Alfred Taudes

Papers

Konnexionistische Kaufakt- und Markenwahlmodelle

A highway freight transport platform for the Chinese freight market — Requirements analysis and case study

Retail Revenue Management

An agent-based investigation into the new product development capability

Incentives to share knowledge