Monitoring Of Key Performance Indicators Research Articles

Моделирование процессов обработки данных экологического мониторинга в облачной инфраструктуре

A model of a cloud-based system for processing environmental monitoring data is proposed. The model takes into account the multi-tier web applications and the heterogeneity of the input flow of applications. As a characteristic of the efficiency of the data processing system, an additive criterion is chosen that takes into account the volume of processed requests and the load on the resources of the cloud infrastructure. The approach under consideration will allow creating a basis for modeling the processes occurring in natural-technical systems (NTS), analyze data processing processes during monitoring of key performance indicators of NТS and ensure that these indicators meet the requirements specified in SLA agreements through the use of alternative data processing strategies.

Reducing surgical cancellations: a successful application of Lean Six Sigma in healthcare

ObjectiveThe COVID-19 pandemic emphasises the need to use healthcare resources efficient and effective to guarantee access to high-quality healthcare in an affordable manner. Surgical cancellations have a negative impact on...

Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory.

Staff competency is a crucial component of the in vitro fertilization (IVF) laboratory quality management system because it impacts clinical outcomes and informs the key performance indicators (KPIs) used to continuously monitor and assess culture conditions. Contemporary quality control and assurance in the IVF lab can be automated (collect, store, retrieve, and analyze), to elevate quality control and assurance beyond the cursory monthly review. Here we demonstrate that statistical KPI monitoring systems for individual embryologist performance and culture conditions can be detected by artificial intelligence systems to provide systemic, early detection of adverse outcomes, and identify clinically relevant shifts in pregnancy rates, providing critical validation for two statistical process controls proposed in the Vienna Consensus Document; intracytoplasmic sperm injection (ICSI) fertilization rate and day 3 embryo quality.

A Sensor Data-Driven Decision Support System for Liquefied Petroleum Gas Suppliers

Currently, efficiency in the supply domain and the ability to make quick and accurate decisions and to assess risk properly play a crucial role. The role of a decision support system (DSS) is to support the decision-making process in the enterprise, and for this, it is yet not enough to have up-to-date data; reliable predictions are necessary. Each application area has its own specificity, and so far, no dedicated DSS for liquefied petroleum gas (LPG) supply has been presented. This study presents a decision support system dedicated to support the LPG supply process from the perspective of gas demand analysis. This perspective includes a short- and medium-term gas demand prediction, as well as the definition and monitoring of key performance indicators. The analysis performed within the system is based exclusively on the collected sensory data; no data from any external enterprise resource planning (ERP) systems are used. Examples of forecasts and KPIs presented in the study show what kind of analysis can be implemented in the proposed system and prove its usefulness. This study, showing the overall workflow and the results for the use cases, which outperform the typical trivial approaches, could be a valuable direction for future works in the field of LPG and other fuel supply.

Assessing the sustainability of General Insurance Business through Real Time Monitoring of KPIs using Recurrent Neural Network

A company’s sustainability is driven significantly by its operational efficiency. Operational efficiency plays a significant role in the growth and the profitability of a company. Thus, operational efficiency of a company forms the basis for the metrics known as the Key Performance Indicators(KPIs). These KPIs bridge the concept of performance an operation and a means to measure the same quantitatively. In this work, we used Recurrent Neural Network (RNN) with the Long Short Term Memory(LSTM) cells for projecting the public disclosure data of select General Insurance(GI) companies operating in India to the future. We use this data to calculate the KPIs pertaining to the operations of general insurance companies and calculate how the operations of the GI company affect its performance at various levels. Since this analysis is done for the projected data, we get a framework to assess the sustainability of the GI companies by monitoring these KPIs in real-time. The complex RNN and LSTM algorithms were implemented with the help of the Google Colaboratory platform by using the GPUs of the Google Hardware with the help of the Cloud Computing framework.

IMPLEMENTASI PLAN-DO-CHECK-ACT (PDCA) BERBASIS KEY PERFORMANCE INDICATORS (KPI): STUDI KASUS DI SMP-SMA INTEGRAL AR-ROHMAH DAU MALANG

This research is to analyze the process of development of quality management by using the cycled approach of PDCA (Plan-Do-Check-Act) based on KPI (Key Performance Indicators). This research was conducted at SMP-SMA Integral Ar-Rohmah "Boarding School" Dau Malang by using descriptive qualitative methods. The object of this research is around 449 students at SMP-SMA Integral Ar-Rohmah Dau Malang. The research period is the even semester of the 2018/2019 school year. The results of this study can be concluded that; Planning (Plan), produces three KPI formulation products, namely; a) KPI monitoring; b) KPI filling formula; and c) KPI report card. Implementation (Do), by requiring all students to participate in filling the KPI monitoring form and activeness of the musyrif room in assisting the filling of KPI. Evaluation (Check) that has been carried out is by means of a random check (random) conducted by the room meeting from the results of filling KPI monitoring. The follow up (Act) is to publish KPI report cards.

