Critical Performance Measures Research Articles

Exploiting Parallelism With Random Linear Network Coding in High-Speed Ethernet Systems

Parallelism has become one of the key architectural features in 40-/100-/400-Gb Ethernet multi lane distribution (MLD) standards. The MLD packetizes and distributes traffic adaptively over parallel lanes and maps them to parallel network interfaces for wide area transmission, typically over optical networks. As such, the MLD creates not only new network topology abstractions but also enables modular implementations of various new features to improve the system performance. In this paper, we study the performance of the parallelized Ethernet in combination with erasure coding and more specifically random linear network coding (RLNC). We present a novel theoretical modeling framework, including the derivation of upper and lower bounds of differential delay and the resulting receiver queue size—a critical performance measure in the high-speed Ethernet. The results show benefits of a combined usage of parallelism and RLNC: with a proper set of design parameters, the differential delay and the receiver buffer size can be reduced significantly, while cross-layer design and path computation greatly simplified.

Optimal motion planning and stopping test for 3-D object reconstruction

In this work, two aspects of motion planning for object reconstruction are investigated. First, the effect of using a sampling-based optimal motion planning technique to move a mobile manipulator robot with 8 degrees of freedom, during the reconstruction process, in terms of several performance criteria is studied. Based on those criteria, the results of the reconstruction task using rapidly exploring random tree (RRT) approaches are compared, more specifically RRT* smart versus RRT* versus standard RRT. Second, the problem of defining a convenient stopping probabilistic test to terminate the reconstruction process is addressed. Based on our results, it is concluded that the use of a RRT* improves the measured performance criteria compared with a standard RRT. The simulation experiments show that the proposed stopping test is adequate. It stops the reconstruction process when all the portions of object that are possible to be seen have been covered with the field of view of the sensor.

A tool for time, variance and energy related performance estimations in a shuttle-based storage and retrieval system

• The mean and variance of travel time of lifts and shuttles are estimated well. • The mean energy consumption and energy regeneration per transaction are estimated. • By the provided tool, such systems’ performances can be calculated promptly. • It is the first time such an analytical-based tool for an SBS/RS is studied. • The results are highly accurate (1% APE) when compared with the simulation results. The aim of this study is to present a tool that can calculate critical performance measures of different Shuttle-Based Storage and Retrieval System (SBS/RS) designs. Specifically, our aim is to develop an analytical model-based tool that can estimate the mean and variance of travel time of lifts and shuttles, as well as the mean amount of energy consumption and energy regeneration per transaction for a pre-defined SBS/RS design. The methodology illustrated in this paper can also be utilized for any system having a straight discrete travel pattern with equal distance stop points. We are motivated in this study by a relatively new automated storage and retrieval technology, SBS/RS. By the provided tool, one can evaluate these systems’ performance promptly by changing the input design parameters (e.g., discrete travel lengths, velocity of vehicles, acceleration/deceleration of vehicles, number of tiers, number of bays, etc.) of these systems. The analytical models used in the tool are verified via simulation for several SBS/RS design scenarios. As a result, it is found that the proposed tool produces highly accurate results, specifically less than 1% Absolute Percentage Error from the simulation results.

The Impact of Performance Measurement on the Selection of Enforcement Targets by Competition Authorities: The Russian Experience in an International Context

ABSTRACTThis article provides evidence about the influence of performance measurement criteria on the choice of enforcement targets by law enforcement authorities, utilizing a rich dataset of decisions by the Russian competition authority in the period 2008–2015. The authors provide a comparative analysis of performance measurement by several competition authorities throughout the world. Then a hypothesis is tested suggesting that a competition authority, motivated by the criterion of “enforcement success,” tends to select relatively easy implemented enforcement targets, which lead to decisions with relatively low probability of being annulled if appealed. This is so, even though other enforcement targets would generate superior welfare effects. Thus, our analysis indicates that putting undue emphasis on “enforcement success” as a performance criterion may not lead to desirable welfare outcomes.

