Total Quality Facilities Management and Innovation: A Synergistic Approach

The ideas of quality and performance management and innovation in facilities management service provision are not new. Total Quality Management (TQM) is widely recognised throughout the world as a concept capable of providing competitive advantage. Innovation has also received considerable attention as having a crucial role in securing sustainable competitive advantage. However, there has been little consideration of the potential for integration of TQM practices with innovation principles in determining facilities management performance. TQM and innovation appear to corroborate each other and are becoming increasingly important in facilities management. This study takes a theoretical approach to critically review the relationship between TQM and innovation and to determine the relationship between TQM and Innovation in regard to facilities service provision. The theoretical implication is that FM service providers may adopt a synergistic approach to TQM and innovation, leading to sustained competitive advantage in terms of better positioning themselves within the saturated FM marketplace

Preference for Plants in an Office Environment

Plants in the workplace are known to bring a number of benefits including psychological as well as aesthetic and air quality benefits. Therefore, plants can have an impact on overall organisational performance. However, findings of previous studies have rarely been applied in the FM context and yet strategic FM delivery in improving workplace productivity is essential for business survival. The paper explores the importance of interior plants in maintaining the physical and psychological well-being of office occupants utilising a survey of participants’ perceptions of photographs of an office with various levels of planting installed from no plants up to very high levels of planting. The paper provides preliminary results of a longer programme of research into the benefits of plants within the FM context. The work demonstrates that a reasonable level of interior planting in offices is preferred over offices with no plants. These perceived benefits may have a direct impact on overall organisational performance and therefore incorporating elements of nature within building design and management may in future be considered imperative to achieving the desired strategic outcomes of the organisation

Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator

When an unbiased estimator of the likelihood is used within a Metropolis--Hastings chain, it is necessary to trade off the number of Monte Carlo samples used to construct this estimator against the asymptotic variances of averages computed under this chain. Many Monte Carlo samples will typically result in Metropolis--Hastings averages with lower asymptotic variances than the corresponding Metropolis--Hastings averages using fewer samples. However, the computing time required to construct the likelihood estimator increases with the number of Monte Carlo samples. Under the assumption that the distribution of the additive noise introduced by the log-likelihood estimator is Gaussian with variance inversely proportional to the number of Monte Carlo samples and independent of the parameter value at which it is evaluated, we provide guidelines on the number of samples to select. We demonstrate our results by considering a stochastic volatility model applied to stock index returns.Comment: 34 pages, 9 figures, 3 table

Perspectives: Aligning Business Needs with Older Workers\u27 Preferences and Priorities

Perspectives: Aligning Business Needs with Older Workers’ Preferences and Priorities An Issue Brief Prepared by Marcie Pitt-Catsouphes and Michael A. Smyer for What An Aging Workforce Can Teach Us About Workplace Flexibility July 18, 2005

Likelihood based inference for diffusion driven models

This paper provides methods for carrying out likelihood based inference for diffusion driven models, for example discretely observed multivariate diffusions, continuous time stochastic volatility models and counting process models. The diffusions can potentially be non-stationary. Although our methods are sampling based, making use of Markov chain Monte Carlo methods to sample the posterior distribution of the relevant unknowns, our general strategies and details are different from previous work along these lines. The methods we develop are simple to implement and simulation efficient. Importantly, unlike previous methods, the performance of our technique is not worsened, in fact it improves, as the degree of latent augmentation is increased to reduce the bias of the Euler approximation. In addition, our method is not subject to a degeneracy that afflicts previous techniques when the degree of latent augmentation is increased. We also discuss issues of model choice, model checking and filtering. The techniques and ideas are applied to both simulated and real data.Bayes estimation, Brownian bridge, Non-linear diffusion, Euler approximation, Markov chain Monte Carlo, Metropolis-Hastings algorithm, Missing data, Simulation, Stochastic differential equation.

Deformation restraint and the mechanics of soil behavior

Statistical analysis of data from the literature indicates that variability of natural soil deposits often is a predominant factor contributing to inaccuracy of geotechnical predictions. A review of 46 case histories showed that settlement predictions for shallow foundations by consolidation theory resulted in errors ranging from 100 percent underpredictions to 30 percent overpredictions. Prediction error was also found to be a random process described by the Gaussian probability distribution function. With variability reconciled, consolidation theory was found to underpredict settlements by 22 percent. Although geotechnical design methodologies which include soil variability have been proposed, obstacles to practical application appear to be the absence of well defined probabilistic design goals and the inability of obtaining sufficient data to define variability with existing tests;In this research, probabilistic design targets for ultimate bearing capacity of shallow foundations were developed from criteria forming the basis of the current American Concrete Institute building code. Deformation restraint testing which involves subjecting cylindrical specimens to variable confining stress dictated by the amount of radial deformation was investigated as a potential method for rapid, single-specimen evaluation of limit strength parameters. The Iowa K-Test is a convenient form of deformation restraint testing where the degree of radial restraint or the restraint function is arbitrarily established by the character of the apparatus. An apparatus which allows imposition of an infinite variety of restraint functions was developed, and tests on loess suggests the existence of a unique, restraint function which allows development of a stress path corresponding to a multispecimen, constant-confining-stress strength envelope. This unique function was also found to be consistent with a theoretical stress-strain relation developed from Drucker\u27s hypothesis;The degree of radial restraint was also found to have significant influence on axial stress-strain response of the soil. A fivefold difference was observed for restraints imposed by previous K-Test apparatus. A methodology in which the restraint function is defined from elastic theory is proposed, and experimentation illustrates that such systematic definition results in deformation response which is in close agreement with that realized from Lambe\u27s stress path method