David J. Edwards
Other affiliations: Loughborough University, University of Birmingham, Edith Cowan University ...read more
Bio: David J. Edwards is an academic researcher from Birmingham City University. The author has contributed to research in topics: Building information modeling & Project management. The author has an hindex of 43, co-authored 329 publications receiving 6982 citations. Previous affiliations of David J. Edwards include Loughborough University & University of Birmingham.
Papers published on a yearly basis
TL;DR: There is a paucity of literature that examines building information modelling (BIM) for asset management within the architecture, engineering, construction and owner-operated (AECO) sector.
TL;DR: It was revealed that risk management and sustainable construction were the most popular AHP application areas in CM and that AHP is flexible and can be used as a stand-alone tool or in conjunction with other tools to resolve construction decision-making problems.
Abstract: The analytic hierarchy process (AHP) has gained increasing attention in construction management (CM) domain as a technique to analyse complex situations and make sound decisions. However, AHP per s...
TL;DR: In this paper, a seamless project supply chain management (SCM) model is proposed that integrates the design and production processes of construction projects, and the proposed model is subjected to validation by a sample of industry practitioners and their comments are presented and reflected upon.
Abstract: Presents a holistic approach to construction project SCM. Specifically, a seamless project supply chain management (SCM) model is proposed that integrates the design and production processes of construction projects. The proposed model was subjected to validation by a sample of industry practitioners and their comments are presented and reflected upon. Direction for future research is provided and such work aims to: explore the legalities of implementing the model; develop an appropriate benchmarking framework; explore the use of quality function development; and probity issues when selecting project team members.
TL;DR: The first comprehensive scientometric study appraising the state of research on AI-in-the-AECI is presented, indicating that genetic algorithms, neural networks, fuzzy logic, fuzzy sets, and machine learning have been the most widely used AI methods in AEC.
TL;DR: In this article, the authors examined the influence of a risk/reward model on the behavior of project participants, and concluded that risk sharing is pivotal to obtaining a successful project outcome for the procurement of civil engineering infrastructure projects when using an alliance.
Abstract: A risk/reward model is described as that which aligns project participants' behaviors toward the achievement of a project's performance objectives through the use of incentives. A risk/reward model typically includes the following mechanisms: risk/reward shared percentages among nonowner participants, project cost risk/reward, noncost risk/reward, risk cap, and achievability of performance targets. This paper examines the influence of a risk/reward model on the behavior of project participants. Twenty-nine industry practitioners from eight civil infrastructure project alliances were interviewed. The interviews revealed that individual features of a risk/reward model identified had merits, but the achievability of performance targets model appeared to be the most appropriate for promoting positive behaviors within the project team. Additionally, it was found that all incentive aspects of the model examined led to positive and constructive behaviors occurring due to their perceived fairness and equity of payment structure. Participants indicated that having a commercial interest in an alliance's performance outcomes ensured collaboration and engagement throughout the project's life cycle. It is concluded that risk/reward sharing is pivotal to obtaining a successful project outcome for the procurement of civil engineering infrastructure projects when using an alliance.
01 Jan 2016
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01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
TL;DR: Deming's theory of management based on the 14 Points for Management is described in Out of the Crisis, originally published in 1982 as mentioned in this paper, where he explains the principles of management transformation and how to apply them.
Abstract: 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.
TL;DR: In this article, a professional services was launched having a hope to serve as a total on the internet electronic catalogue that gives usage of many PDF file guide assortment, including trending books, solution key, assessment test questions and answer, guideline sample, exercise guideline, test test, customer guide, user guide, assistance instruction, repair guidebook, etc.
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01 Jan 2008
TL;DR: Nonaka and Takeuchi as discussed by the authors argue 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.
Abstract: 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.