Academic literature on the topic 'KPIs optimization'
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Journal articles on the topic "KPIs optimization"
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Waurisch, Heiko, Nick von Bargen, Nico Ploczicki, Bente Ralfs, Berit Elsner, Reiner Schütt, and Nassipkul Dyussembekova. "Assessment of Grid and System Supportability Based on Spatio-Temporal Conditions—Novel Key Performance Indicators for Energy System Evaluation." Energies 17, no. 7 (March 23, 2024): 1534. http://dx.doi.org/10.3390/en17071534.
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The energy transition introduces new technical standards, laws and regulations regarding the stability and reliability of energy grids and systems. Due to the non-existence of a measuring standard, key performance indicators (KPIs) were developed to enable the measurement and comparison of individual energy grid (namely electricity, heat and gas grid) and system supportabilities while also promoting well-founded decision-making and optimization efforts. Inconsistencies in definitions concerning fundamental energy terms and the correlations between them inhibit the effective usage of the KPIs. Therefore, the overarching issue of the security of energy supply and its related subjects were also approached. The primary subject of this paper is the development of two new KPIs to measure and compare the energy grid and system supportability. These KPIs are based on spatio-temporal conditions in their respective grids. The usage and benefits of the developed KPIs are exemplarily highlighted by analyzing the impact of a scenario with the integration of a large-scale heat pump into the electricity and heat grid. The energy grid supportability is determined for each grid, whereas the energy system supportability takes the interactions of the electricity and heat grid into account. The developed KPIs are intended to enable stakeholders to identify areas with optimization potential in energy grids and systems. Moreover, the KPIs can be used to create a standardized evaluation method for regulatory requirements.
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Islam, Md Rakibul, Syed Mithun Ali, Amir Mohammad Fathollahi-Fard, and Golam Kabir. "A novel particle swarm optimization-based grey model for the prediction of warehouse performance." Journal of Computational Design and Engineering 8, no. 2 (February 24, 2021): 705–27. http://dx.doi.org/10.1093/jcde/qwab009.
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Abstract Warehouses constitute a key component of supply chain networks. An improvement to the operational efficiency and the productivity of warehouses is crucial for supply chain practitioners and industrial managers. Overall warehouse efficiency largely depends on synergic performance. The managers preemptively estimate the overall warehouse performance (OWP), which requires an accurate prediction of a warehouse’s key performance indicators (KPIs). This research aims to predict the KPIs of a ready-made garment (RMG) warehouse in Bangladesh with a low forecasting error in order to precisely measure OWP. Incorporating advice from experts, conducting a literature review, and accepting the limitations of data availability, this study identifies 13 KPIs. The traditional grey method (GM)—the GM (1, 1) model—is established to estimate the grey data with limited historical information but not absolute. To reduce the limitations of GM (1, 1), this paper introduces a novel particle swarm optimization (PSO)-based grey model—PSOGM (1, 1)—to predict the warehouse’s KPIs with less forecasting error. This study also uses the genetic algorithm (GA)-based grey model—GAGM (1, 1)—the discrete grey model—DGM (1, 1)—to assess the performance of the proposed model in terms of the mean absolute percentage error and other assessment metrics. The proposed model outperforms the existing grey models in projecting OWP through the forecasting of KPIs over a 5-month period. To find out the optimal parameters of the PSO and GA algorithms before combining them with the grey model, this study adopts the Taguchi design method. Finally, this study aims to help warehouse professionals make quick OWP estimations in advance to take control measures regarding warehouse productivity and efficiency.
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Imoize, Agbotiname Lucky, Friday Udeji, Joseph Isabona, and Cheng-Chi Lee. "Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment." Future Internet 15, no. 5 (May 12, 2023): 181. http://dx.doi.org/10.3390/fi15050181.
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Mobile broadband (MBB) services in Lagos, Nigeria are marred with poor signal quality and inconsistent user experience, which can result in frustrated end-users and lost revenue for service providers. With the introduction of 5G, it is becoming more necessary for 4G LTE users to find ways of maximizing the technology while they await the installation and implementation of the new 5G networks. A comprehensive analysis of the quality of 4G LTE MBB services in three different locations in Lagos is performed. Minimal optimization techniques using particle swarm optimization (PSO) are used to propose solutions to the identified problems. A methodology that involves data collection, statistical analysis, and optimization techniques is adopted to measure key performance indicators (KPIs) for MBB services in the three locations: UNILAG, Ikorodu, and Oniru VI. The measured KPIs include reference signal received power (RSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), and signal-to-noise ratio (SINR). Specific statistical analysis was performed, and the mean, standard deviation, skewness, and kurtosis were calculated for the measured KPIs. Additionally, the probability distribution functions for each KPI were plotted to infer the quality of MBB services in each location. Subsequently, the PSO algorithm was used to optimize the KPIs in each location, and the results were compared with the measured data to evaluate the effectiveness of the optimization. Generally, the optimization process results in an improvement in the quality of service (QoS) in the investigated environments. Findings also indicated that a single KPI, such as RSRP, is insufficient for assessing the quality of MBB services as perceived by end-users. Therefore, multiple KPIs should be considered instead, including RSRQ and RSSI. In order to improve MBB performance in Lagos, recommendations require mapping and replanning of network routes and hardware design. Additionally, it is clear that there is a significant difference in user experience between locations with good and poor reception and that consistency in signal values does not necessarily indicate a good user experience. Therefore, this study provides valuable insights and solutions for improving the quality of MBB services in Lagos and can help service providers better understand the needs and expectations of their end users.
