Evaluation Of Key Performance Indicators Research Articles

Abstract C130: Evaluation of key performance indicators of a community-focused patient navigation program in an outpatient cancer center

Abstract Background: In 2021, Dana-Farber Cancer Institute’s (DFCI) Cancer Care Equity Program (CCEP) implemented a Community-Focused Patient Navigation (CFPN) program to support adult medical oncology patients from historically marginalized groups by proactively identifying and addressing patients’ barriers to cancer treatment. The program has been sequentially implemented in five cancer treatment centers, gastrointestinal, breast, thoracic, and gynecologic oncology and hematologic malignancies with plans to expand further. The purpose of this analysis is to evaluate CFPN against key performance indicators (KPIs) in gastrointestinal oncology, where the program was implemented first. Methods: Patients included in the analysis met CFPN eligibility criteria: adult medical oncology patients from zip codes in the Greater Boston area with significant socio-economic disparities and who agreed to receive support from the patient navigator. Program KPIs included in the analysis were appointment no show rate, New Patient Intake Questionnaire (NPIQ) response rate, and social work encounter rate. A pre- post analysis was used to identify trends in the KPIs of interest among navigated patients from the start of the program in October 2021 through May 2024 compared to similar groups of patients seen one year prior to the launch of the program who would have met CFPN eligibility criteria if the program had existed at that time. The data were taken from the cancer center’s electronic medical record system and visualized in a dashboard. Results: Preliminary findings show a significant decrease in appointment no show rate from 4.07% to 2.29% (p<0.05) and an increase in NPIQ response rate from 20.97% to 34.74% (p<0.05) among all navigated patients after the program was implemented compared to one year prior to the program launch. While not statistically significant, there was an increase from 28.23% to 43.16% in the social work encounter rate for patients engaged with the patient navigator. Conclusion: Preliminary analysis of CFPN demonstrates a positive trend toward meeting KPIs in gastrointestinal oncology and reveals outcomes that will guide further quantitative and qualitative analysis to identify areas of programmatic success and improvement. Next steps include conducting a more in-depth quantitative analysis on additional KPIs, including patient retention rate, stratified by demographics. CCEP also plans to conduct a qualitative analysis to identify patient and care team perceptions of CFPN through focus groups and/or structured interviews. An abstract describing CFPN program design was accepted for online publication at ASCO. This project was undertaken as a Quality Improvement Initiative at DFCI, and as such was not formally supervised by the Institutional Review Board per policy. Citation Format: Sarah Pohl, Emma Green, Katherine Miller, Ludmila Svoboda, Magnolia Contreras, Christopher Lathan. Evaluation of key performance indicators of a community-focused patient navigation program in an outpatient cancer center [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C130.

EVALUATION OF MAINTENANCE EFFECTIVENESS AND HUMAN FACTORS: CASE STUDY OF A RESEARCH INSTITUTE

Maintenance management is an integral part of any organization’s overall commercial process that develops administrative operational value. Thus, the maintenance function is of immense importance to ensure the safety and effective operation of a facility and equipment in compliance with a statutory obligation, to maximize equipment life, and to reduce the risk of failure. However, for maintenance success work planning, the perception of human factors, and the interrelation of workers and groups in any organisation must be understood. This in turn would determine the allocation of maintenance tasks to team members. This research focused on the evaluation of key performance indicators in relation to the maintenance effectiveness of selected equipment, in a research institution. The impact of human factors on the maintenance was also investigated. Key performance indicators such as Mean Time to Repair (MTTR), Mean Time Between Failure (MTBF), downtime, repair time, and availability were used to quantitatively evaluate the maintenance effectiveness of the equipment, while the systematic approach to empirical research was used to analyse the effect of human factors on maintenance practices. This study was carried out over a period of 6 months on a 4-tonne hydraulic lift, hydraulic hoist jack lift, tire changing machine, fault diagnostic machine, and computerized alignment gauge. Data collected from the maintenance unit was analysed. A total of 11.92 hours was recorded as the MTTR of the five machines. The machines had a high level of availability and a total downtime of 12.01 hours out of operational time of 2369.8 hours over a period of 6 months. The level of motivation and competence among maintenance personnel was assessed to be very good. This is expected to have contributed largely to the effective maintenance practices of workers in the unit. From this study, it can be deduced that the level of motivation and competence is high among maintenance personnel. In terms of productivity, effectiveness, teamwork, decision-making, execution, safety, and responsiveness, the human factor has been shown to have thrived well. This in turn, stimulated the corresponding increase in the availability of machines over the 6 months- of study.

