Common Performance Indicators Research Articles

The Multi-Skilled Resource-Constrained Project Scheduling Problem: A Systematic Review and an Exploration of Future Landscapes

Abstract The Multi-skilled Resource Constrained Project Scheduling Problem (MS-RCPSP) is a complex and multi-faceted problem that involves scheduling activities whilst considering various resource constraints. These constraints include limited availability of workers, equipment, and materials, with each activity requiring a minimal set of skills to be executed. Furthermore, for a better resemblance to reality, workers/machines are assumed to be multi-skilled/multi-purpose posing another dimension of complexity to the problem. The objective is to minimize project duration, cost, or other relevant criteria while accounting for the inherent resources flexibility. This paper provides a systematic review of the literature pertaining to MS-RCPSP, and an in-depth analysis of 171 papers published between 2000 and 2021 inclusive. The conducted bibliometric analysis identifies the top contributing authors, most influential papers, existing research tendencies, and thematic research topics within the field. In addition, this review highlights different aspects of the MS-RCPSP, spanning the significance of performance measures, solution approaches, application areas, and the incorporation of time constraints. While project completion time, cost, and tardiness are common performance indicators, other measures such as multi-skilled staff assignment and schedule robustness are also deemed important. Although various methods have been employed to solve the MS-RCPSP including exact and approximate approaches, the selection of the most-suited approach depends on the problem’s scale, complexity, and constraints, necessitating careful consideration of each method’s strengths and weaknesses. Interestingly, several studies have jointly addressed resource and time constraints in the context of MS-RCPSP, often considering tardiness, and have proposed different algorithms, models, and metaheuristics to tackle these challenges. This paper clearly highlights research gaps and promising avenues for future research. This work provides valuable insights for project managers to effectively schedule tasks in the presence of multiple flexible resources.

Modeling global solar radiation using ambient temperature

Accurate information about solar radiation is regarded as the most important stage in determining the availability of solar energy. As well, it is considered the principal input for various applications of solar energy. Because solar radiation measurements are unavailable at many locations throughout the world, many solar radiation models have been developed to predict global solar radiation. In this regard, this study objects to develop accurate and quick Global Solar Radiation (GSR) models for new locations, which currently lack an accurate model. Additionally, assess the performance of a recently introduced model, one of the best temperature-based models for GSR estimation, in these five new sites. Moreover, the proposed model's generalization capacity is investigated over the whole zone, the Suez Canal Zone, and a comparative analysis of its performance is presented. Models’ estimation is compared to the observed values, and the most common performance indicators are obtained to assess models’ performance. The findings indicate that the developed models in this study can predict global solar radiation accurately. Where the Models’ performance, both the local and general models, are larger than 95 % at all sites except for the local one at Port Said City (coastal site), it is 91 %. Additionally, the developed models have good RMSE and MABE values which range from 0.8 to 1.8 (MJ/m2 day−1) and 0.7 to 1.7 (MJ/m2 day−1), successively. Besides, they have excellent performance with coefficient of determination (R2) values ranging from 0.95 to 0.98, whereas prior research's ranges are 0.884–0.895 for R2 and 2.69–3.367 (MJ/m2 day−1) for RMSE. Therefore, the developed models in this study can be utilized for accurate GSR forecast, with their high applicability which may be achieved by combining them with various long- or short-term weather forecasting approaches, that are mostly used to reliably anticipate weather temperature. As well, these models' precise and speedy computation of GSR can also be employed in the design and performance evaluation of various solar applications.

The Impact of Web Analytics Tools on the Performance of Small and Medium Enterprises

In the era of data analytics, the use of web analytics tools is widely spread among Small and Medium Enterprises (SMEs), especially in the e-commerce sector, to analyze web usage data and measure website performance due to the several features it provides. Most web analytics studies were conducted based on marketing rather than a managerial point of view. Therefore, there is a necessity to study the impact from a managerial perspective by exploring the impact on financial and non-financial performance and the decision-making process. This study assessed the impact of web analytics tools on the overall performance of SMEs, targeting Saudi SMEs and considering the lack of web analytics studies in this sector in Saudi Arabia. The sample involved eight Saudi SMEs who participated by answering interview questions, which were analyzed using thematic analysis. The findings demonstrated four main themes: the current web analytics tools used by Saudi SMEs, the motivations behind their use, and their impact on SMEs' performance and decision-making. The results showed that these tools positively affect the overall performance of SMEs. Google Analytics (GA) was the tool most used by the sample. Seven motives were discovered that encourage SMEs to use web analytics tools. This study also addressed the common Key Performance Indicators (KPIs) used by the participants. Concerning decision-making, it was found that web analytics tools affected and supported SMEs' decisions. Furthermore, this study evaluated the role of Zid, the Saudi e-commerce platform for data analytics and performance measurement.

