Artykuły w czasopismach na temat „And Industry Forecast 2023-2027”
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SAVCHENKO-BELSKII, K. A., E. I. MANTAEVA i A. A. MANTSAEVA. "ESTABLISHMENT OF A TOURIST AND RECREATIONAL CLUSTER IN THE REGION: REASONABILITY AND FORECAST". Scientific Works of the Free Economic Society of Russia 239, nr 1 (24.05.2023): 180–202. http://dx.doi.org/10.38197/2072-2060-2023-239-1-180-202.
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The article assesses the feasibility of establishing a regional economic cluster. The assessment is tested for the tourism industry. It is based on a two-level classification system of Russian regions and simulation modeling. The classification made it possible to single out typological groups of regions with different industry orientations and to identify groups of different industry development levels. Simulation modeling required studying a number of indicators of the tourism industry and identifying patterns and processes occurring in it in a formalized form. Using built models, the results of the tourism industry between 2018–2027 were predicted. Along with that, the investments were provided for the establishment and development of a tourist and recreational cluster.
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HOSSEN, Sayed Mohibul, Mohd Tahir ISMAIL i Mosab I. TABASH. "THE IMPACT OF SEASONALITY IN TEMPERATURE FORECAST ON TOURIST ARRIVALS IN BANGLADESH: AN EMPIRICAL EVIDENCE". GeoJournal of Tourism and Geosites 34, nr 1 (31.03.2021): 20–27. http://dx.doi.org/10.30892/gtg.34103-614.
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In the present study, we aim to investigate how seasonality influences the climate changes on the outdoor thermal comfort for traveling to visit Bangladesh. Wherein, the effect of temperature on tourist arrival is assessed using SANCOVA and SARIMA model at seven attractive sightseeing diverse places in Bangladesh. The highest temperature has appeared in Khulna and Rajshahi with 35.53 °C and 35.85 °C and the lowest temperature was appeared in Rajshahi and Rangamati with 10.40 °C and 11.72 °C, respectively. This result also revealed that the temperature for Dhaka, Chittagong, Cox’s Bazar, Khulna, and Sylhet has extreme values of decreasing, in Dhaka the temperature will be 25.140 °C on January 2023, in Chittagong 260 °C on January 2027, Cox’s Bazar 26.490 °C on January 2030, in Khulna 25.610 °C on January 2023, and in Sylhet 26.560 °C on January 2020. Our findings also indicate that the tourism industry of Bangladesh is more vulnerable to seasonal variation and this seasonality has a 74% effect on tourist’s arrival as well as a 98% effect on overall temperature in Bangladesh.
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XU, QIAN, HUA CHENG i YABIN YU. "Analysis and forecast of textile industry technology innovation capability in China". Industria Textila 72, nr 02 (22.04.2021): 191–97. http://dx.doi.org/10.35530/it.072.02.1759.
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The textile industry of China has been facing with fierce competition and transformational pressures. It is of great significance to study the evolution of textile industry’s technological progress and to predict the trends. The study analyses the technological innovation ability of China’s textile industry based on the data of 270,145 patent applications from 1987 to 2016. At the same time, the Logistic model is used to forecast the technology innovation capability of China’s textile industry. The study found out: the number of Chinese textile patent applications is on a upward trend; enterprises and universities are the most important patentee; the regional distribution of textile technology innovation is uneven; the number of patent applications in the southeast coastal areas is the largest; the distribution of the IPC is also uneven, D06 (fabric treatment) having the largest number of patent applications and the fastest growth rate; China’s textile industry technology innovation has entered a maturity stage in 2018, and will enter the recession stage after 2027 based on the Logistic model.
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Gajdzik, Bożena. "Post-Pandemic Steel Production Scenarios for Poland Based on Forecasts of Annual Steel Production Volume". Management Systems in Production Engineering 31, nr 2 (3.05.2023): 172–90. http://dx.doi.org/10.2478/mspe-2023-0019.
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Abstract The paper presents the results of forecasts made for the volume of steel production in Poland based on actual data for the period from 2006 to 2021 with forecasting until 2026. The actual data used for the forecasts included annual steel production volumes in Poland (crude steel) in millions of tons. Basic adaptive methods were used to forecast the volume of steel production for the next five years. When selecting the methods, the course of the trend of the studied phenomenon was taken into account. In order to estimate the level of admissibility of the adopted forecasting methods, as well as to select the best forecasts, the errors of apparent forecasts (ex post) were calculated. Errors were calculated in the work: RMSE Root Mean Square Error being the square root of the mean square error of the ex-post forecasts yt for the period 2006-2021; ? as the mean value of the relative error of expired forecasts y*t (2006-2021) – this error informs about the part of the absolute error per unit of the real value of the variable yt. Optimization of the forecast values was based on the search for the minimum value of one of the above-mentioned errors, treated as an optimization criterion. In addition, the value of the point forecast (for 2022) obtained on the basis of the models used was compared with the steel production volume obtained for 3 quarters of 2022 in Poland with the forecast for the last quarter. Forecasting results obtained on the basis of the forecasting methods used, taking into account the permissible forecast errors, were considered as the basis for determining steel production scenarios for Poland until 2026. To determine the scenarios, forecast aggregation was used, and so the central forecasts were determined separately for decreasing trends and for increasing trends, based on the average values of the forecasts obtained for the period 2022-2026. The central forecasts were considered the baseline scenarios for steel production in Poland in 2022-2026 and the projected production volumes above the baseline forecasts with upward trends were considered an optimistic scenario, while the forecasted production volumes below the central scenario for downward trends were considered a pessimistic scenario for the Polish steel industry.
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Duanaputri, Rohmanita, Sulistyowati Sulistyowati i Putra Aulia Insani. "Analisis peramalan kebutuhan energi listrik sektor industri di Jawa Timur dengan metode regresi linear". JURNAL ELTEK 20, nr 2 (28.10.2022): 50. http://dx.doi.org/10.33795/eltek.v20i2.352.
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Abstrak
Pada kehidupan sekarang maupun akan datang, energi listrik menjadi kebutuhan pokok masyarakat. Kebutuhan energi listrik selalu mengalami peningkatan, diikuti meningkatnya pertumbuhan penduduk. Permasalahan akan muncul apabila kebutuhan energi listrik tidak diperkirakan. Maka perlu dilakukan peramalan kebutuhan energi listrik untuk memprediksikan ketersediaan energi listrik di masa mendatang. Pada penelitian ini, dilakukan peramalan kebutuhan energi listrik menggunakan metode regresi linier pada sektor industri di Jawa Timur untuk tahun 2023-2027. Berdasarkan hasil perhitungan prediksi dan MAPE (2009-2021), didapatkan metode regresi linier masih baik dan layak digunakan menurut standar MAPE. Kemudian dibandingkan hasil prediksi dan MAPE (2010-2020) antara metode regresi linear dengan metode time series pada penelitian sebelumnya, didapatkan metode time series menghasilkan prediksi dan MAPE lebih baik dibanding metode regresi linier pada pelanggan listrik, sedangkan pada daya tersambung, energi listrik terjual, dan pendapatan penjualan energi listrik didapatkan metode regresi linier menghasilkan prediksi dan MAPE lebih baik dibanding metode time series. Tetapi, penulis menghitung peramalan kebutuhan energi listrik pada sektor industri di Jawa Timur (2023-2027) hanya menggunakan metode regresi linier. Sehingga dihasilkan akan terjadi kenaikan setiap tahun dengan rata-rata untuk pelanggan listrik sebesar 5.264 pelanggan, daya tersambung sebesar 328,49 MVA, energi listrik terjual sebesar 580,64 GWh, dan pendapatan penjualan energi listrik sebesar 1.065.266,21 Juta Rupiah. Menurut hasil tersebut, maka pasokan energi listrik harus tercukupi dengan merencanakan pengembangan atau penambahan kapasitas pembangkit listrik.