Normative accuracy and response time data for the computerized Benton Facial Recognition Test (BFRT-c).

We report normative data from a large (N = 307) sample of young adult participants tested with a computerized version of the long form of the classical Benton Facial Recognition Test (BFRT; Benton & Van Allen, 1968). The BFRT-c requires participants to match a target face photograph to either one or three of six face photographs presented simultaneously. We found that the percent accuracy on the BFRT-c (81%-83%) was below ceiling yet well above chance level, with little interindividual variance in this typical population sample, two important aspects of a sensitive clinical test. Although the split-half reliability on response accuracy was relatively low, due to the large variability in difficulty across items, the correct response times measured in this version-completed in 3 min, on average-provide a reliable and critical complementary measure of performance at individual unfamiliar-face matching. In line with previous observations from other measures, females outperformed male participants at the BFRT-c, especially for female faces. In general, performance was also lower following lighting changes than following head rotations, in line with previous studies that have emphasized participants' limited ability to match pictures of unfamiliar faces with important variations in illumination. Overall, this normative data set supports the validity of the BFRT-c as a key component of a battery of tests to identify clinical impairments in individual face recognition, such as observed in acquired prosopagnosia. However, this analysis strongly recommends that researchers consider the full test results: Beyond global indexes of performance based on accuracy rates only, they should consider the time taken to match individual faces as well as the variability in performance across items.

Examining the Relationship of an All-Cause Harm Patient Safety Measure and Critical Performance Measures at the Frontline of Care

BackgroundIn 2015, the Institute of Medicine Vital Signs report called for a new patient safety composite measure to lessen the reporting burden of patient harm. Before this report, two patient safety organizations had developed an electronic all-cause harm measurement system leveraging data from the electronic health record, which identified and grouped harms into five broad categories and consolidated them into one all-cause harm outcome measure.ObjectivesThe objective of this study was to examine the relationship between this all-cause harm patient safety measure and the following three performance measures important to overall hospital safety performance: safety culture, employee engagement, and patient experience.MethodsWe studied the relationship between all-cause harm and three performance measures on eight inpatient care units at one hospital for 7 months.ResultsThe findings demonstrated strong correlations between an all-cause harm measure and patient safety culture, employee engagement, and patient experience at the hospital unit level. Four safety culture domains showed significant negative correlations with all-cause harm at a P value of 0.05 or less. Six employee engagement domains were significantly negatively correlated with all-cause harm at a P value of 0.01 or less, and six of the ten patient experience measures were significantly correlated with all-cause harm at a P value of 0.05 or less.ConclusionsThe results show that there is a strong relationship between all-cause harm and these performance measures indicating that when there is a positive patient safety culture, a more engaged employee, and a more satisfying patient experience, there may be less all-cause harm.

Deep Learning Technology for Predicting Solar Flares from (Geostationary Operational Environmental Satellite) Data

Solar activity, particularly solar flares can have significant detrimental effects on both space-borne and grounds based systems and industries leading to subsequent impacts on our lives. As a consequence, there is much current interest in creating systems which can make accurate solar flare predictions. This paper aims to develop a novel framework to predict solar flares by making use of the Geostationary Operational Environmental Satellite (GOES) X-ray flux 1-minute time series data. This data is fed to three integrated neural networks to deliver these predictions. The first neural network (NN) is used to convert GOES X-ray flux 1-minute data to Markov Transition Field (MTF) images. The second neural network uses an unsupervised feature learning algorithm to learn the MTF image features. The third neural network uses both the learned features and the MTF images, which are then processed using a Deep Convolutional Neural Network to generate the flares predictions. To the best of our knowledge, this work is the first flare prediction system that is based entirely on the analysis of pre-flare GOES X-ray flux data. The results are evaluated using several performance measurement criteria that are presented in this paper.