대학 일반재정지원사업의 성과기반 재정배분에 대한 국제비교 연구

The purpose of this study is to find ways to improve the performance-based financial allocation of general financial support programs for universities in Korea. To this end, an international comparative research method was used to obtain implications based on the cases of the United States and Finland, which introduced performance-based systems in university financial allocation before Korea. The implications for policy improvement based on the results of international comparative analysis are as follows. First, the performance-based financial allocation system for universities is operated based on absolute evaluation based on quantitative indicators, but it is necessary to apply a method of adding monitoring-based qualitative evaluation only for subgroups that do not meet the standard value. Second, it is necessary to add indicators related to graduation or employment as 'output' indicators as performance indicators for teaching, a core function of universities, but supplement equity indicators to reflect the achievements of universities' practical efforts. Third, it is necessary to review the application method of differentiated common performance indicators by type considering the characteristics of universities. Specifically, two approaches were proposed: ‘applying differential weights’ and ‘applying differentiated performance indicators’. Fourth, a more rational improvement plan for 'policy indicators' directly related to higher education policy goals was proposed. Fifth, when selecting the autonomous performance indicators, it is necessary to present quantitative indicators from which universities can select within a certain range rather than individual qualitative indicators. Lastly, consulting and evaluation of autonomous performance indicators should be set at a certain periodic cycle.

Exploring the completeness of comprehensive geriatric assessment in relation to care outcomes in hospitalized older patients

Exploring the completeness of comprehensive geriatric assessment in relation to care outcomes in hospitalized older patients

Multivariate analysis and the establishment of new common financial performance indicators in publicly traded companies

This article aims to analyze the interdependence of asset management indicators of publicly traded companies from different sectors, through the use of Factor Analysis, one of the aspects of Multivariate Analysis. As a result, the total of 2 underlying factors in relation to the 7 economic and financial indicators selected for the study, namely: general liquidity, dry liquidity, immediate liquidity, current liquidity, asset turnover ratio, stock. The factors were called Financial Liquidity and Profitability, according to the concepts of Production Engineering. The KMO test showed a value of 0.739, classifying the level of appropriation of the analysis as adequate for model validation. Bartlett's test obtained a value of 398,192 (significance level of 1%) and the total explained variance obtained an explanation degree of 83.54%.

Seamless 5G Multi-Hop Connectivity Architecture and Trials for Maritime Applications.

This paper provides a study of the different alternatives that are being considered in the 5G-ROUTES project to establish seamless 5G connectivity in a maritime environment both from an architectural point of view and also from the definition of field trials to evaluate the performance and dependability of the proposed solution. As expected, the main challenge in providing 5G connectivity on the sea is to provide coverage over large areas of open water. Thus, as a starting point, this paper presents a measurement campaign that was conducted to assess the current coverage in the Baltic Sea, which concluded that the current terrestrial networks cannot guarantee sufficient coverage. Next, the solution architecture and trials proposed by 5G-ROUTES are described, which are based on the integration of satellite and leading-edge multi-hop connectivity in 5G networks. Utilizing satellite backhaul can potentially overcome the connectivity challenge from the terrestrial domain to the maritime domain, while multi-hop connectivity ensures that coverage is extended among the different ships that are navigating the sea. Furthermore, this paper describes how the project will evaluate, in field trials tailored to this maritime environment, common connectivity key performance indicators (KPIs) such as latency, throughput, availability and reliability. This paper concludes by providing a vision for applying the obtained results and insights to maritime transportation and other remote areas where the deployment of a suitable 5G infrastructure may be challenging or costly. The findings will be used to guide the design of future 5G networks for marine applications and to identify the most effective methods for providing secure and dependable communication in a maritime setting.

Sustainable supply chains in the heavy vehicle and equipment industry: a multiple-case study of four manufacturers