Collecting Valid and Reliable Data: Fieldwork Monitoring Strategies in a Health Examination Survey

Introduction: Health surveys constitute a relevant information source to access the population’s health status. Given that survey errors can significantly influence estimates and invalidate study findings, it is crucial that the fieldwork progress is closely monitored to ensure data quality. The objective of this study was to describe the fieldwork monitoring conducted during the first Portuguese National Health Examination Survey (INSEF) regarding protocol deviations and key performance indicators (KPI). Methods: Data derived from interviewer observation and from the statistical quality control of selected KPI were used to monitor the four components of the INSEF survey (recruitment, physical examination, blood collection and health questionnaire). Survey KPI included response rate, average time distribution for procedures, distribution of the last digit in a specific measure, proportion of haemolysed blood samples and missing values. Results: Interviewer observation identified deviations from the established protocols, which were promptly corrected. During fieldwork monitoring through KPI, upon implementation of corrective measures, the participation rate increased 2.5-fold, and a 4.4-fold decrease in non-adherence to standardized survey procedures was observed in the average time distribution for blood pressure measurement. The proportion of measurements with the terminal digit of 0 or 5 decreased to 19.6 and 16.5%, respectively, after the pilot study. The proportion of haemolysed samples was at baseline level, below 2.5%. Missing data issues were minimized by promptly communicating them to the interviewer, who could recontact the participant and fill in the missing information. Discussion/Conclusion: Although the majority of the deviations from the established protocol occurred during the first weeks of the fieldwork, our results emphasize the importance of continuous monitoring of survey KPI to ensure data quality throughout the survey.

KPI-TSAD: A Time-Series Anomaly Detector for KPI Monitoring in Cloud Applications

Accurately detecting anomalies and timely interventions are critical for cloud application maintenance. Traditional methods for performance anomaly detection based on thresholds and rules work well for simple key performance indicator (KPI) monitoring. Unfortunately, it is difficult to find the appropriate threshold levels when there are significant differences between KPI values at different times during the day or when there are significant fluctuations stemming from different usage patterns. Therefore, anomaly detection presents a challenge for all types of temporal data, particularly when non-stationary time series have special adaptability requirements or when the nature of potential anomalies is vaguely defined or unknown. To address this limitation, we propose a novel anomaly detector (called KPI-TSAD) for time-series KPIs based on supervised deep-learning models with convolution and long short-term memory (LSTM) neural networks, and a variational auto-encoder (VAE) oversampling model was used to address the imbalanced classification problem. Compared with other related research on Yahoo’s anomaly detection benchmark datasets, KPI-TSAD exhibited better performance, with both its accuracy and F-score exceeding 0.90 on the A1benchmark and A2Benchmark datasets. Finally, KPI-TSAD continued to perform well on several KPI monitoring datasets from real production environments, with the average F-score exceeding 0.72.

Tracking quality: can embryology key performance indicators be used to identify clinically relevant shifts in pregnancy rate?

Can embryology key performance indicators (KPIs) detect shifts in laboratory performance that precede changes in clinical outcomes? Day 5 usable blastocyst rate (D5BUR) is an important embryology KPI that complements total usable blastocyst rate (TBUR) in its ability to rapidly identify adverse embryology outcomes, prior to changes in clinical outcomes. The hypothesis that monitoring performance of an IVF laboratory using statistical process controls (SPCs) can act as an early warning signal of shifts in laboratory conditions is a hypothesis that requires validation. The Vienna consensus report recently defined KPIs for monitoring fresh IVF and ICSI cycles, but the effectiveness of using these KPIs for detecting clinically relevant shifts following changes in laboratory processes is unknown. A retrospective, multicentre, analysis of KPIs for 1971 fresh IVF and ICSI cycles during three consecutive 5-month periods (P1, P2 and P3) during which the culture medium was changed in the middle period. ICSI fertilisation rate, IVF fertilisation rate, D5BUR, TBUR and clinical pregnancy rate (CPR) were tracked monthly and analysed for SPC using Shewhart control charts. Out-of-control KPIs were identified by warning (2-sigma) and control (3-sigma) limits. The effect of the laboratory culture medium change on embryology KPIs and cumulative CPR was investigated using a one-way ANOVA or Pearson Chi-squared test and logistic regression. D5BUR decreased from 32 to 25% after the culture medium was changed, and the decrease was detected within 1 week after the change (P < 0.0001). D5BUR subsequently increased after a change back to the original medium. A decrease in CPR (51-36%) after the medium change was also observed but was not detected until 3 months after the shift in D5BUR (P = 0.0005). Overall, the change in culture medium independently influenced D5BUR, CPR and cumulative CPR. Importantly, TBUR (41%) was not affected by the change in culture medium, remaining within control limits for all three culture periods, indicating that the overall blastocyst rate alone may not sufficiently monitor embryology laboratory performance. The statistical KPI monitoring system demonstrated by the current study may be less effective at identifying KPI shifts in smaller clinics with lower cycle volumes. Live-birth rate per cycle started was not included as a clinical KPI. This study demonstrates that statistical KPI monitoring systems have the potential to provide systematic, early detection of adverse outcomes in ART laboratories after planned or unexpected shifts in conditions. No external funds were used for the study. The authors have no conflicts of interest. Not applicable.

