Adrie J. M. Beulens

Bio: Adrie J. M. Beulens is an academic researcher from Wageningen University and Research Centre. The author has contributed to research in topics: Digital learning & Supply chain. The author has an hindex of 12, co-authored 38 publications receiving 639 citations.

Supply Chain Design in the Food Industry

Abstract: The concepts of hybrid supply chain strategies and the decoupling point are applied to a poultry supply chain experiencing high demand uncertainty in an inflexible production environment. Several solutions are proposed for this supply chain to cope with high demand uncertainty. The customer order decoupling point, the product differentiation point and the information decoupling point play a central role in these solutions. Because of specific characteristics of the poultry supply chain, the opportunities for a leagile supply chain design are limited.

Simulating Knowledge Sharing in Spatial Planning: An Agent-Based Approach

Abstract: This paper presents a multiagent system (MAS) that simulates a multiactor interactive spatial-planning process. The MAS extends an existing approach with the principle of sharing knowl- edge between participating actors while trying to create a shared vision. In the simulation, actors are modelled as agents. They have desires and preferences regarding the future development of their environment. These are used to develop their individual views on what areas are eligible for change. A facilitator agent coordinates the exchange of information by indicating possible solutions and conflicts to the actor agents. The simulation is demonstrated for an allocation problem in a pilot area in the southeast of the Netherlands. Four different scenarios are implemented, which demon- strate the impact of cooperation and hierarchy during an interactive spatial-planning process. Although the model is kept limited in terms of input data, the results show its potential for providing insight into the relations and interaction between actors, rather than predicting the results of an interactive spatial-planning process.

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.

Teaching for quality learning at university

Abstract: by J. Biggs and C. Tang, Maidenhead, England, Open University Press, 2007, 360 pp., £29.99, ISBN-13: 978-0-335-22126-4

Corporate Social Responsibility in the Supply Chain: An Application in the Food Industry

Abstract: The food industry faces many significant risks from public criticism of corporate social responsibility (CSR) issues in the supply chain. This paper draws upon previous research and emerging industry trends to develop a comprehensive framework of supply chain CSR in the industry. The framework details unique CSR applications in the food supply chain including animal welfare, biotechnology, environment, fair trade, health and safety, and labor and human rights. General supply chain CSR issues such as community and procurement are also considered. Ultimately, the framework serves as a comprehensive tool to support food industry practitioners and researchers in the assessment of strategic and operational supply chain CSR practices.