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.
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Tuncer, Gozde, Deniz Mansouri, and Polat Şendur. "The effect of spotwelds and structural adhesives on static and dynamic characteristics of vehicle body design." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 235, no. 12 (March 19, 2021): 3207–19. http://dx.doi.org/10.1177/09544070211004468.
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Spotwelding and structural adhesive applications are two important processes in the automotive industry as they are closely associated with the functional requirements, weight, and cost of the vehicle. Even though there is a vast body of literature on their mathematical models, the effect of these processes on key vehicle performance indices and optimization is rather limited. Besides, the weight benefit of these processes in terms of functional requirements has not been investigated. There are multiple objectives of the paper to fill this gap: (i) to quantify the effect of structural adhesives on the key performance indices (KPIs) of a vehicle body, (ii) to rank the components based on their gauge sensitivities for body KPIs using topometry optimization, (iii) to assess the weight impact of the structural adhesive applications using the gauge sensitivity results, (iv) to determine the optimum layout of the structural adhesive applications using topology optimization, (v) to present a methodology for automotive original equipment manufacturers (OEMs) to determine the “critical welds” on the vehicle body and reduce the number of spotwelds as a potential cost reduction action. For this purpose, a validated finite element model of 2010 Toyota Yaris has been used. Optimization of the structural adhesives and spotwelds was carried-out using SIMP (Solid Isotropic Material with Penalization) based topology optimization. The thickness of each panel is ranked using topometry optimization results. Automotive OEMs can use the proposed methodology to optimize the structural adhesives or spotwelding processes in their product development cycle.
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Cecchini, Alessia, Grazia Maria Pia Masselli, and Sergio Silvestri. "Evaluation and optimization of CES performances: application of Pareto principle to KPIs." Global Clinical Engineering Journal 4, no. 1 (March 1, 2021): 14–21. http://dx.doi.org/10.31354/globalce.v4i1.87.
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In recent times the approach to health care has been mostly influenced by the growing number of biomedical equipment used in hospitals, which needs the presence of the Clinical Engineering Service (CES).
The aim of this work is to suggest a methodology to improve the performance of a CES through the application of Pareto principle to main Key Performance Indicators (KPIs). The methodology is applied by focusing on the use of KPIs that represent a quantifiable measure of achieving goals set by an organization. In this study five KPIs are considered: Uptime, MTTR (mean time to repair), PPM (percentage preventive maintenance), MTBF (mean time between failures) and the COSR (cost of service ratio). The first three indicators express the measure of CES efficiency in ensuring regular maintenance.
The first step consists in retrieving data related to work orders for the years 2015-2016 on 6000 installed devices, carried out by a management software. The second step is to get the results through the use of an environment for numerical calculation and statistical analysis.
In order to identify the main critical issues that may be present, three indicators (Uptime, MTTR and MTBF) are analyzed by applying the Pareto principle (i.e. 20% of the causes produce 80% of the effects). Considering the totality of work orders, therefore, it is possible to concentrate on only 20% of them in order to focus on a small group to understand the correlations between them. Identifying these characteristics means identifying the main critical issues that are present, on which action must be taken, and which affect 80% of the overall behavior. The COSR and PPM indicators, instead, suggest distribution models that allow to focus attention on the most critical devices.
In conclusion, the way to analyze the results is obtained, when possible, by applying Pareto principle. Therefore, a CES will be able to focus on a few causes of poor performance. The achievement of these results could allow the standardization of the method used, enabling it to be applied to any healthcare system.
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Carpenter, Chris. "Collaborative Approach Optimizes Automated-Drilling Performance Measurement." Journal of Petroleum Technology 74, no. 10 (October 1, 2022): 68–70. http://dx.doi.org/10.2118/1022-0068-jpt.
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_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 31579, “A Successful Case Study of a Collaborative Approach in Operational Optimization Through Adoption of Automated-Drilling-Performance Measurement,” by Muhammad Afiq Arif Normin, TDE Group, and Azlesham Rosli and Meor M.H.M. Hashim, SPE, Petronas, et al. The paper has not been peer reviewed. Copyright 2022 Offshore Technology Conference. Reproduced by permission. _ This paper discusses the strategy devised, the approach implemented, and the challenges experienced in the adoption and implementation of an automated drilling-performance-measurement (ADPM) system to be used onsite and remotely. The stages of the optimization of the ADPM system are broken down into prespud operations, operations, and post-well analysis. The successful result of this deployment has seen total actual savings of 2.94 days throughout the campaign. From 2016 to 2021, the operator gained 39.03 days of actual savings for their rig fleet. The ADPM Tool The ADPM tool was introduced in 2016 as part of the operator’s digitalization effort. The tool was integrated into operations at the Wells Real-Time Center (WRTC) and was managed by a wells technical application champion (WTAC). With this integration in place, the operator aimed to deliver all wells, whether vertical, horizontal, or extended-reach, in the most consistent and efficient way without jeopardizing the operation’s safety. To ensure the investment bore dividends, this initiative required a major collaborative effort. The tool’s main input is the rig’s real-time surface data obtained by mud-logging sensors. The real-time data are transmitted to the WRTC server in Kuala Lumpur and stored. Access to the data is provided to the ADPM tool to extract the necessary data required for rig-state detection. The secondary input for the ADPM tool is the daily drilling report (DDR) prepared by drilling supervisors at the rig site. The extraction of information from the DDRs serves two purposes: to provide information on the activity performed on the surface not detected by rig-state detection and to provide a reference for ADPM personnel during data processing and quality control. Each activity code and subcode associated with the DDR also is integrated into the ADPM database. Merging both data has provided more insight to the project team because a comparison between the activity reported in the DDRs and the activity detected by the ADPM can be highlighted. During the initial phase of ADPM deployment, a list of the key performance indicators (KPIs) was defined and established by the WTAC. This set of KPIs covered the tripping, drilling, and casing and liner operations necessary to determine invisible lost time (ILT). For tripping operations, the critical KPIs are slip time, tripping-in speed, and tripping-out speed. All KPIs are differentiated as having been performed in cased hole or open hole. Next is on-bottom drilling, with five focused KPIs. Finally, three KPIs were dedicated to the casing and liner operations: slips time, average pipe moving speed, and gross running rate. As with tripping operations, all KPIs were differentiated as having been performed in cased hole or open hole except slips time. All KPIs were computed from a single data source (the real-time data).
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Bikas, Harry, Michail Aggelos Terzakis, and Panagiotis Stavropoulos. "Manufacturability-Based Design Optimization for Directed Energy Deposition Processes." Machines 11, no. 9 (September 1, 2023): 879. http://dx.doi.org/10.3390/machines11090879.
