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1
Niameshchuk, H., V. Bozhanova, V. Chala, and A. Hlushchenko. "The environmental and resource productivity as the key element of green economy in EU." IOP Conference Series: Earth and Environmental Science 915, no. 1 (November 1, 2021): 012018. http://dx.doi.org/10.1088/1755-1315/915/1/012018.
Повний текст джерелаАнотація:
Abstract The purpose of this study was to analyze the indicators of environmental and resource productivity of European countries to identify leaders and outsiders and study their trends over recent decades. The results of the analysis showed that the indicators are characterized by uneven development, due to the characteristics of countries and their green policies. The key indicators of the Europe 2020 policy have been used for the analysis, namely resource productivity, greenhouse gas emissions per capita, recycling rate of municipal waste, eco-innovation index, final energy consumption in households. Linear trends for the period 1995-2019 with indication of trend equations were constructed for each indicator. The slope of the functions has been analyzed in order to identify the dynamics of trends. According to the results of the analysis, all indicators except greenhouse gas emissions per capita were characterized by positive dynamics, ie increased. It has been determined that the problematic indicator for EU countries is final energy consumption in households, which requires further detailed research.
2
Albala-Bertrand, J. M. "Structural Change behind GDP Growth Rates via Key Indicators: Chile 1996-2015." International Business Research 11, no. 8 (July 6, 2018): 38. http://dx.doi.org/10.5539/ibr.v11n8p38.
Повний текст джерелаАнотація:
This paper deals with the change of some aggregate structural indicators in Chile over the 20-year period 1996-2015. We first produce an accounting growth decomposition to assess the changes in the contribution of capital productivity, capital intensity and labour participation to the growth rate of output per capita as well as the growth rate of labour income participation in national income. We then combine an accounting growth decomposition with a standard production function growth accounting to assess the contribution of both capital productivity and capital intensity to total factor productivity (TFP). To complement the latter, we produce optimal estimates of incremental capital productivity and incremental income elasticity to capital by means of a linear programming exercise. Our main conclusion is that capital intensity, rather than capital productivity or labour participation, has been the main growth contributor. TFP has contributed in a decreasing way from positive to negative over our sub-periods, so adding to and subtracting from GDP growth over time, with the main positive contributor to TFP growth systematically being a proportion of capital intensity.
3
Berglund, Anders, Johannes Blackne, and Niklas Jansson. "Proposing a Feedback System to Enhance Learning Based on Key Performance Indicators." International Journal of Quality Assurance in Engineering and Technology Education 3, no. 1 (January 2014): 1–14. http://dx.doi.org/10.4018/ijqaete.2014010101.
Повний текст джерелаАнотація:
This paper proposes a feedback system that is based on the self-evaluation of perceived productivity as a mechanism for detecting deviations in an engineering design student project. By monitoring key performance indicators, project members used feedback loops to recognize alarming patterns and act accordingly. The study is based on descriptive survey data that addressed three factors of influence: perceived productivity, perception of stage completion, and work-activity distribution. The productivity data was analysed by detecting patterns in the form of peaks and lows and by combining the patterns with qualitative data from observations and documented work activities. Measurements were taken every time the project team got together; 33 occasions during the course of the project, resulting in a total of 280 student responses for productivity (P) and completion (C) and 115 student replies for work activity distribution. The findings provide an extraction of peak values and low values that enables tracking of critical incidents. Through an in-depth activity log, each value was enriched with lessons learned about what took place and the consequences for the project, thus enhancing learning from past activities through systematic feedback sessions. The accumulated set of data provided distinguishable patterns for the project team to interpret. Over time this made student actions more proactive, activity execution more distinct and purposeful, and resource allocation in combination with feedback reflections more refined.
4
Spahija, S., E. Shehi, and G. Guxho. "Evaluation of production effectiveness in garment companies through key performance indicators." Autex Research Journal 12, no. 2 (October 1, 2012): 62–66. http://dx.doi.org/10.2478/v10304-012-0012-x.
Повний текст джерелаАнотація:
Abstract Key Performance Indicators help an organisation to improve productivity and to evaluate its success. Improved productivity is an essential part of increasing the value-added services that companies can offer to clients and hence their competitiveness. By using Key Performance Indicators, a company can establish baseline figures against a number of important areas. They can be considered like a health check on a company or a diagnosis of where a company can improve its performance. The definition of what they are and how they are measured does not change often, but Key Performance Indicators may change as the organisation's goals change. In this paper we present and discuss the way to establish Key Performance Indicators to evaluate the production effectiveness of garment companies. The KPIs are being tested in several garment companies in Albania and actually implemented in 25 companies.
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Zhang, Lu, and Zhiyun Ouyang. "Exploring the Relationships between Key Ecological Indicators to Improve Natural Conservation Planning at Different Scales." Forests 10, no. 1 (January 5, 2019): 32. http://dx.doi.org/10.3390/f10010032.
Повний текст джерелаАнотація:
Biodiversity, regulating ecosystem services (RES), and vegetation productivity are key indicators to instruct natural conservation planning. Decision makers often hope that ecosystems can be protected by focusing on certain key indicators, which requires an understanding of the relationships between the indicators. Using individual case studies, many have argued that these indicators commonly have significant relationships. However, these relationships at different spatial scales are unclear. Therefore, in this study, biodiversity and ecosystem services are modelled by the ecological niche model, the universal soil loss equation, and the equation of water balance in two study areas at different scales. The influence of vegetation productivity on the spatial pattern of other ecological indicators in the two areas is examined by a spatial lag model. The contributions of the driving factors on biodiversity distribution at both scales are identified by a boosted regression tree (BRT) model. The results showed that at the fine scale, the spatial correlations were strongest for species richness, especially mammalian species richness, and water retention. However, biodiversity had no significant relationship with vegetation productivity. In contrast, at a coarser scale, the correlation was stronger between plant diversity and regulating ecosystem services. In addition, plant diversity was significantly correlated with vegetation productivity. These differences between scales were controlled by various explanatory variables. At the fine scale, biophysical and climatic factors had the strongest effects on biodiversity distribution, while Net Primary Productivity (NPP) and ecoregion also had relatively high influences on biodiversity at the coarse scale. This demonstrates the critical importance of spatial scale in selecting conservation indicators. We suggest that rare mammalian species richness or flagship mammal species are suitable as conservation surrogates in fine-scale conservation planning. However, at a coarser scale, selecting vegetation patches with more rare plant species and high productivity for each ecoregion is a workable alternative method for conservation planning.
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Телеев, Джамалутдин, and Dzhamalutdin Teleev. "ANALYSIS OF KEY PARAMETERS OF THE FUNCTIONING OF BUSINESS PROCESSES AS AN EMPIRICAL BASE OF ACCOUNTING AND ANALYTICAL MANAGEMENT." Russian Journal of Management 6, no. 4 (December 25, 2018): 1–5. http://dx.doi.org/10.29039/article_5ca760b7213b73.74562403.
Повний текст джерелаАнотація:
The article presents the results of the analysis of the dynamics of indicators of output of products, works, services, capital productivity and capital ratio, the level of depreciation of fixed assets in the context of economic activities. According to the results of the analysis, the influence of labor productivity and the introduction of new technologies on one of the main indicators of the business process functioning - the output of products, work-services. The necessity of a process of large-scale development and introduction of new technologies with the aim of increasing labor productivity, increasing capital productivity and, ultimately, increasing the output of new products due to qualitative factors has been determined.
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Mikhnenko, P. A. "Economic and statistical analyses of labor productivity growth at russian industrial enterprises: Key factors." Management Science 11, no. 2 (May 26, 2021): 6–23. http://dx.doi.org/10.26794/2404-022x-2021-11-2-6-23.
