Performance Measurement Methods Research Articles

Precision improvement method for onsite performance measurement of variable refrigerant flow system

With the widespread use of variable refrigerant flow (VRF) systems in various commercial and residential buildings, the performance and efficiency of VRF systems have attracted considerable attention in recent years. Although the compressor set energy conservation (CSEC) method exhibits a high accuracy in most cases of VRF performance measurement, several problems limit its accuracy. To improve the accuracy of the CSEC method for performance measurement, two methods are proposed to resolve the measurement difficulty of two-phase suction. The accuracies of the proposed methods are experimentally validated using three VRF configurations. In the experiments, the relative errors for the compressor isentropic efficiency-based method range from ±15%, ±17%, and ±18% for the three configurations. For the compressor volumetric efficiency-based method, the relative errors of the three configurations range from ±15%, ±16%, and ±16%, respectively. The capacity distribution and heat loss of the VRF system are measured and analyzed in the experiment. Heat losses from closed indoor units range from 0% to 21.5% in different cases in the cooling mode and from 0% to 31.1% in the heating mode. Additionally, an estimation method for the temperature measurement deviation is proposed to improve the precision of performance measurement.

Performance analysis of milk processing units to support competitive improvement of the milk processing industry in Padang Panjang City, West Sumatera Province

Dairy cattle productivity has plateaued; average milk production ranges between 8 and 12 liters per day, on a scale of 2-3 maintenance per family breeder breeding. Furthermore, small farms account for 90% of milk production. Permata Ibu’s Milk Processing Unit (UPS) in Padang Panjang is one of the business units that processes milk into other derivative products. It produces processed milk pasteurization, yogurt, and other products. The focus of this research was to analyze UPS Permata Ibu’s processing performance by calculating the amount of added value and profitability. As measured by the value-added method of performance measurement, the processing of fresh milk into pasteurized milk and yogurt at Permata Ibu milk processing unit generates a variety of added values. Pasteurized milk has better processing performance than yogurt processing, as evidenced by its high added value compared to other processed products, with added value ratios of 22,45 percent and 12,50 percent for pasteurized milk and yogurt, respectively. Based on profitability analysis, it is known that pasteurized milk is still better than yogurt because it has a higher profitability value of 30,30% compared to yogurt, which is only 6,50%. Pasteurized milk processing results in higher profitability and added value than yogurt.

Using environmental indicators in performance evaluation of sustainable development health goals

Good health is essential for sustainable development; the other sustainable development goals cannot be achieved with prevalent high rates of debilitating infectious and non-infectious illnesses and without physical, mental and social wellbeing in the population (UN General Assembly, 2012). The third goal of the 2030 Sustainable Development Agenda addresses health and sustainable development by linking health to indicators related to environmental factors, such as air and water pollution. In this research, we use the two most prominent methods for the SDG's performance measurement at the country level: the simple mean Bertelsmann Index – BI (Lafortune et al., 2018; Sachs et al., 2018) and the OECD's Distance Measure Index – DMI (OECD, 2017) by using the air pollution, poisoning, poor sanitation, and unsafe water indicators of the third SDG goal for 107 United Nations countries for the period between 1990 and 2017. The above countries are divided into four income categories, corresponding to the 2020–2021 World Bank income classification: Low Income, Lower–Middle Income, Upper–Middle Income and High Income. Finally, looking at the performance indicators in combination with the country income categories, we conclude a positive relationship between BI and income and a negative relationship between DMI and income.

Assessing Multidimensional Approaches to Measure Program Outcomes in Human Service Organizations.

This study explores scholars’ approaches to measure performance in nonprofit human service organizations. While acknowledging that each human service organization’s unique mission makes it challenging to create a generalizable model across all nonprofit human service organizations, we propose three multidimensional frameworks for performance measurement derived from survey and qualitative data of organizations in this subsector. The frameworks will help researchers and practitioners rethink, adapt to, and reflect on the implications of their current methods of program performance measurement. While contributing to the academic discussion on the measurements used to evaluate human service organizations’ program performance, our research also offers important insights for researchers, managers, marketers, board members, and funders to use moving forward.

Hospital-specific Template Matching for Benchmarking Performance in a Diverse Multihospital System.