PurposeRecently, interest in sustainability has grown globally in the heavy vehicle and equipment industry (HVEI). However, this industry's complexity poses a challenge to the implementation of generic sustainable supply chain management (SSCM) practices. This study aims to identify SSCM's barriers, practices and performance (BPP) indicators in the HVEI context.Design/methodology/approachThe results are derived from case studies of four multinational manufacturers. Within-case and cross-case analyses were conducted to categorise the SSCM BPP indicators that are unique to HVEI supply chains.FindingsThis study's analysis revealed that supply chain cost implications and a deficient information flow between focal firms and supply chain partners are the key barriers to SSCM in the HVEI. This analysis also revealed a set of policies, programmes and procedures that manufacturers have adopted to address SSCM barriers. The most common SSCM performance indicators included eco-portfolio sales to assess economic performance, health and safety indicators for social sustainability and carbon- and energy-related measures for environmental sustainability.Practical implicationsThe insights can help HVEI firms understand and overcome the typical SSCM barriers in their industry and develop, deploy and optimise their SSCM strategies and practices. Managers can use this knowledge to identify appropriate mechanisms with which to accelerate their transition into a sustainable business and effectively measure performance outcomes.Originality/valueThe extant SSCM literature has focused on the light vehicle industry, and it has lacked a concrete examination of HVEI supply chains' sustainability BPP. This study develops a framework that simultaneously analyses SSCM BPP in the HVEI.

Hybrid System of Photovoltaic and Solar Thermal Technologies for Industrial Process Heat

Process heating is the activity with the most energy consumption in the industrial sector. Solar heating (SH) systems are a promising alternative to provide renewable thermal energy to industrial processes. However, factors such as high investment costs and area limitations in industrial facilities hinder their utilization; therefore, hybrid systems that combine two different solar thermal or photovoltaic technologies where each technology operates under conditions that allow a higher overall performance than conventional configurations have been proposed. In this review, we discuss the limitations of conventional SH systems and the potential of hybrid configurations to overcome them. First, the current literature about conventional and hybrid systems is presented. Then, the application of common performance indicators to evaluate hybrid configurations is analyzed. Finally, the limitation, advantages, and potential applications of conventional and hybrid systems are discussed. This work shows that conventional systems are the most promising alternatives in low and high-temperature industrial applications. At the same time, in medium and processes, hybrid configurations have great potential to increase the performance of SH systems and help to boost their adoption in the industrial sector. There are few studies about hybrid systems in industrial applications, and further research is required to determine their potential.

THE IMPACT OF FINANCIAL DETERMINANTS ON MALAYSIAN REITS’ PERFORMANCE

Property investment in Malaysia has experienced tremendous growth and is considered one of the prime investments by investors due to its stable return growth over the years. This study examined the effects of six financial determinants (earnings per share, debts to assets, price-to-book value, dividend yield, market capitalisation, and return on equity) on the annual return of Malaysian Real Estate Investment Trusts (REITs). The common performance indicator of company can be seen in the percentage change in stock price plus when the dividend paid at the end of the year is used to measure the annual return performance of REITs. A total of 154 firm year observations for a sample of 14 Malaysian REIT companies were examined for a period of 11 years (2008-2018) from the Bloomberg terminal. Multiple regression analysis was used to analyse the data. Findings showed that there is a positive relationship between earning per share, price-to-book value and dividend yield and REITs’ annual return. On the other hand, this study showed that there is no significant relationship between debts to assets and REITs’ annual return. The results also showed that market capitalisation and return on equity are negatively related to Malaysian REITs’ annual returns. Overall, the results highlighted two key features. First, earnings per share and return on equity should be used by investors and management to assess the profitability and operational efficiency of REITS. Second, the investors should use dividend yield as one of the key investment criteria when assessing REIT investment decisions. This study contributed to the literature in REITs by increasing the effects of the explanatory power of financial determinants on REITs’ annual returns.

In search of the collaborative advantage of public‐private partnerships: A comparative analysis of Dutch transport infrastructure projects

AbstractThe theory of collaborative advantage is a major theory rationalizing the role of Public‐Private Partnerships (PPPs). However, the evidence for the theory in the case of PPPs is weak: the literature lacks studies that compared the actual performance and collaboration of PPPs to that of traditionally procured public projects. The aim of this article is to test the theory of a collaborative advantage of PPPs, focusing on three common performance indicators: time, cost, and perceived project performance. Data were collected on Dutch transport infrastructure projects. PPPs were compared to traditionally procured projects with non‐parametric tests and robust regression using weighted least squares estimator. It was found that PPPs have better cost performance but that this performance advantage cannot be attributed to collaboration. Therefore, it is concluded that no evidence was found for the theory of a collaborative advantage of PPPs.

