Key Production Indicators Research Articles

Automated methodology for calculating key production indicators of oil field development for business planning model

Background: In order to secure the stable and efficient operations of oil-producing companies, it is essential to forecast the profitability and ROI of the enterprise, as well as to regulate development for the next 5 years. Accurate forecasts facilitate more informed planning and decision-making, directly influencing the economic sustainability and competitiveness of the enterprise. Aim: The purpose of this study is to develop an innovative approach to automate the methodology for calculating key development indicators in a business planning model. Materials and methods: The study utilizes methods for collecting and analyzing production and geological data, empirical forecasting models, and statistical analysis techniques to enchance the accuracy and reliability of forecasts. This approach employs modern algorithms and technologies to process large volumes of data, which allows for more accurate and reasonable forecasts of key production indicators of the field development. Results: This methodology can be applied to forecast a five-year business plan and evaluate its expected implementation. It is integrated into the ‘Production planning and monitoring’ of the ABAI information system, which allows direct data export from the database, automates the monthly monitoring of production indicators, and generate reports for further export. Conclusion: The proposed method for automated planning of key production indicators of the development enhances the accuracy and efficiency of forecasting, thereby improving the quality of planning and evaluating the implementation of the business plan. This contributes to more informed and strategically validated management of oil production processes. Automation of planning processes reduces the labor costs traditionally associated with manual analysis and calculations, freeing up resources for more strategic purposes. This enables rapid responses to changes in production conditions and prompt adjustments of plans. As a result, managers can allocate resources more efficiently, minimize risks, and increase the overall productivity of oil production operations.

An Investigation into Statistical Correlations Between Coal Production and Key Productivity Indicators of Surface Miners in Indian Opencast Mines

An Investigation into Statistical Correlations Between Coal Production and Key Productivity Indicators of Surface Miners in Indian Opencast Mines

Visualization of Key Performance Indicators in the Production System in the Context of Industry 4.0

Industry 4.0 has revolutionized the methods to automate and analyze a process, as is the case of implementing Key Performance Indicators (KPIs) that reflect the correct functioning of a working task. Besides, the use of the Internet of Things (IoT), Cyber-Physical Systems (CPS), and other technologies in Industry 4.0 provide an enormous amount of data. For these reasons, this article presents the design of a methodology that contains stages of process analysis, design, implementation, and validation to obtain an interface that shows key production indicators in a simple and friendly way. The cloud platform receives the information from the machine using OPC and MQTT protocols implemented in a low-cost hardware device without changing the current automation hardware platform.

Study on benchmarking of power production management in large river basin hydropower development enterprises

Focusing on quantitative analysis and combining with qualitative analysis, this paper selects five benchmarking enterprises for quantitative evaluation of key production indicators, and completes the transverse and overall comparison of data at all levels of production management. It also carries out qualitative analysis by positioning research for Enterprise B, and points out directions of production management optimization. With benchmarking and targeted diagnosis and optimization, production and operation management level of the enterprise can be improved.