Hospital-specific template matching is a newer method of hospital performance measurement that may be fairer than regression-based benchmarking. However, it has been tested in only limited research settings. The objective of this study was to test the feasibility of hospital-specific template matching assessments in the Veterans Affairs (VA) health care system and determine power to detect greater-than-expected 30-day mortality. Observational cohort study with hospital-specific template matching assessment. For each VA hospital, the 30-day mortality of a representative subset of hospitalizations was compared with the pooled mortality from matched hospitalizations at a set of comparison VA hospitals treating sufficiently similar patients. The simulation was used to determine power to detect greater-than-expected mortality. A total of 556,266 hospitalizations at 122 VA hospitals in 2017. A number of comparison hospitals identified per hospital; 30-day mortality. Each hospital had a median of 38 comparison hospitals (interquartile range: 33, 44) identified, and 116 (95.1%) had at least 20 comparison hospitals. In total, 8 hospitals (6.6%) had a significantly lower 30-day mortality than their benchmark, 5 hospitals (4.1%) had a significantly higher 30-day mortality, and the remaining 109 hospitals (89.3%) were similar to their benchmark. Power to detect a standardized mortality ratio of 2.0 ranged from 72.5% to 79.4% for a hospital with the fewest (6) versus most (64) comparison hospitals. Hospital-specific template matching may be feasible for assessing hospital performance in the diverse VA health care system, but further refinements are needed to optimize the approach before operational use. Our findings are likely applicable to other large and diverse multihospital systems.

A Novel Method for Performance Measurement of Public Educational Institutions Using Machine Learning Models

Lack of education is a major concern in underdeveloped countries because it leads to poor human and economic development. The level of education in public institutions varies across all regions around the globe. Current disparities in access to education worldwide are mostly due to systemic regional differences and the distribution of resources. Previous research focused on evaluating students’ academic performance, but less has been done to measure the performance of educational institutions. Key performance indicators for the evaluation of institutional performance differ from student performance indicators. There is a dire need to evaluate educational institutions’ performance based on their disparities and academic results on a large scale. This study proposes a model to measure institutional performance based on key performance indicators through data mining techniques. Various feature selection methods were used to extract the key performance indicators. Several machine learning models, namely, J48 decision tree, support vector machines, random forest, rotation forest, and artificial neural networks were employed to build an efficient model. The results of the study were based on different factors, i.e., the number of schools in a specific region, teachers, school locations, enrolment, and availability of necessary facilities that contribute to school performance. It was also observed that urban regions performed well compared to rural regions due to the improved availability of educational facilities and resources. The results showed that artificial neural networks outperformed other models and achieved an accuracy of 82.9% when the relief-F based feature selection method was used. This study will help support efforts in governance for performance monitoring, policy formulation, target-setting, evaluation, and reform to address the issues and challenges in education worldwide.

Gaussian Process Regression Based Robust Optimization with Observer Uncertainty for Reconfigurable Self-x Sensory Electronics for Industry 4.0

Abstract This paper presents a robust optimization technique for the reconfigurable measurement of sensory electronics for industry 4.0 to obtain a robust solution even in the presence of observer uncertainty using a cost-effective performance measurement method. The extrinsic evaluation of the proposed methodology is performed on an indirect current-feedback instrumentation amplifier (CFIA), which is a fundamental part of sensory systems. To reduce the CFIA device performance evaluation set-up cost, a low-cost test stimulus is applied to the circuit under test, and the output response of the circuit is examined to correlate with the device’s performance parameters. Due to the complexity of the smart sensory electronics search space, the meta-heuristic optimization algorithm is being selected as an optimizer. For objective space or observer uncertainty, the Gaussian process regression from the Bayesian statistical regression process is used to estimate the uncertainty level efficiently. Six different classical metrics have been used to evaluate the regression model accuracy. The highest achieved average expected error metrics value is 0.313, and the minimum value of correlation performance metrics is 0.908. The device is implemented using 0.35 μm austriamicrosystems technology.

Quantitative Analysis of EEG Slow Wave Activity Based on MinPeakProminence Method

Persistent, unchanging, and non-reactive focal or generalized abnormal Slow Wave (SW) activities in an awake adult patient are examined pathologically. Although these waves in Electroencephalogram (EEG) are much less prominent than transient activities in some areas, it is not possible to understand them easily by looking at the EEG. For this reason, reliable computer programs that can sort out Slow Waves (SWs) correctly are needed. In this study, a new method based on MinPeakProminence that can detect abnormal SW activities was developed. To test the performance of the study, the data collected from Selcuk University Hospital (22 subjects - epilepsy and various neurological diseases) and Bonn Hospital (only normal A dataset) were used. Various statistical performance measurement methods were used to search the results. The results of this analysis revealed that the classification success, sensitivity and specificity values obtained with the SUH dataset were 96.5%, 93.3% and 96.1%, respectively. In the results of the experiments made with the Bonn dataset, 100% classification success was achieved. Besides, according to the analyses, it was found that SWs are frequently seen in the posterior regions of the brain, especially in the parietal and occipital regions in the SUH dataset.

