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.

Product intelligence in warehouse management: A case study

The need for more flexible, adaptable and customer-oriented warehouse operations has been increasingly identified as an important issue by today's warehouse companies due to the rapidly changing preferences of the customers that use their services. Motivated by manufacturing and other logistics operations, in this paper we argue on the potential application of product intelligence in warehouse operations as an approach that can help warehouse companies address these issues. We discuss the opportunities of such an approach using a real example of a third-party-logistics warehouse company and we present the benefits it can bring in their warehouse management systems. © 2013 Springer-Verlag.

Classifying data quality problems in asset management

© Springer International Publishing Switzerland 2015. Making sound asset management decisions, such as whether to replace or maintain an ageing underground water pipe, are critical to ensure that organisations maximise the performance of their assets. These decisions are only as good as the data that supports them, and hence many asset management organisations are in desperate need to improve the quality of their data. This chapter reviews the key academic research on data quality (DQ) and Information Quality (IQ) (used interchangeably in this chapter) in asset management, combines this with the current DQ problems faced by asset management organisations in various business sectors, and presents a classification of the most important DQ problems that need to be tackled by asset management organisations. In this research, eleven semi structured interviews were carried out with asset management professionals in a range of business sectors in the UK. The problems described in the academic literature were cross checked against the problems found in industry. In order to support asset management professionals in solving these problems, we categorised them into seven different DQ dimensions, used in the academic literature, so that it is clear how these problems fit within the standard frameworks for assessing and improving data quality. Asset management professionals can therefore now use these frameworks to underpin their DQ improvement initiatives while focussing on the most critical DQ problems.

How to avoid the problems of target-setting

Purpose: Advocates and critics of target-setting in the workplace seem unable to reach beyond their own well-entrenched battle lines. While the advocates of goal-directed behaviour point to what they see as demonstrable advantages, the critics of target-setting highlight equally demonstrable disadvantages. Indeed, the academic literature on this topic is currently mired in controversy, with neither side seemingly capable of envisaging a better way forward. This paper seeks to break the current deadlock and move thinking forward in this important aspect of performance measurement and management by outlining a new, more fruitful approach, based on both theory and practical experience. Design/methodology/approach: The topic was approached in three phases: assembling and reading key academic and other literature on the subject of target-setting and goal-directed behaviour, with a view to understanding, in depth, the arguments advanced by the advocates and critics of target-setting; comparing these published arguments with one's own experiential findings, in order to bring the essence of disagreement into much sharper focus; and then bringing to bear the academic and practical experience to identify the essential elements of a new, more fruitful approach offering all the benefits of goal-directed behaviour with none of the typical disadvantages of target-setting. Findings: The research led to three key findings: the advocates of goal-directed behaviour and critics of target-setting each make valid points, as seen from their own current perspectives; the likelihood of these two communities, left to themselves, ever reaching a new synthesis, seems vanishingly small (with leading thinkers in the goal-directed behaviour community already acknowledging this); and, between the three authors, it was discovered that their unusual combination of academic study and practical experience enabled them to see things differently. Hence, they would like to share their new thinking more widely. Research limitations/implications: The authors fully accept that their paper is informed by extensive practical experience and, as yet, there have been no opportunities to test their findings, conclusions and recommendations through rigorous academic research. However, they hope that the paper will move thinking forward in this arena, thereby informing future academic research. Practical implications: The authors hope that the practical implications of the paper will be significant, as it outlines a novel way for organisations to capture the benefits of goal-directed behaviour with none of the disadvantages typically associated with target-setting. Social implications: Given that increased efficiency and effectiveness in the management of organisations would be good for society, the authors think the paper has interesting social implications. Originality/value: Leading thinkers in the field of goal-directed behaviour, such as Locke and Latham, and leading critics of target-setting, such as Ordóñez et al. continue to argue with one another - much like, at the turn of the nineteenth century, proponents of the "wave theory of light" and proponents of the "particle theory of light" were similarly at loggerheads. Just as this furious scientific debate was ultimately resolved by Taylor's experiment, showing that light could behave both as a particle and wave at the same time, the authors believe that the paper demonstrates that goal-directed behaviour and target-setting can successfully co-exist. © Emerald Group Publishing Limited.

Content Based Integration of Construction Site Images in Architecture, Engineering, Construction, and Facilities Management (AEC/FM) Model based Systems

This book explores the processes for retrieval, classification, and integration of construction images in AEC/FM model based systems. The author describes a combination of techniques from the areas of image and video processing, computer vision, information retrieval, statistics and content-based image and video retrieval that have been integrated into a novel method for the retrieval of related construction site image data from components of a project model. This method has been tested on available construction site images from a variety of sources like past and current building construction and transportation projects and is able to automatically classify, store, integrate and retrieve image data files in inter-organizational systems so as to allow their usage in project management related tasks. objects. Therefore, automated methods for the integration of construction images are important for construction information management. During this research, processes for retrieval, classification, and integration of construction images in AEC/FM model based systems have been explored. Specifically, a combination of techniques from the areas of image and video processing, computer vision, information retrieval, statistics and content-based image and video retrieval have been deployed in order to develop a methodology for the retrieval of related construction site image data from components of a project model. This method has been tested on available construction site images from a variety of sources like past and current building construction and transportation projects and is able to automatically classify, store, integrate and retrieve image data files in inter-organizational systems so as to allow their usage in project management related tasks.