Application of the Delphi technique in healthcare maintenance

PurposeThe purpose of this paper is to examine the research design, issues and considerations in the application of the Delphi technique to identify, refine and rate the critical success factors and performance measures in maintenance-associated infections.Design/methodology/approachIn-depth literature review through the application of open and axial coding were applied to formulate the interview and research questions. These were used to conduct an exploratory case study of two healthcare maintenance managers, randomly selected from two National Health Service Foundation Trusts in England. The results of exploratory case study provided the rationale for the application of the Delphi technique in this research. The different processes in the application of the Delphi technique in healthcare research are examined thoroughly.FindingsThis research demonstrates the need to apply and integrate different research methods to enhance the validity of the Delphi technique. The rationale for the application of the Delphi technique in this research is because some healthcare maintenance managers lack knowledge about basic infection control (IC) principles to make hospitals safe for patient care. The result of first round of the Delphi exercise is a useful contribution in its own rights. It identified a number of salient issues and differences in the opinions of the Delphi participants, noticeably between healthcare maintenance managers and members of the infection control team. It also resulted in useful suggestions and comments to improve the quality and presentation of the second- and third-round Delphi instruments.Practical implicationsThis research provides a research methodology that can be adopted by researchers investigating new and emerging issues in the healthcare sector. As this research demonstrates, the Delphi technique is relevant in soliciting expert knowledge and opinion to identify performance measures to control maintenance-associated infections in hospitals. The methodology provided here could be applied by other researchers elsewhere to probe, investigate and generate rich information about new and emerging healthcare research topics.Originality/valueThe authors demonstrate how different research methods can be integrated to enhance the validity of the Delphi technique. For example, the results of an exploratory case study provided the rationale for the application of the Delphi technique investigating the key performance measures in maintenance-associated infections. The different processes involved in the application of the Delphi technique are also carefully explored and discussed in depth.

Seeing is believing: A graphical reference framework for multi-criteria evaluation

This work proposes a method for assessment of evaluation processes, applicable to systems in which both measurable and policy-based performance criteria coexist. It intends to provide a mapping procedure, based solely on measurable criteria, aiming at visual inspection of the outcome, which allows the introduction of two types of indicators at different phases of the process. The definition of performance-based zones is suggested: two of them clearly correspond to high/low performance, in which the overall evaluation result is essentially established on the basis of measurable indicators, i.e. on the basis of objective criteria, and an in-between area, more prone to a less deterministic outcome. The method of evaluation is applied in three successive steps: (i) build the representation and identify the performance zones, (ii) introduce additional policy criteria to complete a first draft of the outcome, and, finally (iii) assess relative positioning, correct if necessary, and finalize. Performance maps are presented and inspected for a set of Research and Development institutions, to illustrate examples of application of the methodology proposed.

Granular Fuzzy Rule-Based Models: A Study in a Comprehensive Evaluation and Construction of Fuzzy Models

Fuzzy models are regarded as numeric constructs and as such are optimized and evaluated at the numeric level. In this study, we depart from this commonly accepted position and propose a granular evaluation of fuzzy models and present an augmentation of fuzzy models by forming information granules around numeric values of the parameters and constructions of the models. The concepts and algorithms of granular fuzzy models are discussed in the setting of Takagi–Sugeno rule-based architectures. We show how different protocols of forming and allocating information granules lead to the improvement of the granular performance of the models. Different from the standard numeric performance measure of fuzzy models coming in the form of the root mean squared error index, two performance measures are introduced that are pertinent to granular constructs, namely coverage and specificity. Furthermore, we propose a global indicator implied by these two measures, called an area under the curve, being computed for the characteristics of the granular model expressed in the coverage-specificity coordinates. A series of experimental studies is reported, which offers a comprehensive overview of the introduced performance measure criteria as well as the underlying realization of the granular fuzzy models.

Reordering Webpage Objects for Optimizing Quality-of-Experience

The quality of experience (QoE) perceived by users is a critical performance measure for Web browsing. “Above-The-Fold” (ATF) time has been recently recognized and widely used as a direct measure of user-end QoE by a number of studies. To reduce the ATF time, the existing works mainly focus on reducing the delay of networking. However, we observe that the webpage structures and content orders can also significantly affect theWeb QoE. In this paper, we propose a novel optimization framework that reorders the webpage objects to minimize the user-end ATF time. Our core idea is to first identify the webpage objects that consume the ATF time but have no impact on the page experience and then change the positions of these objects to achieve the minimum ATF time. We implement this framework and evaluate its performance with popular websites. The results show that the ATF time is greatly reduced compared with the existing works, especially for complex webpages.