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Additive Manufacturing (AM) is the process of joining materials by selectively depositing them layer upon layer for the purpose of manufacturing parts or assemblies which are based on a 3D digital model. The nature of these processes results in the morphing of complex component geometries, enabling a high degree of design freedom and resulting in lightweight structures with increased performance. These processes, however, experience many limitations regarding manufacturability. The aim of this study is to develop a method and tool that optimizes the design of a component to avoid overhanging geometries and the need for supports during the Additive Manufacturing process. A workflow consisting of steps for topology optimization, orientation optimization, material addition, and machine code generation is described and implemented using Rhinoceros 3D and Grasshopper software. The proposed workflow is compared to a conventional workflow regarding manufacturing Key Performance Indicators (KPIs) such as part volume, support volume, and build time. A significant reduction is observed regarding all the KPIs by using the proposed method. Examining the results from both the conventional workflow and the proposed one, it is clear that the latter has unquestionable advantages in terms of effectiveness. In the particular case study presented, a total volume reduction of around 80% is observed. The reduction in the total volume (including the required support volume) leads to a significant reduction in the material used as well as in the build time, consequently resulting in cost reduction.
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Maduekwe, Victor Chidiebere, and Sunday Ayoola Oke. "Novel Taguchi scheme–based DEMATEL methods and DEMATEL method for the principal performance indicators of maintenance in a food processing industry." International Journal of Intelligent Computing and Cybernetics 14, no. 3 (April 29, 2021): 363–97. http://dx.doi.org/10.1108/ijicc-12-2020-0209.
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PurposeKey performance indicators (KPIs) of maintenance systems serve as benchmarks to workers and organizations to compare their goals for decision-making purposes. Unfortunately, the effects of one KPI on the other are least known, restraining decisions on prioritization of KPIs. This article examines and prioritizes the KPIs of the maintenance system in a food processing industry using the novel Taguchi (T) scheme-decision-making trial and evaluation laboratory (DEMATEL) method, Taguchi–Pareto (TP) scheme–DEMATEL method and the DEMATEL method.Design/methodology/approachThe causal association of maintenance process parameters (frequency of failure, downtime, MTTR, MTBF, availability and MTTF) was studied. Besides, the optimized maintenance parameters were infused into the DEMATEL method that translates the optimized values into cause and effect responses and keeping in view the result of analysis. Data collection was done from a food processing plant in Nigeria.FindingsThe results indicated that downtime and availability have the most causal effects on other criteria when DEMATEL and T-DEMATEL methods were respectively applied to the problem. Furthermore, the frequency of failure is mostly affected by other criteria in the key performance indication selection using the two methods. The combined Taguchi scheme and DEMATEL method is appropriate to optimize and establish the causal relationships of factors.Originality/valueHardly any studies have reported the joint optimization and causal relationship of maintenance system parameters. However, the current study achieves this goal using the T-DEMATEL, TP-DEMATEL and DEMATEL methods for the first time. The applied methods effectively ease decisions on prioritization of KPIs for enhancement.
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Juan, Angel A., Majsa Ammouriova, Veronika Tsertsvadze, Celia Osorio, Noelia Fuster, and Yusef Ahsini. "Promoting Energy Efficiency and Emissions Reduction in Urban Areas with Key Performance Indicators and Data Analytics." Energies 16, no. 20 (October 22, 2023): 7195. http://dx.doi.org/10.3390/en16207195.
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With the increasing demand for sustainable urban development, smart cities have emerged as a promising solution for optimizing energy usage, reducing emissions, and enhancing the quality of life for citizens. In this context, the combined use of key performance indicators (KPIs) and data analytics has gained significant attention as a powerful tool for promoting energy efficiency and emissions reduction in urban areas. This paper presents a comprehensive conceptual framework in which a series of KPIs are proposed to serve as essential metrics for guiding, monitoring, and assessing energy efficiency and emissions reduction levels in smart cities. Some of the included KPIs in the analysis are 'annual energy consumption per person', 'reduction in greenhouse gas emissions', 'public transport use', and 'adoption of renewable energy'. By incorporating these KPIs, city planners and policymakers can gain valuable insights into the effectiveness of sustainability initiatives. Furthermore, the paper explores how the integration of KPIs with data analytics can be used for monitoring and assessing the overall performance of the city in terms of energy efficiency, emissions reduction, and the enhancement of urban living conditions. Visualization tools, such as radar plots, and time series analysis forecasting methods allow data to be processed and patterns to be identified, enabling informed decision-making and efficient resource allocation. Real-life case studies of ongoing smart city projects are presented in the paper, which also provides a KPI comparison among different European cities, as well as models to forecast the evolution of KPIs related to energy usage and emissions reduction in different European cities.
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El Mahjoubi, Ahmed, Tomader Mazri, and Nabil Hmina. "4.5G Capacity & QoS Optimization: A New Proposal to Enhance the Principal KPIs of 5G/IoT." International Journal of Online Engineering (iJOE) 14, no. 11 (November 10, 2018): 193. http://dx.doi.org/10.3991/ijoe.v14i11.9256.
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RF capacity management and optimization is an important part of the network performances of the future. Technically, 4.5G/5G mobile network is expected to enable people-thing and thing-thing interconnections by combining communication technologies and networks. Our Lab has been actively promoting research on 4G & 5G performances, in which the RF Capacity management issue is of great interest. New 4G/5G tests put forward a lot of requirements for RF network performances in terms of KPIs, QoS, as well as user experience. Based on Morocco’s current situation, this article first discusses the LTE cell resources monitoring and optimization, Furthermore, we will introduce LTE OMSTAR tool, and LTE cell resources optimization scenarios are summarized.
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Biser, Stefan, Guido Wortmann, Swen Ruppert, Mykhaylo Filipenko, Mathias Noe, and Martin Boll. "Predesign Considerations for the DC Link Voltage Level of the CENTRELINE Fuselage Fan Drive Unit." Aerospace 6, no. 12 (November 20, 2019): 126. http://dx.doi.org/10.3390/aerospace6120126.
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Electric propulsion (EP) systems offer considerably more degrees of freedom (DOFs) within the design process of aircraft compared to conventional aircraft engines. This requires large, computationally expensive design space explorations (DSE) with coupled models of the single components to incorporate interdependencies during optimization. The purpose of this paper is to exemplarily study these interdependencies of system key performance parameters (KPIs), e.g., system mass and efficiency, for a varying DC link voltage level of the power transmission system considering the example of the propulsion system of the CENTRELINE project, including an electric motor, a DC/AC inverter, and the DC power transmission cables. Each component is described by a physically derived, analytical model linking specific subdomains, e.g., electromagnetics, structural mechanics and thermal analysis, which are used for a coupled system model. This approach strongly enhances model accuracy and simultaneously keeps the computational effort at a low level. The results of the DSE reveal that the system KPIs improve for higher DC link voltage despite slightly inferior performance of motor and inverter as the mass of the DC power transmission cable has a major share for a an aircraft of the size as in the CENTRELINE project. Modeling of further components and implementation of optimization strategies will be part of future work.
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Sánchez, Almudena, Rocío Acedo-Hernández, Matías Toril, Salvador Luna-Ramírez, and Carlos Úbeda. "A Trace Data-Based Approach for an Accurate Estimation of Precise Utilization Maps in LTE." Mobile Information Systems 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/2752364.
