Calculation Of Key Performance Indicators Research Articles

Методическая основа комплексной оценки результативности бизнес-процессов в управлении качеством предприятия

The article explains the specifics of forming a methodology for the comprehensive assessment of key perfor-mance indicators of business processes in quality management. The relevance of the study is due to the fact that the existing methods of foreign and domestic scientists for calculating and evaluating business process indicators do not affect their relationship with the quality management of enterprises and do not describe the procedure for their automation. In addition, the author argues for the methodology of comprehensive assess-ment of key performance indicators of business processes of the quality management system, consisting of several stages: identification of processes, determination of their performance, expert evaluation of indicators, carrying out calculations according to specified formulas. An example of automating the calculation of key per-formance indicators of the business process “monitoring and measurement” of an industrial enterprise is pre-sented. This methodology allows to consider the strengths and weaknesses of the enterprise’s business pro-cesses for further development of corrective action plans to improve the quality management system.

A General-Purpose Distributed Analytic Platform Based on Edge Computing and Computational Intelligence Applied on Smart Grids.

Currently, in many data landscapes, the information is distributed across various sources and presented in diverse formats. This fragmentation can pose a significant challenge to the efficient application of analytical methods. In this sense, distributed data mining is mainly based on clustering or classification techniques, which are easier to implement in distributed environments. However, the solution to some problems is based on the usage of mathematical equations or stochastic models, which are more difficult to implement in distributed environments. Usually, these types of problems need to centralize the required information, and then a modelling technique is applied. In some environments, this centralization may cause an overloading of the communication channels due to massive data transmission and may also cause privacy issues when sending sensitive data. To mitigate this problem, this paper describes a general-purpose distributed analytic platform based on edge computing for distributed networks. Through the distributed analytical engine (DAE), the calculation process of the expressions (that requires data from diverse sources) is decomposed and distributed between the existing nodes, and this allows sending partial results without exchanging the original information. In this way, the master node ultimately obtains the result of the expressions. The proposed solution is examined using three different computational intelligence algorithms, i.e., genetic algorithm, genetic algorithm with evolution control, and particle swarm optimization, to decompose the expression to be calculated and to distribute the calculation tasks between the existing nodes. This engine has been successfully applied in a case study focused on the calculation of key performance indicators of a smart grid, achieving a reduction in the number of communication messages by more than 91% compared to the traditional approach.

A Discrete Process Modelling and Simulation Methodology for Industrial Systems within the Concept of Digital Twins

A generic well-defined methodology for the construction and operation of dynamic process models of discrete industrial systems following a number of well-defined steps is introduced. The sequence of steps for the application of the method as well as the necessary inputs, conditions, constraints and the results obtained are defined. The proposed methodology covers the classical offline modelling and simulation applications as well as their online counterpart, which use the physical system in the context of digital twins, with extensive data exchange and interaction with sensors, actuators and tools from other scientific fields as analytics and optimisation. The implemented process models can be used for what-if analysis, comparative evaluation of alternative scenarios and for the calculation of key performance indicators describing the behaviour of the physical systems under given conditions as well as for online monitoring, management and adjustment of the physical industrial system operations with respect to given rules and targets. An application of the proposed methodology in a discrete industrial system is presented, and interesting conclusions arise and are discussed. Finally, the open issues, limitations and future extensions of the research are considered.

Making personnel selection smarter through word embeddings: A graph-based approach

This paper employs techniques and algorithms from the fields of natural language processing, graph representation learning and word embeddings to assist project managers in the task of personnel selection. To do so, our approach initially represents multiple textual documents as a single graph. Then, it computes word embeddings through representation learning on graphs and performs feature selection. Finally, it builds a classification model that is able to estimate how qualified a candidate employee is to work on a given task, taking as input only the descriptions of the tasks and a list of word embeddings. Our approach differs from the existing ones in that it does not require the calculation of key performance indicators or any other form of structured data in order to operate properly. For our experiments, we retrieved data from the Jira issue tracking system of the Apache Software Foundation. The evaluation results show, in most cases, an increase of 0.43% in the accuracy of the proposed classification models when compared against a widely-adopted baseline method, while their validation loss is significantly decreased by 65.54%.