Analisis Strategi Bisnis Menggunakan SWOT, BMC, dan Balance Scorecard pada UMKM AC

The food and beverage industry contributes significantly to Indonesia's Gross Domestic Product (GDP), with UMKM AC, located in Bogor City, being one of the contributors. This research formulates a business strategy for UMKM AC using the Business Model Canvas (BMC), SWOT analysis, and evaluation of Key Performance Indicators (KPIs) with the Balanced Scorecard (BSC). Data were collected through questionnaires and literature studies. The BMC analysis results indicate strengths in customer segmentation and community relations. The internal and external factor analysis shows that UMKM AC is in a position to grow and develop. The SWOT analysis identifies opportunities for UMKM AC, focusing on strategies that have a positive impact. The BSC evaluation shows overall good performance, but there are some strategic goals that need further improvement. This research highlights the importance of strategic planning and performance evaluation for SMEs in facing industry competition.

Performance Evaluation of Railway Infrastructure Managers: A Novel Hybrid Fuzzy MCDM Model

Modern challenges such as the liberalization of the railway sector and growing demands for sustainability, high-quality services, and user satisfaction set new standards in railway operations. In this context, railway infrastructure managers (RIMs) play a crucial role in ensuring innovative approaches that will strengthen the position of railways in the market by enhancing efficiency and competitiveness. Evaluating their performance is essential for assessing the achieved objectives, and it is conducted through a wide range of key performance indicators (KPIs), which encompass various dimensions of operations. Monitoring and analyzing KPIs are crucial for improving service quality, achieving sustainability, and establishing a foundation for research and development of new strategies in the railway sector. This paper provides a detailed overview and evaluation of KPIs for RIMs. This paper creates a framework for RIM evaluation using various scientific methods, from identifying KPIs to applying complex analysis methods. A novel hybrid model, which integrates the fuzzy Delphi method for aggregating expert opinions on the KPIs’ importance, the extended fuzzy analytic hierarchy process (AHP) method for determining the relative weights of these KPIs, and the ADAM method for ranking RIMs, has been developed in this paper. This approach enables a detailed analysis and comparison of RIMs and their performances, providing the basis for informed decision-making and the development of new strategies within the railway sector. The analysis results provide insight into the current state of railway infrastructure and encourage further efforts to improve the railway sector by identifying key areas for enhancement. The main contributions of the research include a detailed overview of KPIs for RIMs and the development of a hybrid multi-criteria decision making (MCDM) model. The hybrid model represents a significant step in RIM performance analysis, providing a basis for future research in this area. The model is universal and, as such, represents a valuable contribution to MCDM theory.

EVALUASI KINERJA UTAMA PEMERINTAH KABUPATEN BEKASI PROVINSI JAWA BARAT

This research discusses the misalignment between strategic planning and annual planning in achieving key performance indicators (KPIs) at the national and regional levels, with a focus on Bekasi District, West Java. This alignment is critical to achieving community welfare. Gaps were found between the performance targets in the National Medium-Term Development Plan (RPJMN) and the Government Work Plan (RKP), especially on economic growth, poverty levels, and unemployment. An evaluation of the KPIs in Bekasi District also showed non-optimality, with a number of KPIs not achieved. Qualitative research methods were used to evaluate this. Clear KPI establishment, accurate data collection, regular monitoring, and root cause analysis are important steps in KPI evaluation. The results show that the KPI setting process in Bekasi District still does not fulfill the SMART (Specific, Measurable, Assignable, Realistic, Time Related) criteria thoroughly.There are weaknesses in senior management commitment, an unoptimized performance culture, and a lack of Critical Factor Success (CSF) approach in the formulation of performance indicators.In addition, the process of simulating the achievement of KPIs and midterm evaluation of the RPJMD was not adequately implemented; however, this process was not fully followed at both the national and regional levels. In addition, the process of simulating the achievement of KPIs and the midterm evaluation of the RPJMD were not adequately implemented. However, this process was not fully followed at both the national and regional levels.