Abstract
In present and future life, electrical energy becomes basic needs of community. Electrical energy needs always increased, followed by increased population growth. Problem will appear if electrical energy needs is not expected. Therefore, it is necessary to forecast electrical energy needs to predict the availability of electrical energy in future. In this study, calculation of forecasting electrical energy needs using linear regression methods in industrial sector in East Java for 2023-2027. Based on calculation results of prediction and MAPE (2009-2021), it is obtained linear regression method is still good and worthy of use according to MAPE standard. Then comparison results of prediction and MAPE (2010-2020) between linear regression method with time series method in previous study, it was obtained that time series method produced predictive and MAPE is better than linear regression methods on electricity customers, while in power connected, electric energy sold, and earnings of electrical energy sales obtained linear regression method produces predictive and MAPE better than time series method. However, authors calculation of electrical energy needs in industrial sector in East Java (2023-2027) only using linear regression methods. So there will be increase every year with average for electricity customers of 5,264 customers, power connected of 328.49 MVA, electric energy sold of 580.64 GWh, and earnings of electrical energy sales of 1,065,266.21 million rupiah. According to results, supply of electrical energy should be fulfilled by planning development or additional power plant capacity.
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S. Milovanov, Svyatoslav. "Clinical Trials Trends of 2023 Year and Visionary to the Future". International Journal of Clinical Investigation and Case Reports 02, nr 01 (2023): 13–19. http://dx.doi.org/10.55828/ijcicr-21-04.
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Introduction: The importance of studying historical changes in the development of human activity is substantiated by the need to systematize such changes and the possibility of predicting them. Historical changes are extended in time and do not have clear boundaries, requiring greater involvement in their study and the prerequisites for their appearance. Clinical research is more than just the practical application of medical changes and discoveries. They make changes in medical practice but are subject to change. Changes in the clinical research industry are tendentious and develop gradually, requiring study and forecasting. According to the generally accepted temporal gradation of the forecast, there is an operational forecast of up to one month, a short-term forecast of up to one year, a medium-term forecast of up to five years, a long-term forecast of up to 20 years and a long-term forecast over long-term, and a short-term forecast is common in the clinical research industry. We analyzed publications in open sources from 1930 to 2023 by keywords in the Russian-language literature trends in the clinical trial industry and the English-language literature trends in the clinical trial industry. Discussion and Conclusion: Trends in the development of clinical trials until the end of 2023 can be divided into two groups, those related to changes in the conduct of clinical trials and changes in the products of clinical trials in nosologies. If in the first group, the trends remain similar to 2022, the ongoing digitalization of operations, the shift of centralized research towards decentralization, and the shift in protocol design towards patient-centricity, then in the second group, the number of expected drugs has decreased, and there is a shift of drugs towards biologics and gene therapy drugs.
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Rubtsova, Natalia. "Microfinance in the Russian Federation: Changes in Industry Indicators in the Context of Global Challenges". Baikal Research Journal 15, nr 1 (30.03.2024): 13–24. http://dx.doi.org/10.17150/2411-6262.2024.15(1).13-24.
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The purpose of the study was to predict the state of microfinance organizations in the Russian Federation, verify the trends that determine their development in the context of global geopolitical challenges. The research was carried out using logical and empirical methods. The article analyzes changes in the main indicators of the functioning of microfinance organizations (MFOs) in the Russian Federation over a ten-year period (2014–2023). Based on an analysis of changes in key indicators characterizing domestic microfinance organizations (the number of microfinance organizations, the volume of microloans issued and their structure in the context of online and offline formats, the main segments of microfinance), the author comes to the conclusion that microfinance activities in the Russian Federation are highly resistant to negative impacts environmental factors. The scientific novelty of the article lies in the verification of the main trends in the development of domestic microfinance, which include tightening regulation of microfinance organizations by the Central Bank of Russia, further consolidation, industry concentration, development of non-core activities, BNPL services, dominance of online microcredit, deterioration in the quality of debt servicing, reducing the investment attractiveness of the industry. In conclusion, the author identified the forecast values of the main performance indicators of MFOs for the period 2024–2027, and possible restrictions on the future development of this.
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Zhang, Bowen. "Analysis of Bilibili's Competitive Strategy in the New Trends". BCP Business & Management 34 (14.12.2022): 849–55. http://dx.doi.org/10.54691/bcpbm.v34i.3104.
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According to the "2022-2027 China Internet Video Industry Market Depth Research and Investment Strategy Forecast Report" published by the China Research Institute of Industry, as of the end of June 2021, the size of China's Internet users broke one billion, reaching 1.011 billion people, an increase of 0.22 billion people compared to the end of December 2020, the massive size of Internet users to promote the development of China's online video industry. The size of the short video market will increase more quickly between 2020 and 2022, with a compound annual growth rate of about 44%. The market size will grow at a slower rate during 2023-2025, but will still maintain a CAGR of 16%. China's short video market is expected to reach nearly 600 billion yuan in 2025 [1]. More than a quarter of a day is spent watching short videos on mobile devices in China. Along with visuals and audio, short video has emerged as the "third language" of the mobile Internet. Short-form video has rapidly increased in the new Internet economy. Bilibili's future development has attracted much attention. With the development of the Internet economy and the increase in significant video websites, whether Bilibili can continue its competitive advantage and successfully achieve business transformation has become controversial. This research will analyse Bilibili's business model through a SWOT analysis and make feasible suggestions for its future development.
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Tahir, Saad, i Asher Ramish. "Xarasoft (Pvt) Ltd – vision 2027 to implement a digital supply chain for industry 4.0". Emerald Emerging Markets Case Studies 12, nr 1 (15.02.2022): 1–22. http://dx.doi.org/10.1108/eemcs-05-2021-0180.
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Learning outcomes This case study aims to be taught at an MBA level. Specifically, those students who are majoring in supply chain would benefit the most from this case study. This case study has elements of supply chain management, supply chain strategy, warehousing and logistics, and a digital supply chain for Industry 4.0. The learning outcome of this case study could be seen if the students are able to identify the challenges and opportunities of a digital supply chain for Industry 4.0 and how it could be implemented methodically. Teaching Objective 1: Students should be able to identify what challenges organizations face if they implement a digital supply chain for Industry 4.0. Teaching Objective 2: Students should be able to identify what opportunities can be tapped if Big Data Analytics are used in a supply chain teaching. Objective 3: Students should layout a methodical plan of how an analogue company can gradually achieve the objective of implementing a digital supply chain for Industry 4.0 in procurement function. Case overview/Synopsis Based in the Lahore region of Pakistan, Xarasoft is a footwear manufacturing company which has undertaken a decision to transcend to a digital supply chain for Industry 4.0 by 2027. Asif, who is the Head of the Department of Supply Chain, has to come up with a plan to present in the next meeting with the CEO. Xarasoft is a company that preferred to work in an analogue routine. The company set production targets and sold goods through marketing. With no forecast or exact demand, the company had decided to procure 140 million units of raw material and carrying a huge inventory, a percentage of which had to be thrown away as it started to degrade. While the company did have machinery on the production floor, they were operated manually and were a generation behind. Asif faced the question of what challenges he would face and exactly how would a digital supply chain for Industry 4.0 be implemented in the company. Complexity academic level Masters level supply chain courses Supplementary materials Teaching notes are available for educators only. Subject code CSS 9: Operations and Logistics.
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López, Gustavo Rosal. "SS62-02 REFLECTIONS ON THE USE OF EXOSKELETONS IN THE HEALTHCARE SECTOR". Occupational Medicine 74, Supplement_1 (1.07.2024): 0. http://dx.doi.org/10.1093/occmed/kqae023.0361.
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Abstract For a few years now, the concept of Human 2.O has been very present in industry. Among the lines of work of Human 2.0, perhaps the best known is that of supporting the capabilities that humans have. Thus, we can talk about increasing human cognitive capabilities (example - augmented reality) and also physical capabilities (example -exoskeletons). And this last case is the one that we are going to evaluate in this study. The exoskeleton market was valued at USD 354.22 million in 2021, and it is expected to reach USD 1620.04 million in 2027, registering a CAGR of 12.5% during the forecast period (2022-2027). The development and production of exoskeletons requires the collaboration of experts from different fields, including engineers, medical professionals and designers. It is a task undertaken by specialized companies that focus on developing advanced exoskeletons that meet the needs of users. And finally, with all this analysis we have to think about the future of exoskeletons in the healthcare sector. Are they really going to satisfy the current needs of workers in the sector? Can their costs be assumed by health organizations? What will happen to the possible rejection of their use by patients? This and other questions must be answered in a very short period of time.
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Dany-Knedlik, Geraldine, Oliver Holtemöller, Stefan Kooths, Torsten Schmidt i Timo Wollmershäuser. "Gemeinschaftsdiagnose Herbst 2023: Kaufkraft kehrt zurück – politische Unsicherheit hoch". Wirtschaftsdienst 103, nr 10 (1.10.2023): 680–83. http://dx.doi.org/10.2478/wd-2023-0189.