Day 5 useable blastocyst rate is an early indicator of clinically significant shifts in the embryo culture system

The ability to track performance of an IVF laboratory using statistical process controls has not been validated and remains an important goal for an early warning signal of shifts in laboratory conditions. The Vienna consensus report recently defined Key Performance Indicators (KPIs) for monitoring fresh IVF and ICSI cycles, but the effectiveness of these KPIs for detecting clinically relevant shifts following changes in laboratory processes is unknown1. The aim of this study was to assess whether embryology KPIs detect shifts in laboratory performance that result in changes in clinical outcomes. Retrospective, multicentre, analysis of KPIs for fresh IVF and ICSI cycles. Three consecutive 5-month periods of embryo culture media were investigated 1) embryo culture in Continuous Single Culture Complete (CSCM-C) (Irvine Scientific) 2) embryo culture in G-TL (Vitrolife) 3) embryo culture returned to CSCM-C (Irvine Scientific). Six primary embryology KPIs were tracked for 1523 cycles from June 2016 to August 2017; ICSI fertilization rate, IVF fertilization rate, useable day 5 blastocyst rate, overall useable blastocyst rate, clinical pregnancy rate and cumulative clinical pregnancy rate. Meaningful KPI shifts were identified by upper and lower warning (2-sigma) and control (3-sigma) limits. KPI shifts were analyzed by ANOVA and multivariate regression. During the 15 months analyzed, the novel KPI, useable day 5 blastocyst rate, fell below the 3-sigma lower control limit for 5 months. The decrease in useable day 5 blastocyst rate, from 43% to 32%, occurred following a change from CSCM-C (Irvine Scientific) to G-TL (Vitrolife) and was detected one week after the change (p < 0.0001). Useable day 5 blastocyst rate subsequently recovered after a change back to CSCM-C (p < 0.0001). A decrease in clinical pregnancy rate (54% to 41%) was also observed, but this was not detected until 3 months after the useable day 5 blastocyst rate shift (p = 0.01). Although culture media type independently effected useable day 5 blastocyst rate and clinical pregnancy rate, the cumulative clinical pregnancy rate was similar among the three time periods. Importantly, overall useable blastocyst rate (day 5-7) remained within control limits (52%) for all three periods, indicating that the overall rate alone may not sufficiently monitor embryology laboratory performance. This study validates a statistical KPI monitoring system to provide systematic, early detection of culture condition shifts in ART laboratories. Useable day 5 blastocyst rate is identified as an important KPI, which complements overall useable blastocyst rate (day 5-7).

A Lean PSS design and evaluation framework supported by KPI monitoring and context sensitivity tools

Over the last decade, Product-Service System (PSS) has been established as a prominent business model which promises sustainability. A great amount of literature work has been devoted to PSS issues, but there is fairly limited published work on integrated and easily applicable evaluation methodologies for PSS design, as well as a lack of Lean PSS approaches. Contributing to these directions, the present work introduces a framework for the evaluation and improvement of the Lean PSS design using key performance indicators (KPIs), Lean rules, and sentiment analysis, aiming to feed all the stages of PSS design lifecycle. According to the evaluation phase, a certain appropriate set of KPIs is selected and suggested to the PSS designer via a context-sensitivity analysis (CSA) tool through a pool, which have been identified after intensive literature survey, and systematically classified into five main categories: design, manufacturing, customer, environment, and sustainability. According to the same phase, sentiment analysis has been used to identify the polarity of the customer opinions regarding the PSS offerings. During the phase of Lean design assistance, Lean rules are selected using CSA and are suggested to the designer to ensure the minimization of wasteful activities. Enabler for the context awareness is the availability of feedback gathered from the manufacturing, shop-floor experts, and the different types of customers (business or final-product consumers), as well as the PSS lifecycle which the designer treats. The proposed framework is implemented in a software prototype and is applied in a mold-making industrial case study.