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Znamenák, Jaroslav, Gabriela Križanová, Miriam Iringová, and Pavel Važan. "A Proposal for Production Data Collection on a Hybrid Production Line in Cooperation with MES." Research Papers Faculty of Materials Science and Technology Slovak University of Technology 24, no. 39 (December 1, 2016): 137–44. http://dx.doi.org/10.1515/rput-2016-0028.
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Abstract Due to the increasing competitive environment in the manufacturing sector, many industries have the need for a computer integrated engineering management system. The Manufacturing Execution System (MES) is a computer system designed for product manufacturing with high quality, low cost and minimum lead time. MES is a type of middleware providing the required information for the optimization of production from launching of a product order to its completion. There are many studies dealing with the advantages of the use of MES, but little research was conducted on how to implement MES effectively. A solution to this issue are KPIs. KPIs are important to many strategic philosophies or practices for improving the production process. This paper describes a proposal for analyzing manufacturing system parameters with the use of KPIs.
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Yu, T. X., Yan Fei Xiang, Min Wang, and Li Ming Yang. "Key Performance Indicators of Tubes Used as Energy Absorbers." Key Engineering Materials 626 (August 2014): 155–61. http://dx.doi.org/10.4028/www.scientific.net/kem.626.155.
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Based on our extensive studies on the experimental, theoretical and numerical results on various tubes under axial compression/impact in the last few years, we propose a set of Key Performance Indicators (KPIs) to assess and compare the energy absorbing performance of tubular structures with various configurations, so as to guide the design of energy absorbers whilst to archive a certain degree of optimization. The KPIs have five factors: Effective stroke ratio (ESR), Non-dimensional Load-carrying capacity (NLC), Effectiveness of energy absorption (EEA), Specific energy absorption capacity (SEA), Stableness of load-carrying capacity (SLC).The paper presents a series of diagrams to compare the energy absorbing performance of various tubes in terms of the four KPIs as described above. The work is valuable to engineering designs and applications, as well as to the further studies of the topic.
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Sirait, Fadli, Akhmad Wahyu Dani, Yuliza Yuliza, and Ulil Albab. "OPTIMIZATION IN QUALITY OF SERVICE FOR LTE NETWORK USING BANDWIDTH EXPANSION." SINERGI 23, no. 1 (February 27, 2019): 47. http://dx.doi.org/10.22441/sinergi.2019.1.007.
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QoS (Quality of Service) of LTE networks can bring the providers to provide broadband services with high performance to end user. Furthermore, the expected data rate transfer is up to 300 Mbit/s per user while the range of bandwidth varies from 1.4 MHz to 20 MHz. The network worked in 1800 MHz bands, 64 QAM modulation technique and used 10 MHz and 15 MHz channel bandwidth. There is a congestion problem for LTE network with 10 MHz channel bandwidth due to high utilization. The paper tries to analyze the QoS parameters, named Key Performance Indicators (KPI) for LTE Networks to solve the problem using bandwidth expansion. The KPIs parameter that is measured by drive test is accessibility, retainability, PRB downlink utilization, and user number. Based on the KPIs measurements results, it is showed that the proposed method to expand the bandwidth from 10 MHz to 15 MHz can avoid congestion problem and impact on improving the performance of LTE network.
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Xiang, Yanfei, Min Wang, Tongxi Yu, and Liming Yang. "Key Performance Indicators of Tubes and Foam-Filled Tubes Used as Energy Absorbers." International Journal of Applied Mechanics 07, no. 04 (August 2015): 1550060. http://dx.doi.org/10.1142/s175882511550060x.
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Based on a systematic investigation on the experimental, theoretical and numerical results on various tubes under axial compression/impact including our own tests, a set of key performance indicators (KPIs) for assessing and comparing the energy absorbing performance of tubular structures with various configurations is proposed, so as to guide the design of energy absorbers whilst to facilitate parameter optimization. The five KPIs proposed on the basis of mechanical analyses are effective stroke ratio (ESR), nondimensional load-carrying capacity (NLC), specific energy absorption (SEA), effectiveness of energy absorption (EEA) and undulation of load-carrying capacity (ULC). Moreover, by considering the influence of foam filling, these five KPIs are also modified and extended to the foam-filled tubes. The paper presents a series of diagrams to compare the energy absorbing performance of various tubes in terms of the five KPIs as described above. It transpires that the energy absorption performance of circular tubes is superior to that of square tubes. It is also confirmed that the mass of foam fillers results in reductions of SEA and EEA, though foam fillers will greatly improve the NLC of empty tubes. The novelty of the present study is displayed on the following aspects: (1) uniquely defining the effective stroke by the maximum point of "energy efficiency" f so as to avoid ambiguity which appeared in the literature; (2) instead of a single indicator such as SEA, proposing a set of five KPIs to comprehensively assess the performance of energy absorbers and (3) validating the usefulness of the proposed KPIs by comparing the performance of various tubular structures used as energy absorbers.