TRAINING IN ECONOMICS FOR ACQUIRING AND DEVELOPING BUSINESS INTELLIGENCE COMPETENCES AT HIGHER SCHOOLS

The research focuses on some relevant aspects of training at higher schools for building digital competences which facilitate the effective application of modern digital tools for solving planned tasks and achieving objectives. The emphasis falls on the necessary competences for working with integrated resource planning systems (ERP). Special attention is paid to the acquisition and development of the so-called Business Intelligence (BI) competences at higher schools. At the beginning of the research, we have synthesized the necessary activities for quality training as the final objective is that the learners acquire a totality of knowledge, skills and experience for working and managing some heterogeneous in their nature processes, as well as some means and technologies required for the transformation of data into managerial information, whereas the data itself – into managerial decisions and action plans. The next stage consists of differentiation among the various types of skills – business skills, analytic skills and IT skills, as well as the specific aspects of their acquisition and development through the training at higher schools. The conclusion makes an assessment of the trends, respectively, the topics of training on specific tools for the solution of managerial tasks and the successful realization of strategic aims. We take into account the training in performing business analyses through dashboards and the work with key productivity indicators (KPI). Parallel to this, we have researched the degree of coverage of this matter in existing curricula and courses at higher schools in Central and Eastern Europe using the method of random sampling without replacement. The logic of carrying out the research corresponds to the principle that the training shall comply with the needs and requirements of the practice. The basis for determining the research sample is based on the subject matter of the research. We have encompassed enterprises of the private sector in countries from Central and Eastern Europe. The sample is limited to small, medium and large enterprises which actively use digital tools in making managerial decisions (respectively 16% / 65% / 19%) from the Czech Republic, Romania, Greece and Bulgaria using the method of random sampling without replacement. The methodology of the research secures its reliability by addressing the survey to a specific category of individuals, including the opinions of managers and employees on the digital competences of future specialists with a degree in Economics. The results are used to make a narrow selection of those areas in the content of specific courses which shall be updated and to point out those curricula which shall be updated in the medium term.

Material incentives for innovative activity of enterprise personnel based on key productivity indicators of innovation

Development and implementation of innovation is one of the means of providing competitive advantages for a business in a market economy. Innovative activity of a company ought to be expedient from the economic viewpoint and result in reduction of costs and maximizing the profitability of the company’s activity in the long-term perspective. One of essential factors of economic effectiveness of innovation is innovation-active personnel able to generate ideas and quickly adapt to changes, willing and motivated to participate in implementing innovation strategy of the company’s development. Not all employees a priori possess such features, and therefore the company ought to develop and take measures to facilitate their innovative activity. The article introduces the method of material incentives for innovative activity of the personnel suggested by the authors. The method is based on adapting a well-known system of key productivity indicators to innovative activity of a company. Personnel of the company is divided into three categories of employees according to their roles in realization of the organization’s innovative policy, and the authors defined a set of four or five key productivity indicators of their activity for each category. The authors defined a range of quantitative interpretation of the achievable result for each indicator. They suggested a mechanism for calculating the total amount of the incentive bonus which takes into account key performance indicators coefficients of innovative productivity and their value, the size of the bonus wage fund for the employee, the employee’s affiliation to one of the personnel categories established, the number of rewarded employees and the frequency of premium payments. Implementation of the suggested mechanism will ensure objective assessment and reward for the contributions made by all the interested employees into innovative development of the company increasing effectiveness of the production.

Multiagent modeling of production logistics in the creation of high-tech products

Economic instability and stochastic behavior of the market lead to the need to develop new methods and models to reduce the life cycle of high-tech products. Therefore, the urgent task is a detailed consideration and analysis of each main stage of the life cycle of creating complex technical products. The current study develops a method for optimizing production processes. The subject of research is a production planning and management as the main stage of the life cycle. The method proposed in the publication is based on the component architecture of the created innovative high-tech product, which is obtained because of decomposition at the initial stage of design. Modern production is of distributed nature. While planning production, it is necessary to pay attention to the management and synchronization of material and other types of flows in the logistics chain of distributed production. To form a set of basic and subsidiary operations of distributed production, it is necessary to consider the logistical features, the level of decomposition of the component architecture of the created product, as well as the type of component in the production cycle. Logistics operations are conducted following the requirements for the relevant indicators, namely, the cost of the operation, quality, competitiveness, innovation, the risk of the operation. The main criterion for optimization is the duration of operations. The work uses multi-agent simulation modeling of a sequential logistics chain of production processes, as well as mathematical models and methods: system analysis, optimization using integer programming, multi-criteria optimization, and expert evaluations. The method reduces the duration of the production cycle for the manufacture of individual components of the complex architecture of high-tech products by determining the optimal set of production and ancillary logistics operations for each component, considering the requirements for key production indicators, which minimize production time of high-tech products.