Knowledge-based Estimation of Maintenance Time for Complex Engineered-to-Order Products Based on KPIs Monitoring: A PSS Approach

Maintenance is the most common product-oriented Product Service System (PSS) offering, as well as a core activity of the manufacturing system lifecycle, since it accounts for as much as 60 to 70% of its total costs. Commonly, in the Small Medium Enterprises (SMEs), the maintenance activities of customised engineering-to-order (ETO) products highly depend on the experience of engineers and shop-floor experts, without considering specific tools and algorithms that can capture the knowledge and reuse it in an efficient way. Moreover, the estimation of the maintenance time for a new maintenance project, which is among the main offerings in the maintenance, is solely based on the engineer's experience and knowledge. Aiming to support the knowledge capturing and its reuse in the maintenance activities, as well as to improve the performance of the provided maintenance PSS, the present work proposes a methodology for knowledge-based estimation of maintenance time based on Key Performance Indicators (KPI) monitoring. Data captured through the KPIs monitoring tool were collected in a knowledge repository, and were processed using a Case-Based Reasoning (CBR) technique, estimating the required maintenance time. A validation of the proposed methodology was performed based on real-life data from a mold-making European Small Medium Enterprise (SME). Preliminary results indicated a significant reduction in the number of iterations between customers and the engineering department, compared to the traditional approach followed by the company, and improved accuracy of maintenance time estimation, which led to increased customer satisfaction.

АЛГОРИТМ ОЦЕНКИ РЕМОНТА И ЭКСПЛУАТАЦИИ ПУТЕВЫХ МАШИННЫХ КОМПЛЕКСОВ НА ОСНОВЕ МОНИТОРИНГА КЛЮЧЕВЫХ ПОКАЗАТЕЛЕЙ ЭФФЕКТИВНОСТИ

АЛГОРИТМ ОЦЕНКИ РЕМОНТА И ЭКСПЛУАТАЦИИ ПУТЕВЫХ МАШИННЫХ КОМПЛЕКСОВ НА ОСНОВЕ МОНИТОРИНГА КЛЮЧЕВЫХ ПОКАЗАТЕЛЕЙ ЭФФЕКТИВНОСТИ

Lean rules extraction methodology for lean PSS design via key performance indicators monitoring

Lean PSS (Product-Service System) design comprises a promising strategy for delivering sustainable PSS offerings, considering several well-established lean practices. However, automated ways to apply lean practices and more specifically lean rules in industry are limited. This work proposes a methodology for improving the leanness of PSS design, by combining real-time KPI monitoring with lean principles and practices. Through a correlation of typical wastes with the metrics used in the calculation of KPIs, the Total Leanness Index (TLI) of the procedures is defined. Based on automatically identified trade-off values for TLI, lean rules are extracted to improve the performance of PSS lifecycle phases. The proposed lean rules extraction methodology (LeanREM) is validated through a case study of power waste reduction and the concurrently maintenance time decrease in a mould-making company.

Современные принципы работы системы управления персоналом

This work describes basic principles of human resource (HR) management improvement by using new information systems and monitoring of key performance indicators (KPI). HR management systems are based on software uniting information from various applications to the one universal database. Relation between HR and competence modules via single database is the main difference from custom and proprietary previous developments that makes this software flexible, while KPI estimation leads to detection of potential improvements. Proposed approach differs from known solutions by containing all main processes of HR management. Earlier on developed systems were characterized by narrow only one goal concerned with as example payroll preparation optimization or work time management. Modern systems cover all problems relating with HR departments including monitoring and improvement of process effectiveness, organization hierarchy management and simplification of all type financial transactions. This work presents researches mainly focused on improvement of large enterprise processes as well as it may be applied for small and medium-sized businesses. Here modern HR management systems are considered under KPI monitoring to estimate a progress in toward strategic goals.

