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.