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Abstract According to the Joint Economic Forecast, Germany’s gross domestic product will decline by 0.6% in 2023. This is a strong downward revision of 0.9 percentage points from the forecast made in spring 2023. The most important reason for this revision is that industry and private consumption are recovering more slowly than has been expected in spring. Germany has been in a downturn for more than a year. The sharp rise in energy prices in 2022 put an abrupt end to the recovery from the pandemic. However, wage increases have meanwhile followed the price hike, energy prices have fallen, and exporters have partially passed on their higher costs, so that purchasing power is returning. Therefore, the downturn is expected to subside by the end of the year.
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Widjanarko, Bambang, Awan Panjinata, Agus Sukoco i Joko Suyono. "Analyzing the Financial Performance of PT. Steel Pipe Industry of Indonesia Tbk". International Journal of Industrial Engineering, Technology & Operations Management 1, nr 2 (31.12.2023): 86–92. http://dx.doi.org/10.62157/ijietom.v1i2.32.
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The financial report is a vital tool for acquiring insights into a company's financial position and business performance. Through financial statement analysis, crucial indicators pertaining to the company's financial health are unveiled, rendering it a valuable resource for guiding financial decision-making processes and offering a comprehensive portrayal of the company's performance. This study evaluates the financial performance of PT Steel Pipe Industry of Indonesia Tbk. and forecasts the company's sales turnover over the next five years. This research adopts a quantitative descriptive approach, utilizing secondary data spanning from 2018 to 2022 from the PT Steel Pipe Industry of Indonesia Tbk. The data analysis process encompasses several stages, including (i) Ratio Analysis of Financial Reports from 2018 to 2022, (ii) Compilation of sales data, (iii) Projections of sales figures using the least squares method, and (iv) Forecasting profits for the period from 2023 to 2027. The findings of this study indicate that the PT Steel Pipe Industry of Indonesia Tbk. is facing challenges in its financial performance, as the ratio values consistently fall below industry-standard financial metrics. However, the company has demonstrated resilience in maintaining its profitability levels, evidenced by a 6% increase in profit percentage in 2021 compared to 2020. This can be attributed to the company's consistent profit generation efforts, resulting in year-on-year profit growth.
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CHERNIAVSKYI, Ivan. "FORECASTING THE EXPORT POTENTIAL OF UKRAINIAN GRAIN INDUSTRY ENTERPRISES, TAKING INTO ACCOUNT THE LEVEL OF DEVELOPMENT OF DOMESTIC SELECTION". Ukrainian Journal of Applied Economics 4, nr 4 (30.10.2019): 199–208. http://dx.doi.org/10.36887/2415-8453-2019-4-23.
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The scientific article is devoted to the problem of forecasting the grain production volume and enterprise export potential of the Ukrainian grain industry (taking into account the development level of domestic breeding) for the medium term under 3 alternative scenarios. Grain sowing area was forecasted by 2023 using an adaptive forecasting model. The results of the conducted research show that by 2023 the area of corn sowing will increase to 4922 thousand hectares. The corn yield forecast for 2023 has been developed, which shows that the yield level will increase substantially. So, if in 2017 the yield of this crop was 61 hwt / ha, then on average in 2023 it will be 76.6 hwt / ha. It is established that yield is one of the main factors for increasing the volume of grain production. The forecast suggests that on average, corn grain exports from Ukraine tend to grow in the future, namely that it will grow by 11.8 % in 2023 compared to the 2018/2019 marketing year and amount to 30835 thousand tons. Under the optimistic scenario, corn production in the year 2023 will be 42536 thousand tons, which is 54.4 % more than in the 2018/2019 marketing year. The results of the study show that in the future (by 2023), the volumes of maize seeds use will increase – on average they will increase by 8 %, and by the optimistic forecast – by 25 % compared to the 2018/2019 marketing year and will amount to 585 thousand tons. The analysis shows that in 2019 there was a rapid development of both world and Ukrainian breeding. Thus, analyzing the varieties of the main grain crops, it should be noted that in the analyzed period the number of registered varieties suitable for distribution in Ukraine increased by 11.7 times, including the domestic ones – 10.5 times. Accordingly, maize grew 21.8 times and 14.4 times, respectively. It is worth noting a fairly high proportion of domestic wheat varieties, whose share is 72 %. What cannot be said for other crops, in particular, the share of domestic varieties is only 47 %. In the medium to long term, Ukraine can increase its seed exports by a dozen times. Opportunities for capacity-building of seed plants are steadily increasing every year. Large seed companies, such as “Pioneer”, “Monsanto”, “Syngenta”, “Moisadur”, “Euralis Semens” and domestic “Mais”, “Eridon”, “Selena”, “Eurostandard”, have potential to attract foreign investments to bring Ukraine to the forefront in sales of cereal seeds (wheat, barley, oats, rye and corn) and oilseeds (sunflower, soybean, mustard and rapeseed). Key words: cereals, corn, corn seeds, forecasting, adaptive forecasting models, export potential, acreage, yield.
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Wu, Xiaoran. "A Financial Valuation and Strategic Forecast on Nike, Inc." Highlights in Business, Economics and Management 34 (10.06.2024): 58–63. http://dx.doi.org/10.54097/3sg56a84.
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The objective of this article is to evaluate Nike Inc.'s overall performance in the past three years and make predictions about its future prospects. Initially, this paper will analyze the three most significant accounting policies outlined in Nike's annual report for 2023. This analysis will assist us in determining the company's effectiveness in managing its assets, utilizing them, generating profits, and identifying long-term development trends. Furthermore, this paper will conduct a comparative assessment of various financial ratios with competitors within the Nike industry to gauge the company's performance in the previous year. This evaluation will provide valuable insights into Nike's financial position, operational achievements, and market standing. Finally, based on the comprehensive evaluation of Nike's overall performance in 2023, it will offer forecasts regarding its market value and future performance over the next two years. As a globally renowned sports goods brand known for its remarkable success thus far, Nike has strategically implemented a product portfolio strategy that has propelled it forward in terms of market advancements. The conclusion drawn from this article aims to enhance other sports goods brands' understanding of effective marketing strategies employed by Nike while fostering growth across the entire industry.
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Liu, Yibing. "Financial Analysis and Strategic Forecast of Toyota". Advances in Economics, Management and Political Sciences 43, nr 1 (10.11.2023): 218–24. http://dx.doi.org/10.54254/2754-1169/43/20232167.
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One of the main forces boosting the nations economic expansion is the automobile industry. It has developed a respectable market share in the worlds four-wheeler businesses, which are essential to certain economic sectors. The one-year study period was the fiscal year of 2023, and the key information was gathered from the annual reports of the corporations-Toyota and its competitors. The paper analysis the Toyota Motor Corporation in four parts. Collected the evidence from the annual report, industry report and so in, the paper analysis the automobile corporation deeply. Firstly, the Introduction wraps up the companys history, development, business overview, and strategy. In accounting analysis, this paper analyzes some critical parts. I choose three critical items from the income statement and balance sheet to discuss the accounting records, Intangible incurred by R&D expenses, Vehicles and equipment on operating leases, and Revenue recognition. In the third part, this paper analyzes Toyota from four perspectives, liquidity, solvency, efficiency, and profitability with three ratios respectively. It argues for the current and future business, which gives the investor a deep understanding of Toyota and the automobile industry.
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Barač Miftarević, Sandra. "Medical Tourism in Croatia". Journal of applied health sciences 8, nr 1 (3.02.2022): 121–31. http://dx.doi.org/10.24141/1/8/1/11.
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Recently, medical tourism became one of the rapidly growing industries globally with 25% growth yearly with the value of over 200 billion euros. North America, Asia and Europe hold the most significant share of this value. According to The Medical Tourism Market – Global Industry Analysis Report, the forecast by 2027 will be a value of 272.70 billion US dollars. Croatia has strong potential for developing the medical tourism industry as an integral and essential part of the whole tourism industry in Croatia. But, lack of political will and public sector efforts decrease these opportunities. Fundamental healthcare reform is needed and improves outdated infrastructure with low service quality, including accommodation and accompanying catering and recreational facilities. Health care tourism is not competitive in this exceptionally demanding market. Singapore, India and Turkey can be excellent examples of doing thing rights, showing the path to success to the Croatian medical tourism industry. Where is Croatia right now, and what can be done to move forward is a big question. Several authors offer possible solutions that can lead to achieving objectives and goals stated in the National Strategy for Development of Healthcare and Action Plan until 2028. The future development of the medical tourism industry is an exciting area both in applicative and scientific fields, which can encourage further scientific efforts to explore more deeply the subject.
