Vincent C. Liu and Brian H. Kleiner
Looks at the success of total quality management (TQM) and the principles of completeness. Considers the quality requirements and the performance standards as essential factors…
Abstract
Looks at the success of total quality management (TQM) and the principles of completeness. Considers the quality requirements and the performance standards as essential factors within a total quality plan and states that the adaptation of the theory to the particular industry is key to success. Looks at TQM in the medical and legal profession and suggests that the practice of implementation can be helped or hindered by outside influences such as the state of the business and the culture it possesses.
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Kamran Ahmed, A. John Goodwin and Kim R. Sawyer
This study examines the value relevance of recognised and disclosed revaluations of land and buildings for a large sample of Australian firms from 1993 through 1997. In contrast…
Abstract
This study examines the value relevance of recognised and disclosed revaluations of land and buildings for a large sample of Australian firms from 1993 through 1997. In contrast to prior research, we control for risk and cyclical effects and find no difference between recognised and disclosed revaluations, using yearly‐cross‐sectional and pooled regressions and using both market and non‐market dependent variables. We also find only weak evidence that revaluations of recognised and disclosed land and buildings are value relevant.
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Natalie M. Scala, Thais da Costa Lago Alves, Dominique Hawkins, Vincent Schiavone and Min Liu
This paper introduces the weighting, analysis and validation method used in the development of the Maturity Model for Collaborative Scheduling (MMCS). The scoring and ranking…
Abstract
Purpose
This paper introduces the weighting, analysis and validation method used in the development of the Maturity Model for Collaborative Scheduling (MMCS). The scoring and ranking process introduced by the MMCS fills a gap in the literature by supporting the selection of collaborative scheduling (CS) practices that yield more weight toward the achievement of higher maturity levels in the development and implementation of CS. The ranking process can then be used during pre/post project execution to track collaborative scheduling in practice against the model’s weighting and provide the project team with constructive feedback and actionable steps for reaching the next highest level of collaboration.
Design/methodology/approach
The MMCS, which focuses on five pillars (key areas of interest for CS) and related swim lanes (specific attributes), covers a broad range of areas in the construction industry and was coded into a survey. The relative weights of pillars and swim lanes were then established using the Delphi method with the group of subject matter experts (SMEs), analyzed using multi-objective decision analysis (MODA) and validated using 241 answers to a survey with questions drawn from the MMCS, including organizations across the industry in the United States.
Findings
The project scoring defines bounds for bronze, silver and gold levels of collaboration in scheduling. Project evaluations can then be used to identify areas for continuous improvement and enhanced collaboration. We offer recommendations and best practices for project improvement.
Originality/value
Two original contributions resulted from this work: (1) a method to elicit weights based on a combination of Delphi, MODA and survey methods was used to develop and validate a scale with three different maturity levels to support the use and continuous improvement of CS practices and (2) a validated model was used to assess the maturity level of CS in construction projects alongside specific recommendations to move upward in terms of maturity. In practice, project leaders can use this model to assess project performance, advance the project’s maturity and guide continuous improvement efforts for enhanced collaboration.
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Natalie M. Scala, Min Liu, Thais da Costa Lago Alves, Vincent Schiavone and Dominique Hawkins
The overall contribution of this work is to provide a usable maturity model for collaborative scheduling (CS) that extends the literature, identifies inconsistencies in schedule…
Abstract
Purpose
The overall contribution of this work is to provide a usable maturity model for collaborative scheduling (CS) that extends the literature, identifies inconsistencies in schedule development, and improves collaboration in the construction industry.
Design/methodology/approach
Via subject matter expert elicitation and focus groups, the maturity model establishes five pillars of collaboration—scheduling significance, planners and schedulers, scheduling representation, goal alignment with owner, and communication. The maturity model is then validated through iterative feedback and chi-squared statistical analysis of data obtained from a survey. The five pillars are tied to the literature and previous work in CS.
Findings
The analysis shows that current industry projects are not consistent in collaboration practice implementation, and the maturity model identifies areas for collaboration improvement. The study's contributions to the body of knowledge are (1) developing a maturity model-based approach to define and measure the current level of collaboration and (2) discovering the level of consistency in scheduling collaboration practice implementation.
Practical implications
The findings provide a benchmark for self-evaluation and peer-to-peer comparison for project managers. The model is also useful for project managers to develop effective strategies for improvement on targeted dimensions and metrics.
Originality/value
The construction engineering and management (CEM) literature does not contain targeted models for scheduling collaboration in the context of maturity and, broadly speaking, neither does the literature at large. The literature also lacks actionable items as presented for the maturity model for collaborative scheduling (MMCS).
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Anthony Scanlan, Daniel O’Hare, Mark Halton, Vincent O’Brien, Brendan Mullane and Eric Thompson
The purpose of this paper is to present analysis of the feedback predictive encoder-based analog-to-digital converter (ADC).
Abstract
Purpose
The purpose of this paper is to present analysis of the feedback predictive encoder-based analog-to-digital converter (ADC).
Design/methodology/approach
The use of feedback predictive encoder-based ADCs presents an alternative to the traditional two-stage pipeline ADC by replacing the input estimate producing first stage of the pipeline with a predictive loop that also produces an estimate of the input signal.
Findings
The overload condition for feedback predictive encoder ADCs is dependent on input signal amplitude and frequency, system gain and filter order. The limitation on the practical usable filter order is set by limit cycle oscillation. A boundary condition is defined for determination of maximum usable filter order. In a practical implementation of the predictive encoder ADC, the time allocated to the key functions of the gain stage and loop quantizer leads to optimization of the power consumption.
Practical implications
A practical switched capacitor implementation of the predictive encoder-based ADC is proposed. The power consumption of key circuit blocks is investigated.
Originality/value
This paper presents a methodology to optimize the bandwidth of predictive encoder ADCs. The overload and stability conditions may be used to determine the maximum input signal bandwidth for a given loop quantizer. Optimization of power consumption based on the allocation of time between the gain stage and the successive approximation register ADC operation is investigated. The lower bound of power consumption for this architecture is estimated.