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For network planning and optimization purposes, mobile operators make use of Key Performance Indicators (KPIs), computed from Performance Measurements (PMs), to determine whether network performance needs to be improved. In current networks, PMs, and therefore KPIs, suffer from lack of precision due to an insufficient temporal and/or spatial granularity. In this work, an automatic method, based on data traces, is proposed to improve the accuracy of radio network utilization measurements collected in a Long-Term Evolution (LTE) network. The method’s output is an accurate estimate of the spatial and temporal distribution for the cell utilization ratio that can be extended to other indicators. The method can be used to improve automatic network planning and optimization algorithms in a centralized Self-Organizing Network (SON) entity, since potential issues can be more precisely detected and located inside a cell thanks to temporal and spatial precision. The proposed method is tested with real connection traces gathered in a large geographical area of a live LTE network and considers overload problems due to trace file size limitations, which is a key consideration when analysing a large network. Results show how these distributions provide a very detailed information of network utilization, compared to cell based statistics.
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Brochado, Ângela F., Eugénio M. Rocha, and Diogo Costa. "A Modular IoT-Based Architecture for Logistics Service Performance Assessment and Real-Time Scheduling towards a Synchromodal Transport System." Sustainability 16, no. 2 (January 15, 2024): 742. http://dx.doi.org/10.3390/su16020742.
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Logistics is significantly impacted by quality/quantity issues associated with data collection and data sharing restrictions. Nonetheless, public data from national entities and internet-of-things (IoT) solutions enable the development of integrated tools for performance analysis and real-time optimization of logistics networks. This study proposes a three-module data-driven system architecture that covers (a) logistics data collection tools, (b) logistics services performance evaluation, and (c) the transition to synchromodal systems. Module 1 integrates multisource data from national logistics platforms and embedded devices placed within intermodal containers. A multigraph representation of the problem is conceived. Environmental, economic, and operational data are generated and injected into a digital twin. Thus, key performance indicators (KPIs) are computed by simulation or direct transformation of the collected data. Module 2 uses Multi-directional Efficiency Analysis, an optimization algorithm that benchmarks multimodal transportation routes of containers using prior KPIs. Outputs are a technical performance index relevant to logistics clients and improvement measures for logistics service providers. A real case study application of the solution proposed for Module 2 is presented. Module 3 provides real-time scheduling and assignment models using CP-sat solvers, accommodating varying system dynamics and resource availability, minimizing makespan and operational costs.
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Simon, Victor, Carl-Anders Johansson, and Diego Galar. "Aggregation of Electric Current Consumption Features to Extract Maintenance KPIs." Management Systems in Production Engineering 25, no. 3 (September 26, 2017): 183–90. http://dx.doi.org/10.1515/mspe-2017-0027.
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AbstractAll electric powered machines offer the possibility of extracting information and calculating Key Performance Indicators (KPIs) from the electric current signal. Depending on the time window, sampling frequency and type of analysis, different indicators from the micro to macro level can be calculated for such aspects as maintenance, production, energy consumption etc. On the micro-level, the indicators are generally used for condition monitoring and diagnostics and are normally based on a short time window and a high sampling frequency. The macro indicators are normally based on a longer time window with a slower sampling frequency and are used as indicators for overall performance, cost or consumption. The indicators can be calculated directly from the current signal but can also be based on a combination of information from the current signal and operational data like rpm, position etc. One or several of those indicators can be used for prediction and prognostics of a machine’s future behavior. This paper uses this technique to calculate indicators for maintenance and energy optimization in electric powered machines and fleets of machines, especially machine tools.
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Christoforidou, Popi, George Bariamis, Maria Iosifidou, Eri Nikolaidou, and Petros Samaras. "Energy Benchmarking and Optimization of Wastewater Treatment Plants in Greece." Environmental Sciences Proceedings 2, no. 1 (August 31, 2020): 36. http://dx.doi.org/10.3390/environsciproc2020002036.
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Wastewater treatment, as a crucial component of the urban water environment, consists of several energy-consumptive stages, therefore efficiency and energy savings measures are essential to maintain them as environmentally sustainable and economically viable. Operational and technical data from WWTPs in Greece have been collected as well as a sample from 61 facilities with key energy profile components. Energy consumption was assessed by specific key performance indicators (KPIs); specific energy consumption expressed per population equivalent (from 3 to 150 kWh/PE), per cubic meter treated (from 0.2 to 2.0 kWh/m3) and per unit of organic load removed (from 0.03 to 7.13 kWh/CODremoved).
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Wang, Xiaoling, Xiaofang Zhang, Ning Li, Chengfeng Han, Zhuping Yuan, and Huanyu Gao. "A Time Series Prediction Approach Based on Hybrid Tuning for Database Performance Indicator in AIOps." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 5 (October 2020): 1030–37. http://dx.doi.org/10.1051/jnwpu/20203851030.
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One of the most important applications of the intelligent operation and maintenance of a cloud database is its trend prediction of key performance indicators (KPI), such as disk use, memory use, etc. We propose a method named AutoPA4DB (Auto Prophet and ARIMA for Database) to predict the trend of the KPIs of the cloud database based on the Prophet model and the ARIMA model. Our AutoPA4DB method includes data preprocessing, model building, parameter tuning and optimization. We employ the weighted MAPE coverage to measure its accuracy and use 6 industrial datasets including 10 KPIs to compare the AutoPA4DB method with other three time-series trend prediction algorithms. The experimental results show that our AutoPA4DB method performs best in predicting monotonic variation data, e.g.disk use trend prediction. But it is unstable in predicting oscillatory variation data; for example, it is acceptable in memory use trend prediction but has poor accuracy in predicting the number of database connection trends.
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Bagchi, Arkya, Akash Chowdhary, Abhishek Takki, and Raminder Kaur. "Simulation and Optimization of Ultrasonic Transducer." International Journal of Electrical and Electronics Research 10, no. 3 (September 30, 2022): 423–28. http://dx.doi.org/10.37391/ijeer.100303.
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The ultrasonic transducers have numerous applications in industries, including medical probes for performing ultrasound scans. One of the significant drawbacks of the ultrasonic transducer is the wastage of a large portion of energy, due to high acoustic impedance, while transmitting ultrasonic waves to the target object. The present study is aimed to investigate the material design of the piezo-composite transducer and improve its performance. Different piezo-composite transducers were simulated in the COMSOL environment by varying input parameters, and three key performance indicators (KPI) were calculated. Many constraint-based multivariable optimization algorithms have been used to maximize the KPIs. A set of parameters, such as Sensitivity and Fractional Bandwidth, have been found to increase the performance of piezo-composite transducer model and its overall efficiency. This study is intended to impinge unidirectional property to the transducer which is found to be beneficial in more accurate medical as well as structural reports and cost savings.