Повний текст джерелаАнотація:
Increasing labor productivity (LP) is an actual task of the Russian economy and management of enterprises. Today, the country has a growing productivity gap between the most efficient and lagging companies. The aim of the study is to identify the key factors which determine the magnitude and growth rate of this indicator in the interests of revealing promising areas and ways of managing its growth. The paper describes the analysis of financial and economic indicators that are factors of sustainable growth in LP at domestic industrial enterprises, leading in this indicator in the period 2017–2019. The methodological basis has been the provisions of the production factors and productivity theories. The research method is based on the theorem of statistical hypotheses. The object of this research has been 94 Russian leading companies in terms of LP growth under reviewing period. An economic and statistical model of LP growth factors has been developed and the conditional probabilities of hypotheses have been calculated that characterize the ranges of growth of this indicator upon the occurrence of events corresponding to an increase in the values of financial and economic indicators. The key factors and the ranges of their change are highlighted for four ranges of the productivity rate: small, moderate, high and record. It is shown that the determining factors of a low rate are a revenue growth in the range from –23 to 57%, a decrease in the duration of an operating cycle to –7% and a growth in asset turnover up to 37%. The factors of the record rate of LP growth are revenue gain in the range 97–297%; operating cycle reduction in the range from –42 to –88% and the turnover of assets gain in the range 114–342%. The capital-labor ratio gain is considered with low probability as a factor of the moderate or high LP gain in combination with an increase in revenue and asset turnover. High correlation of the operating cycle reduction and increase the asset turnover can serve as an indicator of LP increase programs success. The proposed approach allowed to identify significant factors of LP growth at leading companies that had not been obvious during applying a regression analysis.
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Streltsova, Darya Alekseevna. "Key performance indicators of production activity efficiency in the implementation of lean production methods in mechanical-engineering enterprises." Interactive science, no. 10 (44) (October 19, 2019): 51–56. http://dx.doi.org/10.21661/r-508275.
Повний текст джерелаАнотація:
The article is devoted to the consideration of methods of lean production in relation to mechanical-engineering enterprises; the main key indicators of efficiency, productivity and key performance indicators (KPI), their similarities and differences. The key performance indicators, the most preferred in the analysis and management of production at mechanical-engineering enterprises, are defined in the article.
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Schnellbach, P., and G. Reinhart. "Evaluating the Effects of Energy Productivity Measures on Lean Production Key Performance Indicators." Procedia CIRP 26 (2015): 492–97. http://dx.doi.org/10.1016/j.procir.2014.07.094.
Повний текст джерела
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Trukhmanov, V. B., E. A. Pervushkina, A. A. Statuev, V. N. Belov, and A. M. Volodin. "System of key performance indicators as a method for executive decision-making." E3S Web of Conferences 296 (2021): 02003. http://dx.doi.org/10.1051/e3sconf/202129602003.
Повний текст джерелаАнотація:
Key performance indicators, KPI are a proven cutting-edge tool for process productivity and staff performance monitoring. Therefore, today it is possible to find the use of this tool in the most varied spheres and industries. However, the system implementation is a long and painstaking process, which requires financial, labor and time expenditures. But in case of its successful implementation, KPI technology will provide the balance of advantage for the company management and contribute to its growth. The article presents the selection mechanism of KPI system development and implementation methods. Using key performance indicators, the organization is able to manage the process and modify it, as well as define objectives for employees and motivate them to achieve their objectives. KPIs are based on measuring quantitative benefits. Control shall be based on comprehensive and reliable information about the company’s process productivity. This is the only way to take quick management decisions, make adjustments to the operation of processes that will contribute to the achievement of strategic objectives as a whole.
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Zadhoosh, Mohammad, and Amir Afshin Fattahi. "Simultaneous effect of RCM key indicators, related to equipment lifecycle for maintenance strategies (in production systems)." International Journal of Engineering & Technology 4, no. 3 (July 25, 2015): 439. http://dx.doi.org/10.14419/ijet.v4i3.4006.
Повний текст джерелаАнотація:
In the modern industrial world, effective role of maintenance activities in the survival of organizations and enhancing their productivity is undeniable.Variety of equipments in the industrial organizations and their exclusivities in terms of longevity and failures modes, clear the importance of choosing a strategy for the maintenance of any equipment. Thus, knowing the type of the failure is so important for maintenance manager and engineers.Important introduced indicators in this domain such as indicator of reliability, MTBF and MTTR can be used to predicting and detecting approximate failure time and analyzing life cycle status.On the other hand, due to fluctuating time of system availability because of work continuity and loss of a performance indicator, by combining RCM indicators and Overall Equipment Effectiveness (OEE) indicator the most compatible strategy could be chosen. The aim of this article which differs from other studies in this field is to apply a composed indicator, including different parameters of RCM, which is obtained based on the equipment life status.
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Kuvshinov, M. S., and T. A. Vinogradova. "Material incentives for innovative activity of enterprise personnel based on key productivity indicators of innovation." Russian Journal of Industrial Economics 14, no. 4 (December 31, 2021): 463–70. http://dx.doi.org/10.17073/2072-1633-2021-4-463-470.
Повний текст джерелаАнотація:
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.
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GORYACHUK, V. F., and Y. A. NAZARENKO. "GROSS VALUE ADDED AS THE MAIN INDICATOR PRODUCTIVITY OF ENTERPRISES." Economic innovations 22, no. 2(75) (June 20, 2020): 25–33. http://dx.doi.org/10.31520/ei.2020.22.2(75).25-33.
Повний текст джерелаАнотація:
Topicality. Traditionally, output and profit are used as the main indicators of enterprise productivity, but they have significant shortcomings. All this determines the relevance of the search for new indicators that will provide an adequate idea of the productivity of enterprises and a single conceptual basis for assessing the results of economic activity at the micro, mega and macro levels. Aim and tasks. To analyze the existing indicators for assessing the productivity of enterprises, to identify their main short comings. Consider the experience of the Republic of Belarus in the use of gross value added as an indicator of productivity of enterprises. Justify the use of gross value added as a conceptual basis for assessing the results of economic activity at the micro, mega and macro levels. Identify the main indicators for assessing the productivity of enterprises on the basis of gross value added. Research results. As the main indicators of the enterprise, use "output" and "profit". The first indicator consists of gross value added and intermediate consumption and therefore it depends on the performance of other enterprises. In this case, the external contribution can be decisive and therefore the indicator "output" does not give an adequate idea of the productivity of enterprises. Another key indicator of enterprise activity is profit. With a focus on it, wages, social security financing and employee training are the costs of the enterprise and the smaller they are, the greater the company's profits. But in modern conditions to ensure sustainable economic development of the individual enterprise and the country as a whole, it is important to consider that employees are carriers of intellectual capital. The loss or reduction of this capital can lead to more significant losses than the loss of physical and financial capital. Under such conditions, it is advisable to change the benchmark, namely the use of gross value added as the main indicator of productivity of the enterprise. It is on the basis of this indicator that the main result of economic activity of the country (gross domestic product) and regions (gross regional product) is determined. Gross value added is a very important indicator in terms of meeting the needs of all stakeholders in the performance of the enterprise: employees, business owners, the state. The indicator "gross value added" is the main indicator of the enterprise in terms of the interests of society as a whole, and the indicator "profit" - the main in terms of the interests of business owners. The presence of these two indicators reflects the contradiction between the interests of society and business owners. Conclusion. Gross value added has a greater analytical value than the indicators "output" and "profit", because, firstly, it gives a more adequate picture of the results of enterprises, secondly, it determines the potential for capital accumulation and thirdly provides a single conceptual basis for evaluation of economic performance at the micro, mega and macro levels.
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Kusnadi, Kusnadi. "INCREASED PRODUCTIVITY OF HUMAN RESOURCES BASED ON KEY PERFORMANCE INDICATOR AND TRAINING." Dinasti International Journal of Digital Business Management 1, no. 6 (November 1, 2020): 904–10. http://dx.doi.org/10.31933/dijdbm.v1i6.572.