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Zhang, Shaoliang, Miguel Ángel Gomez, Qing Yi, Rui Dong, Anthony Leicht, and Alberto Lorenzo. "Modelling the Relationship between Match Outcome and Match Performances during the 2019 FIBA Basketball World Cup: A Quantile Regression Analysis." International Journal of Environmental Research and Public Health 17, no. 16 (August 7, 2020): 5722. http://dx.doi.org/10.3390/ijerph17165722.
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The FIBA Basketball World Cup is one of the most prominent sporting competitions for men’s basketball, with coaches interested in key performance indicators (KPIs) that give a better understanding of basketball competitions. The aims of the study were to (1) examine the relationship between match KPIs and outcome in elite men’s basketball; and (2) identify the most suitable analysis (multiple linear regression (MLR) vs. quantile regression (QR)) to model this relationship during the men’s basketball tournament. A total of 184 performance records from 92 games were selected and analyzed via MLR and QR, using 10th, 25th, 50th, 75th and 90th quantiles. Several offensive (Paint Score, Mid-Range Score, Three-Point Score, Offensive Rebounds and Turnovers) and defensive (Defensive Rebounds, Steals and Personal Fouls) KPIs were associated with match outcome. The QR model identified additional KPIs that influenced match outcome than the MLR model, with these being Mid-Range Score at the 10th quantile and Offensive Rebounds at the 90th quantile. In terms of contextual variables, the quality of opponent had no impact on match outcome across the entire range of quantiles. Our results highlight QR modelling as a potentially superior tool for performance analysts and coaches to design and monitor technical–tactical plans during match-play. Our study has identified the KPIs contributing to match success at the 2019 FIBA Basketball World Cup with QR modelling assisting with a more detailed performance analysis, to support coaches with the optimization of training and match-play styles.
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de Matos, Bárbara, Rodrigo Salles, Jérôme Mendes, Joana R. Gouveia, António J. Baptista, and Pedro Moura. "A Review of Energy and Sustainability KPI-Based Monitoring and Control Methodologies on WWTPs." Mathematics 11, no. 1 (December 29, 2022): 173. http://dx.doi.org/10.3390/math11010173.
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Humanity faces serious problems related to water supply, which will be aggravated by population growth. The water used in human activities must be treated to make it available again without posing risks to human health and the environment. In this context, Wastewater Treatment Plants (WWTPs) have gained importance. The treatment process in WWTPs is complex, consisting of several stages, which consume considerable amounts of resources, mainly electrical energy. Minimizing such energy consumption while satisfying quality and environmental requirements is essential, but it is a challenging task due to the complexity of the processes carried out in WWTPs. One form of evaluating the performance of WWTPs is through the well-known Key Performance Indicators (KPIs). The KPIs are numerical indicators of process performance, being a simple and common way to assess the efficiency and eco-efficiency of a process. By applying KPIs to WWTPs, techniques for monitoring, predicting, controlling, and optimizing the efficiency and eco-efficiency of WWTPs can be created or improved. However, the use of computational methodologies that use KPIs (KPIs-based methodologies) is still limited. This paper provides a literature review of the current state-of-the-art of KPI-based methodologies to monitor, control and optimize energy efficiency and eco-efficiency in WWTPs. In this paper, studies presented on 21 papers are identified, assessed and synthesized, 12 being related to monitoring and predicting problems, and 9 related to control and optimization problems. Future research directions relating to unresolved problems are also identified and discussed.
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Ho, Man Ying (Annie), Joseph H. K. Lai, Huiying (Cynthia) Hou, and Dadi Zhang. "Key Performance Indicators for Evaluation of Commercial Building Retrofits: Shortlisting via an Industry Survey." Energies 14, no. 21 (November 4, 2021): 7327. http://dx.doi.org/10.3390/en14217327.
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Key performance indicators (KPIs) are quintessentially useful for performance evaluation, but a set of pragmatic KPIs for holistic evaluation of retrofits for commercial buildings is hitherto unavailable. This study was conducted to address this issue. Built upon the findings of a systematic literature review and a focus group meeting in the earlier stages of the study, a questionnaire survey covering 19 KPIs for environmental (embracing energy), economic, health and safety, and users’ perspective evaluations of building retrofits was developed. Data of the survey, collected from facility management (FM) practitioners in Hong Kong, underwent a series of statistical analyses, including Kruskal–Wallis H test, Mann–Whitney U test, and Spearman Rank Correlation. The analysis results revealed the levels of importance of KPIs perceived by different groups of FM practitioners and the rankings of KPIs. Based upon these results, eight KPIs were shortlisted, which are energy savings, payback period, investment cost, actual-to-target ratio of the number of statutory orders removed, actual-to-target ratio of the number of accidents reduced, target indoor air temperature, target indoor air quality (IAQ) class, and target workplane illuminance. These KPIs serve as keystones for further development of an analytic evaluation scheme for commercial building retrofit performance assessment. The methodology of this study can also serve as a reference for similar KPI studies in other research domains.
Dissertations / Theses on the topic "KPIs optimization"
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Oudrhiri, Ali. "Performance of a Neural Network Accelerator Architecture and its Optimization Using a Pipeline-Based Approach." Electronic Thesis or Diss., Sorbonne université, 2023. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2023SORUS658.pdf.