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YUMASHEVA, E. I. "Market of Finishing and Thermal Insulation Materials in 2023". Stroitel'nye Materialy 819, nr 11 (listopad 2023): 75–79. http://dx.doi.org/10.31659/0585-430x-2023-819-11-75-79.
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The 15th annual conference of the Construction Information company took place in St. Petersburg on October 12–13, 2023. More than 120 representatives from 72 organizations from three countries took part in its work – commercial directors, heads of marketing departments, dealer centers, supply and sales specialists. Traditionally, the conference examines the results of the work of the Russian construction complex as a whole over the past year, as well as a number of sub-sectors of the building materials industry. The main results of the work of the industry of dry building mixtures, gypsum finishing materials, mineral and polymer-based thermal insulation materials, facade systems, paint and varnish materials, voiced in the reports of conference participants, are presented. The forecast for the development of these sub-sectors for the short term is also shown.
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Kasatkina, Ekaterina, Daiana Vavilova i Rinat Faizullin. "Development of econometric models to forecast indicators of the livestock industry". E3S Web of Conferences 548 (2024): 03002. http://dx.doi.org/10.1051/e3sconf/202454803002.
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The article discusses the importance of animal husbandry in ensuring food security and maintaining a high quality of life. In the current study, statistical monthly data on animal husbandry in the Udmurt Republic from 2018 to 2023 is analyzed to create models for forecasting key indicators: the average daily milk yield, the number of cows, and the total volume of milk production. The model of the average daily milk yield takes into account seasonal fluctuations, temperature, and time trends, with an average relative error of just 1.55%. The autoregressive model for predicting the number of cattle with a lag of 12 months has shown high accuracy with an average relative approximation error of 0.19%. The econometric model of total milk production takes into account the average daily milk yield and other factors, demonstrating high accuracy in its forecasts. These results are important to support decision-making on the development of animal husbandry and the agricultural sector in general.
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Kuznetsov, V. V., O. A. Kholodov i T. I. Sharovatova. "Forecast parameters of the crop industry development in the Rostov region". Vektor nauki Tol'yattinskogo gosudarstvennogo universiteta. Seriya Ekonomika i upravlenie, nr 4 (2023): 5–17. http://dx.doi.org/10.18323/2221-5689-2023-4-5-17.
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The paper describes the forecast parameters of the sustainable development of the crop industry in the Rostov region, which determine the key trends of the agricultural sector of the region. The data of the Ministry of Agriculture and Food of the Rostov region, research papers and scientific publications on this problem were used as the analytical base for conducting the research. The research involved the analysis of the current state of the crop production industry in the Rostov region and an assessment of its resource potential. Studies have shown that the effective use of the existing resource potential of the industry in the region in conditions of its significant dependence on the imported seed material and technologies makes it possible to ensure sustainable qualitative dynamics of its development. The paper argues that the use of a scientifically based agricultural system makes allows stimulating positive dynamics of economic growth without additional financial investments. This scientifically based approach is the basis for the development of the forecast parameters for the crop production industry development. The process of forecasting based on trend modeling of crop yields and rationalization of the cropping plan, methods of chain substitutions, and expert assessments resulted in three forecast scenarios for the development of the industry: the first (target), the second (inertial) and the third (mixed). The implementation of the first (target) option involves an increase in the yield of cultivated crops, taking into account the use of high-quality seed material and optimal weather conditions, as well as the transition to a scientifically based structure of cropping pattern. The inertial option is based on the rationalization of the agricultural land structure while maintaining the current yield. The mixed variant is characterized by an increase in yield with a constant structure of the area of sowing. The most preferred and promising option for the crop production industry in the Rostov region is the implementation of the target option. Ignoring the science-based approach in the long term prevents from full unlocking the regional potential of the industry.
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Şeker, Ferhat. "Combining the power of artificial intelligence and mathematical modelling: A hybrid technique for enhanced forecast of tourism receipts". European Journal of Tourism Research 36 (1.11.2023): 3614. http://dx.doi.org/10.54055/ejtr.v36i.3246.
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Despite being one of the most visited countries in the world, Türkiye's share of tourism revenue does not rank among the top ten. Therefore, it would be worth researching tourist expenditures and analysing this data could provide valuable insights. This research develops a novel approach to estimating and modelling tourism receipts by analysing expenditure types. Artificial intelligence-based methods, such as machine learning, have been increasingly used in the tourism literature to improve various aspects of the industry. However, little research has been conducted using a hybrid method to model and estimate tourist expenditure. This paper is the first to combine conventional mathematical analysis, specifically first-order two-variable polynomial equations, with artificial intelligence-based machine learning algorithms in a tourism setting. The research results indicate that expenditure types such as accommodation and food & beverage significantly impact Türkiye's tourism revenue and Türkiye's total tourism revenue will not exceed 45 billion dollars by 2027. This study provides a valuable and practical contribution to improving the accuracy and efficiency of methods for managing tourism economics, particularly in European countries where the economy heavily relies on income generated by tourism. Additionally, it fills a gap in studies focused on tourists' expenditure types by combining artificial intelligence and traditional analysis, making it a unique piece of research.
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Loseva, A. V., i G. I. Gadzhimirzoev. "Dynamics and prospects of development of China’s fishing industry and its role in the global economy". Trudy VNIRO 194 (22.01.2024): 218–27. http://dx.doi.org/10.36038/2307-3497-2023-194-218-227.
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Objective: to reflect the retrospective, current and prospective state of the fishing industry in China, to assess the existing trends and highlight their key factors.Method: general scientific methods of analysis and synthesis, as well as methods of statistical processing, analysis and visualization of data were used.Novelty: the main factors and conditions that ensure the progressive growth of the fishing industry in China are identified; a forecast model for the growth of fish production is constructed.Results: The key indicators of China’s fishing industry in retrospect and the current state are analyzed; a quantitative characteristic of China’s position in the global fishing industry and trade in fish products is given; a quarterly forecast of the output of the country’s fishing industry is constructed based on modeling of dynamics series with seasonal components. The key guidelines of the Chinese government regarding the development of the industry, implemented within the framework of five-year planning, aimed at reducing the burden on the environment, restructuring the industry towards increasing the scale of aquaculture, qualitative improvement of the industry on the basis of innovative technological potential and scientific developments, are disclosed.Conclusions are drawn about the multiplicative effect of modern transformations in the fishing industry of China, significant both for the national and for the global economy as a whole.
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Xiao, Qiang, i Hongshuang Wang. "Prediction of WEEE Recycling in China Based on an Improved Grey Prediction Model". Sustainability 14, nr 11 (1.06.2022): 6789. http://dx.doi.org/10.3390/su14116789.
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Accurate waste electrical and electronic equipment (WEEE) recycling forecast is an essential reference for optimizing e-waste industry layout and division of labor policies, conducive to better guiding enterprises’ recycling activities and improving the efficiency of WEEE recycling in China. The nonlinear grey Bernoulli model (NGBM (1,1)) was constructed by analyzing the recycling data characteristics of WEEE from 2012 to 2020, and a particle swarm optimization (PSO) algorithm was introduced to solve the model parameters and optimize the background value coefficients. The prediction results were compared with other grey prediction models to verify the effectiveness of the improved NGBM (1,1) model for WEEE recycling prediction in China and the applicability of the PSO algorithm for improving the prediction accuracy of each grey model. Statistical data were used to forecast the WEEE recycling volume in China from 2021 to 2023, and the results show that the value of WEEE recycling will continue to grow at 9%. The value of recycling will reach 16 billion yuan by 2023, while the value of WEEE recycling will see a slight decline. Based on the calculation results, the WEEE recycling industry development trend is predicted to guide the promotion of the WEEE industry recycling program and the national circular economy program.
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Thomakos, Dimitrios D., Marilou Ioakimidis i Konstantinos Eleftheriou. "Forecasting Tourism Demand for Medical Services". Journal of Developing Areas 57, nr 3 (czerwiec 2023): 315–20. http://dx.doi.org/10.1353/jda.2023.a907749.