Повний текст джерелаАнотація:
This study aims to discuss methods in an effort to increase human resource productivity in a company based on Key Performance Indicators (KPI) and providing training. The concept of human resource development is a way that is carried out with the intention of making human resources a source of competitive advantage, based on practical experience that effective human resource development can increase work productivity, morale, and the potential in institutions, development programs. The assumption of work productivity can be seen as a mental attitude that always has the view that today's life must be better than yesterday and tomorrow must be better than today. Employees who have this attitude will be encouraged to be dynamic, creative, and open while remaining critical and responsive to new ideas and changes. This requires an appropriate human resource development strategy. Work productivity is the result of the interaction between work ability (ability) and motivation (motivation). Work motivation can be influenced by the physical condition, social conditions of the work environment, as well as the basic needs of employees. Productivity can be interpreted, among other things, from the aspect of individual appearance. Thus, employees who have the ability and high motivation in carrying out their duties will be able to support the organization by becoming a productive employee characterized by fulfilling job qualifications, positive job orientation, maturity, highly motivated, and able to get along effectively.
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Amrina, Uly, and Alfa Firdaus. "THE SELECTION OF PRODUCTIVITY KEY PERFORMANCE INDICATORS FOR CAR MANUFACTURING COMPANIES USING INTEGRATED PERFORMANCE MEASUREMENT SYSTEM." SINERGI 22, no. 2 (June 27, 2018): 101. http://dx.doi.org/10.22441/sinergi.2018.2.005.
Повний текст джерелаАнотація:
The increase in car market 17% in 2020, and ASEAN Free Trade policy only 5% for imported products, cause tightened competition in the automotive market. Car manufacturing companies face problems in selecting their performance indicators related to competitive challenges. The purpose of this research is to develop productivity key performance indicators in car manufacturing that conform to stakeholder requirements as a strategy to win the market. The conditions are manifested in a mapping of manufacturing symbols using the Integrated Performance Measurement System (IPMS) method. That manufacturing productivity indicators will become the focus of the top management to be controlled. There are four stages to go through, starting with the identification of stakeholder requirement, which produces six criteria and 12 stakeholder requirements and mapped into 16 goals and 24 key performance indicator (KPI). The second stage is benchmarking, and preparation of KPI hierarchy and the third stage is the description of each KPI in the form of KPI specification table. The fourth stage is the weighting of the six main criteria that result in 3 priority criteria which must be reported monthly between the manufacturing division (painting), finance and administration. Those are employee safety, manufacturing process effectiveness (painting) and waste elimination activities. The priority is obtained based on questionnaires answered by ten stakeholders (experts) processed with Analytical Hierarchy Process (AHP) approach and has been tested consistency in the number 0.09. These three criteria are described in 15 KPIs: performance rate, availability rate, quality rate, trained operator ratio, training value ratio> 80, delay ratio, attendance ratio, downtime losses, setup and adjustment losses, idle time, defect losses, yield losses, manpower efficiency, environmental impact and work accident ratio.
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Filipenko, Anton. "ECONOMIC PRODUCTIVITY: FACTOR APPROACHES." Actual Problems of International Relations, no. 147 (2021): 55–64. http://dx.doi.org/10.17721/apmv.2021.147.1.55-64.
Повний текст джерелаАнотація:
Abstract. The article focuses on economic productivity and have stressed the theory of marginal utility (productivity). In modern researches such aggregate indicators as a total factor productivity and a multifactor productivity prevail. Total Factor Productivity is measured by combining the effects of all there sources used in the production of goods and services (labour, capital, materials, energy, etc.) and dividing it into the output. Multifactor productivity (MFP) is the ratio of total output to a subset of inputs. A subset of inputs might consist of only labour and materials or it could include capital. MFP is the residual contribution to output growth of an industry or economy after calculating the contribution from all its factor inputs. The OECD methodology examines key single-factor (aggregated) indicators of labour and capital productivity, considering total output and costs and, most importantly, the value added, which reflects the real increase in the welfare of the nation.
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Sezer, Ahmet Anil. "Contractor use of productivity and sustainability indicators for building refurbishment." Built Environment Project and Asset Management 5, no. 2 (May 5, 2015): 141–53. http://dx.doi.org/10.1108/bepam-11-2013-0065.
Повний текст джерелаАнотація:
Purpose – The purpose of this paper is to analyse the use of building refurbishment productivity and sustainability indicators among construction contractors. Design/methodology/approach – Theories of organisational culture and incentives predict that contractor size affects dependency on routines and the implementation of company policies on site. Eight semi-structured interviews were conducted with three general or site managers of small and medium enterprises (SMEs) and five site managers of large contractors involved in housing and office refurbishment projects in Sweden. Findings – Both SMEs and large contractors track resource use on refurbishment sites. Resource use is measured through cost recording systems and a few area-based key indicators. SMEs may have sustainability policies, but routines derived from such policies were weak on site. However, for SMEs, refurbishment technology choices are often made by the client’s designer, and sustainability requirements are included in the contract specifications. For large contractors, sustainability policies are more developed and are reflected in site routines. Large contractors are usually public companies with stakeholders who expect such routines. Practical implications – Contractors that develop strategies for increasing productivity and sustainability can benefit from a deeper understanding of implementation issues. The outcome of this paper is useful while designing new indicators and building certification schemes. Originality/value – Much has been written about key performance indicators for construction, but little attention has been paid to indicators for building refurbishment, or the use of measurement routines.
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Chen, Yasheng, and Johnny Jermias. "Linking Key Performance Indicators to New International Venture Survival." Journal of International Accounting Research 15, no. 3 (September 1, 2016): 31–48. http://dx.doi.org/10.2308/jiar-51604.
Повний текст джерелаАнотація:
ABSTRACT Based on the four major challenges firms face in the early stage of their life cycle, we identify and use financial and non-financial performance measures to predict the survivability of new international ventures. We use a sample of 3,729 new manufacturing ventures from the Chinese Foreign Invested Enterprises Database. The study sample consists of wholly owned ventures of multi-national corporations (MNCs) and joint ventures between pairs comprising foreign and local investors in China. The results are consistent with the study's hypotheses. Using the Cox (1972) survival model, we find that employee training, employee productivity, accounts receivable collection period, export intensity, and sales growth are positively related to new venture survival. This study contributes to the existing business venturing and accounting literature in three ways. First, it fills the gap in the existing literature on bankruptcy prediction by focusing on firms in the early stage of their life cycle. Second, it uses survivability as a measure of business success. Survivability is a more comprehensive measure of firm performance than traditional financial measures during the start-up stage because during this stage firms tend to carry large losses that make financial measures inappropriate. Finally, this study has the potential to help new venture managers improve a firm's chances of success by using customized performance measures that fit its unique situation. JEL Classifications: D21; G32; M41.
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Glavatskikh, O. B. "THE ROLE OF KEY PERFORMANCE INDICATORS IN LABOR STIMULATION." Bulletin of Udmurt University. Series Economics and Law 31, no. 3 (June 8, 2021): 357–63. http://dx.doi.org/10.35634/2412-9593-2021-31-3-357-363.
Повний текст джерелаАнотація:
The problem of choosing tools to improve labor efficiency has always been and remains one of the most important and relevant in the modern theory and practice of personnel management. A well-designed system of personnel incentives as a method of improving work efficiency contributes to increasing productivity and improving the effectiveness of the organization as a whole [1]. In the context of digital transformation and the introduction of lean management principles, managers are making increasingly high demands on their staff. Against this background, the search for optimal methods of financial incentives is becoming more relevant than ever [2]. Today, in the Russian business practice, one of the most popular tools for improving the performance of personnel is the system of material incentives based on the developed system of key performance indicators (KPIs) [3]. This article reveals the essence and meaning of key performance indicators; considers the results of sociological research on the effectiveness of the system of material incentives for personnel working in sales, its shortcomings, the factors affecting the labor activity of the personnel. The author offers an easy and understandable for employees way of formation and introduction of KPI system taking into account specifics of sales personnel activity, details main stages of KPI system introduction, considers criteria of sales managers evaluation, gives a specific example of payroll calculation taking into account KPIs.
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Kuzovleva, Nina Fedorovna, Lyudmila Mikhailovna Prokofieva, and Diana Evgenyevna Simakova. "ASSESSMENT OF RELATIONSHIP BETWEEN KEY INDICATORS OF SUSTAINABLE DEVELOPMENT OF OIL COMPANIES IN AN UNSTABLE ECONOMY." SOFT MEASUREMENTS AND COMPUTING 1, no. 4 (2021): 54–66. http://dx.doi.org/10.36871/2618-9976.2021.04.007.