Queue Length Estimation for Freeway Facilities: Based on Combination of Point Traffic Detector and Automatic Vehicle Identification Data

Queue length is a critical performance measure for assessing and managing transportation network performance. Two new methods that integrate data from point traffic detectors and automatic vehicle identification (AVI) readers to estimate the queue length of freeway segments for both off-line and real-time applications are developed in this study. One method estimates the queue length between two detectors by using linear interpolation between the travel time measurement based on AVI data when the link is fully queued and when no queue is present. In the second method, a segment with a partial queue is divided into two subsegments: the first is assumed to be similar to upstream traffic conditions and the second to downstream traffic conditions. Then, the length of each part is calculated from AVI speed data. The performance of these methods is assessed and compared in two case studies that are based on simulation data and real-world data. The results show that using a combination of point detector data and AVI data produces accurate estimates of queue length. The queue estimation method based on cumulative volumes collected with point detectors alone also produces reasonably good estimates but requires additional ramp detection and assumptions regarding moving queue density. The two combination methods produce results that are close to each other based on simulation data and real-world data. The segmentation method produces better results based on real-world data.

Developing an Appropriate Performance Measurement Framework for Total Quality Management (TQM) in Construction and Other Industries

<div><p><em>Total quality management (TQM) is a set of opinions and ideas that widely called “management philosophy”. This management technique is able to assist the construction firms in improving continuously the organization's performance, in order to satisfy customers and survive in the market. The success of TQM implementation is tightly dependent on identifying and selecting the appropriate critical success factors (CSFs), quality tools, and performance measures (KPIs) within TQM framework. Doubtlessly, a set of suitable performance measures (indicators) has a significant role to verify and ensure that TQM implementation can successfully achieve its aims in the organization. Thus, the main purpose of this paper was to develop a suitable framework to assess the effects of TQM implementation on organization's performance in construction industry. For this aim, the study was conducted a comprehensive literature review to specify the existing key performance indicators (KPIs) from 26 TQM frameworks that formulated by scholars. In data analysis, the only 20 KPIs were found of 26 TQM studies. The extracted KPIs (20) classified based on four dimensions of balanced scorecard (BSC), and then arranged from highest to lowest frequency for each perspective of BSC. Finally, a total of ten KPIs were determined and presented within BSC system as an appropriate performance measures framework, which enables evaluating critical areas that very vital to the success of TQM implementation in construction firms at project and enterprise levels.</em></p></div>

An empirical investigation of critical success factors and performance measures for green manufacturing in cement industry

Purpose The purpose of this paper is to aim for the development and analysis of green manufacturing (GM)-based framework on the identified critical success factors (CSFs) and performance measures (PMs) in the context of the Indian cement industry. Design/methodology/approach The research follows survey method for data collection. For framework development, it uses factor analysis on the identified CSFs and regression along with the appropriate measures for checking statistical consistency and validity. Findings This is the first research towards GM framework for the Indian cement industry. Till date, no framework is available which could guide researchers and practitioners of this environment unfriendly industry. Study exposes lack of connectivity between CSFs and PMs for a GM framework and highlights weaknesses of cement industry in this regard. It offers a generalised GM framework linking PMs with top management, human resource management, organisational culture, green practices, process management and supply chain management. Practical implications The framework is expected to help both researchers and practitioners from cement, construction and other industries who are serious towards GM implementation and are looking for appropriate mechanism. This framework if implemented properly will result in enhanced productivity. Originality/value This work is one of the few and pioneering efforts to investigate GM linking CSFs and PMs in Indian manufacturing sectors and the first in cement industry. Not many studies are available in the context of cement industry, which is the lifeblood of infrastructure and construction sectors. The importance of the work increases as it is conducted in the Indian context, which is undeniably an important economy of the world.