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ABSTRACT: Medical tourism is considered nowadays as a multi-billion industry which can promote a country's economic growth. Therefore, forecasting the scheduled tourism demand for medical services is of great importance for policy makers. Doing so, however, is not an easy task due to the following reasons: Data on medical tourism are (i) not easily accessible; (ii) not typically distinguished from tourists' non-scheduled (unintentional) use of a country's medical services; and (iii) usually not publicly available for long time periods. In this paper, we present a novel way to forecast tourism demand (intentional and unintentional) foro medical services —a rough but informative proxy of medical tourism— using limited data. To perform the analysis, we use data on the percentage of hospital discharges of non-residents for 17 European countries over the period 2008-2019 retrieved from Eurostat. Our methodological approach is based on a forecasting technique recently developed by Kyriazi, Thomakos and Guerard ; the adaptive learning forecasting. According to this method, MSE (Mean Squared Error)-performance enhancements can be achieved using any forecast as input —as long as that input is not a 'perfect' forecast— by learning from past forecast errors. Within this context, even the most basic forecast, the no-change or naïve forecast, can be used as input to the adaptive learning procedure. Kyriazi, Thomakos and Guerard approach is very well suited to our research question because (i) the no-change forecast is a natural candidate in a short time series where models cannot be estimated with sufficient accuracy, (ii) the no-change forecast is obviously far from being the 'perfect' forecast, and (iii) the adaptive learning process can be initialized by the no-change forecast and then learn by its own past forecast errors. Our results show that adaptive learning forecasting leads to performance enhancements that range from the 5% to more than 20% relative to the no-change benchmark. This finding indicates the efficiency of the adaptive learning method in forecasting medical tourism demand; an important subcategory of tourism demand for which data are not easily accessible and freely available historical data are existing for short time periods.
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Zhou, Zhenghan. "A Review of the Global Economic Shock in 2022 and a Prediction of the Global Economic Development in 2023". Advances in Economics, Management and Political Sciences 21, nr 1 (13.09.2023): 142–52. http://dx.doi.org/10.54254/2754-1169/21/20230246.
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In 2022, affected by global macroeconomic fluctuations, the economies of some countries around the world have experienced obvious fluctuations, thereby promoting inflation in various countries and limiting the rapid development of some industries around the world. This paper reviews the global economic shock in 2022 and analyzes the impact of China's economy on global economic development from the perspective of the new energy sector. As an important part of new energy, the photovoltaic industry can be analyzed to obtain a development forecast of the global economy in 2023. By analyzing the development of the photovoltaic industry, it is concluded that China's economy and the global economy will show a recovery trend in 2023.
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Zasuhina, Ol'ga, Anna Terekhova, Grigory Shitenkov, Leonid Golovatiukov, Nikolay Malinin i Alexandra Khukhryanskaya. "FORECAST, DEVELOPMENT AND PROSPECTS FOR DIGITALIZATION IN THE ELECTRIC POWER INDUSTRY". Bulletin of the Angarsk State Technical University 1, nr 17 (19.12.2023): 22–27. http://dx.doi.org/10.36629/2686-777x-2023-1-17-22-27.
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Solutions, trends and opportunities for digitalization in the energy industry, and information se-curity issues are considered. An analysis of new national standards in the information sphere is provided
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Palka, Dorota, Peter Blistan i Henryk Badura. "Forecast of the Maximum Methane Concentration in the Longwall Outlet and in the Ventilation Roadway. Case Study". Management Systems in Production Engineering 31, nr 4 (1.12.2023): 398–403. http://dx.doi.org/10.2478/mspe-2023-0044.
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Abstract The mining process of the coal seam wall is accompanied by the release of methane into the mine atmosphere. This process is highly variable and depends on the methane content in the seam and the methane saturation of the rocks surrounding the seam. This is the specificity of the Polish hard coal mining industry. In the article, prognostic formulas for the maximum methane concentration at the outlet of the longwall ventilation gallery were developed. In the presented article, these formulas were used to predict methane concentration at the longwall outlet and in the ventilation gallery at a distance of up to 10 m in front of the longwall. In order to assess the accuracy of the forecasts, their results were compared with the forecast at the exit of the ventilation roadway. The obtained results are so accurate that it is worth repeating this type of check also using measurements in other longwalls. It will allow to reduce the risk of methane explosion during operation.
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Zhou, Kai, Zihao Li i Zhaofeng Liu. "Trajectory Of Development in China's New Energy Vehicle Industry Through Data Analysis and Expectation". Highlights in Science, Engineering and Technology 96 (5.05.2024): 132–38. http://dx.doi.org/10.54097/06ad5c42.
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Amidst a global shift towards sustainable transportation, this study conducts an in-depth analysis of data about China's new energy vehicle (NEV) sector from 2013 to 2022. This research primarily evaluates the standards and key factors influencing the NEV industry's evolution. Spearman correlation and decision tree models indicate that the average price of new energy vehicles and the government subsidies for them have the most significant impact on the development of the new energy industry in China. Expanding on these insights, a robust LASSO linear regression model was developed to further explore these dynamics. Additionally, an ARIMA time series model was employed, leveraging historical data to forecast the factors likely to influence the NEV industry in the coming decade. Integrating these forecasts into the initial evaluation model, the study anticipates a positive growth trajectory for China's NEV development, especially between 2023 and 2025. This research not only sheds light on the current state of the NEV industry in China but also provides valuable predictions for its future direction, contributing to the broader understanding of sustainable vehicle evolution in the global context.
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Lukomets, Artem. "TOOLS FOR IMPROVING RESOURCES IN PLANT PRODUCTION". Vestnik of Kazan State Agrarian University 18, nr 3 (2.10.2023): 180–85. http://dx.doi.org/10.12737/2073-0462-2023-180-185.
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Targeted and regulated management of crop production development and its resource support on the basis of the organizational and economic mechanism contributes to the achievement of goals with optimal resource consumption. As the methodological base of the study, general scientific methods, works of domestic and foreign scientists on crop production and products of its processing were used. The article considers tools for improving resource support as elements of the organizational and economic mechanism for the development of the industry. Tools for improving the resource support of crop production are an integral part of the organizational and economic mechanism and should be aimed at creating conditions for the reproduction process in the industry and import substitution of certain types of resources. The proposed tools are organized by groups corresponding to subsystems of the crop industry: technological, economic, social, environmental, organizational. The developed forecast for the development of the crop industry and the proposed measures of the organizational and economic mechanism were assessed by calculating structural shifts. Structural shifts make it possible to estimate the efficiency of shifts in the actual and predicted structure. The calculation of the shift efficiency indicates that the structure of commodity production in the forecast year within the forecast confidence interval will not change significantly, which indicates that the structure of crop production is balanced. The projected growth of crop production within the confidence interval will affect the growth of the industry's profitability by only 0.99% to 57.15%. Profitability growth is projected due to an increase in the share of oilseeds and a reduction in the share of other crop products in the structure of marketable products.
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Byastinova, L. M. "The use of Multifactor Regression Models in Forecasting in the Agricultural Sector of Yakutia". Vestnik of North-Eastern Federal University Series "Economics Sociology Culturology, nr 4 (20.12.2023): 102–10. http://dx.doi.org/10.25587/2587-8778-2023-4-102-110.
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Under the constantly changing economic conditions of management, ensuring food security in the country, as well as in the regions becomes especially relevant. Agriculture is the only industry that is able to bring countries into a state of complete self-sufficiency, which is clearly indicated in the Doctrine of Food Security of Russia. With this in mind, the intensification of agricultural sectors, through the introduction of new technologies and improving the quality of land use is a particularly topical challenge. In this regard, the state faces the issues of the development of the industry and scientific forecast of its main indicators, taking into account the existing factors and conditions. This article provides a detailed methodology for developing a forecast for the agricultural sector using multivariate regression models. The main factors influencing the indicators of agriculture are considered, the main ones are highlighted and a multifactorial regression model is developed to forecast the indicator of gross agricultural output for the next five years. Recommendations are given for improving the application of this method in relation to the region under consideration.
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Chen, Yuanyuan, Mohammad Affendy Arip i Nor Afiza Abu Bakar. "Cold Chain Logistics Demand Forecasting for Fresh Agricultural Foods in Fujian Province, China". International Journal of Religion 5, nr 5 (5.04.2024): 78–84. http://dx.doi.org/10.61707/e1m9vh53.