Повний текст джерелаАнотація:
This paper is devoted to analysis of the relationship between the level of company's capitalization and individual indicators which characterize sustainable development – replenishment of petroleum commodities as a result of geological exploration, labor productivity and frequency of accidents. The authors have made an attempt to determine the closeness of the relationship of these indicators using correlation and regression analysis. Current materials of financial and nonfinancial reports on levels of indicators of PJSC LUKOIL for the period 2015–2019 were used as input information.
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Burtseva, T. A. "Measures of Regional Labour Productivity." Voprosy statistiki 28, no. 1 (February 19, 2021): 18–27. http://dx.doi.org/10.34023/2313-6383-2021-28-1-18-27.
Повний текст джерелаАнотація:
In this article, in the form of publication, are presented key study findings that cover both methodological issues of measurement and comprehensive analysis of regional labor productivity, along with extended formulations of study results. Based on the experience of domestic and foreign statisticians, the author substantiates the system of regional labour productivity indicators required for its integrated assessment. In the author’s opinion, this system is needed in a formation of a single coordinated approach to the calculation of labour productivity from the perspective of branches, enterprises, and constituent entities of the Russian Federation.The introduction argues for the relevance of the issues related to measuring regional labour productivity, which is necessary to evaluate the progress of the Federal Project “Systemic Measures to Improve Labor Productivity” implemented under the national project “Labor Productivity and Employment Support”.The author justifies the solution to the problem of clustering regions by regional labour productivity indicator using a combined approach, which explains the trends in the economic development of the regions of Russia in the selected clusters.The final part of the article concludes that the study is relevant in assessing the factors of regional labour productivity needed to identify growth reserves or to prevent stagnation in economic development, measured, in particular, by means of a basic indicator of the economic production efficiency in constituent entities of the Russian Federation.
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Seadon, Jeff, and John E. Tookey. "Drivers for construction productivity." Engineering, Construction and Architectural Management 26, no. 6 (July 15, 2019): 945–61. http://dx.doi.org/10.1108/ecam-05-2016-0127.
Повний текст джерелаАнотація:
Purpose The New Zealand construction sector is similar to many other countries with a few large companies and many small and micro enterprises. It seeks to achieve a 20 per cent increase in productivity by 2020 which requires a step change in how the sector operates and buy-in from key stakeholders. The purpose of this paper is to provide a set of levers to improve productivity in the construction sector and develop an implementation schedule. Design/methodology/approach This paper adopts a systems approach taking account of the nature of the building sector and the whole life cycle of a building from design to end-of-life. Information gained from the post-construction phases informs the pre-construction and construction phases. Findings Productivity is an integrated model whereby increases in process efficiency are executed with quality materials and workmanship, in a manner that is affordable for both the client and contractor and sustainable over time. A series of interviews and workshops produced 10 nodal points and 19 crucial levers which were prioritised for implementation. Additionally, indicators were developed to monitor progress over time and provide information for further corrective action to the system. Practical implications The effect of using a few targeted levers in unison provided significantly more gains than individual applications. Modelling real world responses to process stimuli outlined in this paper is extremely valuable. This provided the opportunity for key construction stakeholders to estimate the effects of decision making during a project. Originality/value Previous studies identified factors affecting productivity. Piecemeal approaches to improve productivity have resulted in systemic failure. A whole of life approach provides valuable insights to improve productivity in the construction and pre-construction phases which have a flow-on effect through the life cycle. Importantly, this research proposes drivers, an implementation scheme and indicators that provide leverage on nodal points to improve productivity.
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Vladova, Alla, and Elena Shek. "Data preprocessing for machine analysis of sales representatives’ key performance indicators." Business Informatics 15, no. 3 (September 30, 2021): 48–59. http://dx.doi.org/10.17323/2587-814x.2021.3.48.59.
Повний текст джерелаАнотація:
Significant transformation of the operational activity of product and service distributors is driven by changes in data-receiving and processing technology. At present, the work of these companies’ representatives is digitized to a large extent: for example, the road time, the number and places of meetings with customers are automatically recorded. At the same time, the productivity of managers who do not make direct sales is usually evaluated with the help of surveys, experts and costly double visits, although the existence of large data samples makes possible the use of statistical analysis to identify both insufficient and inflated values of performance indicators. Source data: a relational database that accumulates information about 28 categorical, quantitative, geolocation and temporal parameters of sale representatives’ activities for the last year. Based on available data, we created synthetic features (the latitude and longitude features produced the index, region, street, and house features; based upon identifiers we calculated the sum of activities of sales representatives; according to temporary features we defined the season of the year, the day of the week and the period of day features). The methodology for statistical analysis consists of three main stages: collection and processing of primary data; summary and grouping processed information; setting statistical hypotheses and interpreting the results. A probabilistic approach was used to model the level of distortion of sale representatives’ activities. As a result, with the built tag cloud we highlighted: the most popular season for advertising campaigns; the most productive departments and sale representatives; days of the week with the largest number of contacts to customers. We established a significant number of records about meetings with clients at the weekends. As a result of the data mining, we made a statistical hypothesis about the possibility of identifying the sale representatives who distort the number and parameters of meetings. A set of synthetic integer, real and categorical features was created to identify hidden relationships. Doubtful data (such as working at weekends or at night) were revealed. The resulting aggregated dataset is grouped by a sale representative’s activity ID and the distribution of this feature is plotted. For each sale representative, integer and real features are summarized and outliers that characterize inefficient performance or distortion of data have been detected. Thus, the presence of a large sample of data on the history of movements and activities allowed us to evaluate the productivity of the distribution company’s sales representatives based upon indirect features.
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Sidorkina, S. V. ""TOP-DOWN" APPROACH TO WORKFORCE PLANNING CONSIDERING THE TARGET INDICATORS OF LABOR PRODUCTIVITY OF THE ENTERPRISE." Social and labor researches 42, no. 1 (2021): 86–96. http://dx.doi.org/10.34022/2658-3712-2021-42-1-86-96.
Повний текст джерелаАнотація:
The subject of the research is the workforce planning of an enterprise as an aspect of managing the organization’s labor productivity. The aim of the article is to describe and illustrate, using a hypothetical example, a topdown approach to workforce planning, considering the target budget indicators of the enterprise, taking into account the requirement to increase labor productivity while ensuring the financial security of employees. The author uses such methods as analysis of scientific literature and Internet sources, analysis of regulatory acts of state executive authorities, mathematical modeling, analysis of the dynamic characteristics of an enterprise. As the results of the study the author analyzed the main aspects of the problem of labor productivity management, highlighted in modern domestic and foreign scientific publications; explored the advantages and disadvantages of the methodology for calculating the indicators of labor productivity of the enterprise, approved within the framework of the national project "Labor productivity and employment support"; investigated the influence of the headcount indicator on the volume of personnel costs; demonstrated the applicability and economic feasibility of planning the target headcount of the organization using the "top-down" method. This method is based on dynamic analysis of actual and planned key performance indicators of the company’s budget, taking into account the objectives of increasing productivity and financial safety of employees. The findings of the study can be used for the purposes of reasonable staff planning in the budget process of business and public sectors, for educational purposes in courses on personnel planning, personnel costs, and HR-analytics.
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Ryazanova, G., and P. Tolkachev. "THE EFFICIENT USE OF HUMAN RESOURCES IN RUSSIA IN CONDITIONS OF CATCHING-UP DEVELOPMENT." Vestnik Universiteta, no. 10 (November 28, 2019): 67–72. http://dx.doi.org/10.26425/1816-4277-2019-10-67-72.