Performance measurement tool (PMT) to control maintenance-associated infections

Purpose Healthcare maintenance (HM) services have an important role in the control of hospital-acquired infections (HAIs). Notwithstanding, many HM managers still do not measure the performance of HM services in infection control (IC). This research, therefore, aims to establish the level of importance of critical success factors (CSFs) and performance measures in HM IC. A performance measurement tool (PMT) was also developed to improve the performance of the HM unit in IC. Design/methodology/approach The CSFs and performance measures identified in the literature were categorised into the four perspectives of balanced scorecard and analysed through Delphi. The Delphi participants were presented with 67 performance measures and asked to rate their level of importance in HM in IC. In total, they identified 53 important performance measures to control maintenance-associated HAIs. The results obtained from the Delphi study were used to categorise the performance measures into four means zones. The mean zones were assigned weights (1-4), and the level of importance of the CSFs was established through weighted average. Liaison and communication, IC practices and maintenance resource availability emerged as the most important CSFs in HM in IC. Conversely, customer satisfaction emerged as the least important. Information gathered about the CSFs and performance measures was used to develop a PMT in HM in IC. Findings The following CSFs, liaison and communication, IC practices and maintenance resource availability, emerged as the most important in HM in IC. Conversely, customer satisfaction emerged as the least important. Information gathered about the CSFs and performance measures was used to develop a PMT in HM in IC. Originality/value Through the application of the PMT, performance in IC can be estimated at different levels in the HM unit. The PMT allows managers to focus on the most crucial CSFs and performance measures that drive performance in HM in IC. The PMT could also be used for benchmarking purposes.

Development of objective-criteria-scenario triplets and design fires for performance-based Fire Safety Design

Detailed guidance for conducting performance-based fire engineering analyses for the built environment is lacking, in particular for how to identify and specify performance-criteria, fire scenarios and design fires. To help fill this gap, a method is proposed for establishing sets of ‘objective-criteria-scenario’ triplets and design-basis fires for different building-occupant systems, which can be used in regulation or directly by engineers. Application of the method forces the user to identify fire safety objectives for specific targets within a building, measurable performance criteria related to the targets and the objectives, detailed fire scenarios, and representative design fire curves, which are used to assess the building fire safety systems performance in meeting fire safety objectives. The method is briefly overviewed and particular emphasis is given to how historical data and experimental fire test data can be used to develop scenarios and associated design fire curves.

Environmental and economic performance assessment of desalination supply chain

The interest in desalination has been growing steadily for the recent decades due to the global increased demand for fresh water. Viewing the desalination network as a supply chain permits decision makers to evaluate the performance of desalination processes end to end. However, there are few articles considered evaluating the desalination supply chain as a whole. Thus, the purpose of this study is to develop performance measurement criteria and metrics that is designed to evaluate the efficiency of desalination supply chain concerning the economic and environmental factors. Different desalination and supply chain performance measures related to economic, and environmental perspective have been reviewed. The performance measures and metrics have been further refined by a panel of experts using Delphi technique. By adopting the analytic hierarchy process technique, a weight factor has been assigned to each perspective and metric according to its significant in desalination supply chain. It is believed that both academics and practitioners would benefit from the framework developed. Finally, it must be realized that desalination is one part of the solution. Only with a synergistic approach involving desalination, water management, consumption reduction and water reuse could efficiently solve the water demand challenge in future.