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Ces dernières années, les réseaux de neurones ont gagné en popularité en raison de leur polyvalence et de leur efficacité dans la résolution d'une grande variété de tâches complexes. Cependant, à mesure que les réseaux neuronaux continuent de trouver des applications dans une gamme toujours croissante de domaines, leurs importantes exigences en matière de calcul deviennent un défi pressant. Cette demande en calcul est particulièrement problématique lors du déploiement de réseaux neuronaux sur des dispositifs embarqués aux ressources limitées, en particulier dans le contexte du calcul en périphérie pour les tâches d'inférence. De nos jours, les puces accélératrices de réseaux neuronaux émergent comme le choix optimal pour prendre en charge les réseaux neuronaux en périphérie. Ces puces offrent une efficacité remarquable avec leur taille compacte, leur faible consommation d'énergie et leur latence réduite. Dans le cadre du calcul en périphérie, diverses exigences ont émergé, nécessitant des compromis dans divers aspects de performance. Cela a conduit au développement d'architectures d'accélérateurs hautement configurables, leur permettant de s'adapter aux demandes de performance distinctes. Dans ce contexte, l'accent est mis sur Gemini, un accélérateur configurable de réseaux neuronaux conçu avec une architecture imposée et mis en œuvre à l'aide de techniques de synthèse de haut niveau. Les considérations pour sa conception et sa mise en œuvre ont été motivées par le besoin de configurabilité de la parallélisation et d'optimisation des performances. Une fois cet accélérateur conçu, il est devenu essentiel de démontrer la puissance de sa configurabilité, aidant les utilisateurs à choisir l'architecture la plus adaptée à leurs réseaux neuronaux. Pour atteindre cet objectif, cette thèse a contribué au développement d'une stratégie de prédiction des performances fonctionnant à un niveau élevé d'abstraction, qui prend en compte l'architecture choisie et la configuration du réseau neuronal. Cet outil aide les clients à prendre des décisions concernant l'architecture appropriée pour leurs applications de réseaux neuronaux spécifiques. Au cours de la recherche, nous avons constaté qu'utiliser un seul accélérateur présentait plusieurs limites et que l'augmentation de la parallélisme avait des limitations en termes de performances. Par conséquent, nous avons adopté une nouvelle stratégie d'optimisation de l'accélération des réseaux neuronaux. Cette fois, nous avons adopté une approche de haut niveau qui ne nécessitait pas d'optimisations fines de l'accélérateur. Nous avons organisé plusieurs instances de Gemini en pipeline et avons attribué les couches à différents accélérateurs pour maximiser les performances. Nous avons proposé des solutions pour deux scénarios : un scénario utilisateur où la structure du pipeline est prédéfinie avec un nombre fixe d'accélérateurs, de configurations d'accélérateurs et de tailles de RAM. Nous avons proposé des solutions pour mapper les couches sur les différents accélérateurs afin d'optimiser les performances d'exécution. Nous avons fait de même pour un scénario concepteur, où la structure du pipeline n'est pas fixe, cette fois il est permis de choisir le nombre et la configuration des accélérateurs pour optimiser l'exécution et également les performances matérielles. Cette stratégie de pipeline s'est révélée efficace pour l'accélérateur Gemini. Bien que cette thèse soit née d'un besoin industriel spécifique, certaines solutions développées au cours de la recherche peuvent être appliquées ou adaptées à d'autres accélérations de réseaux neuronaux. Notamment, la stratégie de prédiction des performances et l'optimisation de haut niveau du traitement de réseaux neuronaux en combinant plusieurs instances offrent des aperçus précieux pour une application plus largeIn recent years, neural networks have gained widespread popularity for their versatility and effectiveness in solving a wide range of complex tasks. Their ability to learn and make predictions from large data-sets has revolutionized various fields. However, as neural networks continue to find applications in an ever-expanding array of domains, their significant computational requirements become a pressing challenge. This computational demand is particularly problematic when deploying neural networks in resource-constrained embedded devices, especially within the context of edge computing for inference tasks. Nowadays, neural network accelerator chips emerge as the optimal choice for supporting neural networks at the edge. These chips offer remarkable efficiency with their compact size, low power consumption, and reduced latency. Moreover, the fact that they are integrated on the same chip environment also enhances security by minimizing external data communication. In the frame of edge computing, diverse requirements have emerged, necessitating trade-offs in various performance aspects. This has led to the development of accelerator architectures that are highly configurable, allowing them to adapt to distinct performance demands. In this context, the focus lies on Gemini, a configurable inference neural network accelerator designed with imposed architecture and implemented using High-Level Synthesis techniques. The considerations for its design and implementation were driven by the need for parallelization configurability and performance optimization. Once this accelerator was designed, demonstrating the power of its configurability became essential, helping users select the most suitable architecture for their neural networks. To achieve this objective, this thesis contributed to the development of a performance prediction strategy operating at a high-level of abstraction, which considers the chosen architecture and neural network configuration. This tool assists clients in making decisions regarding the appropriate architecture for their specific neural network applications. During the research, we noticed that using one accelerator presents several limits and that increasing parallelism had limitations on performances. Consequently, we adopted a new strategy for optimizing neural network acceleration. This time, we took a high-level approach that did not require fine-grained accelerator optimizations. We organized multiple Gemini instances into a pipeline and allocated layers to different accelerators to maximize performance. We proposed solutions for two scenarios: a user scenario where the pipeline structure is predefined with a fixed number of accelerators, accelerator configurations, and RAM sizes. We proposed solutions to map the layers on the different accelerators to optimise the execution performance. We did the same for a designer scenario, where the pipeline structure is not fixed, this time it is allowed to choose the number and configuration of the accelerators to optimize the execution and also hardware performances. This pipeline strategy has proven to be effective for the Gemini accelerator. Although this thesis originated from a specific industrial need, certain solutions developed during the research can be applied or adapted to other neural network accelerators. Notably, the performance prediction strategy and high-level optimization of NN processing through pipelining multiple instances offer valuable insights for broader application
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Skocaj, Marco. "An Inter-Frequency Handover Optimization Algorithm for LTE Networks – Design and Test." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
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The proposed thesis work tackles an optimization procedure for inter-frequency handover in LTE networks. In order to do that, a two steps algorithm based on local KPIs observation was designed and is therefore presented in detail. The algorithm consists of a two step procedure, comprising a numerical processing of the observed KPIs and a monitoring phase in order to balance and limit possible negative effects such as ping-pong handovers, outage conditions or reduced end-user throughput.