Повний текст джерелаАнотація:
The relationship of labor productivity with other macroeconomic indicators has been analyzed: the level of wages in current prices and taking into account the Paasche deflator index; dynamics of the gross domestic product; the dynamics of the share of food in the consumer basket; the indicators of changes in clean investments, that is a key factor in economic growth in the national economy. On the basis of empirical data, the trends of productivity growth in comparison with the growth of other macroeconomic indicators algebraically and graphically have been shown. It has been concluded, that the development of human resources is necessary, since low productivity is a brake on the economic growth of the country in the conditions of catching-up development.
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Oleksandr Redych. "DECISION-MAKING IN THE MANAGEMENT OF THE TAX AUTHORITIES ON THE BASIS OF INTEGRATED PERFORMANCE INDICATORS." International Journal of Innovative Technologies in Economy, no. 3(23) (April 30, 2019): 32–40. http://dx.doi.org/10.31435/rsglobal_ijite/30042019/6454.
Повний текст джерелаАнотація:
The paper highlights the results of applying the methodology of integral convolution of key indicators of economic efficiency of territorial subdivisions of the tax authority of the state: results of tax collection, productivity, cost, dynamics of indicators. The criteria for grouping of territorial tax divisions according to the values of the indicator of effectiveness for the purpose of making tactical and strategic management decisions are formed. The paper formulates recommendations on the use of the proposed approach in goal- setting when making managerial decisions based on the "sigma" management.
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Kusrini, Elisa, Fadrizal Novendri, and Vembri Noor Helia. "Determining key performance indicators for warehouse performance measurement – a case study in construction materials warehouse." MATEC Web of Conferences 154 (2018): 01058. http://dx.doi.org/10.1051/matecconf/201815401058.
Повний текст джерелаАнотація:
Warehouse performance measurement is needed to improve performance of logistics system. In order to improve warehouse performance, it is necessary to identify Key Performance Indicator (KPI). Different warehouses have different KPI, therefore this research aims to identify the most important KPI of warehouse so that warehouse manager can determine corrective actions in their warehouse efficiently and effectively. In this research, 25 KPI of warehouse are identified in five warehouse activities based on Frazelle model. The most important KPI are then determined in each warehouse activity using Analytical Hierarchy Process (AHP). Warehouse performance are measured and final score is determined using SNORM. Improvement steps are proposed base on benchmarking among warehouses. Warehouse performance measurement is conducted in 5 construction’s material warehouses located in Yogyakarta, Indonesia. From this study, it is found that most important KPI for receiving is productivity (receive per man-hour), KPI for put away is cycle time (put away cycle time), KPI for storage is utilization (% location and cube occupied), KPI for order picking is cycle time (order picking cycle time) and KPI for shipping is productivity (order prepared for shipment per man-hour). Improving warehouse performance could be done by comparing warehouse performance with the best performance among peer group.
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Nurcahyo, Rahmat, Saripuji Pustiwari, and Djoko Sihono Gabriel. "Developing A Strategy Map Based on Sustainability Balanced Scorecard Framework for Manufacturing Industry in Indonesia." International Journal of Engineering & Technology 7, no. 2.34 (June 8, 2018): 48. http://dx.doi.org/10.14419/ijet.v7i2.34.13910.
Повний текст джерелаАнотація:
This study proposes a structural evaluation to link key performance indicator into a strategy map based on sustainability balanced scorecard framework for manufacturing industry in Indonesia. With four perspectives (finance, stakeholder, internal business process and learning and growth) on Sustainability Balanced Scorecard, the evaluation of relationship between perspectives and indicators of manufacturing industry are synthesized from relevant literature and experts. The Decision Making Trial and Evaluation Laboratory (DEMATEL) method employed to identify critical central and influential factors, to determine the causal relationship and finally to develop a visual strategy map to improve corporate sustainability. The three most important indicators are sustainability award, certification of environmental and social standards and resource productivity. The result can help prioritizing the performance indicators and show which areas that need improvement most.
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Evseeva, Marina. "Comparative effectiveness of high-tech and medium-tech business models: Key indicators and value sustainability." Upravlenets 11, no. 3 (July 10, 2020): 59–71. http://dx.doi.org/10.29141/2218-5003-2020-11-3-5.
Повний текст джерелаАнотація:
Due to the external environment being unpredictable and dynamic, the business model becomes obsolete in a short time and its logic loses effectiveness. This seriously threatens business continuity and affects income flows. The paper develops indicators for assessing the effectiveness of business models. Methodologically, the study rests on the stakeholder theory, valuebased management and technological growth theory. The work uses mathematical methods for calculating financial and economic indicators, as well as a general scientific method of analysis. To test the metod, we examine 188 companies operating in the high-tech and medium-tech industry markets. Having systematized the theoretical positions, we conclude that the evaluation of business model effectiveness should be based on measuring the sustainability of the value created for various groups of the company stakeholders. This allowed us to propose a set of indicators of business model effectiveness built on yield spread and factor productivity. The program of empirical evaluation also included an indicator of the value of intellectual capital, computed on the basis of the company’s profitability deviation from the market norm. The author supposes that, by producing intellectual and relational rents, intangible capital helps increase the effectiveness of the business model, which is reflected in all sustainability indicators. The research results prove the analytical value to be satisfactory. In total, the high-tech market business models are more efficient. The distribution of performance indicators met the assumptions about the impact of a fragmented environment: a more homogeneous industry environment is instrumental in aligning business model performance. The findings of the study are of practical importance for business and contribute to the construction of a control system based on business model effectiveness management.
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Colim, Ana, Rita Morgado, Paula Carneiro, Nélson Costa, Carlos Faria, Nuno Sousa, Luís A. Rocha, and Pedro Arezes. "Lean Manufacturing and Ergonomics Integration: Defining Productivity and Wellbeing Indicators in a Human–Robot Workstation." Sustainability 13, no. 4 (February 11, 2021): 1931. http://dx.doi.org/10.3390/su13041931.
Повний текст джерелаАнотація:
Lean Manufacturing (LM), Ergonomics and Human Factors (E&HF), and Human–Robot Collaboration (HRC) are vibrant topics for researchers and companies. Among other emergent technologies, collaborative robotics is an innovative solution to reduce ergonomic concerns and improve manufacturing productivity. However, there is a lack of studies providing empirical evidence about the implementation of these technologies, with little or no consideration for E&HF. This study analyzes an industrial implementation of a collaborative robotic workstation for assembly tasks performed by workers with musculoskeletal complaints through a synergistic integration of E&HF and LM principles. We assessed the workstation before and after the implementation of robotic technology and measured different key performance indicators (e.g., production rate) through a time study and direct observation. We considered 40 postures adopted during the assembly tasks and applied three assessment methods: Rapid Upper Limb Assessment, Revised Strain Index, and Key Indicator Method. Furthermore, we conducted a questionnaire to collect more indicators of workers’ wellbeing. This multi-method approach demonstrated that the hybrid workstation achieved: (i) a reduction of production times; (ii) an improvement of ergonomic conditions; and (iii) an enhancement of workers’ wellbeing. This ergonomic lean study based on human-centered principles proved to be a valid and efficient method to implement and assess collaborative workstations, foreseeing the continuous improvement of the involved processes.
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Kostiuk, V., and I. Milkin. "METHOD OF MODELING AND FACTOR ANALYSIS OF LABOR PRODUCTIVITY." Series: Economic science 2, no. 162 (March 26, 2021): 64–70. http://dx.doi.org/10.33042/2522-1809-2021-2-162-64-70.
Повний текст джерелаАнотація:
The article deals with the method of modeling and factor analysis of labor productivity. It has been emphasized that labor productivity is one of the most important indicators characterizing the operation and performance of any enterprise. In the system of generalizing indicators of production efficiency, this indicator occupies a special position since it characterizes the achieved level of development of the material and technical foundation of production, efficient use of production potential, and technological advances. It has been emphasized that the change of this indicator depends on various factors characterizing the efficiency of labor and technical potential of an enterprise. Thus, the factor analysis of this indicator is crucial, i.e. studying the influence of structural and production factors on its change. The efficiency of the use of fixed assets, in particular production equipment, has a decisive influence on the change of labor productivity. The most efficient use of machinery and equipment contributes to a significant increase in performance. The article emphasizes that a crucial means of analytical research is the modeling of this indicator. In the course of modeling factor systems of labor productivity, it is necessary to consider the key factors characterizing the structure and efficiency of labor and technical potential of an enterprise, which directly influence the change of the labor productivity of an employee. This is, first of all, the enterprise personnel structure (the number of workers out of the total number of employees), the average output of one worker, labor capital, capital efficiency of fixed assets, their active part, utilization rates of production equipment, and the productivity of existing equipment. To understand the impact of the above factors on the change of productivity of one employee, a step-by-step factor analysis, using the method of chain substitution, has been suggested. The given method of modeling and factor analysis of labor productivity makes it possible to determine the influence of crucial factors on the change of this indicator, study the patterns of such influence, identify internal growth reserves, and ground appropriate managerial decisions on further use of labor and material resources of an enterprise.