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Mushtaq, Umar, and Md Khalil Shahid. "Optimization of Revenue Assurance and Fraud Management System by Designing New KPIs: Case PTCL." International Journal of Computer Applications 89, no. 8 (March 1, 2014): 8–11. http://dx.doi.org/10.5120/15520-4129.
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Ahmad Akbar, Suheri, and Josua Betrand Sinaga. "Design Website for Digital Promotion SMEs Product by Optimize SEO Techniques." International Journal Of Computer Sciences and Mathematics Engineering 2, no. 2 (November 30, 2023): 231–40. http://dx.doi.org/10.61306/ijecom.v2i2.46.
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Small and medium-sized enterprises (SMEs) frequently face challenges in effectively promoting and selling their products online. This study aims to develop an optimized website design for digital promotion of SME products, incorporating enhanced search engine optimization (SEO) techniques. The paper first reviews key barriers inhibiting SMEs from harnessing digital channels, including limited expertise and resources in web development, SEO, and online marketing. Core principles of effective website design are then introduced, covering aspects like user-friendly interfaces, high-quality content, efficient site architecture, and responsive layouts. Building on this, SEO best practices are outlined based on an analysis of major ranking signals and algorithmic behavior. An integrated methodology is subsequently proposed for planning and creating SEO-optimized websites tailored to SME product promotion. The methodology incorporates defining goals and KPIs, competitor analysis, keyword research and mapping, optimized content development, technical site optimization, performance tracking, and continuous improvement. To validate the efficacy of this methodology, the study implements it in developing an e-commerce website for a small homemade food business. KPIs including organic traffic, conversions, and revenues are tracked before and after site development. The results demonstrate significant increases across key indicators, highlighting the effectiveness of the proposed approach in bolstering SME digital promotion through optimized website design and SEO. The methodology and findings aim to provide an accessible, impactful framework for SME adoption of digital channels.
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Leisenberg, Manfred. "Strategies and Tools for Marketing Based on Mobile Services and Devices–An Overview." International Business Research 9, no. 4 (March 5, 2016): 66. http://dx.doi.org/10.5539/ibr.v9n4p66.
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<p>The selection of appropriate tools has great influence on the success of mobile marketing. Monitoring and tracking make it possible to assess the results of the use of these tools. This article will compare what tools and monitoring methods can be used in target-oriented companies on the basis of their inclusion in social media optimization. Advantages of particular tools are discussed. A comparison of properties of mobile websites and native apps regarding development cost, change cycles, speed and other characteristics is provided. The continuous improvement of the mobile marketing processes by application of the Mobile Social Media Optimization is described. In order to determine the optimization success typical Key Performance Indicators are introduced. Different technical procedures that are used in order to determine these KPIs are presented.</p>
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Larsen, O. K., M. Z. Pomianowski, G. Chiesa, E. Belias, T. de Kerchove d’Exaerde, F. Flourentzou, F. Fasano, and P. Grasso. "E-DYCE - Dynamic approach to the dynamic energy certification of buildings." Journal of Physics: Conference Series 2600, no. 3 (November 1, 2023): 032015. http://dx.doi.org/10.1088/1742-6596/2600/3/032015.
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Abstract The energy performance certification (EPC) scheme, introduced in the European Union approximately 20 years ago, has become the focus of the upcoming revision of the Energy Performance of Buildings Directive (EPBD). Despite its widespread use, the current EPC scheme has several shortcomings that need to be addressed. The Energy flexible DYnamic building Certification (E-DYCE) project has developed a dynamic approach to address these issues. The methodology includes a dynamic assessment of a building’s energy needs and comfort conditions under standard and different from standard conditions of building use to support Performance Gap (PG) analyses. The E-DYCE approach includes a dynamic building performance simulation with comfort and energy-related key-performance indicators (KPIs) measured and calculated according to E-DYCE DEPC methodology. These KPIs can inform end-users about indoor environmental quality conditions decisive for building energy performance, aid building managers in detecting dysfunctions resulting in PG, and include energy performance indexes for heating, cooling, lighting, domestic hot water, and more. Overall, the E-DYCE approach offers dynamic, reliable, and customer-tailored information and optimization possibilities to end-users while potentially resolving known shortcomings of the existing EPC schemes.
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McGinley, Orlaith, Paul Moran, and Jamie Goggins. "An Assessment of the Key Performance Indicators (KPIs) of Energy Efficient Retrofits to Existing Residential Buildings." Energies 15, no. 1 (January 4, 2022): 334. http://dx.doi.org/10.3390/en15010334.
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Quantifying the wider benefits of energy efficient building retrofits is crucial to incentivise householder retrofit investments. This research recognises the value of key performance indicators (KPIs) for assessing and demonstrating retrofitting benefits and provides an assessment of KPIs for evaluating retrofits. An integrated framework for evaluating retrofits using a set of economic, social, and environmental KPIs is proposed. This KPI framework is then applied in a pre- and post-retrofit assessment of five case study dwellings located in Ireland, revealing its usefulness in demonstrating the wider benefits of retrofitting to householders, with a view to driving retrofit investment. Three of these case study dwellings had state-of-the-art retrofit technologies installed as part of the works, including heat pumps and solar PV systems. In addition to demonstrating the wider benefits of retrofitting, the framework allowed for the identification of potential causes for differences in performance of these technologies across households, as well as patterns of underperformance. Such insights are useful for the future design of these technologies and retrofit packages, as well as policy measures, which support householders in the adoption and use of these measures. The results demonstrate that householders experience various benefits from retrofitting. Showcasing the different benefits that householders receive from retrofitting, and their satisfaction with the retrofit works, can serve to de-risk retrofit investments, and inspire others to seek similar benefits through retrofitting. Applying the developed framework to a larger, comparable sample size, can distinguish the retrofit packages, which perform best across the KPIs and various household profiles. Furthermore, the application of the developed framework can serve as an evidence base for retrofit designers, contractors, and policy makers in the design of retrofit packages and policy measures that will maximise the benefit for householders.
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Andreadou, Nikoleta, Dimitrios Thomas, Antonio De Paola, Evangelos Kotsakis, and Gianluca Fulli. "Holistic Evaluation of Demand Response Events in Real Pilot Sites: From Baseline Calculation to Evaluation of Key Performance Indicators." Energies 16, no. 16 (August 18, 2023): 6048. http://dx.doi.org/10.3390/en16166048.
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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.
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Balaras, Constantinos A., Kalliopi G. Droutsa, Elena G. Dascalaki, Simon Kontoyiannidis, Andrea Moro, Elena Bazzan, and Paola Borgaro. "Auditing and Rating Sustainability of Mediterranean Buildings, Neighbourhoods and Cities." Energies 17, no. 1 (December 22, 2023): 82. http://dx.doi.org/10.3390/en17010082.