Impact of green manufacturing practices on organisational performance in Indian context: An empirical study

This study presents an empirical assessment and guides about measuring impact of GM (green manufacturing) practices on organisational performance in Indian context. The research approach is to extract critical GM factors from field data, to evaluate those factors which have positive impact on the organisational performance and to develop a model linking both critical factors and performance measures. The research follows survey method for data collection. A diagnostic research survey instrument is designed and used for data collection from the Indian manufacturing companies that have implemented/experienced GM. Authors not only developed model but also attempted its optimisation, checked its robustness through sensitivity analysis and worked out its ability to predict patterns and possible trends through artificial neural network (ANN) simulation. Further, this model is validated through a case company data to work out its intensity and efficacy as a demonstrative application in reducing environmental footprints. The major contribution of this study is in the identification, prioritisation of guiding factors, its modelling, and validation, which influence various manufacturing practices, and drastically affect organisational performance. The study offers useful insights and guides about how industries should link both aspects critical GM factors and performance measures to channelize their GM initiatives, in order to improve environmental, operational and financial performance.

The Effect of Intensive Care Unit Admission Patterns on Mortality-based Critical Care Performance Measures.

Current mortality-based critical care performance measurement focuses on intensive care unit (ICU) admissions as a single group, conflating low-severity and high-severity ICU patients for whom performance may differ and neglecting severely ill patients treated solely on hospital wards. To assess the relationship between hospital performance as measured by risk-standardized mortality for severely ill ICU patients, less severely ill ICU patients, and severely ill patients outside the ICU. Using a statewide, all-payer dataset from the Pennsylvania Healthcare Cost Containment Council, we analyzed discharge data for patients with nine clinical conditions with frequent ICU use. Using a validated severity-of-illness measure, we categorized hospitalized patients as either high severity (predicted probability of in-hospital death in top quartile) or low severity (all others). We then created three mutually exclusive groups: high-severity ICU admissions, low-severity ICU admissions, and high-severity ward patients. We used hierarchical logistic regression to generate hospital-specific 30-day risk-standardized mortality rates for each group and then compared hospital performance across groups using Spearman's rank correlation. We analyzed 87 hospitals with 22,734 low-severity ICU admissions (mean per hospital, 261 ± 187), 10,991 high-severity ICU admissions (mean per hospital, 126 ± 105), and 6,636 high-severity ward patients (mean per hospital, 76 ± 48). We found little correlation between hospital performance for high-severity ICU patients versus low-severity ICU patients (ρ = 0.15; P = 0.17). There were 29 hospitals (33%) that moved up or down at least two quartiles of performance across the ICU groups. There was weak correlation between hospital performance for high-severity ICU patients versus high-severity ward patients (ρ = 0.25; P = 0.02). There were 24 hospitals (28%) that moved up or down at least two quartiles of performance across the high-severity groups. Hospitals that perform well in caring for high-severity ICU patients do not necessarily also perform well in caring for low-severity ICU patients or high-severity ward patients, indicating that risk-standardized mortality rates for ICU admissions as a whole offer only a narrow window on a hospital's overall performance for critically ill patients.

APLIKASI WEB PENGUKURAN KINERJA LEMBAGA KEUANGAN MIKRO MENGGUNAKAN METODE FUZZY-AHP, WPM DAN BALANCED SCORECARD

Performance measurement system is one of the existing models to monitor the successful implement-tation of a predetermined objective strategy leadership of the company including Microfinance Insti-tutions (MFIs). In this case, the Balanced Scorecard is a performance measurement method considered suitable for Microfinance Institutions in IndonesiaTherefore, researchers are trying to apply the Balanced Scorecard performance measurement models in the form of performance measurement application program of Microfinance Institutions using the web -based programming languages PHP and MySQL which the Microfinance Institutions can perform measurements on himself (self assessment) so that the measurement of the performance of Micro fi-nance Institutions can be run effectively and efficiently. The test is done by doing research in Micro-finance Institutions located in Sidoarjo. The sample data is Microfinance Institutions in Sidoarjo.For performance measurement criteria based on the Balanced Scorecard method namely financial pers-pective, customer perspective, internal process perspective and learning and growth perspective. While the method of Fuzzy-Analytic Hierarchy Process (Fuzzy-AHP) and Weighted Product Model used for weighting the criteria and sub-criteria and the final assessment. Weighting is done based on the criteria and sub-criteria prioritization interest rate. The results of the analysis and design of the system will be applied to the measurement of application performance Web-based Microfinance Institutions.