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Sanusi, Anwar. "State Owned Enterprises (SOEs), The Problems and Solutions in Financial and Asset Management." Scholedge International Journal of Multidisciplinary & Allied Studies ISSN 2394-336X 3, no. 2 (March 11, 2016): 19. http://dx.doi.org/10.19085/journal.sijmas030202.
Повний текст джерелаАнотація:
<p>State-owned enterprises (SOEs) faced with the demands to produce a better performance by increasing internal working environment. SOE managers need to measure employee productivity associated with the job description, key result areas, and Key Performance Indicators. This method had a goal to determine the indicators of organizational performance and how SOEs achieved the standards of achievement and to plan reward achievement of productivity. This study tested the hypothesis through validation testing theory or application of theory in certain circumstances.</p><p>The samples were taken from the overall population of SOE employees in Jakarta. It used purposive method in selecting data the data which retrieved by taking certain data through several criteria.</p><p>The study result showed that the respondent perception of communication, work attitude, work ethic, internal control level, risk management practices, and monitoring & evaluation has a significant relationship with work productivity of the employees. </p>
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Vavlas, Nikolaos-Christos, Toby W. Waine, Jeroen Meersmans, Paul J. Burgess, Giacomo Fontanelli, and Goetz M. Richter. "Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series." Remote Sensing 12, no. 15 (July 24, 2020): 2385. http://dx.doi.org/10.3390/rs12152385.
Повний текст джерелаАнотація:
High-frequency Earth observation (EO) data have been shown to be effective in identifying crops and monitoring their development. The purpose of this paper is to derive quantitative indicators of crop productivity using synthetic aperture radar (SAR). This study shows that the field-specific SAR time series can be used to characterise growth and maturation periods and to estimate the performance of cereals. Winter wheat fields on the Rothamsted Research farm in Harpenden (UK) were selected for the analysis during three cropping seasons (2017 to 2019). Average SAR backscatter from Sentinel-1 satellites was extracted for each field and temporal analysis was applied to the backscatter cross-polarisation ratio (VH/VV). The calculation of the different curve parameters during the growing period involves (i) fitting of two logistic curves to the dynamics of the SAR time series, which describe timing and intensity of growth and maturation, respectively; (ii) plotting the associated first and second derivative in order to assist the determination of key stages in the crop development; and (iii) exploring the correlation matrix for the derived indicators and their predictive power for yield. The results show that the day of the year of the maximum VH/VV value was negatively correlated with yield (r = −0.56), and the duration of “full” vegetation was positively correlated with yield (r = 0.61). Significant seasonal variation in the timing of peak vegetation (p = 0.042), the midpoint of growth (p = 0.037), the duration of the growing season (p = 0.039) and yield (p = 0.016) were observed and were consistent with observations of crop phenology. Further research is required to obtain a more detailed picture of the uncertainty of the presented novel methodology, as well as its validity across a wider range of agroecosystems.
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Łukowiak, Remigiusz, Witold Grzebisz, Jakub Ceglarek, Adam Podolski, Cezary Kaźmierowski, and Jan Piekarczyk. "Spatial Variability of Yield and Nitrogen Indicators—A Crop Rotation Approach." Agronomy 10, no. 12 (December 12, 2020): 1959. http://dx.doi.org/10.3390/agronomy10121959.
Повний текст джерелаАнотація:
The division of an arable field into zones of different productivity requires a reliable, discriminatory tool. This hypothesis was validated by analyzing the spatial variability of yield and N indicators in the crop rotation of winter oilseed rape (WOSR)/winter triticale (WTR) during 2016/2017 and 2017/2018 in a field of 30 ha (Przebędowo, Poland). The direct, measurable variables were: yield, N accumulated in—seeds/grain and crop residues, mineral N in spring, and harvest. The basic N indicators were total N uptake (TN), N-partial factor productivity, and N balance (Nb). The attainable yields of WOSR and WTR were 4.93 and 6.51 t ha−1, and a yield gap of −2.04 and −2.10 t ha−1. The management of 50 kg of the non-used N by crops, i.e. nitrogen gap (NG) could cover 36% and 65% of the yield gap (YG), respectively. The Nb, based on N input (Nin = Nmin + Nf) and TN, was the key field indicator, defining both yield and NG. Geostatic parameters, i.e., the nugget to sill ratio, spatial dependence range, and mean correlation distance, were very stable (≤0.2–0.17; 94–100 m; 28 m for WOSR and WTR). The spatial stability of Nb, irrespective of the crop and growing conditions, corroborates its suitability for discriminating high and low-productivity field zones.
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Əbil oğlu Dünyamalıyev, Sevindik, Zahid Xəlil oğlu Mustafayev, Lidiya Məhəmməd qızı Şahverdiyeva, and Xalid Rüstəm oğlu Hətəmov. "İnfluence on the quality of corn plants of mineral fertilizers under rainfed conditions secured by the moisture of Shaki-Zagatala region." NATURE AND SCIENCE 08, no. 03 (May 20, 2021): 8–11. http://dx.doi.org/10.36719/2707-1146/08/8-11.
Повний текст джерелаАнотація:
The article shows the use of effective norms and ratios of organic and mineral fertilizers to obtain high grain and green mass yields from proud varieties of corn in mountain forest brown soils provided with moisture. Indicators of plant quality and yield were determined for all options. The highest productivity in terms of productivity and quality was recorded in the version of N90P90K90 + Fon 20 t / ha. Key words: corn, agrochemical properties, analysis, productivity, nitrogen, phosphorus, potassium
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Trachuk, A. V., and N. V. Linder. "INNOVATIVE ACTIVITY OF INDUSTRIAL ENTERPRISES: MEASUREMENT AND EFFECTIVENESS EVALUATION." Strategic decisions and risk management 10, no. 2 (July 30, 2019): 108–21. http://dx.doi.org/10.17747/2618-947x-2019-2-108-121.
Повний текст джерелаАнотація:
The paper considers the indicators and multi-scale assessment of innovative activity of industrial companies. The research methodology consistently includes the analysis of the factors of innovative activity; collection and analysis of information about the indicators for assessing the innovative activity of industrial enterprises taking into account the defined factors of innovative activity; compiling of the short and aggregated lists of indicators for assessing the innovation activity taking into account the determined factors; defining an aggregate indicator of the innovative activity of an industrial enterprise. To define the aggregate indicator of the innovative activity we analysed the published reports of 57 companies, which have been the leaders in the industrial sectors for the last 15 years. Moreover, we analysed 40 scholarly publications focused on the assessment of the innovative activities and interviewed 16 experts, which are heads of large industrial companies. In addition, we analysed key performance indicators (KPI) of innovative activity recommended by the American Productivity & Quality Center (APQC). As a result of the research, we have proposed a methodology of the multi-scale assessment of the innovative activity of industrial companies. We have selected 5 key performance indicators to calculate the integral indicator. Furthermore, we have developed an algorithm to calculate the integral indicator of innovative activity. In addition to the integral indicator, we recommend to use 3 indicative values, which influence the integral indicator: a) comprehensive indicator of the development of scientific research, research institutes, academic organisations, technology platforms, per cent; b) number of proposals to generate new technologies, technical and technological solutions from affiliated companies over the year; c) number of new competences acquired by a company from innovative activities.The production of this integral indicator of the innovative activity will allow to the industrial enterprises a more informative assessment of their innovative activities and innovation behaviour transformations.