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Sustainability rating systems for the built environment involve a multitude of indicators that are based on different types of data. This work capitalizes on an existing multicriteria assessment method and supporting decision-making tool at building and neighbourhood scale, to develop an enhanced method and tool at city scale. The main sustainability issues at building and city scales include site and infrastructure, energy and resources consumption, environmental loadings, climate change, environmental quality, water, waste, transportation, services, social aspects, economy and governance. Approximately 300 indicators distributed among the different scales are used to describe and quantify the various facets of sustainability. Specifically, the building scale includes a pool of 80 indicators of which 17 are key performance indicators (KPIs), the neighbourhood scale has 133 indicators of which 14 are KPIs and the city scale has a total of 99 indicators of which 10 are KPIs that were new additions to the existing method. The emphasis in this paper is given on elaborating the key performance indicators for cities and demonstrating their applicability through a case study. The common method and tools provide a flexible assessment system for local authorities and stakeholders to develop and assess sustainability plans.
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Geese, Marc. "Automotive Image Quality Concepts for the next SAE levels: Color Separation and Contrast Detection Probability." Electronic Imaging 2020, no. 16 (January 26, 2020): 19–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.16.avm-019.
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In this paper, we present an overview of automotive image quality challenges and link them to the physical properties of image acquisition. This process shows that the detection probability based KPIs are a helpful tool to link image quality to the tasks of the SAE classified supported and automated driving tasks. We develop questions around the challenges of the automotive image quality and show that especially color separation probability (CSP) and contrast detection probability (CDP) are a key enabler to improve the knowhow and overview of the image quality optimization problem. Next we introduce a proposal for color separation probability as a new KPI which is based on the random effects of photon shot noise and the properties of light spectra that cause color metamerism. This allows us to demonstrate the image quality influences related to color at different stages of the image generation pipeline. As a second part we investigated the already presented KPI Contrast Detection Probability and show how it links to different metrics of automotive imaging such as HDR, low light performance and detectivity of an object. As conclusion, this paper summarizes the status of the standardization status within IEEE P2020 of these detection probability based KPIs and outlines the next steps for these work packages.
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Amir, Malik Muhammad Haris, Salvatore Maresca, Gaurav Pandey, Antonio Malacarne, Antonella Bogoni, and Mirco Scaffardi. "Performance Evaluation of Photonics-Based Coherent MIMO Radar Systems for Maritime Surveillance." Inventions 8, no. 4 (August 7, 2023): 99. http://dx.doi.org/10.3390/inventions8040099.
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Multiple-input multiple-output (MIMO) radars offer significant advantages over conventional standalone radars in terms of target detection and localization capabilities. However, to fully exploit their potential, such systems require excellent time and phase synchronization among the central unit and the radar heads. Only recently, thanks to microwave photonics (MWP) techniques, it has been possible to develop the first coherent MIMO radar demonstrators. In this paper, a simulation tool in MATLAB programming language is proposed to model coherent MIMO radars based on MWP techniques in maritime surveillance applications. Moreover, a novel approach for estimating the radar cross section of extended maritime targets is presented. The system performance in co-located and distributed MIMO configuration, as well as in single- and multi-band operations, is evaluated by means of relevant key performance indicators (KPIs). Simulations are carried out in two close-to-reality scenarios. In the first, a co-located MIMO radar is mounted on top of a patrolling vessel. In the second, a distributed MIMO radar is deployed inside a port. The proposed KPIs are analyzed in terms of both geometric and frequency diversities of the system, laying the foundations for general system optimization criteria valid in any given surveillance application.
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Kaganski, S., and S. Toompalu. "Development of key performance selection index model." Journal of Achievements in Materials and Manufacturing Engineering 1, no. 82 (May 1, 2017): 33–40. http://dx.doi.org/10.5604/01.3001.0010.2077.
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Purpose: The main idea of this paper is to introduce the refined model for selection of the Key performance indicators (KPI). The KPI selection model can be considered as a tool for analysis of the enterprise, which should be able to simplify the choice of the right metrics for the company, where study has been conducted. The Enterprise analysis model (EAM) will provide the information regarding weak spots on the production and provide further steps to the management. Those actions will save time and reduce resources that are necessary to implement metrics in company. Design/methodology/approach: Main activities performed include: optimization of EAM; Fuzzy AHP and SMARTER criteria’s for ranking the KPIs; reliability analysis and weights appointment to questions and KPIs. In addition, the expert group has participated in the analysis of this work and has made a high impact on the results. Findings: The main result of this work is the final version of the KPI selection model. Research limitations/implications: The future research should be focused on optimization of the model and in adding additional module for automatic data collection. The Production Monitoring System (PMS) that should help to collect data about the status of the machine park, taking into account the downtime, overall equipment efficiency (OEE) and etc. Practical implications: The proposed model can be used in SME (small and medium enterprises) in order to improve the productivity. The concept was tested in particular company. Originality/value: The KPI selection model combine different methodologies into one general approach. Due to this fact, the process of finding right metrics can be reduced significantly. The proposed approach allows saving resources for the research of metrics.
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Ehlinger, Victoria Marie, Thomas Roy, Alex King, Sarah Baker, Eric B. Duoss, Adam Z. Weber, and Victor A. Beck. "Optimization of Porosity Distribution in Gas Diffusion Electrodes for CO2 Electrolysis." ECS Meeting Abstracts MA2022-01, no. 46 (July 7, 2022): 1967. http://dx.doi.org/10.1149/ma2022-01461967mtgabs.
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The criteria dictating the performance of gas diffusion electrodes (GDEs) for CO2 electrolysis are not well understood, due to the complex and highly coupled relationships of the underlying physical and chemical phenomena. In addition, a number of key performance indicators (KPIs) have been identified (e.g., Faradaic efficiency, reaction selectivity, single-pass conversion, and productivity), and optimizing for any single metric often leads to inherent tradeoffs. Consequently, much recent work has focused on understanding how operational and architectural parameters control GDE performance. The porous catalyst supports and diffusion media in these devices are critical components as they mediate the transport and reactive processes. The microstructure of these components influences the balance between the electrochemical surface area, which dictates CO2 consumption, and mass transfer of the aqueous and gaseous species. Traditional porous media is often spatially homogeneous and provides limited opportunity to tailor this balance. In contrast, novel advanced manufacturing methods have now enabled researchers to create electrodes with variable porosity that can be tuned for optimal performance. To explore the impact of locally altering the porous media structure, we leverage previous modeling work by Weng et al. to allow for spatially varying porosity. Further, we couple the forward simulation to adjoint based optimization algorithms to determine optimal porosity distribution in the diffusion media and catalyst layer of a GDE. The cost functions for the optimization are derived from the previously mentioned KPIs. Finally, the performance of the resultant spatially varying porosity GDEs is compared to the performance of homogenous porosity GDEs, and we identify key features of the porosity distribution leading to improved performance. Weng, Lien-Chun, Alexis T. Bell, and Adam Z. Weber. "Modeling gas-diffusion electrodes for CO2 reduction." Physical Chemistry Chemical Physics 20.25 (2018): 16973-16984. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-830082
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Schubert, Christina, Wiem Fekih Hassen, Barbara Poisl, Stephanie Seitz, Jonathan Schubert, Estanis Oyarbide Usabiaga, Pilar Molina Gaudo, and Karl-Heinz Pettinger. "Hybrid Energy Storage Systems Based on Redox-Flow Batteries: Recent Developments, Challenges, and Future Perspectives." Batteries 9, no. 4 (March 31, 2023): 211. http://dx.doi.org/10.3390/batteries9040211.