Assessment of Infiltration from Private Sewer Laterals: Case Study in Jurmala, Latvia

The presence of excess water in centralized sewerage systems is known to have a multitude of unfavorable effects on the daily operation of the wastewater infrastructure. The additional volume of I/I-water decreases the hydraulic capacity of wastewater collection networks, reduces the efficiency of wastewater treatment processes, and increases the costs of transporting and treating the wastewater. Currently, most I/I studies in Latvia are conducted on the scale of the wastewater treatment plant service area and determine only the common performance indicators for a given year. However, data of such resolution are not sufficient to identify problem areas within the networks and to introduce cost-effective measures. The contribution of private sewer laterals to the overall I/I volume is an area of particular interest. Although it is possible to locate and quantify I/I from individual house connections, in practice, given the financial and time constraints, it is not feasible to apply a case-by-case approach. Thus, a simple method to predetermine the problematic parts of the system before conducting on-site inspections is needed. This study investigates the link between groundwater levels and observed night-time wastewater flows on a sub-catchment scale by performing a linear regression analysis (940 data points in total). The results show a direct correlation (R > 0.70 in all cases) between said parameters and highlight the impacts of poorly built and ill-maintained house connections. The presented approach can be widely adopted by system operators to help identify potential sources of diluted wastewater and to aid in the development of priority-based renovation plans.

Low-light image enhancement based on deep learning: a survey

Images taken under low light or dim backlight conditions usually have insufficient brightness, low contrast, and poor visual quality of the image, which leads to increased difficulty in computer vision and human recognition of images. Therefore, low illumination enhancement is very important in computer vision applications. We mainly provide an overview of existing deep learning enhancement algorithms in the low-light field. First, a brief overview of the traditional enhancement algorithms used in early low-light images is given. Then, according to the neural network structure used in deep learning and its learning algorithm, the enhancement methods are introduced. In addition, the datasets and common performance indicators used in the deep learning enhancement technology are introduced. Finally, the problems and future development of the deep learning enhancement method for low-light images are described.

High-priority expected waiting times in the delayed accumulating priority queue with applications to health care KPIs

We provide the first analytical expressions for the expected waiting time of high-priority customers in the delayed APQ by exploiting a classical conservation law for work-conserving queues. Additionally, we describe an algorithm to compute the expected waiting times of both low-priority and high-priority customers, which requires only the truncation of sums that converge quickly in our experiments. These insights are used to demonstrate how the accumulation rate and delay level should be chosen by health care practitioners to optimize common key performance indicators (KPIs). In particular, we demonstrate that for certain nontrivial KPIs, an accumulating priority queue with a delay of zero is always preferable. Finally, we present a detailed investigation of the quality of an exponential approximation to the high-priority waiting time distribution, which we use to optimize the choice of queueing parameters with respect to both classes’ waiting time distributions.

Sustainability, Criticality, and Circularity in PV: Using SDG 12 as a Lens to Map Sectoral Gaps

Multiple activities in the photovoltaic (PV) sector demonstrate a will to improve environmental impacts, reduce critical material uses and waste generated by linear supply chains. A common understanding of the remaining priority gaps in sustainability is, however, lacking, hindering the implementation of strategic courses of action. The United Nations Sustainable Development Goal 12 (SDG 12), targeting sustainable consumption and production, constitutes an entry point for PV stakeholders to examine their impacts holistically by reconciling ecological sustainability, criticality, and circularity. This article introduces a framework to map sustainability strategies in PV under the lens of SDG 12. A gap analysis is conducted to identify the current sustainability performances of PV value chains. The readiness level of the sector to close each gap is then evaluated, thus highlighting the “low hanging fruits” and the more consequent efforts required to achieve sustainability. Overall, a lack of common key performance indicators is noticed, resulting in a fragmented and uncertain sustainability landscape. A need for more coherence, sustainability leadership, and collaboration at the sectoral level is identified for all topics of SDG 12. In particular, the implementation of circularity strategies is linked to substantial technoeconomic hurdles, which need to be addressed.

Optimized Tree Strategy with Principal Component Analysis Using Feature Selection-Based Classification for Newborn Infant's Jaundice Symptoms.

One of the most important and difficult research fields is newborn jaundice grading. The mitotic count is an important component in determining the severity of newborn jaundice. The use of principal component analysis (PCA) feature selection and an optimal tree strategy classifier to produce automatic mitotic detection in histopathology images and grading is given. This study makes use of real-time and benchmark datasets, as well as specific approaches for detecting jaundice in newborn newborns. According to research, the quality of the feature may have a negative impact on categorization performance. Additionally, compressing the classification method for exclusive main properties can result in a classification performance bottleneck. As a result, identifying appropriate characteristics for training the classifier is required. By combining a feature selection method with a classification model, this is possible. The major outcomes of this study revealed that image processing techniques are critical for predicting neonatal hyperbilirubinemia. Image processing is a method of translating analogue images to digital formats and manipulating them. The primary goal of medical image processing is to collect information useful for disease detection, diagnosis, monitoring, and therapy. Image datasets can be used to validate the performance of newborn jaundice detection. When compared to conventional approaches, it offers results that are accurate, quick, and time efficient. Accuracy, sensitivity, and specificity, which are common performance indicators, were also predictive.