Exploring the status of diversity in policies and practices of Spanish universities. An asymmetric dual model

Higher education across the world is currently in the throes of assuming a commitment to diversity. However, certain critical positions maintain that such evolution is still guided by market principles. Within such a context, this paper explores what attention is given to diversity in Spanish university policies and practices and how it relates to key productivity indicators. To do so, a study with a descriptive and correlational design was conducted, based on analysing institutional documents and surveying chief diversity officers, techniques which provided evidence about diversity philosophy and practices, respectively. The results revealed at least an average level of institutionalisation of diversity, although it did not demonstrate, in most of the areas, any association with indicators derived from a consolidated ranking by productivity in Spain. The conclusion is that Spanish universities have adopted an asymmetric dual model, in which neo-liberal ideas maintain their hegemony while, although subordinately, certain innovations have been consolidated in parallel in order to protect a number of vulnerable groups under the rhetoric of equity and social justice.

Effect of PRRSV stability on productive parameters in breeding herds of a swine large integrated group in Spain

BackgroundIn breeding herds, porcine reproductive and respiratory syndrome (PRRS) clinically manifests as increased abortions, number of stillbirths, and pre-weaning mortality, and as a direct consequence, results in a decrease of the number of piglets weaned per sow per year. Breeding farm classification according the PRRS virus (PRRSV) status (unstable or stable) is a key control strategy for this disease. The aim of this study was to evaluate the production improvement related to achieving a PRRSV stable status in breeding herds in Spain. For this purpose, epidemiological and productivity data were collected from a systematic PRRSV monitoring program in 35 breeding herds from a large integrated swine group in Spain. A comparative statistical analysis was conducted using four key production indicators (KPI) between different PRRSV status and a generalized linear mixed model: weekly abortions/1000 sows (ABTHS), born-alive rate (BAR), pre-weaning mortality rate (PWMR), and number of weaned piglets per 1000 sows (WPTHS).ResultsFrom the 35 monitored farms during a total period of 58 weeks, we collected 49 to 58 weeks of production data and PRRSV classification status for each study farm. This represented a total of 1997 (741 unstable and 1256 stable) weekly data collected that was eligible for the KPI comparative study. PRRSV stability was associated with significant improvement in BAR (+ 1.10 %, p < 0.001), PWMR (-0.88 %, p < 0.002) and WPTHS (+ 24.52, p < 0.0001).ConclusionsThese results demonstrate for the first time the improved production due to achieving PRRSV stability in breeding herds under field conditions in a European country. Increased number of born-alive piglets and a reduction of piglet pre-weaning mortality represents an increase of 1.28 weaned piglets per sow per year if PRRSV stability was achieved and maintained for one-year period in a breeding farm.

ВНУТРЕННЯЯ ОТЧЕТНОСТЬ АГРОХОЛДИНГОВ

The effective and well-coordinated work of agricultural holdings is to a large extent determined by the internal structure of the system, the clear interaction of its constituent structures. The effective development of holding structures is impossible without the organization of an effective accounting and analysis system, which has its own characteristics in agricultural holdings. The formation of an accounting and analysis system in holding structures requires a vertical orientation, which implies the existence of a parent organization that ensures the centralization of all information flows through the development of intercompany management reporting. An important process for managing an agricultural holding is the formation of a regulation for interaction among holding participants in it, where the inter-holding reporting system plays an important role, without which it is impossible to optimize all financial and commodity flows, organize operational monitoring of the financial and economic activities of the agricultural holding, and accordingly, make timely and correct decisions. It will be relevant to divide the system of intra-holding reporting into three main blocks (on finance, costs and key production indicators), in each of which the appropriate forms are identified and tested.

Modeling and optimization of sugarcane juice clarification process

Sugarcane juice clarification is an important operation in the production process of sugar industry. At present, the two major production indicators (clear juice color value and gravity purity of the mixed juice) in sugarcane juice clarification process still cannot be measured online, and can only be obtained by offline test. In addition, when production condition changes, the setting and adjustment of the key operational variables are made by experienced workers. The delay caused by the offline test and the randomness of the key operational variables caused by manual adjustment will affect the quality of final product. To address these two issues, we firstly constructed a data-driven model based on the deep kernel extreme learning machine (DK-ELM) to predict the key production indicators for clarification process. The model which combined the kernel method and the multi-layer extreme learning machine (ML-ELM) proved successful in prediction of clear juice color value and gravity purity. Then, we proposed a strategy for optimization of operational variables, avoiding the randomness caused by manual setting. A multi -objective function including the purity and color value was constructed. The niche multi-objective particle swarm optimization (PSO) algorithm was used to solve the multi -objective function under typical working conditions in the production process to get optimal set of key operational variables. The results showed that the niche multi-objective particle swarm optimization (PSO) algorithm can provide optimum production indexes for sugarcane juice clarification.

Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm

Soft-sensor technology plays a vital role in tracking and monitoring the key production indicators of the grinding and classifying process. Least squares support vector machine (LSSVM), as a soft-sensor model with strong generalization ability, can be used to predict key production indicators in complex grinding processes. The traditional crossvalidation method cannot obtain the ideal structure parameters of LSSVM. In order to improve the prediction accuracy of LSSVM, a golden sine Harris Hawk optimization (GSHHO) algorithm was proposed to optimize the structure parameters of LSSVM models with linear kernel, sigmoid kernel, polynomial kernel, and radial basis kernel, and the influences of GSHHO algorithm on the prediction accuracy under these LSSVM models were studied. In order to deal with the problem that the prediction accuracy of the model decreases due to changes of industrial status, this paper adopts moving window (MW) strategy to adaptively revise the LSSVM (MW-LSSVM), which greatly improves the prediction accuracy of the LSSVM. The prediction accuracy of the regularized extreme learning machine with MW strategy (MW-RELM) is higher than that of MW-LSSVM at some moments. Based on the training errors of LSSVM and RELM within the window, this paper proposes an adaptive hybrid soft-sensing model that switches between LSSVM and RELM. Compared with the previous MW-LSSVM, MW-neural network trained with extended Kalman filter(MW-KNN), and MW-RELM, the prediction accuracy of the hybrid model is further improved. Simulation results show that the proposed hybrid adaptive soft-sensor model has good generalization ability and prediction accuracy.

Simulation and Real-time Visualization of Truck-Loader Haulage Systems in an Open Pit Mine using AnyLogic

In this study, we proposed a method to simulate a truck-loader haulage system in open-pit mines and visualize the results in real time using the software AnyLogic. An open-pit limestone mine in Korea was selected as the study area. The time parameters for each unit operation of the haulage system were measured via a field survey. A discrete event simulation model for the mine was then developed using AnyLogic. We intuitively confirmed the simulation results with key production indicators and visualized the truck-loader haulage operation over time. This study was thus able to overcome the limitations of previous studies, which could not measure key production indicator values and the status of haulage systems in real time during simulations.

Development of a Productivity Assessment Tool for Hospital Settings in Iran: A Delphi Study

Background: Measurement of the organizational performance would be effective and helpful for improvement, in the case that it is based on an appropriate and principled system. Such a system uses specific indicators to measure the productivity and performance of the organizations. In fact, these indicators act as useful tools for strategic planning in managerial levels. Given the importance of the issue, this study aimed to develop the key productivity indicators for hospitals affiliated to Isfahan University of Medical Sciences. These indicators are designed to be applied in evaluation, implementation, and improvement processes of the organizations under study. Methods: This was a qualitative study conducted among the hospitals affiliated to Isfahan University of Medical Sciences in 2015. Purposive sampling method was applied and the study participants were selected through snowballing. Then, the participants were organized in a focus group consisting of 16 experts in the field of hospital performance and efficiency indicators to achieve the research objectives by using indexing matrix method. Finally, validity of the developed indices was confirmed using Delphi method. Results: A total of 42 productivity indicators were formulated. Of these, nine indices were input (structural), which included financial, human resources, and energy consumption indicators. The next 29 indices were output and mainly belonged to the financial, efficiency, and quality area. The final four indices included process indicators and consisted of length of stay in emergency department, waiting time for out of hospital counseling, and waiting time for the presence of resuscitation team at the patient bedside during the cardiopulmonary resuscitation. Conclusion: Development of the indicators in three areas of input, process, and output helped managers and policy makers to access reliable and valid instrument for measuring organizational productivity.