Performance Evaluation of a Department in the Manufacturing Industry Based on Balanced Scorecard

The success of a company, especially in the manufacturing industry, is closely linked to the performance of each department. The final objective of this research is to determine KPIs, measure department performance on each KPI based on the perspective of a Balanced Scorecard-based performance assessment approach, and then determine achievements. The process of generating Key Performance Indicators (KPIs) commences with conducting unstructured interviews to get KPIs for each perspective. Subsequently, a survey was devised to conduct a systematic evaluation of Key Performance Indicators (KPIs) for each perspective, employing the Analytical Hierarchy Process methodology. The computations yielded weights for the four perspectives: financial, customer, learning, and internal business process. Each perspective was assigned a weight value of 0.25, 0.50, 0.125, and 0.125, respectively.

Effectiveness of the activities of the federal project “Clean air” by the quality of atmospheric air and risk for the health (by means of the example of the city Bratsk)

Introduction. The study is relevant due to the necessity to adequately assess the effectiveness of activities performed within the “Clean Air” Federal Project using ambient air quality and public health risks as key performance indicators. 
 The purpose of the work was to comparatively assess ambient air quality and airborne health risks for Bratsk population over 2020 –2022 when activities planned within the “Clean Air” Federal Project were being implemented in the city.
 Materials and methods. The analysis was based on data obtained by instrumental measurements of ambient air quality performed within social-hygienic monitoring programs. More than thirty thousand measuring results were considered; they covered 20 chemicals and were taken at two monitoring posts. Carcinogenic and acute and chronic non-carcinogenic health risks were assessed in conformity with the existing regulatory documents.
 Results. Between 2020 and 2022, a decrease in ground levels of particulate matter, sulfur dioxide, nitrogen oxides, hydrogen chloride, and some other technogenic chemicals was registered at social-hygienic monitoring posts in Bratsk. These chemicals were subject to specific activities stipulated by the “Clean Air” Federal Project and performed by economic entities. However, public health risks remained impermissible. Thus, an acute risk of respiratory diseases equaled 3.24 during 2022 and a chronic risk of respiratory diseases reached 5.43 in the same year whereas their permissible level should not exceed 3.0. 
 Limitations. Analysis is possible only if there is a sufficient number of instrumental studies at points located in residential buildings to assess the risk (least 300 one-time or 75 daily samples for each impurity). 
 Conclusion. The study results indicate still necessary to take further efforts to provide a safer environment for Bratsk population and to continue monitoring of chemicals that create impermissible health risks. The most important task is to manage systemic operative data exchange between systems for environmental and social-hygienic monitoring to provide adequate assessment of a sanitary-hygienic situation as well as objective evaluation of key performance indicators of the “Clean Air” Federal Project

Application of Factor Analysis and Structure Equation Modelling for Evaluation of Key Performance Indicators for Development of Sustainable Transportation Infrastructure

Application of Factor Analysis and Structure Equation Modelling for Evaluation of Key Performance Indicators for Development of Sustainable Transportation Infrastructure

Holistic Evaluation of Demand Response Events in Real Pilot Sites: From Baseline Calculation to Evaluation of Key Performance Indicators

Explicit demand response plays a significant role in the future energy grid transition, as it involves end consumers in smart grid activities and, at the same time, exploits the potential of flexibility, giving the opportunity to grid operators to accommodate a total amount of energy without the need to reinforce the grid infrastructure. For evaluating the successfulness of a demand response program, thus, evaluating its advantages, it is fundamental to have an accurate baseline curve consumption along with meaningful key performance indicators. In this work, we propose a novel way of calculating the baseline consumption using artificial intelligence techniques. In particular, regression models have been applied to a database of historical data. In order to present a complete evaluation of demand response programs, we present five key performance indicators (KPIs). The KPIs have been selected so as to depict the successfulness of the explicit demand response program. We suggest a novel way of evaluating two of the five KPI using a quantitative approach. We also apply the proposed methodology for baseline calculation and KPIs evaluation in a practical example: two pilot sites have been used and real-life scenarios of demand response events have been applied for this scope to actual nonindustrial consumers and especially residential consumers. The baseline has been calculated for these pilot sites and the KPIs have been evaluated for them. The presented results complete the picture of evaluating a real-life demand response program and show the effectiveness of the selected approach. The proposed schemes for baseline calculation and KPI evaluation can be used by the scientific community for evaluating future demand response programs, especially in the residential sector.