The algorithm was validated with a tests campaign performed on a real network infrastructure over the months of August and September.
The final objective of this optimization procedure, in the end, can be measured in terms of increased end-user throughput. For this purpose, all conducted tests are thoroughly analyzed and the obtained results are commented.
In the final sections and appendixes some foreseen evolutions and enhancements of the model are discussed.
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Vasquez, Coronado Pedro Pablo. "Optimization of the Haulage Cycle Model for Open Pit Mining Using a Discrete-Event Simulator and a Context-Based Alert System." Thesis, The University of Arizona, 2014. http://hdl.handle.net/10150/321594.
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The loading cycle in an Open Pit mine is a critical stage in the production process that needs to be controlled in detail for performance optimization. A comprehensive Alert System designed to notify supervisors of cycle times that are below the required performance standards is proposed. The system gives an alert message when one or several trucks are idle or the time of completing production tasks are over a predefined value. This alert is identified by the system and compared with pre-established Key Performance Indicators (KPIs) in order to determine corrective actions. The goal is to determine the strategies that help the production supervisor to optimize the haulage cycle model. A discrete-event simulator has been built in order to analyze different scenarios for route design and queue analysis. A methodology that utilizes different algorithms has been developed in order to identify the least productive times of the fleet. These results are displayed every time the simulation has finished. This research focuses on the optimization of haulage. However, the system is intended for implementation in subsequent stages of the production process, and the resulting improvement could impact mine planning and management as well. Topographic and drilling exploration data from a mine located hypothetically in the state of Arizona, were used to build a block model and to design an open pit; an Arena-based simulation was used to generate operating cycles that represent actual operations (As-Is model). Once the Alert System is implemented, adjustments were applied, and a new simulation was performed taking into consideration these adjustments (To-Be model), including comparative analysis and statistical results.
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Carvalho, Bruno Miguel Machado. "Forecasting techniques: application to cellular networks." Master's thesis, 2018. http://hdl.handle.net/10773/28303.
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Due to the growing competitiveness and aggressiveness of the market, the network operators’ strategy is increasingly based on improving infrastructures and optimizing existing resources, in a way that provides the best experience to
the user. To do this, the operator analyzes the Key Performance Indicators (KPIs) and uses forecasting methods to predict and plan the modifications needed in the network. With this as basis, this work focuses on the study and analysis of different forecasting methods and their implementation in Python, so that the operator
can obtain automate real-time predictions of the future behavior of his network.Devido ao aumento da competitividade e agressividade do mercado, cada vez mais a estratégia dos operadores de redes móveis passa pelo melhoramento das infraestruturas e otimização dos recursos já existentes, de modo a proporcionar a melhor experiência aos seus utilizadores. Para isto, recorrem à análise de indicadores chave de desempenho (KPIs) e ao uso de métodos de previsão para prever e planear alterações a realizar na sua rede. Tendo isto como base, esta dissertação foca-se no estudo e análise de diferentes métodos de previsão e sua implementação em Python, de maneira a obter previsões do futuro comportamento da rede em tempo real e de forma automatizada.
Mestrado em Engenharia Eletrónica e Telecomunicações
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Василевич, Д. А., and D. A. Vasilevich. "Моделирование системы оценки функциональности компонентов для повышения эффективности межструктурных коммуникаций : магистерская диссертация." Master's thesis, 2019. http://hdl.handle.net/10995/77671.
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As a part of this study a solution is being developed to evaluate the effectiveness of the components of the MES system. The bottom line is to assess the degree of use of standard functionalities (taking into account their weight ratio) as compared to design modifications for different projects. Analysis of the data provided to the user allows making clear, based on real indicators conclusions about the effectiveness of each component (or module) of the system, allowing to highlight the weak points of the system and determine the trajectory of the further development of each component and the entire software product.В рамках данной работы разрабатывается решение для оценки эффективности компонентов MES-системы. Суть заключается в оценке степени использования стандартных функциональностей (с учетом коэффициента их весомости) по сравнению с проектными доработками для разных проектов. Анализ предоставляемых пользователю данных позволяет делать четкие, основанные на реальных показателях выводы об эффективности каждого компонента (или модуля) системы, позволяя выделять слабые места системы и определять траектория дальнейшего развития как каждого компонента, так и всего программного продукта в целом.