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China's current cold chain infrastructure for fresh agricultural products faces numerous challenges, particularly within the context of Fujian Province. The cold chain logistics sector in the region is characterized by limited development and requires immediate improvements in its foundational supporting infrastructure. The establishment of a comprehensive cold chain logistics system tailored for fresh agricultural goods remains incomplete, resulting in inefficiencies within the supply network. An in-depth examination of the necessity for refrigerated transportation networks for fresh agricultural products through scientific inquiry reveals the potential for strategic investments in the industry. To address this gap, a study employing the GM (1,1) model is conducted to forecast the future demand for cold chain logistics in fresh agricultural items specifically within Fujian Province, China, over the next five years. The findings of the study indicate that by 2027, the demand for cold chain logistics services for fresh produce in Fujian Province is projected to reach 4765.6 million tons. These insights furnish valuable information for optimizing investment planning in cold chain logistics infrastructure and formulating pertinent legislative measures to stimulate industry growth. In summary, the integration of these findings into the context of Fujian Province underscores the significance of enhancing cold chain logistics capabilities to address existing challenges and capitalize on future opportunities within the region's fresh agricultural sector.
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Khrulkov, D. V. "Using the Indicator ‘Economiic Level of Technology’ in Managing Industrial and Technological Systems in Consumer Goods Industry". Vestnik of the Plekhanov Russian University of Economics 20, nr 2 (13.04.2023): 202–11. http://dx.doi.org/10.21686/2413-2829-2023-2-202-211.
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The article studies issues of assessing efficiency of production and technological systems (enterprises) in consumer goods industry. Sanction restrictions built new challenges for customer goods industry but at the same time opened new opportunities. The drawback of the current approaches to managing enterprises of customer goods industry is the absence of dynamic appraisal of the technological process quality, which can cause a delayed response to crisis phenomena. To provide efficient management it is proposed to use the indicator ‘economic level of technology’ (ELT) that implies complex assessment of quality and efficiency of the production and technological system. Advantages of this indicator include possibility to forecast the development of production on the basis of due regard to laws of technological process development. With the help of calculations illustrated by two enterprises of microlevel, i.e. the transnational company Nike and the Russian company Dochki Synochki, as well as the US macroeconomic system it was proved that this indicator of efficiency can forecast arising of crucial trends in operational work of the enterprise and economy in general. Taking into account problems facing enterprises of customer goods industry ELT indicator could be used to raise effectiveness of managerial decision-making on the enterprise level and industry and state level in view of target support of development.
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Oba, K. M. "A Predictive Model for POP Cement Prices in the Nigerian Construction Industry". Journal of Engineering Research and Reports 25, nr 12 (16.12.2023): 14–23. http://dx.doi.org/10.9734/jerr/2023/v25i121037.
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This study was aimed at formulating a model to predict the price of Plaster of Paris (POP) cement using a multiple linear regression modelling technique. The prices of POP cement were predicted between the fiscal years 2024 and 2030, given the prices between the fiscal years 2017 and 2023. Secondary data were obtained for the Interest Rates, Inflation Rates, Naira exchange rates against the US Dollar, Population growth rates, and Gross Domestic Product (GDP) growth rates between 2017 and 2023. Primary data were obtained to investigate the prices at which POP was sold between the fiscal years 2017 and 2023. Exponential trends forecasting was used to forecast the above decision variables or factors affecting the price of POP cement between 2024 and 2030. A multiple linear regression model was derived for the prediction of the POP cement prices between the said years. The model was found fit, adequate, and of a high predictive attribute with an R2 value of 0.99. This study will help in the proactive planning of effective cost management for building construction projects in which POP cement was used. It will reduce problems and challenges of cost overrun on construction projects in the Nigerian construction industry.
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Sonea, Cosmin, Dana Tapaloaga, Raluca Aniela Irimia Gheorghe, Maria Rodica Gurau i Paul-Rodian Tapaloaga. "Milk Production Forecast Analysis in Romania - A Problem to Possible Solutions Approach". Annals of "Valahia" University of Târgovişte. Agriculture 15, nr 1 (1.04.2023): 9–12. http://dx.doi.org/10.2478/agr-2023-0003.
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Abstract Romania is a country located in southeastern Europe, known for its rich history, culture, and agriculture. One of the most important agricultural sectors in this country is milk production. In recent years, the milk production industry has faced numerous of challenges. In this direction, the aim of our paper was to use the exponential smoothing method of forecasting in order to analyse the milk production future trends, based on empirical data provided by FAOSTAT, and to provide some insights regarding possible solutions. According to our observations, in the following period (2023-2033) the milk production tends to slowly decline, situation that impose some measures. In conclusion, regarding the forecasted situation, Romania can avoid the milk production decline and improve the current situation by adopting and implementing some preventive measures.
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Wang, Shiyuan. "Analysis and Prediction of Online Beer Sales Based on SARIMA Model". BCP Business & Management 36 (13.01.2023): 359–66. http://dx.doi.org/10.54691/bcpbm.v36i.3454.
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With the boom of e-commerce in China, online shopping has become the mainstream way of shopping in Chinese. To explore the impact of online shopping on beer sales, this paper uses a time series SARIMA model to analyze online beer sales data from January 2020 to September 2022 obtained from Internet platforms and predicts online beer sales from October 2022 to September 2023. This paper first introduces the current research on beer sales in China, and then briefly analyzes the current situation of the beer industry. Thirdly, based on the real data of beer online sales on the Internet platform, SARIMA model is used to forecast the sales volume of next year. The result shows that beer online sales are expected to show an upward trend, with the industry being the most competitive in June 2023, and a small sales peak both in November 2022 and January 2023 due to the e-commerce carnival. Therefore, beer online sales are significantly affected by seasonality and platform promotions.
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Walicka, Monika. "Mapping Strategies of Net Working Capital in the Energy Sector in Poland – Transformation Perspective". Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu 68, nr 2 (2024): 51–61. http://dx.doi.org/10.15611/pn.2024.2.05.
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The aim of the article is to identify net working capital management strategies used in the energy sector. The analysis covered all companies listed on the Warsaw Stock Exchange under the WIG-Energy industry index. The study was conducted on the basis of financial reports for 2022-2023, with data from the latest reported quarters, i.e. Q1 and Q2 of year 2023, used in strategy mapping. The ratio analysis method was used with reference to industry indicators, and the dynamics of changes in the examined indicators was taken into account. The research results indicate that currently energy entities mainly use agressive strategies (64%). The sector's net working capital forecast indicates that the strong growth trend that has been seen since 2020 will gradually begin to slow down from 2023. As a result of using the indicator mapping technique, significant concentration of indictors can be noted in the sector, with only two entities differing from the rest of the sector in this regard.
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Gu, Weifan, Baohua Guo, Zhezhe Zhang i He Lu. "Civil Aviation Passenger Traffic Forecasting: Application and Comparative Study of the Seasonal Autoregressive Integrated Moving Average Model and Backpropagation Neural Network". Sustainability 16, nr 10 (14.05.2024): 4110. http://dx.doi.org/10.3390/su16104110.
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With the rapid development of China’s aviation industry, the accurate prediction of civil aviation passenger volume is crucial to the sustainable development of the industry. However, the current prediction of civil aviation passenger traffic has not yet reached the ideal accuracy, so it is particularly important to improve the accuracy of prediction. This paper explores and compares the effectiveness of the backpropagation (BP) neural network model and the SARIMA model in predicting civil aviation passenger traffic. Firstly, this study utilizes data from 2006 to 2019, applies these two models separately to forecast civil aviation passenger traffic in 2019, and combines the two models to forecast the same period. Through comparing the mean relative error (MRE), mean square error (MSE), and root mean square error (RMSE), the prediction accuracies of the two single models and the combined model are evaluated, and the best prediction method is determined. Subsequently, using the data from 2006 to 2019, the optimal method is applied to forecast the civil aviation passenger traffic from 2020 to 2023. Finally, this paper compares the epidemic’s impact on civil aviation passenger traffic with the actual data. This paper improves the prediction accuracy of civil aviation passenger volume, and the research results have practical significance for understanding and evaluating the impact of the epidemic on the aviation industry.
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Młody, Michał, Milena Ratajczak-Mrozek i Maja Sajdak. "Industry 4.0 technologies and managers’ decision-making across value chain. Evidence from the manufacturing industry". Engineering Management in Production and Services 15, nr 3 (1.09.2023): 69–83. http://dx.doi.org/10.2478/emj-2023-0021.