Performance Measurement Methods in Business Entities

This study aims to present the performance of an entity. This is tracked using indicators that show the efficiency and effectiveness of the company’s activity. Several ways of measuring performance are presented. In economic entities, in order to measure performance, it is necessary to build a performance model, collect data, analyse and interpret them, extract and communicate information. The indicators that are used by management to reflect the results of the business are performance indicators and steering indicators. Performance indicators are systematised in a dashboard that forms the basis of the analysis of the company’s activity.

Comparative assessment of PEE methods and new performance measurement for RDH

The paper mainly analyzes four typical reversible data hiding (RDH) algorithms that use PEE technology in combination: Conventional Prediction-error-expansion(C-PEE), Adaptive Prediction-error-expansion(A-PEE), Pairwise Prediction-error-expansion (P-PEE), Improve Pairwise Prediction-error-expansion(IP-PEE). And the experimental comparison results are also given. In addition, in the research process, we found that the peak signal-to-noise ratio (PSNR) and embedding capacity (EC) of many algorithms are very close, due to lack of benchmark tools for technical evaluation in the RDH field, thus this paper proposes a new performance measurement method based on the PSNR and EC measurement, we call it the prediction error accuracy rate, Determined by the ratio of the capacity that meets the embedding conditions to the total capacity of the image, combined with the measurement of this method, can better determine whether the performance of the algorithm is optimal. Finally, in this area, challenge and future directions are also given.

Review of construction safety performance measurement methods and practices: a science mapping approach

The construction industry is struggling with poor safety performance. Also, the existing construction safety performance (CSP) measurement methods are no longer effective by reaching their point of diminishing returns, which ultimately points to the need for a novel and reliable method for CSP measurement. To do so, a systematic and comprehensive review of existing methods is a prerequisite. Therefore, this study aims to conduct a comprehensive review of CSP research using a science mapping approach. The CSP research literature was retrieved from the Scopus literature database and analyzed using VOSviewer and NVIVO tools, to visualize the domain-specific body of knowledge. The investigation highlighted the most productive and influential contributors, highly focused research levels, project types and phases, research instruments and data sources, CSP measurement methods, key safety practices, current research trends, future research directions, theoretical and practical implications. This study has observed the CSP research paradigm shift from reactive to proactive approach. To this end, 11 safety dimensions that comprise 109 safety practices were identified, and a proactive CSP management approach was proposed.

Development of Key Performance Indicators in Ammunition Demilitarization Facility Using the Balanced Score Card

Ammunition Demilitarization facility (ADF) should be set up the feasible goals and continue to operate, taking into account non-profit characteristics. However, due to the lack of performance measurement methods in ADF, which are essential to national policy at a significant cost each year, the reliability of the evaluation results can be insufficient. In this paper, the Balanced Score Card (BSC) method was applied that could be evaluated to reflect the financial and non-financial features. The relevant literature research and army regulations reflected the results of various interviews of the expert group. The extraction of success performance area in ADF was confirmed using the BSC method and the Decision Variable (DV) candidate was created to use regression for selecting the DV. Additionally, the key performance indicator was presented by verification the feasibility of content by conducting the survey of experts. The implications of this paper are as follows. First, the proposed BSC model was found to be suitable for practical use in ADF reflecting the non-profit characteristics. Second, accurate evaluation of ADF can contribute to long-term development of ADF. Finally, it can be applied to the management process of the other military sector, so it can be expected to play a role in providing basic data and spreading it to other areas.