Enhancing healthcare quality and outcomes for peritoneal dialysis patients in Thailand: An evaluation of key performance indicators and PDOPPS cohort representativeness.

To assess whether the peritoneal dialysis (PD) centres included in the Peritoneal Dialysis Outcomes and Practise Patterns Study (PDOPPS) in Thailand are representative of other PD centres in the country, based on 8 key performance indicators (KPIs 1-8). A retrospective analysis was conducted comparing PD-related clinical outcomes between PD centres included in the PDOPPS (the PDOPPS group) and those not included (the non-PDOPPS group) from January 2018 to December 2019. Logistic regression analysis was used to identify predictors associated with achieving the target KPIs. Of 181 PD centres, 22 (12%) were included in the PDOPPS. PD centres in the PDOPPS group were larger and tended to serve more PD patients than those in the non-PDOPPS group. However, the process and outcome KPIs (KPIs 1-8) were comparable between the 2 groups. Large hospitals (≥120 beds), providing care to ≥100 PD cases and having experience for >10 years were independent predictors of achieving the peritonitis rate target of <0.5 episodes/year. Most PD centres in Thailand showed weaknesses in off-target haemoglobin levels and culture-negative peritonitis rate. The PD centres included in Thai PDOPPS were found to be representative of other PD centres in Thailand in terms of clinical outcomes. Thus, Thai PDOPPS findings may apply to the broader PD population in Thailand.

DSF Core: Integrated Decision Support for Optimal Scheduling of Lifetime Extension Strategies for Industrial Equipment.

This paper proposes a generic algorithm for industries with degrading and/or failing equipment with significant consequences. Based on the specifications and the real-time status of the production line, the algorithm provides decision support to machinery operators and manufacturers about the appropriate lifetime extension strategies to apply, the optimal time-frame for the implementation of each and the relevant machine components. The relevant recommendations of the algorithm are selected by comparing smartly chosen alternatives after simulation-based life cycle evaluation of Key Performance Indicators (KPIs), considering the short-term and long-term impact of decisions on these economic and environmental KPIs. This algorithm requires various inputs, some of which may be calculated by third-party algorithms, so it may be viewed as the ultimate algorithm of an overall Decision Support Framework (DSF). Thus, it is called "DSF Core". The algorithm was applied successfully to three heterogeneous industrial pilots. The results indicate that compared to the lightest possible corrective strategy application policy, following the optimal preventive strategy application policy proposed by this algorithm can reduce the KPI penalties due to stops (i.e., failures and strategies) and production inefficiency by 30-40%.

Evaluation of key performance indicators of Indian airlines using fuzzy AHP method

Due to intense competition, decreasing profit margins and demanding customers in the air transport business, airlines have to measure their performance in order to remain competitive as well as sustainable in the airline market. The performance of an airline depends on many factors these are known as key performance indicators (KPIs). This study makes an attempt to evaluate the KPIs of airlines. Evaluation of KPIs is done using a combined approach based on fuzzy theory and analytic hierarchy process (AHP) method. A case example of Indian airlines is conducted to illustrate the proposed model applicability. The results of the study indicates that the priority order of the KPIs is SSR > OMR > CR > FBR, which show that safety and security related indicators are found most important in this priority list and financial and business related parameters ranked last. This study makes an important contribution to various airline companies by solving significant problems in order to enhance their performances in the competitive market with proposed methodology. This proposed method considered fuzzy framework that can handle impreciseness and uncertainty. Sensitivity analysis is also performed to test the robustness of the proposed model.