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Abstract The paper aims to identify how Industry 4.0 technologies affect the quality and speed of the managers’ decision-making process across the different stages of the value chain, based on the example of the manufacturing sector. The paper adopts qualitative research, based on nine in-depth interviews with key informants, to capture senior executives’ experiences with implementing Industry 4.0 technologies in their organisations. The research is focused on three manufacturing industries: the automotive, food and furniture industries. The research shows that depending on the stage of the value chain, different Industry 4.0 technologies are more suitable for the support of managers’ decisions. Various Industry 4.0 technologies support decision-making at different stages of the manufacturing value chain. In the Design stage, 3D printing and scanning technologies play a crucial role. In the case of Inbound Logistics, robotisation, automation, Big Data analysis, and Business Intelligence are most useful. During the Manufacturing stage, robotisation, automation, 3D printing, scanning, Business Intelligence, cloud computing, and machine-to-machine (M2M) integration enable quick decision-making and speed up production. Sensors and the Internet of Things (IoT) optimise distribution in the Outbound Logistics stage. And finally, Business Intelligence supports decisions within the Sales and Marketing stage. It is also the most versatile technology among all particular stages. The paper provides empirical evidence on the Industry 4.0 technology support in decision-making at different stages of the manufacturing value chain, which leads to more effective value chain management, ensuring faster and more accurate decisions at each value-chain stage. When using properly selected Industry 4.0 technologies, managers can optimise their production processes, reduce costs, avoid errors and improve customer satisfaction. Simultaneously, Industry 4.0 technologies facilitate predictive analytics to forecast and anticipate future demand, quality issues, and potential risks. This knowledge allows organisations to make better decisions and take proactive actions to prevent problems.
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ZHILINA, Ekaterina V., Anzhelika A. NIKITINA, Elena A. KHUNAFINA i Ilyuza M. KHANOVA. "Regional specific features of the consumer sector of the economy". Regional Economics: Theory and Practice 19, nr 8 (16.08.2021): 1406–19. http://dx.doi.org/10.24891/re.19.8.1406.
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Subject. This article discusses the development of meat food market in Russia. Objectives. The article aims to define trends in the development of the meat industry, forecast the meat food consumption in Russia, and analyze the effect of various factors on the meat food consumption level. Methods. For the study, we used a statistical analysis. Results. The article presents the forecast of meat food consumption until 2023 and describes the dependence of meat consumption on a number of factors. Conclusions. Changes in consumer behavior patterns are affecting the meat food market. The direct relationship between the real incomes of the population and the level of consumption has a significant impact on the demand for meat products.
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Rodin, Evgeniy. "Project Activities in the Construction Industry: the Specifics of Competition and the Development Forecast". Bulletin of Kemerovo State University. Series: Political, Sociological and Economic sciences 2023, nr 2 (11.08.2023): 189–96. http://dx.doi.org/10.21603/2500-3372-2023-8-2-189-196.
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The purpose of the study is to analyze the construction industry processes under the influence of recent changes to identify the most competitive strategy and forecast further market development. The article might be of interest to the construction market players in the field of public procurement. Competition in the construction industry remains very high and intensifies over time, which is why many players are leaving the market. Among the main success factors in the market are revenue management, order portfolio, customer base, and high specialization. The author analyzes the competition in construction market project activities in the field of public procurement on the basis of the M. Porter’s 5 factors of competition. Based on a survey of industry experts, the main success factors in the market are identified. The success factors are new knowledge that can be used both to improve the work of current market participants and to help new players to enter the market.
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Huang, Yi. "The Application of Exponential Smoothing in GDP Forecasting". Frontiers in Business, Economics and Management 11, nr 3 (26.10.2023): 102–6. http://dx.doi.org/10.54097/fbem.v11i3.13196.
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GDP forecasting is the estimation of gross domestic product (GDP) over a period of time in the future, which is important for national governments, businesses, and investors respectively. The exponential smoothing method, also known as an exponentially weighted average method, is a forecasting method with the advantages of a clear process and convenient calculation. In this paper, the literature method is used, and "gross domestic product", "GDP", and "exponential smoothing method" are used in various databases and literature review websites, "Macroeconomics" and "forecasting" as the keywords to search, comb the literature, categorize and summarize the application of exponential smoothing method in GDP forecasting, and at the same time, the experimental method is used to design an experiment to forecast the future growth of GDP in the next three years by using the data published by the National Bureau of Statistics of China (NBSC). The results of the experiment are that the value-added of the primary industry is expected to continue to grow from about 9227.246 billion Yuan in 2023 to about 100139.20 billion Yuan in 2025. The value added of the secondary industry is projected to continue to grow from about 510,795,800,000,000 Yuan in 2023 to about 563,968,676,000,000 Yuan in 2025. The value added to the tertiary sector is projected to continue to grow from about 679,630.84 billion yuan in 2023 to about 753,204.88 billion yuan in 2025. Correspondingly, the Gross Domestic Product (GDP) will continue to grow, from about 1,281,982,888 million yuan in 2023 to about 1,417,312,284 million yuan in 2025. By analyzing the forecast results, it can be seen that all three major industries in China will maintain growth in the next three years. Depending on the different attributes of the three major industries, the government or enterprises will take appropriate measures to ensure the growth of the industries as well as to cope with various risks. This forecast is based on past data, but the actual economic situation may be affected by many external factors such as the international trade environment and the global economic situation. Governments and businesses will therefore need to pay close attention to these factors in order to adjust their strategies in a timely manner.
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Ma, Mingshang. "Forecasting New Energy Vehicle Sales in China based on GM (1,1) Model". Highlights in Science, Engineering and Technology 72 (15.12.2023): 155–62. http://dx.doi.org/10.54097/z8426s60.
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With the intensification of the conflict between economic development and green energy, the prediction of sales figures of new energy vehicles, especially electric vehicles, has become one of the key concerns in society. This study aims to help businesses understand market trends and adjust marketing strategies and planning in a timely manner through sales volume forecasting. The research data in this paper is sourced from the monthly sales volume data of new energy vehicles in China from January to June 2023, obtained from the global automotive industry platform Mark Lines. Taking into account various factors and adopting a time series approach, the GM(1,1) model is utilized to forecast the sales of the research subject. for the future time period in 2023 on a monthly basis. The results indicate that this method can accurately predict the research subjects in this study, the new energy vehicle industry is expected to enter a faster development stage. This provides an important reference for promoting the vigorous development of China in the new energy vehicle industry.
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Doanh, Doung Cong, Zdenek Dufek, Joanna Ejdys, Romualdas Ginevičius, Pawel Korzynski, Grzegorz Mazurek, Joanna Paliszkiewicz, Krzysztof Wach i Ewa Ziemba. "Generative AI in the Manufacturing Process: Theoretical Considerations". Engineering Management in Production and Services 15, nr 4 (1.12.2023): 76–89. http://dx.doi.org/10.2478/emj-2023-0029.
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Abstract The paper aims to identify how digital transformation and Generative Artificial Intelligence (GAI), in particular, affect the manufacturing processes. Several dimensions of the Industry 4.0 field have been considered, such as the design of new products, workforce and skill optimisation, enhancing quality control, predictive maintenance, demand forecasting, and marketing strategy. The paper adopts qualitative research based on a critical review approach. It provides evidence of the GAI technology support in the mentioned areas. Appropriate use of emerging technology allows managers to transform manufacturing by optimising processes, improving product design, enhancing quality control, and contributing to overall efficiency and innovation in the industry. Simultaneously, GAI technologies facilitate predictive analytics to forecast and anticipate future demand, quality issues, and potential risks, improve a marketing strategy and identify market trends.
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and Mobile Computing, Wireless Communications. "Retracted: Forecast Analysis of the Overall Structure Characteristics and Development Potential of the Sports Industry Based on Wireless Communication Networks". Wireless Communications and Mobile Computing 2023 (24.01.2023): 1. http://dx.doi.org/10.1155/2023/9827393.
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Куренков, Пётр Владимирович, Ирина Андреевна Солоп, Евгения Андреевна Чеботарева i Сергей Анатольевич Сафронов. "PROBLEMS OF DEVELOPMENT OF THE TRANSPORT INDUSTRY IN THE CONDITIONS OF PROCESSES OF GLOBALIZATION AND REGIONALIZATION". Транспорт: Наука, техника, управление, nr 7 (2.07.2024): 3–6. http://dx.doi.org/10.36535/0236-1914-2023-07-1.
