Frontier experiences from industry-academia consortia

Technology roadmapping has been applied successfully in many industrial organizations. Designed to facilitate and communicate technology strategy and planning, roadmaps (or, as in Europe, route maps) can take a variety of specific forms, depending on the type (opportunities, capabilities, products, technologies, etc.) and particular company context. While roadmaps are generally manifest in a number of "program elements or levels" superimposed upon a timeline, experienced mappers often claim that it is "roadmapping" rather than "the roadmap" that generates the value. This special report focuses primarily on product and technology roadmaps. Following an introduction to the evolution, purpose and applications of corporate/industry roadmapping, four industry-developed articles examine roadmapping in Lucent Technologies, Rockwell Automation, the pharmaceutical/biotechnology industry, and UK-based Domino Printing Sciences.

Managing Information Risks in asset management - Experiences from an in-depth case study in the utility industry

Information is one of the most important resources in our globalized economy. The value of information often exceeds the value of physical assets. Information quality has, in many ways, an impact on asset management organisations and asset managers struggle to understand and to quantify it, which is a prerequisite for effective information quality improvement. Over the past few years, we have developed an innovative management concept that addresses these new asset management challenges: a process for Total Information Risk Management (TIRM), which has been already tested in a number of asset management industries. The TIRM process enables to manage information quality more effectively in asset management organisations as it focuses specifically on the risks that are imposed by information quality. In this paper, we show how we have applied the TIRM process in an in-depth study at a medium-sized European utility provider, the Manx Electricity Authority (MEA), at the Isle of Man.

Identification of the steering control behaviour of five test subjects following a randomly curving path in a driving simulator

The paper is concerned with the identification of theoretical preview steering controllers using data obtained from five test subjects in a fixed-base driving simulator. An understanding of human steering control behaviour is relevant to the design of autonomous and semi-autonomous vehicle controls. The driving task involved steering a linear vehicle along a randomly curving path. The theoretical steering controllers identified from the data were based on optimal linear preview control. A direct-identification method was used, and the steering controllers were identified so that the predicted steering angle matched as closely as possible the measured steering angle of the test subjects. It was found that identification of the driver's time delay and noise is necessary to avoid bias in identification of the controller parameters. Most subjects' steering behaviour was predicted well by a theoretical controller based on the lateral/yaw dynamics of the vehicle. There was some evidence that an inexperienced driver's steering action was better represented by a controller based on a simpler model of the vehicle dynamics, perhaps reflecting incomplete learning by the driver. Copyright © 2014 Inderscience Enterprises Ltd.