Rationale and design of individualized quality improvement based on the Computer Analysing system to improve Stroke management quality Evaluation (CASE): a multicenter historically controlled study

BackgroundGuideline-based medical care has been identified to improve outcomes in stroke. However, data acquisition and medical quality management during hospital stay still need to be improved in China. We have developed a computer-based medical data collecting system, together with automated calculation of key performance indicators (KPIs) and regular individualized education, and thus aim to explore whether it can improve the medical care quality of acute ischemic stroke (AIS) during hospital stay in stroke centers.MethodsThe individualized quality improvement based on the Computer Analysing system to improve Stroke management quality Evaluation (CASE) trial is a prospective, multicenter, historical control study among 30 stroke centers in China. In this trial, the data is directly extracted from the saved original medical record of each AIS patient during hospital stay, regardless of different Electronic Medical Record System (EMRS) in each center. Then, the automated calculation of KPIs and the regular education via teleconference per month allow the clinicians to examine the causes of non-compliance of guideline-based care and develop programs to decrease their frequency.DiscussionWe compare KPIs between pre-intervention stage and post-intervention stage (without or with education) among stroke centers. If proved effective, this approach might be generalized around China and even worldwide, where a unified EMRS is difficult to be applied and in-patient care needs to be improved.Trial registrationClinicalTrials.gov NCT03684629. Registered on 9 December 2018. Retrospectively registered.

PENGUKURAN KINERJA TEKNOLOGI INFORMASI PERUSAHAAN ABC MENGGUNAKAN IT BALANCED SCORECARD DAN BTRIPPLEE FRAMEWORK

Information technology (IT) applied by the company can help increase the effectiveness and efficiency associated with the company's operational activities. Measurement of IT implementation is carried out on ABC companies engaged in information technology services in every business process. To integrate business processes with the current implementation of information technology, IT measurements are needed through the concept of the Balanced Scorecard (BSC) in ABC companies. In ABC companies, IT performance measurement is very important to know the extent of the company's business development stage so that it can achieve the organization's vision and mission in the short and long term. In terms of effectiveness and efficiency, ABC companies have information technology and prioritize meeting business and IT strategies. A number of business transactions carried out every day can run the risk of failure of IT implementation which impacts on the company's progress. The method used to measure IT maturity in ABC company is IT BSC combined with BTripleE Framework. From the results of the analysis conducted, there are various findings related to the risk and failure of IT implementation in the company so that an evaluation of IT is obtained which is used as a responsive action towards the IT implementation. After that, the description of the BTripleE Framework form is applied in the use of IT BSC that considers 4 perspectives through the calculation of Key Performance Indicators (KPI), including corporate contribution, future orientation, operational excellence, and customer orientation.Keywords: IT Performance Measurement, IT Balanced Scorecard, BtrippleE Framework, Key Performance Indicator

Computable metrics of personal performance in software development teams

The present article describes setup, configuration and usage of the key performance indicators (KPIs) of members of project teams involved into the software development life cycle. Key performance indicators are described for the full software development life cycle and imply the deep integration with both task tracking systems and project code management systems, as well as a software product quality testing system. To illustrate, we used the extremely popular products - Atlassian Jira (tracking development tasks and bugs tracking system) and git (code management system). The calculation of key performance indicators is given for a team of three developers, two testing engineers responsible for product quality, one designer, one system administrator, one product manager (responsible for setting business requirements) and one project manager. For the key members of the team, it is suggested to use one integral key performance indicator per the role / team member, which reflects the quality of the fulfillment of the corresponding role of the tasks. The model of performance indicators is inverse positive - the initial value of each of the indicators is zero and increases in the case of certain deviations from the standard performance of official duties inherent in a particular role. The calculation of the proposed key performance indicators can be fully automated (in particular, using Atlassian Jira and Atlassian Bitbucket (git) or any other systems, like Redmine, GitLab or TestLink), which eliminates the human factor and, after the automation, does not require any additional effort to calculate. Using such a tool as the key performance indicators allows project managers to completely eliminate bias, reduce the emotional component and provide objective data for the project manager. The described key performance indicators can be used to reduce the time required to resolve conflicts in the team, increase productivity and improve the quality of the software product.

