Performance measurement and continuous improvement : are they linked to manufacturing strategy?

To maintain or achieve competitiveness and profitability, a manufacturing firm or enterprise must respond to a range of challenges, including rapid improvements in technology; declining employment and output; globalisation of markets and environmental requirements. In addition, substantial changes in government policy have had important impacts in many countries, as have the increasing levels of global trade. Manufacturing enterprises need to have a clear understanding of what their customers want and why customers purchase their products rather than purchase from their competitors. They need to fully understand the aims of the business in terms of its customers, market segments, product attributes, geographical markets and performance. Continuous Improvement (CI) methods have become widely adopted and regarded as providing an important component of increased company competitiveness. This article examines the extent to which continuous improvement activities have contributed to the different areas of business performance.

Learning barriers in continuous product innovation

In today's dynamic and turbulent environment companies are required to increase their effectiveness and efficiency, exploit synergy and learn product innovation processes in order to build competitive advantage. To be able to stimulate and facilitate learning in product innovation, it is necessary to gain an insight into factors that hinder learning and to design effective intervention strategies that may help remove barriers to learning. This article reports on learning barriers identified by product innovation managers in over 70 companies in the UK, Ireland, Italy, Netherlands, Sweden and Australia. The results show that the majority of the barriers identified can be labelled as organisational defensive routines leading to a chain of behaviours; lack of resources leads to under-appreciation of the value of valid information, absence of informed choice and lack of personal responsibility. An intervention theory is required which enables individuals and organisations to interrupt defensive patterns in ways that prevents them from recurring.

Continuous infusion of ticarcillin-clavulanate for home treatment of serious infections : clinical efficacy, safety, pharmacokinetics and pharmacodynamics

Continuous infusion (CI) ticarcillin–clavulanate is a potential therapeutic improvement over conventional intermittent dosing because the major pharmacodynamic (PD) predictor of efficacy of β-lactams is the time that free drug levels exceed the MIC. This study incorporated a 6-year retrospective arm evaluating efficacy and safety of CI ticarcillin–clavulanate in the home treatment of serious infections and a prospective arm additionally evaluating pharmacokinetics (PK) and PD. In the prospective arm, steady-state serum ticarcillin and clavulanate levels and MIC testing of significant pathogens were performed. One hundred and twelve patients (median age, 56 years) were treated with a CI dose of 9.3–12.4 g/day and mean CI duration of 18.0 days. Infections treated included osteomyelitis (50 patients), septic arthritis (6), cellulitis (17), pulmonary infections (12), febrile neutropenia (7), vascular infections (7), intra-abdominal infections (2), and Gram-negative endocarditis (2); 91/112 (81%) of patients were cured, 14 (13%) had partial response and 7 (6%) failed therapy. Nine patients had PICC line complications and five patients had drug adverse events. Eighteen patients had prospective PK/PD assessment although only four patients had sufficient data for a full PK/PD evaluation (both serum steady-state drug levels and ticarcillin and clavulanate MICs from a bacteriological isolate), as this was difficult to obtain in home-based patients, particularly as serum clavulanate levels were found to deteriorate rapidly on storage. Three of four patients with matched PK/PD assessment had free drug levels exceeding the MIC of the pathogen. Home CI of ticarcillin–clavulanate is a safe, effective, convenient and practical therapy and is a therapeutic advance over traditional intermittent dosing when used in the home setting.

Modelling sustainable and optimal solutions for building services integration in early architectural design : confronting the software and professional interoperability deficit

Decisions made in the earliest stage of architectural design have the greatest impact on the construction, lifecycle cost and environmental footprint of buildings. Yet the building services, one of the largest contributors to cost, complexity, and environmental impact, are rarely considered as an influence on the design at this crucial stage. In order for efficient and environmentally sensitive built environment outcomes to be achieved, a closer collaboration between architects and services engineers is required at the outset of projects. However, in practice, there are a variety of obstacles impeding this transition towards an integrated design approach. This paper firstly presents a critical review of the existing barriers to multidisciplinary design. It then examines current examples of best practice in the building industry to highlight the collaborative strategies being employed and their benefits to the design process. Finally, it discusses a case study project to identify directions for further research.

