Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Component Reliability Estimation From Partially Masked and Censored System Life Data Under Competing Risks.

This research presents new approaches to the estimation of component reliability distribution parameters from partially masked and/or censored system life data. Such data are common in continuous production environments. The methods were tested on Monte Carlo simulated data and compared to the only alternative suggested in literature. This alternative did not converge on many masked datasets. The new methods produce accurate parameter estimates, particularly at low masking levels. They show little bias. One method ignores masked data and treats them as censored observations. It works well if at least 2 known-cause failures of each component type have been observed and is particularly useful for analysis of any size datasets with a small fraction of masked observations. It provides quick and accurate estimates. A second method performs well when the number of masked observations is small but forms a significant portion of the dataset and/or when the assumption of independent masking does not hold. The third method provides accurate estimates when the dataset is small but contains a large fraction of masked observations and when independent masking is assumed. The latter two methods provide an indication which component most likely caused each masked system failure, albeit at the price of much computation time. The methods were implemented in user-friendly software that can be used to apply the method on simulated or real-life data. An application of the methods to real-life industrial data is presented. This research shows that masked system life data can be used effectively to estimate component life distribution parameters in a situation where such data form a large portion of the dataset and few known failures exist. It also demonstrates that a small fraction of masked data in a dataset can safely be treated as censored observations without much effect on the accuracy of the resulting estimates. These results are important as masked system life data are becoming more prevalent in industrial production environments. The research results are gauged to be useful in continuous manufacturing environments, e.g. in the petrochemical industry. They will also likely interest the electronics and automotive industry where masked observations are common

Innovation report : a methodology for estimating gear pump wear-out reliability using pump pressure ripple and an extremely small sample size - the case study of a heavy-duty diesel engine lubrication gear pump

Design for Reliability (DfR) encourages testing products early in the New Product Development (NPD) process to identify and resolve weaknesses quickly. An organisation can then track reliability growth and intervene to ensure the changes in product robustness are in line with a timely release to market. However, for products with long life spans (such as a Heavy-Duty engine (HDE) lubrication gear pump), the evaluation of reliability with an extremely small number of prototype samples is problematic. Budget constraints, product size, and test facilities can limit the possibilities of accurately assessing the initial reliability forming a test planning paradox. The research in this thesis proposes an innovate methodology to minimise this test planning paradox, specific to a gear pump. The method uses step-stress accelerated degradation testing and Bayesian inference to estimate degradation parameters using only a sample size of two. Post-testing, numerical simulation is used to build a degradation model with larger sample sizes and produce a survival distribution at the quantile of interest. Increasing pump outlet pressure above normal usage accelerates the pump wear and pressure ripple measurements are used to monitor the performance degradation. On inspection, the pumps exhibit erosion on the housing and micro pitting of the gear flanks. The innovative use of a Maximal Overlap Discrete Wavelet Transforms (MODWT) with an Autoregressive Moving Average (ARMA 2,1) extracts a feature from the pressure ripple that provides a stochastic, linear and non-monotone degradation path that is appropriately modelled using a Brownian Motion simulation model. Regression analysis provides a drift and diffusion covariate functional relationship to pump outlet pressure. Given the stress-varying environment of an HDE, Monte Carlo simulations overcome the complexity of replicating vehicle drive cycle and produces a credible reliability estimate validated against a similarly designed high mileage pump. The application of this original methodology offers the opportunity to minimise the test planning paradox and satisfies populating the reliability growth chart. It is foreseen the method can be adopted for a wide range of positive displacement pumps where is it possible to measure pressure ripple

Reliability Abstracts and Technical Reviews January-December 1967

No abstract availabl