Evaluation of key performance indicators of Indian airlines using fuzzy AHP method

Evaluation of key performance indicators of Indian airlines using fuzzy AHP method

Computational framework for the techno-economic analysis of monoclonal antibody capture chromatography platforms

We developed a computational framework that integrates commercial software components to perform customizable technoeconomic feasibility analyses. The use of multiple software packages overcomes the shortcomings of each to provide a detailed simulation that can be used for sensitivity analyses and optimizations. In this paper, the framework was used to evaluate the performance of monoclonal antibody capture processes. To this end, the simulation framework incorporated dynamic models for the affinity chromatography step that were validated with experimental breakthrough curves. The results were integrated with an Intelligen SuperPro Designer process simulation for the evaluation of key performance indicators of the operations. As proof of concept, the framework was used to perform a sensitivity analysis and optimization for a case study in which we sought to compare membrane and resin chromatography for disposable and reusable batch capture platforms. Two membranes and one resin were selected for the capture media, which yielded six process alternatives to compare. The objective functions were set to be cost of goods, process time, and buffer utilization. The results of the optimization of these process alternatives were a set of operating conditions that display tradeoffs between competing objectives. From this application exercise, we conclude that the framework can handle multiple variables and objectives, and it is adaptable to platforms with different chromatography media and operating modes. Additionally, the framework is capable of providing ad hoc analyses for decision making in a specific production context.

Spatial decision support system for low-carbon sustainable cities development: An interactive storytelling dashboard for the city of Turin

The evaluation of city sustainability considering economic, social, and environmental subsystems is a fundamental field in urban planning. It directly affects the quality of urban economies toward efficiency and innovation with minimal resource consumption. This requires the use of appropriate SDSS (Spatial Decision Support Systems) which take into account inclusive approaches to engage local stakeholders. The study aims at developing an interactive dashboard to aid decision-makers in sustainable urban planning processes to co-create future transition strategies with the employment of spatialized outputs. The dashboard enables the evaluation of the sustainability performance of the city by the selection and assessment of KPIs (Key Performance Indicators) as a part of the MOLOC European Interreg Project. The study proposes a multidisciplinary GIS-based framework for the spatial evaluation of KPIs which can support scenario development by providing targeted suggestions for local development planning. Outcomes represent a useful interactive SDSS tool within a storytelling dashboard which is combinable with multi-criteria decision models at the city scale. The spatial distribution of results evidences the strengths and weaknesses of the city and facilitates the implementation of low-carbon action plans, avoiding unnecessary investments. The methodology illustrated was applied to the city of Turin as the case study, however, it can be implemented in other European urban contexts intending to achieve sustainable spatial planning since the methodological framework modulates to the territorial specificities of a place, always including all aspects of sustainability to be assessed.

Fault Injection with Multiple Fault Patterns for Experimental Evaluation of Demand-Controlled Ventilation and Heating Systems.

Heating, ventilation, and air-conditioning (HVAC) systems are large-scale distributed systems that can be subject to multiple faults affecting the electronics, sensors, and actuators, potentially causing high energy consumption, occupant discomfort, degraded indoor air quality and risk to critical infrastructure. Fault injection (FI) is an effective experimental method for the validation and dependability evaluation of such HVAC systems. Today's FI frameworks for HVAC systems are still based on a single fault hypothesis and do not provide insights into dependability in the case of multiple faults. Therefore, this paper presents modeling patterns of numerous faults in HVAC systems based on data from field failure rates and maintenance records. The extended FI framework supports the injection of multiple faults with exact control of the timing, locality, and values in fault-injection vectors. A multi-dimensional fault model is defined, including the probability of the occurrence of different sensor and actuator faults. Comprehensive experimental results provide insights into the system's behavior for concrete example scenarios using patterns of multiple faults. The experimental results serve as a quantitative evaluation of key performance indicators (KPI) such as energy efficiency, air quality, and thermal comfort. For example, combining a CO2 sensor fault with a heater actuator fault increased energy consumption by more than 70%.

Evaluation of key performance indicators of Internet of Things and Cloud Computing for infrastructure projects in Gujarat, India through Consistent Fuzzy Preference Relations approach

The construction industry is the world's most significant and dominant industry. Although it is a significant and dominant industry and utilizes the latest technology like the Internet of Things (IoT) and Cloud Computing, it is still considered as being weak. So, evaluating the Key Performance Indicators (KPIs) is crucial for adopting these technologies. The objective of this paper is to identify the KPIs of IoT and Cloud Computing for infrastructure projects based on the previous literature study and the authors' personal experience and evaluate them using the multi-criteria decision-making method like Consistent Fuzzy Preference Relation (CFPR). The case studies used for this research are 138 infrastructure projects of Gujarat, India. The finding of the study identified that all through 109 KPIs the “Nature and Willingness of the Organization” is the most severe KPI for the application of the Internet of Things (IoT) and Cloud Computing for the infrastructure project. The rank of all 109 KPIs has been obtained, and for the top ten KPIs' corrective and preventive mitigation measures have been proposed to implement future projects smoothly.