In Situ Evaluation of the U-Value of a Window Using the Infrared Method

The amount of heat lost through the envelope of a building is one of the most important variables that affects the energy performance evaluation of a house. In addition, it is especially important to estimate and accurately diagnose the amount of heat produced by windows. In Korea, windows’ U-values reflect a building’s initial design values and thermal characteristics that determine the thermal performance of an existing building, and is a factor that can overestimate the energy performance of a building. Therefore, there is a need for a field measurement method that can accurately measure the total U-value of windows in an existing house. This study provides a method of quantitatively measuring the total U-value of windows using the infrared (IR) method in ISO 9869-2. As a result of measuring the U-value using the infrared (IR) method, the Korean Standard (KS F 2278) for window performance test result values and the root mean square error (cvRMSE) for the U-value measurements using the IR method showed a high accuracy of about 3.29%. In addition, we confirmed that the IR method is an effective (cvRMSE about 7% improvement) method that can measure the comparison result faster than the heat flow meter (HFM) method, which is a conventional thermal performance measurement method.

Borsa İstanbul da İşlem Gören Perakende Ticaret Şirketleri Hisse Senetlerinin Performansının Treynor Oranı İle Ölçümü

Equity investment in developing countries is an important alternative to generating returns above risk-free returns. Investors who take risks by investing in stocks, expect returns above risk-free returns. Accordingly, the preference of the equity to be invested is extremely important. The Treynor performance measurement method, which takes into consideration the stock risk, risk-free return and average return of the stock, is an important method that can be used in this field. In our study, the performance of stocks of retail trade companies traded on Borsa Istanbul was measured according to Treynor ratios calculated over monthly returns of the past 5 years. The results of the measurements were compared with the results of September and October 2019. Only four stocks have positive Treynor ratios, yield higher than risk-free returns, others have negative Treynor ratios, yield less risk-free returns. Study shows that, stocks which have positive Treynor ratio generated higher return in September and October 2019 higher than other stocks.

Comparative Analysis of Factors Affecting Employee Performance According to Job Performance Measurement Method: The Case of Performing Artists

Comparative Analysis of Factors Affecting Employee Performance According to Job Performance Measurement Method: The Case of Performing Artists

Systematics Review on the Application of Social Media Analytics for Detecting Radical and Extremist Group

Recently, social media platforms such as Twitter, Tumblr, Facebook, YouTube, blogs and discussion forums are being mistreated by radical groups to promote their ideologies and encourage radicalization. Social medias also have been used to create online extremist community and recruit new followers. In this paper work, the authors conduct a schematics literatures review on all available techniques and perform a comprehensive analysis on the application of social media analytic for detecting radical group to understand the circumstances, trends and its gaps. Further, the author provides the characterization, classification and meta-analysis in order to achieve a better understanding of the literature on the extremist detection through intelligent social media. It is found that for over the last 10 years many researchers have been conduct deep investigation on the use of social media analytics on predicting and identifying online radicalization. Besides, data source, features, geolocation, language, machine learning techniques, and tools have been applied on the recent literatures to detect those cyber-extremist activists. This paper also highlighting the performance measurement methods that have been used by researcher for detecting extremist group and radical communities. The goal of this research is to provide an academic base for ongoing research in the developing machine learning algorithm for detecting extremist and radical contents in social medias.

აგენტიფიკაცია საქართველოს საგანმანათლებლო სექტორში

Agencification has been a recent trend on the agenda of public administration reforms in a number of countries. The concept of Agencification implies the rearrangement of the relation between the central government and the agencies as a result of delegation of tasks. Agencification process has been mostly driven by international organizations such as the OECD, IMF and the World Bank with the aim to introduce a “business-like” efficiency-oriented model in public sector in order to improve the performance and to raise the accountability. The following study explores the Agencification processes in the education sector of Georgia. Six agencies operating under the aegis of Ministry of Education and Science of Georgia have been examined. The empirical study shows that the relationship between the Ministry and the studied agencies goes beyond the classical model of principal-agent relationship and is based more on collaboration and cooperation principles. Shortcomings regarding the legal framework, old-fashioned performance measurement and performance management methods, constant reforms and structural changes put challenge to Agencification process. Furthermore, in the context of Georgia, there is no evidence that external actors – international organizations had an impact on the Agencification process in this very specific policy field. It was more an internal decision.