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Recently, the appeal of Hybrid Energy Storage Systems (HESSs) has been growing in multiple application fields, such as charging stations, grid services, and microgrids. HESSs consist of an integration of two or more single Energy Storage Systems (ESSs) to combine the benefits of each ESS and improve the overall system performance, e.g., efficiency and lifespan. Most recent studies on HESS mainly focus on power management and coupling between the different ESSs without a particular interest in a specific type of ESS. Over the last decades, Redox-Flow Batteries (RFBs) have received significant attention due to their attractive features, especially for stationary storage applications, and hybridization can improve certain characteristics with respect to short-term duration and peak power availability. Presented in this paper is a comprehensive overview of the main concepts of HESSs based on RFBs. Starting with a brief description and a specification of the Key Performance Indicators (KPIs) of common electrochemical storage technologies suitable for hybridization with RFBs, HESS are classified based on battery-oriented and application-oriented KPIs. Furthermore, an optimal coupling architecture of HESS comprising the combination of an RFB and a Supercapacitor (SC) is proposed and evaluated via numerical simulation. Finally, an in-depth study of Energy Management Systems (EMS) is conducted. The general structure of an EMS as well as possible application scenarios are provided to identify commonly used control and optimization parameters. Therefore, the differentiation in system-oriented and application-oriented parameters is applied to literature data. Afterwards, state-of-the-art EMS optimization techniques are discussed. As an optimal EMS is characterized by the prediction of the system’s future behavior and the use of the suitable control technique, a detailed analysis of the previous implemented EMS prediction algorithms and control techniques is carried out. The study summarizes the key aspects and challenges of the electrical hybridization of RFBs and thus gives future perspectives on newly needed optimization and control algorithms for management systems.
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Cinar, Eyup, Sena Kalay, and Inci Saricicek. "A Predictive Maintenance System Design and Implementation for Intelligent Manufacturing." Machines 10, no. 11 (October 31, 2022): 1006. http://dx.doi.org/10.3390/machines10111006.
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The importance of predictive maintenance (PdM) programs has been recognized across many industries. Seamless integration of the PdM program into today’s manufacturing execution systems requires a scalable and generic system design and a set of key performance indicators (KPIs) to make condition monitoring and PdM activities more effective. In this study, a new PdM system and its implementation are presented. KPIs and metrics are proposed and implemented during the design to enhance the system and the PdM performance monitoring needs. The proposed system has been tested in two independent use cases (autonomous transfer vehicle and electric motor) for condition monitoring applications to detect incipient equipment faults or operational anomalies. Machine learning-based data augmentation tools and models are introduced and automated with state-of-the-art AutoML and workflow automation technologies to increase the system’s data collection and data-driven fault classification performance.
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Abolhassani, Amir, Gale Boyd, Majid Jaridi, Bhaskaran Gopalakrishnan, and James Harner. "“Is Energy That Different from Labor?” Similarity in Determinants of Intensity for Auto Assembly Plants." Energies 16, no. 4 (February 10, 2023): 1776. http://dx.doi.org/10.3390/en16041776.
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This paper addresses the question “Is energy that different from labor?” from the perspective of efficiency. It presents a novel statistical analysis for the auto assembly industry in North America to examine the determinants of relative energy intensity, and contrasts this with a similar analysis of the determinants of another important factor of production, labor intensity. The data used combine two non-public sources of data previously used to separately study key performance indicators (KPIs) for energy and labor intensity. The study found these two KPIs are statistically correlated (the correlation coefficient is 0.67) and the relationship is one-to-one. The paper identifies 11 factors that may influence both energy and labor intensity KPIs. The study then contrasts which of the empirical factors the two KPIs’ share and how they differ. Two novel statistical methods, Huber estimators and Multiple M-estimators, combined with regularized algorithms, are identified as the preferred methods for robust statistical models to estimate energy intensity. Based on our analysis, the underlying determinants of energy efficiency and labor productivity are quite similar. This implies that strategies to improve energy may have spillover benefits to labor, and vice versa. The study shows vehicle variety, car model types, and launch of a new vehicle penalize both energy and labor intensity, while flexible manufacturing, production volume, and year of production improve both energy and labor intensity. In addition, the study found that the plants that produce small cars are more energy-efficient and productive compared to plants that produce large vehicles. Moreover, in a given functional unit, i.e., on a per-unit basis, Japanese plants are more energy-efficient and productive compared to American plants. Plant managers can use the proposed data-driven approach to make the right decisions about the energy efficiency targets and improve plants’ energy efficiency up to 38% using hybrid regression methods, mathematical modeling, plants’ resources, and constraints.
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Schwantes, Rebecca, Jakob Seger, Lorenz Bauer, Daniel Winter, Tobias Hogen, Joachim Koschikowski, and Sven-Uwe Geißen. "Characterization and Assessment of a Novel Plate and Frame MD Module for Single Pass Wastewater Concentration–FEED Gap Air Gap Membrane Distillation." Membranes 9, no. 9 (September 6, 2019): 118. http://dx.doi.org/10.3390/membranes9090118.
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Membrane distillation (MD) is an up and coming technology for concentration and separation on the verge of reaching commercialization. One of the remaining boundaries is the lack of available full-scale MD modules and systems suitable to meet the requirements of potential industrial applications. In this work a new type of feed gap air gap MD (FGAGMD) plate and frame module is introduced, designed and characterized with tap water and NaCl–H2O solution. The main feature of the new channel configuration is the separation of the heating and cooling channel from the feed channel, enabling a very high recovery ratio in a single pass. Key performance indicators (KPIs) such as flux, gained output ratio (GOR), recovery ratio and thermal efficiency are used to analyze the performance of the novel module concept within this work. A recovery rate of 93% was reached with tap water and between 32–53% with salt solutions ranging between 117 and 214 g NaCl/kg solution with this particular prototype module. Other than recovery ratio, the KPIs of the FGAGMD are similar to those of an air gap membrane distillation (AGMD) channel configuration. From the experimental results, furthermore, a new MD KPI was defined as the ratio of heating and cooling flow to feed flow. This RF ratio can be used for optimization of the module design and efficiency.
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Pramangioulis, Dionysios, Konstantinos Atsonios, Nikos Nikolopoulos, Dimitrios Rakopoulos, Panagiotis Grammelis, and Emmanuel Kakaras. "A Methodology for Determination and Definition of Key Performance Indicators for Smart Grids Development in Island Energy Systems." Energies 12, no. 2 (January 14, 2019): 242. http://dx.doi.org/10.3390/en12020242.