Estimation and monitoring of key performance indicators of manufacturing systems using the multi-output Gaussian process

Recently, the estimation and monitoring of manufacturing key performance indicators (KPIs) have drawn significant attention. In this article, a KPI estimation and monitoring method using a multi-output Gaussian process (MGP) is proposed. The Gaussian process (GP) is an effective non-parametric flexible tool for data-driven statistical modelling for various systems. The unique features of the proposed method is that the MGP enjoys the high flexibility and desirable analytical properties of the GP while also capturing the correlation between different KPIs, thus providing better estimation accuracy and error quantification. The advantageous features of the proposed method are demonstrated through a numerical study as well as a case study with real world data in the estimation and monitoring of throughput for a multiclass production operation.

Incorporating permaculture and strategic management for sustainable ecological resource management

Utilization of natural assets to the best efficient level without changing natural balance has become a critical issue for researchers as awareness on climate change takes central position in global debate. Conventional sustainable resource management systems are based on neoclassical economic approach that ignores the nature's pattern and therefore are not actually capable of sustainable management of resources. Environmentalists are lately advocating incorporation of Permaculture as holistic approach based on ethics, equitable interaction with eco-systems to obtain sustainability. The paper integrates philosophy of permaculture with strategic management frameworks to develop a pragmatic tool for policy development. The policy design tool augments management tasks by integrating recording of natural assets, monitoring of key performance indicators and integration of sectorial policies in real time, bringing out policy as a truly live document. The tool enhances the edifice process, balancing short term viewpoints and long term development to secure renewability of natural resources.

A Hierarchical structure of key performance indicators for operation management and continuous improvement in production systems

Key performance indicators (KPIs) are critical for manufacturing operation management and continuous improvement (CI). In modern manufacturing systems, KPIs are defined as a set of metrics to reflect operation performance, such as efficiency, throughput, availability, from productivity, quality and maintenance perspectives. Through continuous monitoring and measurement of KPIs, meaningful quantification and identification of different aspects of operation activities can be obtained, which enable and direct CI efforts. A set of 34 KPIs has been introduced in ISO 22400. However, the KPIs in a manufacturing system are not independent, and they may have intrinsic mutual relationships. The goal of this paper is to introduce a multi-level structure for identification and analysis of KPIs and their intrinsic relationships in production systems. Specifically, through such a hierarchical structure, we define and layer KPIs into levels of basic KPIs, comprehensive KPIs and their supporting metrics, and use it to investigate the relationships and dependencies between KPIs. Such a study can provide a useful tool for manufacturing engineers and managers to measure and utilize KPIs for CI.

Quality control of CT systems by automated monitoring of key performance indicators: a two-year study.

The purpose of this study was to develop a method of performing routine periodical quality controls (QC) of CT systems by automatically analyzing key performance indicators (KPIs), obtainable from images of manufacturers' quality assurance (QA) phantoms. A KPI pertains to a measurable or determinable QC parameter that is influenced by other underlying fundamental QC parameters. The established KPIs are based on relationships between existing QC parameters used in the annual testing program of CT scanners at the Karolinska University Hospital in Stockholm, Sweden. The KPIs include positioning, image noise, uniformity, homogeneity, the CT number of water, and the CT number of air. An application (MonitorCT) was developed to automatically evaluate phantom images in terms of the established KPIs. The developed methodology has been used for two years in clinical routine, where CT technologists perform daily scans of the manufacturer's QA phantom and automatically send the images to MonitorCT for KPI evaluation. In the cases where results were out of tolerance, actions could be initiated in less than 10 min. 900 QC scans from two CT scanners have been collected and analyzed over the two‐year period that MonitorCT has been active. Two types of errors have been registered in this period: a ring artifact was discovered with the image noise test, and a calibration error was detected multiple times with the CT number test. In both cases, results were outside the tolerances defined for MonitorCT, as well as by the vendor. Automated monitoring of KPIs is a powerful tool that can be used to supplement established QC methodologies. Medical physicists and other professionals concerned with the performance of a CT system will, using such methods, have access to comprehensive data on the current and historical (trend) status of the system such that swift actions can be taken in order to ensure the quality of the CT examinations, patient safety, and minimal disruption of servicePACS numbers: 87.57.C‐, 87.57.N‐, 87.57.Q‐

Data-Driven Approach of KPI Monitoring and Prediction with Application to Wastewater Treatment Process

In this paper, a data-driven scheme of key performance indicator (KPI) monitoring, prediction and KPI related fault detection is applied to the wastewater treatment process (WWTP). By means of a data-driven realization of the so-called left coprime factorization (LCF) of the process, the efficient monitoring and prediction of chemical oxygen demand (COD) concentration in the effluent flow are realized both for the situation that COD is measurable and unmeasurable. The well established Benchmark Simulation Model no. 1 (BSM1) is utilized for the demonstration of the effectiveness of this approach.