Improving automatic speaker verification using SVM techniques

Automatic recognition of people is an active field of research with important forensic and security applications. In these applications, it is not always possible for the subject to be in close proximity to the system. Voice represents a human behavioural trait which can be used to recognise people in such situations. Automatic Speaker Verification (ASV) is the process of verifying a persons identity through the analysis of their speech and enables recognition of a subject at a distance over a telephone channel { wired or wireless. A significant amount of research has focussed on the application of Gaussian mixture model (GMM) techniques to speaker verification systems providing state-of-the-art performance. GMM's are a type of generative classifier trained to model the probability distribution of the features used to represent a speaker. Recently introduced to the field of ASV research is the support vector machine (SVM). An SVM is a discriminative classifier requiring examples from both positive and negative classes to train a speaker model. The SVM is based on margin maximisation whereby a hyperplane attempts to separate classes in a high dimensional space. SVMs applied to the task of speaker verification have shown high potential, particularly when used to complement current GMM-based techniques in hybrid systems. This work aims to improve the performance of ASV systems using novel and innovative SVM-based techniques. Research was divided into three main themes: session variability compensation for SVMs; unsupervised model adaptation; and impostor dataset selection. The first theme investigated the differences between the GMM and SVM domains for the modelling of session variability | an aspect crucial for robust speaker verification. Techniques developed to improve the robustness of GMMbased classification were shown to bring about similar benefits to discriminative SVM classification through their integration in the hybrid GMM mean supervector SVM classifier. Further, the domains for the modelling of session variation were contrasted to find a number of common factors, however, the SVM-domain consistently provided marginally better session variation compensation. Minimal complementary information was found between the techniques due to the similarities in how they achieved their objectives. The second theme saw the proposal of a novel model for the purpose of session variation compensation in ASV systems. Continuous progressive model adaptation attempts to improve speaker models by retraining them after exploiting all encountered test utterances during normal use of the system. The introduction of the weight-based factor analysis model provided significant performance improvements of over 60% in an unsupervised scenario. SVM-based classification was then integrated into the progressive system providing further benefits in performance over the GMM counterpart. Analysis demonstrated that SVMs also hold several beneficial characteristics to the task of unsupervised model adaptation prompting further research in the area. In pursuing the final theme, an innovative background dataset selection technique was developed. This technique selects the most appropriate subset of examples from a large and diverse set of candidate impostor observations for use as the SVM background by exploiting the SVM training process. This selection was performed on a per-observation basis so as to overcome the shortcoming of the traditional heuristic-based approach to dataset selection. Results demonstrate the approach to provide performance improvements over both the use of the complete candidate dataset and the best heuristically-selected dataset whilst being only a fraction of the size. The refined dataset was also shown to generalise well to unseen corpora and be highly applicable to the selection of impostor cohorts required in alternate techniques for speaker verification.

Experience mapping : producing spatially continuous environment representations using RatSLAM

RatSLAM is a system for vision-based Simultaneous Localisation and Mapping (SLAM) inspired by models of the rodent hippocampus. The system can produce stable representations of large complex environments during robot experiments in both indoor and outdoor environments. These representations are both topological and metric in nature, and can involve multiple representations of the same place as well as discontinuities. In this paper we describe a new technique known as experience mapping that can be used online with the RatSLAM system to produce world representations known as experience maps. These maps group together multiple place representations and are spatially continuous. A number of experiments have been conducted in simulation and a real world office environment. These experiments demonstrate the high degree to which experience maps are representative of the spatial arrangement of the environment.

Distance Education and the Integration of E-learning in a Graduate Program

Historically, distance education consisted of a combination of face-to-face blocks of time and surface mailed packages. However, advances in information technology literacy and the abundance of personal computers has placed e-learning in increased demand. The authors describe the planning, implementation, and evaluation of the blending of e-learning with face-to-face education in the postgraduate nursing forum. Experiences of this particular student group are also discussed.

Integration of a road traffic noise model (ASJ) and traffic simulation (AVENUE) for a built-up area

A road traffic noise prediction model (ASJ MODEL-1998) has been integrated with a road traffic simulator (AVENUE) to produce the Dynamic areawide Road traffic NoisE simulator-DRONE. This traffic-noise-GIS based integrated tool is upgraded to predict noise levels in built-up areas. The integration of traffic simulation with a noise model provides dynamic access to traffic flow characteristics and hence automated and detailed predictions of traffic noise. The prediction is not only on the spatial scale but also on temporal scale. The linkage with GIS gives a visual representation to noise pollution in the form of dynamic areawide traffic noise contour maps. The application of DRONE on a real world built-up area is also presented.

A practical implementation of a continuous isotropic spherical omnidirectional drive

This paper presents a continuous isotropic spherical omnidirectional drive mechanism that is efficient in its mechanical simplicity and use of volume. Spherical omnidirectional mechanisms allow isotropic motion, although many are limited from achieving true isotropic motion by practical mechanical design considerations. The mechanism presented in this paper uses a single motor to drive a point on the great circle of the sphere parallel to the ground plane, and does not require a gearbox. Three mechanisms located 120 degrees apart provide a stable drive platform for a mobile robot. Results show the omnidirectional ability of the robot and demonstrate the performance of the spherical mechanism compared to a popular commercial omnidirectional wheel over edges of varying heights and gaps of varying widths.

Continuous biometric authentication : can it be more practical?

Continuous biometric authentication schemes (CBAS) are built around the biometrics supplied by user behavioural characteristics and continuously check the identity of the user throughout the session. The current literature for CBAS primarily focuses on the accuracy of the system in order to reduce false alarms. However, these attempts do not consider various issues that might affect practicality in real world applications and continuous authentication scenarios. One of the main issues is that the presented CBAS are based on several samples of training data either of both intruder and valid users or only the valid users' profile. This means that historical profiles for either the legitimate users or possible attackers should be available or collected before prediction time. However, in some cases it is impractical to gain the biometric data of the user in advance (before detection time). Another issue is the variability of the behaviour of the user between the registered profile obtained during enrollment, and the profile from the testing phase. The aim of this paper is to identify the limitations in current CBAS in order to make them more practical for real world applications. Also, the paper discusses a new application for CBAS not requiring any training data either from intruders or from valid users.