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There is a growing interest over the last decades in the field of autonomous island grids that is driven mainly by climate reasons. The common objective among the members of the European Union (EU) is the increase of Renewable Energy Sources (RES) penetration in the energy mixture, as well as turning the grid into a smart grid. Consequently, more and more state-of-the-art solutions are being proposed for the electricity generation and the optimization of the energy system management, taking advantage of innovations in all energy related sectors. The evaluation of all available solutions requires quantitative assessment, through the adoption of representative Key Performance Indicators (KPIs) for the projects that are related to smart grid development in isolated energy systems, providing the relevant stakeholders with a useful comparison among the proposed solutions. The evaluation approach that is described in this paper emphasizes the role of the various stakeholder groups who face the proposed solutions by different points of view. Apart from the domains of interest that are also observed in previous approaches, the proposed list also contains a set of legal KPIs, since the regulatory framework can either represent a serious barrier or grant a strong incentive for the implementation of state-of-the-art energy technology and grid management solutions in different countries.
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Martínez-López, José Antonio, Ramón López-Urrea, Ángel Martínez-Romero, José Jesús Pardo, Jesús Montero, and Alfonso Domínguez. "Sustainable Production of Barley in a Water-Scarce Mediterranean Agroecosystem." Agronomy 12, no. 6 (June 3, 2022): 1358. http://dx.doi.org/10.3390/agronomy12061358.
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Scarcity of water resources is one of the main constraints on agricultural activity in arid and semi-arid areas. Despite the great technological development over recent decades, farmers are demanding methodologies and tools adapted to their training, management restrictions, and economic capacity. To tackle these challenges, the sustainable production in water-limited environments of Mediterranean agroecosystems (SUPROMED) project combines, in an online platform, a set of models and methodologies for more efficient management of water, energy, and fertilizers. A two-year trial (2020–2021) was conducted in the Castilla-La Mancha region (Spain) to demonstrate, for a barley crop, the effectiveness of SUPROMED as a farm management support tool. The impact of transferring the model for the economic optimization of irrigation water use at farm level (MOPECO model) irrigation scheduling among other methods and tools, integrated in the SUPROMED platform, to farmers was determined by analyzing a set of productive, economic, and environmental key performance indicators (KPIs). The KPIs were selected to show farmers how the efficient use of productive factors could improve the profitability of their farms, thus reducing the impact of agriculture on the environment. In 2020, the management plan proposed by SUPROMED achieved the same yield as traditional management using 32% less water and resulting in a 13% and 66% improvement in gross margin and gross economic irrigation water productivity, respectively. In 2021, the management implemented by a farmer trained in the use of the tools and methodologies in the SUPROMED platform showed improvements in most of the KPIs analyzed, achieving similar results to those obtained by SUPROMED during 2020. The results are promising, indicating that the tools and models proposed in SUPROMED can be easily used by farmers and can improve the economic and environmental sustainability of Mediterranean agroecosystems. The involvement of public administrations, together with local researchers and technicians, is required for the effective promotion and use of these methodologies by the productive sector.
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Efkarpidis, Nikolaos, Andrija Goranović, Chen-Wei Yang, Martin Geidl, Ingo Herbst, Stefan Wilker, and Thilo Sauter. "A Generic Framework for the Definition of Key Performance Indicators for Smart Energy Systems at Different Scales." Energies 15, no. 4 (February 10, 2022): 1289. http://dx.doi.org/10.3390/en15041289.
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The growing integration of intermittent renewable energy sources (RESs) and the increasing trend of shutting down fossil-fuel-based power plants has brought about the need for additional flexibility in energy systems. This flexibility can be provided in various forms, including controllable generation and consumption, storage, conversions, and exchanges with interconnected systems. In this context, an increasing focus is placed on the development of smart energy systems (SESs) that combine different types of distributed energy resources (DERs), information and communication technologies (ICTs), demand side management (DSM), and energy conversion technologies. The utilization of SESs can lead to multiple benefits for the stakeholders involved; therefore, the assessment of their performance is a primary concern. Due to their multidisciplinary nature, there are no known or universally accepted standards for assessing the performance of SESs. Previous efforts only define key performance indicators (KPIs) for individual homogeneous subsystems, focusing on a specific SES type and application area. This paper focuses on the development of a novel comprehensive KPI framework that can be applied to any type of SES, regardless of the application area. The proposed framework consists of four layers that specify the application area, the main SES requirements, and the involved stakeholders’ objectives. Next, the KPIs are identified for each of the stakeholders’ objectives. The proposed KPI framework is applied to the use case of a European research project with different application areas, to demonstrate its features. Finally, a repository of KPIs is identified for each use case with respect to the aforementioned SES requirements.
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Pacana, Andrzej, and Karolina Czerwińska. "A Quality Control Improvement Model That Takes into Account the Sustainability Concept and KPIs." Sustainability 15, no. 12 (June 15, 2023): 9627. http://dx.doi.org/10.3390/su15129627.
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Nowadays, in addition to quality aspects, social and environmental issues are also a source of competitive advantage, as a result of changing socio-economic conditions and observed trends. The purpose of the study was to create a model for improving quality control in line with the concept of sustainable development. The model captures the microfoundational approach, and allows identifying quality capability with the idea of sustainable development. The proposed method is based on analyses of the characteristic parameters of methods for detecting aluminum casting nonconformities using automated or semi-automated nondestructive methods. The interdisciplinary approach takes into account indicators from the leading areas of sustainable development (economic, environmental and social dimensions) to determine the gradation of detection methods. Verification of the model confirmed its usefulness in foundry companies. As a result, a ranking of the total efficiency of the analyzed detection methods used in the quality control of the studied product was obtained. This enabled the development of energy, emission and cost optimization of the inspection process while maintaining the expected level of quality. Further research directions will concern the expansion of the model with additional key indicators related to the concept of pure quality control and automation of the implementation of analysis.
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Schreyer, Kristina, Arman Dehghani, and Eleonora Fendeková. "Improvement of OSH Management through the holistic approach of incident controlling." SHS Web of Conferences 115 (2021): 03014. http://dx.doi.org/10.1051/shsconf/202111503014.
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This paper deals with the performance optimization of occupational safety and health management (OSHM) with the help of the integration of near miss management into existing KPIs of the company. In this context, two independent processes are carried out: risk assessment and near miss management. In this work, the holistic view of these two processes is investigated as an optimization potential OSHM. Based on a literature review, the framework of the qualitative method applied here is established. After definitions and general information concerning OSHM, the processes of risk assessment and near miss management in different organisations (23 international plants for risk assessment and 6 German plants for near miss management) with established processes according to BPM are examined. The chosen approach is combined with the method of the PDCA cycle to relate the results of the investigation to the CIP existing in the OSHM. The result was elaborated in the form of a recommendation for action: optimization of existing processes, integrated hazard and near miss management as a more sustainable solution and proposal for the implementation of a KPI for better control of incidents based on predictive risk assessment.