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Ehrlich, Gabriel, John Haltiwanger, Ron Jarmin, David Johnson, and Matthew D. Shapiro. "Minding Your Ps and Qs: Going from Micro to Macro in Measuring Prices and Quantities." AEA Papers and Proceedings 109 (May 1, 2019): 438–43. http://dx.doi.org/10.1257/pandp.20191004.
Повний текст джерелаАнотація:
Key macro indicators such as output, productivity, and inflation are based on a complex system across multiple statistical agencies using different samples and levels of aggregation. The Census Bureau collects nominal sales, the Bureau of Labor Statistics collects prices, and the Bureau of Economic Analysis constructs nominal and real GDP using these data and other sources. The price and quantity data are integrated at a high level of aggregation. This paper explores alternative methods for reengineering key national output and price indices using item-level data. Such reengineering offers the promise of greatly improved key economic indicators along many dimensions.
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Gackowiec, Paulina, Marta Podobińska-Staniec, Edyta Brzychczy, Christopher Kühlbach, and Toyga Özver. "Review of Key Performance Indicators for Process Monitoring in the Mining Industry." Energies 13, no. 19 (October 4, 2020): 5169. http://dx.doi.org/10.3390/en13195169.
Повний текст джерелаАнотація:
The sustainable development of an organisation requires a holistic approach to the evaluation of an enterprise’s goals and activities. The essential means enabling an organisation to achieve goals are business processes. Properly managed, business processes are a source of revenue and become an implementation of business strategy. The critical elements in process management in an enterprise are process monitoring and control. It is therefore essential to identify the Key Performance Indicators (KPIs) that are relevant to the analysed processes. Process monitoring can be performed at various levels of management, as well as from different perspectives: operational, financial, security, or maintenance. Some of the indicators known from other fields (such as personnel management, finance, or lean manufacturing) can be used in mining. However, the operational mining processes require a definition of specific indicators, especially in the context of increasing the productivity of mining machines and the possibility of using sensor data from machines and devices. The article presents a list of efficiency indicators adjusted to the specifics and particular needs of the mining industry resulting from the Industry 4.0 concept, as well as sustainable business performance. Using the conducted research and analysis, a list of indicators has been developed concerning person groups, which may serve as a benchmark for mining industry entities. The presented proposal is a result of work conducted in the SmartHUB project, which aims to create an Industrial Internet of Things (IIoT) platform that will support process management in the mining industry.
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Miqueo, A., M. Martín, M. Torralba, and J. A. Yagüe. "Labour productivity in mixed-model manual assembly 4.0." IOP Conference Series: Materials Science and Engineering 1193, no. 1 (October 1, 2021): 012104. http://dx.doi.org/10.1088/1757-899x/1193/1/012104.
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Abstract Manual assembly lines productivity is threatened by the increased complexity brought by mass customisation demand trends. Industry 4.0 offers potential solutions to address this situation, but the methodology to implement it is still a subject of study. As a preliminary step, this article aims to identify the dominant factors affecting the Key Performance Indicators of mixed-model assembly lines. To do so, parametric and discrete-events simulation models were developed, and Design of Experiments techniques were used. The results show that the key drivers for assembly line performance are number of work stations and batch size, and that increasing the work content ratio of the products assembled does not interact negatively with other factors. The results presented here pave the way for developing Industry 4.0 projects that address specifically the most relevant factors that affect assembly lines performance.
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Raizada, Sumesh. "Employee Relationship Management: Key to Organizational Growth." Business Management and Strategy 10, no. 1 (June 20, 2019): 167. http://dx.doi.org/10.5296/bms.v10i1.14951.
Повний текст джерелаАнотація:
Employee Relationship Management (ERM) is both a management philosophy as well as strategy, and is extremely vital for the success of business organizations nowadays. It emphasize on effective inter and intra departmental coordination thereby enhancing employee engagement, creativity and productivity. The present paper is conceptual. It shall discuss ERM and explore its relevance in improving the performance of an organization. Paper shall identify various factors that contribute towards measurement and analysis of ERM in the organizations. Attempt has been made to quantify the attributes, propose indices for measurement and explore their linkages with the organizational performance indicators. Paper shall also develop conceptual model and hypotheses related to ERM for further empirical study by researchers and academicians. Both online as well as offline information has been sourced through books and articles.
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Jantoń-Drozdowska, Elżbieta, and Maria Majewska. "Social capital as a key driver of productivity growth of the economy: across-countries comparison." Equilibrium 10, no. 4 (December 31, 2015): 61. http://dx.doi.org/10.12775/equil.2015.035.
Повний текст джерелаАнотація:
The aim of this work was to show the possible impact of social capital on productivity of the economy. That impact can be measured by such indicators of productivity of the economy as used in our study: the GDP, the total value added of the economy (TVE), and the GNI per total labour force. Thus, this paper was organized as follows: its first part presents the relationship between the development of social capital and productivity growth of the country in the light of the economic development theory. In this context, it is pointed out that the significance of social capital as a component of the productivity potential of a given country increases when such country moves to the next stages of economic development. Therefore, social capital becomes a very important driver of the upgrading of national incomes in those countries, in which competitive advantages are based primarily on intellectual capital assets. The another part of the paper describes the methodology and the results of a research conducted on a group of 100 countries in the years 2012-2013 with an aim to illustrate the link between social capital and productivity of the economy as a whole referred to, or indicated, in the first part of the study. The results of the research allowed us to formulate a conclusion that without an appropriate ethical behaviour, not only in business, the productivity growth is hampered because it translates into a lower level of trust and unwillingness to cooperate. In other words, as, among others, W. Bartoszewski stressed, "it is worth to be decent".
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Gareeva, Zemfira, Gulnara Nizamova, Larisa Avdeeva, Irina Soloveva, and Dilara Musina. "Development of the personnel controlling model of oil organizations." SHS Web of Conferences 101 (2021): 02024. http://dx.doi.org/10.1051/shsconf/202110102024.
Повний текст джерелаАнотація:
The purpose of the work is to develop proposals aimed at improving the personnel controlling model of oil organizations as a tool for managing the processes of personnel turnover. The experience of applying the system of personnel controlling in foreign countries has proven to be an effective system, providing the management with the relevant information base for timely tracking of changes in the indicators of personnel management and productivity indicators. There is theoretically confirmed interrelation between personnel turnover indicators and labor productivity that predetermines the necessity of monitoring of the given indicator in the system of personnel controlling. Meanwhile, the turnover rate itself is influenced by different internal and external factors. The suggested directions of improving the personnel controlling model consist in inclusion of the additional module, which provides the analysis of the statistical reporting; suggested system of the quantitative evaluation of the production personnel quality indicators; analytical procedure of revealing the reasons of the key personnel turnover by means of introduction of the block structure of the turnover factors evaluation of this personnel category, revealed by method of expert evaluation of the oil organization workers, which allows ranking those factors and timely develop the means of its solution. Theoretical and methodological basis of the research are the actual concepts of personnel management and personnel controlling systems study and development. Methods of expert evaluation, mathematical and statistical methods, correlation and regression analysis were used to carry out the task of estimation of the proposed recommendations.
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Bellón, Beatriz, Julien Blanco, Alta De Vos, Fabio de O. Roque, Olivier Pays, and Pierre-Cyril Renaud. "Integrated Landscape Change Analysis of Protected Areas and their Surrounding Landscapes: Application in the Brazilian Cerrado." Remote Sensing 12, no. 9 (April 30, 2020): 1413. http://dx.doi.org/10.3390/rs12091413.