Interactions between building information modelling and off-site manufacturing for productivity improvement

PurposeNew methods have been introduced as revolutionary approaches in the construction industry, such as off-site manufacturing (OSM) and building information modelling (BIM). Although these approaches can provide many benefits, there are still barriers to meeting the expectations of improved construction productivity via their implementation. Hence, the purpose of this paper is to critically review the capabilities of OSM and BIM techniques, as well as their potential interactions, in productivity improvement.Design/methodology/approachA scoping review approach was adopted, where 100 peer-reviewed journal articles were collected to analyse the capabilities of OSM and BIM, as well as their potential interactions, in productivity improvement as assessed by key productivity indicators (KPrIs).FindingsThe results reveal seven BIM-based capabilities and six OSM-based capabilities, as well as 12 potential OSM–BIM interactions that have significant potential for satisfying KPrIs.Originality/valueAn integrated framework has also been developed to clarify and conceptualise the roles of OSM–BIM interactions in their designated KPrIs. The research has developed insightful and practical references for strategic planning and management in OSM–BIM-based projects.

T. Gorokhova, Y. Fomenko, Marketing product policy as a factor of competitive capacity of enterprises in post-crisis conditions.

The article considers that the marketing commodity policy is one of the key factors of competitive capacity of enterprise in the modern conditions of market development. The marketing commodity policy can significantly improve the efficiency of companies activities. We researched this issue by example of activity of Metinvest Group (International Vertically Integrated Mining and Metallurgical Company). The financial and production reports of Metinvest for the last 6 years were analyzed. It should be noted that the connection between elements of the enterprise marketing product policy and its financial and production indicators is stable. We conducted the assessment of competitive capacity of enterprises using commodity method with elements of statistical analysis. We determined trends in the production of the main products groups of the enterprises, which affects to effectiveness of company's marketing product policy. The using of statistical analysis of financial and production reports of the selected company can provide the necessary information about the situation with its main product groups and the dynamics of key production indicators. This analysis reveals effective and inefficient trends in marketing product policies (ineffective solutions being eliminated or edited, while effective solutions can be optimized and positively affect the financial stability of the enterprise competitiveness). The additional scientific novelty is a research of influence of marketing product policy in the context of the post-crisis activity of the selected company. We developed and presented the recommendations of improving elements of marketing product policy (information support, new product development, and brand policy) in frame of optimization the company's activities.

Online hybrid modeling method with application for predicting Bauxite production indicators

In the bauxite flotation process, concentrate grade and tailings grade are key production indicators; however, they are difficult to measure online. It is also difficult to develop an effective mathematical model for the process because of the complex non-linear and uncertain relationship among the feed parameters (feed grade, pulp density, slurry particle size, etc.), froth features and production indicators. Therefore, an online hybrid modeling method is proposed by analyzing the multiple parameters that affect the production indicators. First, according to the correlation and redundancy in the feed and froth feature parameters, the kernel principle component analysis (KPCA) is used to reduce the number of the parameters. Then, a neutral network model of the regular extreme learning machine (RELM), which is based on wavelet function, is presented to predict these two indicators. To improve generalization capability and prediction accuracy, information entropy is used to distribute the weight of the two models based on their predicting error. At last, an on-line updating strategy of the hybrid model is constructed in order to investigate the influence of the working conditions. The proposed method is tested on the diasporic-bauxite flotation process and shows high predictive accuracy and generalization capability. It lays the foundation for optimal control of the operation parameters based on mineral grade in the flotation process.