A Comparative Study of Different Sorbents in the Context of Direct Air Capture (DAC): Evaluation of Key Performance Indicators and Comparisons

Direct air capture can be based on an adsorption system, and the used sorbent (chemisorbents or physisorbents) influences process. In this work, two amine-functionalized sorbents, as chemisorbents, and three different metal organic frameworks, as physisorbents, are considered and compared in terms of some key performance indicators. This was carried out by developing a mathematical model describing the adsorption and desorption stages. An independent analysis was carried out in order to verify data reported in the literature. Results show that the equilibrium loading is a critical parameter for adsorption capacity, energy consumption, and cost. The considered metal organic frameworks are characterized by a lower equilibrium loading (10−4 mol/kg) compared to chemisorbents (10−1 mol/kg). For this reason, physisorbents have higher overall energy consumptions and costs, while capturing a lower amount of carbon dioxide. A reasonable agreement is found on the basis of the operating conditions of the Climeworks company, modelling the use of the same amine cellulose-based sorbent. The same order of magnitude is found for total costs (751 USD/tonneCO2 for our analysis, compared to the value of 600 USD/tonneCO2 proposed by this company).

Application of multi-criteria decision making for evaluation of key performance indicators of integrated project delivery and BIM model for an infrastructure transportation project in Western India

Multi-criteria-decision-making is a very effective tool for decision making in complex infrastructure projects. Building-information-modelling (BIM) and integrated-project-delivery (IPD) are adopted by the architecture engineering and construction industry because of their notable benefits to complex infrastructure projects throughout their project life cycle. Poor knowledge of the KPIs hinders integrated BIM and IPD adoption which results in affecting project timelines and budgets. This paper aims at evaluating the key performance indicators (KPI) of IPD and BIM for an infrastructure project of a metro rail construction located in western India. After an extensive literature review and experts’ opinions, a list of 24 KPI was identified. Further, factor comparison method and fuzzy decision-making trial and evaluation laboratory were utilized to prioritize the identified KPIs and disclose their interrelationships based on influential weight, respectively. The findings revealed the 16 most critical key performance indicators (MCKPI) which are vital for BIM and IPD adoption in highly complex infrastructure projects. Their interrelationship was represented by a causal diagram showing cause and effects groups. The results indicated the four most influential and critical KPIs. They were accessibility and accuracy of information by BIM, facilitating access to real-time data, interoperability and compatibility of data, and minimizing claims and disputes. Hence, more consideration should be given to the MCKPIs to enhance the project performance of complex infrastructure projects like metro rail construction.

Performance Evaluation of Key Performance Indicators for UMTS Networks in Calabar, Nigeria

For appropriate regulation and quality assurance, continuous monitoring of cellular network services is necessary. A drive test measurement was carried out in Calabar, to investigate four UMTS networks, taking cognizance of Call Setup Success Rate (CSSR), Drop Call Rate (DCR), Handover Success Rate (HOSR), Call Setup Time (CST), Received Signal Code Power (RSCP) and Signal-to-Interference Ratio (Ec/Io). The collected data was analyzed and the obtained results was compared with the performance threshold levels of the Nigerian Communication Commission (NCC). The four networks had excellent CST, with MTN leading, followed by Airtel, then 9mobile and Globacom. For CSSR, Airtel had excellent performance, as there was no blocked call. This was followed by MTN and Globacom. 9mobile failed in CSSR as it was below the NCC recommended benchmark. There was no drop call in Airtel, MTN and 9mobile, but Globacom’s performance was below the recommended threshold for dropped calls. For HOSR, the four networks showed excellent performance as they were able to handover calls between cells, as recommended by NCC. Considering the RSCP, the four networks displayed excellent services as subscribers experienced good voice and data services. However, the four networks had poor coverage quality (Ec/Io), the worst, being displayed by MTN. This shows that the equipment capability of the networks is low. The network operators are advised to monitor their services often, so as to optimize their networks for a better Quality of Service (QoS).