Book chapters on the topic "KPIs optimization"
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Morri, Nabil, Sameh Hadouaj, and Lamjed Ben Said. "Toward Real-Time Multi-objective Optimization for Bus Service KPIs." In Informatics in Control, Automation and Robotics, 18–36. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26474-0_2.
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Stroe, Ion-Sorin. "Proposed KPIs for Optimization and Value Determination of an e-Business." In Soft Computing Applications, 1069–76. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18416-6_86.
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Chiesa, Giacomo, Francesca Fasano, and Paolo Grasso. "Simulated Versus Monitored Building Behaviours: Sample Demo Applications of a Perfomance Gap Detection Tool in a Northern Italian Climate." In Innovative Renewable Energy, 109–33. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15218-4_6.
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AbstractGreen building technologies and design-correlated choices may significantly contribute to supporting the transition toward net energy flows in the built environment. Nevertheless, large discrepancies are underlined between standard simulated and monitored building behaviours requiring approaches able to simply correlate real building behaviours and simulated ones to further support coherent certification and/or optimization. The paper focusses on the application of a semi-automatic methodology to compare and evaluate thermal behaviours of buildings considering monitored and simulated data. The approach is based on a new Python tool developed by the authors, able to manage EnergyPlus inputs and perform multi-source KPIs calculations. The mentioned tool is used here to support semi-automatic model verifications of real weather data by optimizing model parameters to fit monitored behaviours. The approach is applied in this chapter to two demo buildings, a municipality school and a residential unit, located in the Turin metropolitan area of Piedmont, in Northwest Italy.
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Winkler, Heiner, Susanne Franke, Felix Franke, Iren Jabs, Daniel Fischer, and Matthias Thürer. "Systems Thinking Approach for Production Process Optimization Based on KPI Interdependencies." In IFIP Advances in Information and Communication Technology, 662–75. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43670-3_46.
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Lai, Manuel, and Simona Tusacciu. "Analysis of Production Scenario and KPI Calculation: Monitoring and Optimization Logics of White’R Island." In Advances in Neural Networks, 475–80. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33747-0_47.
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Ali, Ashraf A., and Khalid Al-Begain. "Session Initiation and IP Multimedia Subsystem Performance Evaluation." In Advances in Wireless Technologies and Telecommunication, 36–49. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2113-6.ch003.
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In this chapter analyses the overall system capacity and scalability which is affected by added traffic introduced by more users who are trying to access the system provided services. This could happen in a mission critical communication system during natural disaster or large scale attack, where the system accessibility could be affected due to the sudden increase of number of users. The need for a more detailed study of other SIP and IMS KPIs is vital to have a better understanding of the overall system performance which will enable us to take it a step further toward system performance enhancement and optimization to avoid single point of failure of the system.
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Grover, Neha. "Performance Measurement." In Innovative Solutions for Implementing Global Supply Chains in Emerging Markets, 212–41. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9795-9.ch015.
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The purpose of this chapter is to study what supply chain managers of a retail supply chain measure. In other words aim here to identify the key indicators for measuring retail supply chain performance. A qualitative approach is adopted. Published literature from refereed journals on supply chain performance measurement has been considered. Besides the need for organizations to adopt a holistic approach, firms remain focused on traditional financial measures (gross profit margin, Interest coverage, and debt and equity ratios). The chapter identifies key indicators for performance measurement and classifies them into four major categories: transport optimization, information technology optimization, inventory optimization and resource optimization. These key indicators are arranged precisely for retail industry. From a supply chain perspective, the non financial measures such as on-time delivery, training of employees, warehouse layout, etc. are also important aspects of measuring supply chain performance. Further research can be carried out to validate the relevance and applicability of identified indicators. The study can be further conducted to measure the interrelationships between the KPIs and their impact on financial performance of the firm. In this chapter the author attempts to identify the performance indicators specifically for retail supply chain. The identified measures are further categorized based on its operations.
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Massaro, Alessandro. "Process Mining in Production Management, Intelligent Control, and Advanced KPI for Dynamic Process Optimization." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 1–17. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-7684-0.ch001.
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The book chapter is focused on the definition of efficient models to apply to production processes. Specifically, starting to business process modelling and notation (BPMN) approach, are defined rules and methods to integrate artificial intelligence (AI) and innovative key performance indicators (KPIs) for task checkpoints implementing a dynamic and intelligent decision-making approach. The whole theoretical mechanism constitutes a decision support system (DSS) model supporting risk analyses including aspects related to organization, predictive maintenance, and the use of technologies in the era of Industry 5.0. Particular attention is addressed on methods about the efficient monitoring of production processes by means process mining (PM) workflows. Different examples are provided in the book chapter, by enhancing the aspect related to the DSS logics and implementation of logic conditions. The discussed model opens a new topic about intelligent BPMN and process engineering including AI facilities strengthening decisions in operating processes.
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Peisino, Alessandro, and Simone Tesconi. "Naval Fleet Integrated Logistic Design Optimization: The Italian Navy Experience in Enhancing Feedback from the Field." In Progress in Marine Science and Technology. IOS Press, 2022. http://dx.doi.org/10.3233/pmst220012.