TS-Perf: General Performance Measurement of Trusted Execution Environment and Rich Execution Environment on Intel SGX, Arm TrustZone, and RISC-V Keystone

A trusted execution environment (TEE) is a new hardware security feature that is isolated from a normal OS (i.e., rich execution environment (REE)). The TEE enables us to run a critical process, but the behavior is invisible from the normal OS, which makes it difficult to debug and tune the performance. In addition, the hardware/software architectures of TEE are different on CPUs. For example, Intel SGX allows user-mode only, although Arm TrustZone and RISC-V Keystone run a trusted OS. In addition, each TEE has each SDK for programming. Each SDK offers own APIs and makes difficult to write a common program. These features make it difficult to compare the performance fairly between TEE and REE on different CPUs. To obtain precise performance and behavior in TEE, we propose TS-perf which is a compiler-based performance measurement method. TS-perf accesses the hardware timestamp counter in TEE as well as REE and keeps a precise log. The codes for measurement are inserted in a TEE binary by the compiler options (i.e., profile option, constructor, and destructor). Furthermore, we utilize the separate compilation technique, and the same benchmark binary is used for a fair comparison between TEE and REE. The architecture of TS-perf is general and implemented for three TEE architectures (Arm TrustZone, Intel SGX, and RISC-V Keystone). TS-perf measures the performance of GlobalPlatform’s TEE internal APIs, matrix multiplication, memory access, and storage access. The comparisons show the difference in performance between TEE and REE and the unusual behavior of trusted applications (TAs).

Evaluation the performance of stock funds using multi-criteria decision-making methods

Purpose- The aim of this study is to apply MCDM methods in addition to the traditional methods which are commonly utilized in evaluating the efficiency of stock funds. In this direction, starting from the fact that the normal distribution of stock returns, which is one of the assumptions of Modern Portfolio Theory, is mostly violated in practice, higher order moments are included in the objective functions of MCDM methods in addition to the first and second moments. Methodology-. In this study, in addition to the traditional performance measurement methods such as Sharpe Ratio, Information Ratio, Treynor Ratio and Jensen's Alpha; Adjusted Sharpe Ratio and TOPSIS, MOORA from MCDM methods are utilized by including high-order moments in addition to the first and second moments in the objective functions. In the next step, the ranking process was carried out with respect to traditional and recommended performance evaluation methods. Then, the rankings were evaluated using Spearman Rank Correlation Test. Findings- As a result of the analyses using the stock funds obtained from the TEFAS Turkey Electronic Fund Trading Platform for the period of 2018, 2019 and the first 9 months of 2020, ranking process is carried out using each methods’ and these rankings are compared using the Spearman Rank Correlation Test. According to the results of the Jarque-Bera test, it is seen that the majority of the stock funds included in the sample do not show normal distribution. This situation strengthens our hypothesis and emphasizes the necessity of including higher moments in the analysis. According to Spearman’s Rank Correlation Test results, strong relationships are found among the rankings obtained from traditional performance measurement methods. Moreover, it has been observed that the strength of the relationship between non-traditional methods and traditional methods is very weak. In other words, while the stock funds proposed by a traditional method were supported by other traditional methods, by making suggestions in a different direction TOPSIS and MOORA offered an alternative perspective considering the high-order moments. Conclusion- In this study, it is shown that the higher order moments and alternative ranking methods can be used in evaluating the performance of stock funds. In the view of both performance measurement and the portfolio selection window, these methods are considered to be useful in the analysis of stock funds. Accordingly, this study is intended to provide a perspective for equity fund managers, fund investors, and researchers to evaluate the effectiveness of stock funds.

A dynamic performance measurement system for supply chain management

PurposeThis paper aims to propose a dynamic model for measurement supply chain performance (SCP) based on a dynamic balanced scorecard (DBSC). Balanced scorecard (BSC) can be defined as a popular performance measurement method that can translate the strategy into a set of performance indicators and manage the status of implementing the various strategies. However, BSC is unable to simulate the complicated environment and the dynamic behavior of performance metrics. Therefore, the author combines BSC with system dynamics (SD) to explore a more efficient tool for measurement SCP.Design/methodology/approachA dynamic causal model is proposed based on the causal hypotheses. The developed DBSC enables managers to evaluate and measure the SCP in a much-balanced way. Using DBSC makes it possible that different SCP metrics to be reviewed and distributed into the four above-mentioned perspectives. It also enables supply chain (SC) managers to evaluate different strategies to improve SCP.Findingsinvestigates two strategies to improve SCP as follows: (1) competitive strategy and (2) harvesting big data and using data mining techniques to determine the customer's expectations and then compares the results of these two strategies based on the four perspectives of DBSC and introduce the best strategy. Finally, harvesting big data and data mining is selected as the best strategy.Originality/valueThis study proposes a novel strategic management tool for measurement SCP and simulation of the complicated environment and the dynamic behavior of performance metrics. The proposed DBSC model enables managers to compare different strategies and select the best strategy.