Reviews and industry: A love‐hate relationship

Reviews and industry: A love‐hate relationship

One-year Mortality after Lung Transplant is Not a Reliable Indicator of Lung Transplant Center Performance

<h3>Purpose</h3> Measuring performance in lung transplantation (LTx) is complex, but in the United States the most common performance indicator is one-year mortality. This indicator is shared with centers in quarterly reports and is used by the United Network for Organ Sharing (UNOS) to identify programs for review and trigger penalties including loss of accreditation. Reliability adjustment, a novel technique for quantifying and removing statistical "noise" from quality rankings, is becoming more widely used outside surgery. We sought to evaluate the reliability of 1-year mortality outcomes in LTx. <h3>Methods</h3> We used the Standard Transplant Analysis and Research (STAR) files form UNOS to identify LTx recipients from 2013-2018 in the United States. We first used standard risk adjustment methods to estimate expected 1-year mortality rates at the hospital level. We then examined the effect of adjusting for reliability using empirical Bayes techniques. <h3>Results</h3> We identified 12,769 recipients in 69 centers over the study period. Reliability adjustment greatly diminished apparent variation in hospital outcomes. For risk-adjusted 1-year mortality, there was a large range (range 0 to 89%), which decreased to a 2.5-fold difference (6 to 15%) after reliability adjustment. Reliability adjustment had a large impact on hospital mortality rankings. For example, with rankings based on mortality, 22% (4 hospitals) of the "best" hospitals (top 25%) were reclassified after reliability adjustment . Similarly, 18% (3 hospitals) of the "worst" hospitals (bottom 25%) were reclassified after reliability adjustment. The overall reliability of 1-year mortality was 0.42. <h3>Conclusion</h3> Conclusions: The reliability of 1-year mortality benchmarking in lung transplantation is low. Accounting for reliability yields more accurate measures of LTx center performance.

The study of palm kernel shell updraft gasification for supplying heat to an asphalt mixing plant

Air-gasification converts biomass using air into combustible gaseous components which also known as producer gas. The combustion of those gases provides energy for any purposes. In an asphalt mixing plant, this technology is implemented using palm kernel shell as feedstock to generate heat for aggregate heating as a part of hot-mixed asphalt production. Both Cold Gas Efficiency (CGE) and Carbon Conversion Efficiency (CCE) are the common performance indicators of such process. This study examines a performance of an updraft fixed-bed gasifier to convert the shell into producer gas in supplying heat for increasing aggregate temperature from ambient to about 145 – 165°C at capacity of 800 kg/min. The feedstock flowrate was set at 990 kg/hour and equivalence ratios were varied at 0.04, 0.05 and 0.08. Because the gasification reactions were in equilibrium, the producer gas composition for calculating the CGE was predicted using thermodynamic model with minimizing Gibbs free energy. The feedstock and solid residue of the process were analysed to determine the carbon content for calculating the CCE. It is indicated that the performance of the gasification operation for supplying heat in an asphalt mixing plant is limited by operating conditions.

Towards objective measurement of reproductive performance of traditionally managed goat flocks in the drylands of Ethiopia

Reproductive performance is a key determinant for the efficiency of goat production. Regular monitoring of reproductive efficiency is essential to assess management and to avoid financial losses due to poor performance. To allow more objective measurement and comparisons over time, we propose a novel quantitative approach for defining annual reproductive performance by combining common performance indicators into a goat flock index. Commonly used reproductive performance measures were collected from 242 goat flocks in four districts in dryland of Ethiopia between July 2018 and February 2019. Principal component analysis (PCA) was performed to identify biologically meaningful latent components that explain annual reproductive output (ARO) and annual reproductive wastage (ARW). Together with the remaining annual reproductive performance measures, the ARO and ARW components were included in a PCA to derive an algorithm for a goat annual reproductive performance index (G-ARPI). One component representing variation in kidding interval, PCARO1 and PCARW1 was extracted and normalized to a 10-scale value. The flocks were classified into good performing (15.63%) with index > 8.5, moderately performing (48.21%) with index values ranging from 6.5 to 8.5 and poor performing (36.16%) with index < 6.5. Good performing flocks have higher scores for reproductive output measures, lower scores for reproductive wastage and lower kidding interval. The proposed G-ARPI can be used as an objective tool to compare reproductive performance between management systems, evaluate the costs of poor reproductive management and will be useful for economic models that aim to identify the most cost-efficient intervention option and monitor the impact of interventions. We present here the index for goat production in dryland systems in Ethiopia; the approach can easily be adapted to other production systems elsewhere.