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Defense budget are shrinking and human resources are becoming more critical, while operational needs of Warships increase and change in the life cycle to face new threats. To achieve the best balance between operational availability (Ao) and costs all along the life cycle, the sustainability requires a correct initial definition of the support followed by its continuous optimization, guaranteed by constant monitoring and analysis of the data coming back from the field, by a review of the reliability parameters, maintenance plan and spare parts list. The ITN (Italian Navy) started a change management initiative from beginning of 2000’ through an optimization process by analysing the return from the field data during the TGS FREMM contract, which has lasted for more than 10 years. This process consist of: ∙ An initial definition of the support: configuration management, obsolescence monitoring, maintenance plans and definition of stocks, optimized with OPUS10 tools, whose models are created starting from the logistic databases provided by the private industry; ∙ The evaluation and analysis of return from the field data: measure of the reached availability, KPIs (Key Performance Indicator) evaluation and reliability calculation, trough assessment software tools (Weibull) and recalculation software (Calypso); ∙ The optimization of support: in terms of operational availability and costs, in compliance with the operational requirements. After the FREMM experience, ITN is taking over all the activities performed so far by the private companies, for current and future shipbuilding programs (LSS, PPA and LHD). The authors will go through the process set, tested for FREMM program, will show results after more than 10 years of experience, and will cover all the activities Italian Navy is taking care by itself for new programs, mentioning as well ITN investments and available tools.
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Durcakova, Michaela, Jaak Lavin, and Kristo Karjust. "KPI Optimization for Product Development Process." In Proceedings of the 23rd International DAAAM Symposium 2012, 1079–84. DAAAM International Vienna, 2012. http://dx.doi.org/10.2507/23rd.daaam.proceedings.252.
Full textConference papers on the topic "KPIs optimization"
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Furtado, C. J. A., G. G. Lage, G. R. V. A. da Fonseca, and A. A. R. Patrício. "Water Injection Optimization Based on Operational KPIs." In SPE Water Lifecycle Management Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/219062-ms.
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Waterflood is one of the main techniques to improve the oil recovery. Besides displacing and producing incremental oil, water injection helps to maintain the reservoir pressure close to its original value, keeping productivity in a high level. In offshore fields, seawater is fully available although it needs to have several quality controls to avoid undesirable consequences. Others water sources can be used such as aquifers or produced water, each of which with its specific needs in terms of quality [Chappel, 2020]. In the last decades, injection water process treatment is including more advanced equipment and chemicals to improve the water quality. Therefore, large footprint, high power supply and hard logistic have been required for water injection. Separation process based on membranes is an example of technology that has its use increased recently, mainly for: Nanofiltration membranes for sulfate removal; low sulfate content in water injection may reduce significantly the scale precipitation potential. A side benefit is reducing the biogenic H2S generation (souring) when low SO4 water is injected.Ultrafiltration membranes for solids removal. High solids content can cause pore rocks plugging near wellbore, reducing injectivity. Ceramic ultrafiltration membranes have also been used to remove solids from produced water with the same target.Reverse osmoses membranes to reduce water salinity. Low salinity water may increase the oil recovery when compared with brines such as the seawater. Advanced technologies normally imply into more laborious or time-consuming maintenance. As space in an offshore unit is very limited, facilities design normally considers a set of equipment that allow full capacity even during maintenance, like 5×25% or 6×20%. Although all these cares, uptime can reach low values mainly for units with high volume rates or any critical operational problem. For some kind of equipment, it is possible to bypass it or still operate it far from design conditions. It means that you may keep the injection rate needed, but the water quality will be poor, and you should deal with the consequences. In this work, we will introduce a methodology to check the consequences and benefits of manage water injection with different KPIs (key performance index) and show some cases where this methodology was adopted with success.
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Morri, Nabil, Sameh Hadouaj, and Lamjed Ben Said. "Agent-based Intelligent KPIs Optimization of Public Transit Control System." In 18th International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010616302240231.
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Morri, Nabil, Sameh Hadouaj, and Lamjed Ben Said. "Agent-based Intelligent KPIs Optimization of Public Transit Control System." In 18th International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010616300002994.
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Celebic, Boban, and Ruth Breu. "Using Green KPIs for Large IT Infrastructures' Energy and Cost Optimization." In 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud). IEEE, 2015. http://dx.doi.org/10.1109/ficloud.2015.86.
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Morri, Nabil, Sameh Hadouaj, and Lamjed Ben Said. "Towards an Intelligent control system for public transport traffic efficiency KPIs optimization." In 2020 Global Congress on Electrical Engineering (GC-ElecEng). IEEE, 2020. http://dx.doi.org/10.23919/gc-eleceng48342.2020.9286268.
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Berger, Sascha, Albrecht Fehske, and Gerhard Fettweis. "Force field based joint optimization of strictly monotonic KPIs in wireless networks." In 2012 IFIP Wireless Days (WD). IEEE, 2012. http://dx.doi.org/10.1109/wd.2012.6402845.
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Arif Normin, Muhammad Afiq, Azlesham Rosli, Meor M. Hakeem Meor Hashim, M. Faris Arriffin, and Rohaizat Ghazali. "A Successful Case Study of a Collaborative Approach in Operational Optimization via Adoption of Automated Drilling Performance Measurement." In Offshore Technology Conference Asia. OTC, 2022. http://dx.doi.org/10.4043/31579-ms.