BUSINESS INTELLIGENCE OF THE MECHANIZED OIL WELL STOCK

Background. Modern information systems, designed to facilitate the adoption of optimal solutions, should provide information in a convenient, meaningful form. Processing, transformation and conversion of information to an accessible, visual form is provided by means of Business Intelligence (BI). Business Intelligence are methods and means of presenting raw information in a meaningful and convenient form, easily accessible to perception and capable of making the right decisions. Aims and Objectives. The analysis of the performance of a mechanized well stock, operated by NK «Rosneft» PJSC and supervised by RN-UfaNIPIneft LLC is presented. The level of information usage in decision making in NK «Rosneft» PJSC often does not exceed 20 %, which is explained by its large volume and heterogeneity of the presentation, leading to the complexity of its perception. Results. The principles of implementing BI in the developed tools of analytics are given, where five stages of work with information are distinguished: 1. Information search, loading and storage of the initial information; 2. Interactive analytical processing of information; 3. Warning of deviation from expected results; 4. Business analytics; 5. Business reporting. The implemented tools of regular and flexible accounting are considered. Means for the formation of monthly regulatory reports help to reduce the labor costs for their formation from several days to minutes. The introduction of standardized approaches to the calculation of key performance indicators will facilitate the possibility of their comparison between the various structures of NK «Rosneft» PJSC. Transition from «manual» to automated calculation of a number of parameters will increase their accuracy, which will positively affect the efficiency of the work of NK «Rosneft» PJSC in a whole.

PTH-044 The National Endoscopy Database (NED) Project

Introduction The UK National Endoscopy Database (NED) Project will enable prospective automatic data collection from endoscopy reporting systems in individual hospitals to a national database. Comprehensive national endoscopy data is vital for quality assurance, benchmarking, audit and research. Once fully deployed NED will accept 1 million procedures per year from over 300 hospitals. Methods The National Endoscopy Database is based on the successful JAG Endoscopy Training System (JETS). It has been developed to gather data for calculation of key performance indicators (KPIs) for OGD, flexible sigmoidoscopy, colonoscopy and ERCP. All the major endoscopy reporting system (ERS) manufacturers have engaged with the project and are modifying their systems to allow a mandatory minimum dataset to be automatically uploaded to a central database. The new data schema terms are based on MST 3.0 allowing mapping to a standardised terminology. Results Testing of the upload methodology has demonstrated it is secure, robust, scalable and accurate. To date, two of the major ERS manufacturers have implemented NED compatible software at designated pilot sites. Pilot testing is ongoing. Further roll out and pilot testing is planned over forthcoming months. Conclusion Initial results from the NED project demonstrate that the proposed upload methodology is reliable and accurate. Modifications to existing ERS are currently being performed by all major manufacturers and testing of uploading real time clinical data underway. The ability to centrally collect endoscopy performance data will be an invaluable tool for monitoring and improving endoscopy quality and will provide a platform for endoscopy research. Disclosure of Interest None Declared

Model-based Approach to Automated Calculation of Key Performance Indicators for Industrial Turbines


 
 
 In recent years, the service business of the global turbo- machinery industry has undergone important changes. Many of these changes have been motivated by an increased demand for dedicated and systematic approaches to process safety, reliability, asset integrity and the overall health of the system. This has strengthened the role of key performance indicators (KPIs) as a means of providing guidance for the system’s health state and improve risk management. In order to provide trustable and accurate calculations of these performance indicators in an automated fashion, we argue for a model-based solution that deals with the complexity of diverse configurations and interdependences between system components. This paper presents a solution for calculating KPIs by a semi-automated process based on post-data processing from the site and specific system models. The models consist of a combination of system descriptions in terms of ontologies and complex event processing models. By virtue of our models, state indicator rules for KPI calculations can be formulated at different levels, identifying performance gaps and indicating precisely where action should be taken by the service engineers. With the adopted solution, we discuss the practical implementation and present results of our success story at Siemens AG for the Industrial Gas Turbines.