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Рассматриваются вопросы глобализации экономики России. Отмечено снижение транспортной работы и изменение логистических схем доставки из-за введенных санкций. Приведен прогноз мирового изменения ВВП с распределением по странам. The issues of globalization of the Russian economy are considered. There was a decrease in transport work and a change in logistics delivery schemes due to the sanctions imposed. The forecast of global GDP change with distribution by country is given.
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Xiong, Tao, Miao Li i Jia Cao. "Do Futures Prices Help Forecast Spot Prices? Evidence from China’s New Live Hog Futures". Agriculture 13, nr 9 (23.08.2023): 1663. http://dx.doi.org/10.3390/agriculture13091663.
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China, the largest hog producer and consumer globally, has long experienced significant fluctuations in hog prices, partly due to the lack of rational expectations for future hog spot prices. However, on 8 January 2021, China’s first futures in animal husbandry, the live hog futures, were listed on the Dalian Commodity Exchange. To investigate the forecasting performance of the new live hog futures on forthcoming hog spot prices, we developed six futures-based forecasting models and utilized data on daily hog spot and futures prices from January 2021 to March 2023. Our results show that all six models consistently generate more accurate forecasts than the no-change model across six prediction horizons and four accuracy measures, indicating that China’s new live hog futures prices help forecast forthcoming hog spot prices. Among the futures-based forecasting models, futures spread-based models generally produce the best forecasts for one-, two-, three-, and four-month-ahead forecasting, while the simple linear regression using both spot and futures prices is the best for five- and six-month-ahead forecasting. Our results suggest that live hog futures are a promising and practical tool for various stakeholders in China’s hog industry to develop rational expectations for future hog spot prices.
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Nosov, Vladimir, Maria Tindova, Kirill Zhichkin, Lyudmila Zhichkina, Victoria Karnakova i Lyudmila Voloshchuk. "Production of sugar beet in Russian Federation: analysis and forecast". E3S Web of Conferences 420 (2023): 01008. http://dx.doi.org/10.1051/e3sconf/202342001008.
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The article analyzes the dynamics of indicators characterizing the production of sugar beets in Russia. The Krasnodar region, the Penza region and the Russian Federation as a whole were chosen as the object of study, the sown areas, the gross harvest and the yield of sugar beet were chosen as the subject of the study, and the period from 2005 to 2022 was chosen as the time interval. At the first step of the work, a primary analysis of the studied dynamic series is carried out, which showed a stable growth of indicators with a maximum jump in 2010. Next, regressions are constructed that describe long-term trends in the development of processes, for which, in most cases, quadratic functions are chosen. The next step is modeling the cyclical component and developing multiplicative trend-seasonal models, the error of which was 7.4% -13.8%. As a result of the study, a forecast for the development of the industry for 2023-2024 is built.
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Wang, Chia-Nan, Minh Nhat Nguyen, Anh Luyen Le i Hector Tibo. "A DEA Resampling Past-Present-Future Comparative Analysis of the Food and Beverage Industry: The Case Study on Thailand vs. Vietnam". Mathematics 8, nr 7 (13.07.2020): 1140. http://dx.doi.org/10.3390/math8071140.
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The food and beverage industry plays a significant role in the economic development of developing and emerging countries in Asia through an immense contribution to the national income, employment, value-added inducement, and foreign exchange earnings. Among the developing countries in Asia, Thailand and Vietnam have recently experienced a significant growth in the industry due to their many advantages. However, the nascent stage of this industry was found to be lacking sustainable competitiveness in both countries. Therefore, this study aims to evaluate and forecast the performance efficiency of the food and beverage industry in Thailand and Vietnam to understand how efficient the food and beverage industry to these countries is and formulate suggestions to improve their productivity in accordance with the research findings. To achieve the research objectives, the resampling method in the data envelopment analysis is applied to measure and forecast the efficiency of 20 Vietnamese companies and 20 Thailand firms over the period of 2016 to 2023. The Malmquist productivity index is deployed to calculate the efficiency change over observed periods. The results reveal that Vietnam is found to have a higher efficiency than Thailand due to the outstanding performance of one company but have performed quite poorly due to low scores in technical and productivity change. The findings of this research can give useful information and practical suggestions to improve performance for inefficient companies as well as enhance competitiveness of the efficient companies trying to operate and reach global markets.
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Galtseva, N. V. "The Gold Mining Industry of Magadan Oblast under Sanctions: Risks for the Region". Regional Research of Russia 13, nr 3 (wrzesień 2023): 490–95. http://dx.doi.org/10.1134/s2079970523700788.
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Abstract— The introduction of a new package of sanctions in connection with the start of a military operation in Ukraine in 2022 is particularly changing the conditions for functioning of Russia’s gold mining industry, which uses high-performance foreign equipment for mining and sells its output on the global market. The article considers the factors complicating the industry and which have triggered an increase in production costs and decrease in the sale price of gold: a ban on foreign trade operations for the largest partner banks of subsoil users, an embargo by foreign countries on the purchase of Russian gold and import of foreign equipment and spare parts, and the Russian Central Bank’s decrease in the dollar/ruble exchange rate from May 2022 to January 2023. The case of a specific enterprise in Magadan oblast was used to assess the efficiency of placer gold mining under the changed conditions as of July 2022 and the forecast for 2023. Federal and regional measures were proposed to help smooth the severity of emerging problems in the industry and consequences for the economy of this single-resource northern region.
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Babets, Iryna. "Simulation of the impact of foreign direct investment on the Ukraine’s economy in the conditions of post-war reconstruction". Galic'kij ekonomičnij visnik 80, nr 1 (2023): 131–43. http://dx.doi.org/10.33108/galicianvisnyk_tntu2023.01.131.
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The article examines the dynamics of accumulated foreign direct investment in the economy of Ukraine in 2010–2021 and analyzes the reasons for the significant decrease in FDI inflows to the economy of Ukraine, the withdrawal of significant amounts of capital by foreign investors in periods of deepening economic crisis. The structural changes of foreign direct investment in the Ukrainian economy by types of economic activity and by industry for the period 2015–2021 are analyzed. With the help of regression analysis, modeling of the dependence of economic growth of Ukraine on changes in the volume of foreign direct investments was carried out under the condition that the war ends in 2023. To build a linear regression model, data on the GDP of Ukraine, GDP per capita, the volume of accumulated FDI in the economy and their annual revenues, the volume of FDI in the main sectors of the economy in 2010–2021 and the forecast values of these indicators in 2023–2025. The results of the study confirmed the importance of increasing both the total volume of FDI and the volume of investment involved in high-tech industries industry, to restore the economy of Ukraine after the end of the war. Regression analysis made it possible to reveal a direct connection between the economic growth of Ukraine and the volume of accumulated FDI in the economy and their annual inflow. Based on the model of linear dependence of Ukraine's economic growth on foreign direct investments, forecast scenarios of changes in Ukraine's GDP in actual prices and GDP per capita in 2023 were developed, provided that the volume of accumulated FDI in the economy increases. As a result of the study, no statistically significant dependence of the economic growth of Ukraine on the volume of accumulated FDI in the metallurgical industry and in the in the field of information and telecommunications sector was found. The regression analysis confirmed the significant direct dependence of Ukraine's economic growth on the volume of FDI involved in the machine-building industry, which proves their important role in the implementation of post-war economic recovery projects of Ukraine.
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Rao, Yijie. "Predicting and Analyzing the Bitcoin's Price with the ARIMA Model". Highlights in Science, Engineering and Technology 92 (10.04.2024): 101–6. http://dx.doi.org/10.54097/k1p0ze13.
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Virtual currencies have become one of the most welcomed financial investment properties by investors, managers, and researchers. In this essay, we analyze the Bitcoin price from 2017 to 2023 and forecast the price of the Bitcoin. ARIMA model plays an important role in time series prediction, and many researchers choose to use this model to analyze and forecast historical time series. Therefore, this paper is based on the Autoregressive Integrated Moving Average Model (ARIMA) mode, by using the previous Bitcoin price to forecast the future price of Bitcoin. The outbreak of COVID-19 in late 2019 which then spread from China to the world has had a huge negative impact on the global economy. At the same time, owing to the impact of COVID-19 being significant, this paper will also analyze the influence of the pandemic’s influence on Bitcoin. The result shows that the epidemic has a great influence on it and reflect a different consequence of other industry. Investors and others should be cautious about investing in bitcoin.