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Abstract Digital transformation has always been one of the focuses of the oil & gas industry players in recent years. However, the pandemic and oil downturn last year has put the industry players in a digitization overdrive in the pursuit of leaner and cost-effective operations to stay ahead in these unprecedented times. This paper discusses the strategy, approach, and challenges in the adoption and implementation of the Automated Drilling Performance Measurement (ADPM) onsite and remote approach. This includes Wells Real-Time Center (WRTC), which utilizes the ADPM, an easy access analysis application for operational optimization. The implementation of ADPM falls under the PETRONAS Well Cost Compression focus area of Operational Optimization, which aims to achieve the operational technical limit and non-productive time (NPT) reduction. The stages of operational optimization via ADPM are broken down into pre-spud operations, operations, and post-well analysis. A historical performance study is conducted in pre-spud operations and case study sessions with the project team and Subject Matter Experts (SMEs). Once in operations, best practices for the focused key performance indicator (KPI) and ad-hoc gap analysis are implemented onsite throughout the well construction. Remotely, the KPIs are monitored by WRTC while rig contractors monitor the crew performance. The performance review is studied in post-well analysis, and the best practices are compiled for replication and lesson learned to improve future well's excellence. The evaluation of rig performance is conducted based on the focused KPIs criteria and Rig Scorecard criteria. Implementation of ADPM set clear and defined strategy from top management on digitalization and performance optimization. ADPM also helps foster performance optimization awareness and culture with clearly defined roles, responsibilities, and expectations. For example, the application deployment for the Field B drilling campaign focused on tripping, drilling and casing KPI improvement while utilizing ADPM for data gathering and analysis. The result of this deployment is commendable, with a total actual savings of 2.94 days gained throughout the campaign. From 2016 to 2021, PETRONAS has gained a total of 39.03 days of actual savings for their entire rig fleet.
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Karaouzene, Zoheir, Hicham Megnafi, Lotfi Merad, and Sidi Mohammed Meriah. "Artificial Intelligence in 5G Planning: Optimization of EnodeB Planning Based on 4G KPIs." In 2023 IEEE International Workshop on Mechatronic Systems Supervision (IW_MSS). IEEE, 2023. http://dx.doi.org/10.1109/iw_mss59200.2023.10368904.
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Chell, Brian, Steven Hoffenson, Benjamin Kruse, and Mark R. Blackburn. "Mission-Level Optimization: Complex Systems Design for Highly Stochastic Life Cycle Use Case Scenarios." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22454.
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Abstract Mission engineering is a growing field with many practical opportunities and challenges. The goal of mission engineering is to increase system effectiveness, reduce life cycle costs, and aid in communicating system capabilities to key stakeholders. Optimizing system designs for their mission context is important to achieving these goals. However, system optimization is generally done using multiple key performance indicators (KPIs), which are not always directly representative of, nor easily translatable to, mission success. This paper introduces, motivates, and proposes a new approach for performing mission-level optimization (MLO), where the objective is to design systems that maximize the probability of mission success over the system life cycle. This builds on previous literature related to mission engineering, modeling, and analysis, as well as optimization under uncertainty. MLO problems are unique in their high levels of design, operational, and environmental uncertainty, as well as the single binary objective representing mission success or failure. By optimizing for mission success, designers can account for large numbers of KPIs and external factors when determining the best possible system design.
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Sibaweihi, Najmudeen, and Japan Trivedi. "Distributed Real-Time Multi-Pad Steam Allocation Optimization." In SPE Canadian Energy Technology Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212757-ms.
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Abstract In Steam-Assisted Gravity Drainage (SAGD) recovery, optimal real-time steam allocation from a shared steam generator to the physically coupled multi-pads can significantly improve long-term performance goals. However, multi-pad real-time optimization (RTO) with first-principle models can be computationally intensive. Furthermore, general-purpose optimization algorithms in RTO do not consider the future state beyond the prediction horizons to be optimized and treat the optimization problem as a long-term optimization process. Since steam is the primary cost factor in SAGD, Key Performance Indicators (KPI) such as Net Present Value (NPV), when used in RTO, result in low steam injection impeding steam chamber growth during the build-up and normal SAGD operational phase. Therefore, balancing steam chamber development and economics becomes essential for SAGD well-pads using RTO to meet long-term goals. In this contribution, we implement the Alternating Direction Method of Multipliers (ADMM) and a dynamic data-driven model to reduce the computational cost of RTO. ADMM coordinates in real-time field-wide use of shared steam generation. The shared steam generation is a market commodity traded between the pads, with global coordination in real-time perturbation of their market prices. Four SAGD KPIs are implemented for a multi-pad RTO of the SAGD normal operations phase to see which KPI eventually grows the steam chamber without negatively affecting the long-term economic performance. A SAGD field with four pads with 33 well-pairs shows that for all four pads, an economic-based KPI limits the achievement of long-term goals because it cannot account for the future state beyond the horizon under consideration due to hindered steam chamber growth. For the steam chamber expansion and bitumen recovery KPI, high recovery and economic performance are achieved, but with a high resource requirement, leading to a high carbon footprint. On the other hand, an alternating economic and bitumen recovery KPI achieves high economic performance while minimizing resource requirements that decrease carbon footprint.