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Teske, Sven, and Jaysson Guerrero. "One Earth Climate Model—Integrated Energy Assessment Model to Develop Industry-Specific 1.5 °C Pathways with High Technical Resolution for the Finance Sector." Energies 15, no. 9 (April 30, 2022): 3289. http://dx.doi.org/10.3390/en15093289.
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According to the IPCC, a global carbon budget of 400 GtCO2 is required to limit the temperature rise to 1.5 °C with a 67% likelihood by 2050. The finance industry is increasingly committed to ambitious climate targets. In this article, we describe the detailed methodology and energy model architecture of a MATLAB-based integrated energy assessment model for industry-specific 1.5 °C pathways, with a high technical resolution of target parameters as key performance indicators (KPIs). The additionality of OECM 2.0 is the high technical resolution in terms of the level of detail of industry-specific energy demand and supply parameters that can be modeled—a prerequisite to define industry-specific KPIs. We found that a database of industry-sector-specific energy demands and energy intensities, with a consistent methodology, is required to improve the accuracy of calculations in future research. We supplement the technical documentation with the results for a transport scenario.
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Pepe, Crescenzo, and Silvia Maria Zanoli. "Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review." Energies 17, no. 4 (February 17, 2024): 941. http://dx.doi.org/10.3390/en17040941.
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Intelligent water usage is required in order to target the challenging goals for 2030 and 2050. Hydroelectric power plants represent processes wherein water is exploited as a renewable resource and a source for energy production. Hydroelectric power plants usually include reservoirs, valves, gates, and energy production devices, e.g., turbines. In this context, monitoring and maintenance policies together with control and optimization strategies, at the different levels of the automation hierarchy, may represent strategic tools and drivers for energy efficiency improvement. Nowadays, these strategies rely on different basic concepts and elements, which must be assessed and investigated in order to provide a reliable background. This paper focuses on a review of the state of the art associated with these basic concepts and elements, i.e., digitalization, Industry 4.0, data, KPIs, modelization, and forecast.
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Abdulhamid, Mohanad, and Kisenya Sayianka. "Performance of Automatic Frequency Planning and Optimization Algorithm for Cellular Networks." International Journal of Advanced Network, Monitoring and Controls 8, no. 2 (June 1, 2023): 54–60. http://dx.doi.org/10.2478/ijanmc-2023-0056.
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Abstract Frequency planning is one of the most expensive aspects of deploying a cellular network. If a set of base stations can be deployed with minimal service and planning, the cost of both deploying and maintaining the network will decrease. To ensure that the scarce frequency is utilized to its maximum, planning and optimization are done. This is also carried out to ensure that there is high efficiency in cellular radio systems and little or minimum interference due to co-channeling. This paper focuses on coming up with an automatic way of planning and optimizing the frequency in the cellular network. The approach replaces the inefficient, inaccurate and tedious manual approach. The automatic approach simplifies work for the radio frequency(RF) engineers and also reduces the cost of operation. The automatic approach ensures that the cellular network is extensively deployed in a way that criteria of maximum quality, quantity and good coverage are met. The paper focuses on coming up with an automatic planning and optimization algorithm that minimizes the intra-system interference levels to reasonable ranges within the key performance indicators (KPIs) defined for any acceptable cellular network.
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Fareniuk, Y. "MARKETING STRATEGY OPTIMIZATION IN FMCG MARKET." Bulletin of Taras Shevchenko National University of Kyiv. Economics, no. 219 (2022): 49–57. http://dx.doi.org/10.17721/1728-2667.2022/219-2/7.
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The article contains the results of applying marketing mix modeling based on Data Science technologies for FMCG companies. The market share in packages (sales level) was modeled using regression analysis depending on the key elements of the marketing complex (price, place, promotion), seasonality and media activity of the competitors in all communication channels. Econometric modeling helps to assess the return of media investment by calculating the level of sales generated by media activity in each communication channel and comparing it with the level of media investment, respectively. The influence of distribution on the company’s position in the market and media efficiency has been studied in detail. There is a connection between distribution and media response: less distribution affects the decline in media performance, and vice versa. In conditions of low distribution, it is important to increase the presence in regional communication channels through media pressure in critical sales regions for FMCG brands and try to increase distribution levels nationally. The article contains an assessment of price sensitivity (elasticity) and recommendations for optimizing pricing policy to increase market share by volume or by value depending on the company’s goals. The price elasticity curve was determined by estimating the impact of the price index on the level of sales in packages and deals in money using econometric modeling and simulations of sales levels depending on different options of the price index vs competitors. Based on the research, recommendations for optimization of the marketing and media strategies to maximize sales of FMCG companies are formed. Marketing mix modeling and Data Science provide the most efficient ways to achieve business KPIs.
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Branca, Teresa Annunziata, Ismael Matino, Valentina Colla, Alice Petrucciani, Amarjit Kuor Maria Singh, Antonella Zaccara, Teresa Beone, et al. "Paving the way for the optimization of water consumption in the steelmaking processes: barriers, analysis and KPIs definition." Matériaux & Techniques 108, no. 5-6 (2020): 510. http://dx.doi.org/10.1051/mattech/2021006.
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The efficient use of water resources is one of the main challenges of the steel sector, according to the European Union water policy. On this subject, monitoring and optimization systems, linked to the innovative water treatments, represent important tools to improve water management and the related energy use. The present paper describes a part of the work developed in the early stage of the project entitled “Water and related energy Hub Advanced Management system in steelworks – WHAM”, which is co-funded by the Research Fund for Coal and Steel. The project aims at optimizing water consumption in the steelworks through a holistic combination of on-line monitoring and optimisation and innovative water treatment technologies. As different aspects affect water use in the steelmaking processes, in the first part of the paper, the main technical barriers and factors, that can impact on reuse and recirculation of wastewater and energy efficiency, are analysed. The main constraints on water management in the steel sector, such as fresh water availability, its quality and local legal requirements, were considered in order to maximise the water reuse and recycling. Furthermore, the main barriers, such as environmental issues and several costs, were investigated. In the second part of the paper, a set of Key Performance Indicators are listed. They aim at assessing and monitoring the water management sustainability in a holistic way, both in terms of environmental and economic performances, as well as of new water treatments efficiency and their economic viability. Key Performance Indicators will be used to monitor the efficiency of water management, aiming at achieving significant increase of performances. On the other hand, some of these indicators will be used as objective functions for problems optimization. The computation of the selected Key Performance Indicators will take into account both industrial data and results from simulations that will be carried out after the development of suitable tools in order to assess the feasibility of some relevant process modifications or the applications of new technologies.
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Lai, Guojun, Junsong Lei, Jinlin Liu, Shijie Cao, Haibo Qin, Fanming Zeng, and Ruiping Zhou. "Numerical and experimental study on comprehensive optimization for the KPIs of ship propulsion shafting design based on MDO." Ocean Engineering 222 (February 2021): 108624. http://dx.doi.org/10.1016/j.oceaneng.2021.108624.
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