Economic evaluation of irrigated forage production in a beef cattle operation in the semi-arid tropics of northern Australia

Australia's northern beef industry is a significant contributor to regional economies in northern Australia, but is economically challenged by a range of factors including declining real beef prices, limited market opportunities, stalled productivity growth, increasing levels of enterprise debt, and low returns on investment. The long-term viability of the industry depends on achieving herd productivity gains and lifting enterprise profitability. Typical of the northern Australian beef industry, beef enterprises located in the Gulf of Carpentaria river drainage basins or catchments of north-west Queensland rely on extensive grazing of unimproved native pastures. Feed of reasonable quality is relatively plentiful during the annual wet season, but this is typically followed by feed shortages and sharply declining feed quality in the following dry season. Irrigation of some pasturage could potentially increase the availability and quality of dry-season feed and improve the productivity of these cattle enterprises. To explore this issue, an enterprise-scale bio-economic model was employed to consider several development scenarios involving investments in irrigation infrastructure for a case-study beef enterprise located in the Flinders River catchment in north-west Queensland. Our findings showed improvements in key productivity indicators, such as beef turnoff and enterprise profitability for some forage-based irrigation scenarios. However, the capital cost of installing and operating the infrastructure associated with the assumed scale of irrigation development had a significantly negative effect on returns. The findings suggest that investors will need to be judicious with capital expenditure and investment decisions informed by an understanding of the interplay between irrigation water storage size, expected reliability of filling, and the ability to meet seasonal crop water demand.

Use of intellectual capital as a surrogate for productivity measurement

The ongoing trends in global outsourcing and use of knowledge workers greatly impacted how productivity is measured. As a result, the research explores a possible surrogate for productivity measurement, especially at the organisational level. In this study, the term intellectual capital or IC is selected. The research methodology applies the correlation analysis which pairs the IC with key productivity indicators at the organisational levels. The correlation analysis shows that all selected key organisational productivity indicators such as value–added labour productivity demonstrate the positive relationships with the IC. In conclusion, under the emerging changes in business environment which the intangible assets have been very influential in an organisation's value creation, it is possible to use the IC as a surrogate for productivity measurement.

APPLYING THE METHOD OF MEASURING AIRPORT PRODUCTIVITY IN THE BALTIC REGION

The author of the article describes the theoretical assumptions of the DEA method used for measuring the productivity of airports described in the article ‘Research into the methods of analysing the productivity indicators of transport terminals’ (Jaržemskienė 2009). The essential insights presented in the above mentioned paper reveal that Data Envelopment Analysis (DEA) is a relatively new ‘data oriented’ approach to evaluating the performance of the so-called Decision Making Units (DMU) that convert multiple inputs into multiple outputs. The article focuses on the findings of the study carried out by the author in accordance with those assumptions. Research represents the Data Envelopment Analysis (DEA) method testing 15 selected airports situated in the Baltic Region, including Vilnius, Kaunas, Palanga, Riga, Tallinn, St. Petersburg, Helsinki, Turku, Stockholm, Malme, Copenhagen, Hamburg, Gdansk, Warsaw and Minsk. Airport productivity indicators are ranked considering importance and using the method of Delphi expert survey made of two rounds. The author presented the following indicators (expressed as ‘ratio’) as the major ones estimated by PAX/LAND, AIR/LAND, PAX/AIR, PAX/RW, PAX/RWA, GA and INTER experts. The succeeding indicators were introduced by PAX/TERMAREA, PAX/GATES, AIR/RW, AIR/RWA, AIR/TERMAREA, AIR/GATES, FR/LAND and FR/RW. 10 indicators were accepted as the most important and selected from the current set in the following sequence: AIR/LAND, AIR/RW, PAX/RW, PAX/LAND, AIR/RWA, PAX/AIR, PAX/RWA, AIR/TERMAREA, PAX/GATES and PAX/TERMAREA. AIR/LAND and AIR/RW were submitted as two main indicators. The acronyms are explained as follows: LAND – airport area, RWA – runway length, PAX – the number of passengers, AIR – the number of aircraft take-offs and landings, RW – the number of runways, GATES – the number of gates, FR – the amount of freight served, TERMAREA – the area of passenger terminal, GA – a general aviation market share of airport served aircraft by percentage, INTER – the percentage of international passengers considering all passengers served by airports. After two key productivity indicators were chosen conducting the expert survey, airport productivity was compared applying the DEA method.