Повний текст джерелаАнотація:
Remote sensing tools have been long used to monitor landscape dynamics inside and around protected areas. Hereto, scientists have largely relied on land use and land cover (LULC) data to derive indicators for monitoring these dynamics, but these metrics do not capture changes in the state of vegetation surfaces that may compromise the ecological integrity of conservation areas’ landscapes. Here, we introduce a methodology that combines LULC change estimates with three Normalized Difference Vegetation Index-based proxy indicators of vegetation productivity, phenology, and structural change. We illustrate the utility of this methodology through a regional and local analysis of the landscape dynamics in the Cerrado Biome in Brazil in 2001 and 2016. Despite relatively little natural vegetation loss inside core protected areas and their legal buffer zones, the different indicators revealed significant LULC conversions from natural vegetation to farming land, general productivity loss, homogenization of natural forests, significant agricultural expansion, and a general increase in productivity. These results suggest an overall degradation of habitats and intensification of land use in the studied conservation area network, highlighting serious conservation inefficiencies in this region and stressing the importance of integrated landscape change analyses to provide complementary indicators of ecologically-relevant dynamics in these key conservation areas.
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Polevoy, A. M., L. E. Bozhko, and E. A. Barsukova. "Influence of changes of climate on the productivity of pratal and steppe vegetation in the Forest-steppe area of Ukraine." Bulletin of Kharkiv National Agrarian University named after V.V. Dokychaiev. The series “Crop production, selection and seed production, fruit and vegetable growing” 2019, no. 1 (June 25, 2019): 18–29. http://dx.doi.org/10.35550/visnykagro2019.01.018.
Повний текст джерелаАнотація:
The influence of the climate change on the agro-climatic growth conditions, development and formation of productivity of meadow and steppe vegetation in the forest-steppe zone of Ukraine for three periods has been studied: 2021–2030, 2031–2040, 2041–2050. The calculations of the expected conditions have been performed according to the climate change scenarios RCP4.5 and RCP8.5. The paper provides an assessment of the agro-climatic conditions for the formation of productivity of meadow and steppe vegetation in the current global warming and further climate change conditions until 2050. The assessment has been performed by comparing the average long-term agro-climatic indicators (1980–2010) of the productivity of wild phytocenoses with the same indicators for the future over decades. The calculations of both average long-term productivity of grasses and productivity of grasses in the conditions of climate change are executed according to four types of productivity: potential productivity which in case of optimum maintenance of plants with heat, moisture and mineral food is defined by solar radiation; meteorologically possible yield, which is provided by the temperature regime and the regime of humidification of the territory; really possible yield capacity, which is provided by the natural fertility of the soil; actual yield capacity in the natural conditions. Key words: meadow, steppe vegetation, productivity, humus balance, photosynthetic potential, agroecological categories of yields, climate change.
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Eroshin, Sergey E. "Parametric Monitoring of Defence Industry Organizations as a Strategic Management Tool." Economic Strategies 160, no. 1 (February 20, 2022): 92–97. http://dx.doi.org/10.33917/es-1.181.2022.92-97.
Повний текст джерелаАнотація:
The article proposes an approach to developing strategic management decisions at the parent companies’ level of defence industry groups regarding human resources development. Identification of interrelations between the decisions taken and their consequences is carried out based on analysis of changes in the rank statistics of the enterprises’ key indicators. Using the data on the age distribution of employees of JSC Concern VKO Almaz-Antey, the need to adjust the average age indicator is substantiated. Particular attention is paid to improving the calculation of the parameter of labor productivity within the research organizations of the defence industry.
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Machuca-Villegas, Liliana, Gloria Piedad Gasca-Hurtado, and Mirna Muñoz. "Measures related to social and human factors that influence productivity in software development teams." International Journal of Information Systems and Project Management 9, no. 3 (October 27, 2021): 43–67. http://dx.doi.org/10.12821/ijispm090303.
Повний текст джерелаАнотація:
Software companies need to measure their productivity. Measures are useful indicators to evaluate processes, projects, products, and people who are part of software development teams. The results of these measurements are used to make decisions, manage projects, and improve software development and project management processes. This research is based on selecting a set of measures related to social and human factors (SHF) that influence productivity in software development teams and therefore in project management. This research was performed in three steps. In the first step, there was performed a tertiary literature review aimed to identify measures related to productivity. Then, the identified measures were submitted for its evaluation to project management experts and finally, the measures selected by the experts were mapped to the SHF. A set of 13 measures was identified and defined as a key input for designing improvement strategies. The measures have been compared to SHF to evaluate the development team's performance from a more human context and to establish indicators in productivity improvement strategies of software projects. Although the number of productivity measures related to SHF is limited, it was possible to identify the measures used in both traditional and agile contexts.
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Ramis Ferrer, Borja, Usman Muhammad, Wael Mohammed, and José Martínez Lastra. "Implementing and Visualizing ISO 22400 Key Performance Indicators for Monitoring Discrete Manufacturing Systems." Machines 6, no. 3 (September 1, 2018): 39. http://dx.doi.org/10.3390/machines6030039.
Повний текст джерелаАнотація:
The employment of tools and techniques for monitoring and supervising the performance of industrial systems has become essential for enterprises that seek to be more competitive in today’s market. The main reason is the need for validating tasks that are executed by systems, such as industrial machines, which are involved in production processes. The early detection of malfunctions and/or improvable system values permits the anticipation to critical issues that may delay or even disallow productivity. Advances on Information and Communication Technologies (ICT)-based technologies allows the collection of data on system runtime. In fact, the data is not only collected but formatted and integrated in computer nodes. Then, the formatted data can be further processed and analyzed. This article focuses on the utilization of standard Key Performance Indicators (KPIs), which are a set of parameters that permit the evaluation of the performance of systems. More precisely, the presented research work demonstrates the implementation and visualization of a set of KPIs defined in the ISO 22400 standard-Automation systems and integration, for manufacturing operations management. The approach is validated within a discrete manufacturing web-based interface that is currently used for monitoring and controlling an assembly line at runtime. The selected ISO 22400 KPIs are described within an ontology, which the description is done according to the data models included in the KPI Markup Language (KPIML), which is an XML implementation developed by the Manufacturing Enterprise Solutions Association (MESA) international organization.
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Watt, Michael S., Murray R. Davis, Peter W. Clinton, Graham Coker, Craig Ross, John Dando, Roger L. Parfitt, and Robyn Simcock. "Identification of key soil indicators influencing plantation productivity and sustainability across a national trial series in New Zealand." Forest Ecology and Management 256, no. 1-2 (July 2008): 180–90. http://dx.doi.org/10.1016/j.foreco.2008.04.024.
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Sullivan, S. Mažeika P., and Mary C. Watzin. "Relating stream physical habitat condition and concordance of biotic productivity across multiple taxa." Canadian Journal of Fisheries and Aquatic Sciences 65, no. 12 (December 2008): 2667–77. http://dx.doi.org/10.1139/f08-165.
Повний текст джерелаАнотація:
To explore the potential of assessments of stream geomorphic condition and habitat quality in evaluating lotic productivity, we investigated concordance of stream biotic productivity (aquatic macroinvertebrates, crayfish, fish, and belted kingfishers ( Ceryle alcyon )) and their physical habitat correlates in 18 streams in the Champlain Valley, Vermont, USA. Pearson correlation analysis indicated significant concordance between macroinvertebrate density and fish biomass (r = 0.76), between the density of macroinvertebrates in the orders Ephemeroptera, Plecoptera, and Trichoptera and fish biomass (r = 0.81), and between fish biomass and kingfisher brood weight (r = 0.54). We used principal component analysis followed by linear regression to investigate relationships between physical habitat condition and biotic productivity and to identify key components of physical habitat condition assessments. Our analysis supported the combined use of geomorphic and habitat assessments as a comprehensive indicator of stream physical habitat condition. We found relationships between habitat assessment scores and productivity measures of all taxa except crayfish, suggesting similar responses to physical condition across trophic levels. Our results encourage the use of additional taxa, in addition to widely used macroinvertebrate metrics, as indicators of the composite effects of physical habitat impairment in stream ecosystems.
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Jaržemskienė, Ilona. "APPLYING THE METHOD OF MEASURING AIRPORT PRODUCTIVITY IN THE BALTIC REGION." TRANSPORT 27, no. 2 (June 26, 2012): 178–86. http://dx.doi.org/10.3846/16484142.2012.694079.
Повний текст джерелаАнотація:
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.