Hasil untuk "Risk in industry. Risk management"

Pingting Liu, Liangxing Jin, Xiaogang Li et al.

A multi-objective Improved Radial Movement Optimized Random Forest (MOIRMO-RF) is introduced to address the issues of slow convergence and overfitting in slope stability prediction. Hyperparameter tuning is formulated as a bi-objective search that jointly maximizes accuracy (Acc) and recall (Rec), with a Pareto archive maintained during global exploration. A comprehensive evaluation adopts the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) using Acc, precision (Prec), Rec, F1-score (F1), the area under the ROC curve (AUC) and the Average Precision (AP). The dataset comprises 792 literature-compiled cases with six inputs: slope height ([Formula: see text]), slope angle ([Formula: see text]), unit weight ([Formula: see text]), cohesion ([Formula: see text]), internal friction angle ([Formula: see text]), and pore-pressure ratio ([Formula: see text]). Comparative experiments cover MOIRMO-RF, RF, MOPSO-RF, XGB, MOPSO-XGB, MOIRMO-XGB, MLP, MOIRMO-MLP, and MOPSO-MLP. On the test set, MOIRMO-RF achieves Acc = 0.917, Rec = 0.976, Prec = 0.923, F1 = 0.949, AUC = 0.966 and AP = 0.991, delivering concurrent gains in Rec and AUC without compromising precision, thereby improving discrimination. Under multi-objective optimization, convergence is accelerated, generalization is enhanced, and overfitting is suppressed. External validation of mining slope cases from the Yellow River Basin demonstrates robustness and practical usability, supporting applications in risk assessment and early warning decision-making.

Xiaodan Lin, Fenqiang Chen, Haigang Zhuang et al.

This study aims to develop an optimal underwriting strategy for affordable (H1 and M1) and premium (L1 and M2) electric vehicles (EVs), balancing financial risk and sustainability commitments. The research is motivated by regulatory pressures, risk management needs, and sustainability goals, necessitating an adaptation of traditional underwriting models. The study employs a modified Delphi method with industry experts to identify key risk factors, including accident risk, repair costs, battery safety, driver behavior, and PCAF carbon impact. A sensitivity analysis was conducted to examine premium adjustments under different risk scenarios, categorizing EVs into four risk segments: Low-Risk, Low-Carbon (L1); Medium-Risk, Low-Carbon (M1); Medium-Risk, High-Carbon (M2); and High-Risk, High-Carbon (H1). Findings indicate that premium EVs (L1 and M2) exhibit lower volatility in underwriting costs, benefiting from advanced safety features, lower accident rates, and reduced carbon attribution penalties. Conversely, budget EVs (H1 and M1) experience higher premium fluctuations due to greater accident risks, costly repairs, and higher carbon costs under PCAF implementation. The worst-case scenario showed a 14.5% premium increase, while the best-case scenario led to a 10.5% premium reduction. The study recommends prioritizing premium EVs for insurance coverage due to their lower underwriting risks and carbon efficiency. For budget EVs, insurers should implement selective underwriting based on safety features, driver risk profiling, and energy efficiency. Additionally, incentive-based pricing such as telematics discounts, green repair incentives, and low-carbon charging rewards can mitigate financial risks and align with net-zero insurance commitments. This research provides a structured framework for insurers to optimize EV underwriting while ensuring long-term profitability and regulatory compliance.

Rui Guo, Yongqiang Dai, Junjie Hu

IntroductionMastitis in dairy cows is a significant challenge faced by the global dairy industry, significantly affecting the quality and output of milk from dairy enterprises and causing them to suffer severe economic losses. With the increasing public concern over food safety and the rational use of antibiotics, how to identify cows at risk of disease early has become a key issue that needs to be urgently addressed. Especially subclinical mastitis, due to the lack of obvious external symptoms, makes detection more difficult, so early warning of it is particularly important.MethodsIn this study, a time series prediction method, combined with machine learning techniques, was used to predict the risk of mastitis in dairy cows. The study data were obtained from the production records of 4000 dairy cows in a large farm in Hexi region of Gansu. By constructing time-series features, production indicators such as milk yield, fat rate and protein rate of each cow in two consecutive months, April and May, were utilized to predict its health status in June. To fully exploit the value of the time series features, we designed a multidimensional feature set that included raw indicator values, monthly change rates, and statistical features. After data preprocessing and sample balancing, data from 2821 cows were selected for model training. Finally, the applicability of each model was assessed by comparing and analyzing the prediction performance of six models, namely eXtreme Gradient Boosting(XGBoost), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), K Nearest Neighbors (KNN), Logistic Regression, and Long Short-Term Memory Network (LSTM).ResultsThe XGBoost model demonstrated optimal performance, achieving an area under the ROC curve (AUC) of 0.75 with an accuracy rate of 71.36%. Feature importance analysis revealed three key temporal indicators significantly influencing prediction outcomes: May milk yield (22.29%), standard deviation of fat percentage (20.27%), and fat percentage change rate (19.87%). SHapley Additive exPlanations (SHAP) value analysis further validated the predictive value of these temporal features, providing dairy farm managers with clearly defined monitoring priorities.DiscussionThe XGBoost model demonstrates strong potential as an accurate predictive tool for subclinical mastitis in dairy cows. This study presents an effective early-warning approach through time-series modeling that offers significant practical value for mastitis prevention in dairy farm management.

Yuntao Liang, Yingjie Liu, Changjia Lu et al.

The coal industry is a high risk, high difficulty industry, and the annual global mine accident rate is high, so the safety of coal mine underground operations has been a concern. With the development of technology, the application of intelligent security technology in coal mine safety has broad prospects. In this paper, the research progress of vital signs monitoring and support equipment for underground personnel in coal mines is reviewed. The two main methods to ensure the safety of miners are discussed. They consist of directly monitoring human vital signs through portable devices such as smart helmets and smartwatches and indirectly monitoring underground environmental parameters. In addition, the application of information technology, sensor technology and artificial intelligence in vital signs monitoring is briefly discussed, and some future research directions are proposed. For example, through big data and artificial intelligence technology, vital signs data can be compared with historical data, individual health trends and potential risks can be analyzed, and we can provide personalized health management programs for miners. These technologies not only improve the safety of underground coal mine operation, but also provide an important guarantee for the realization of intelligent and safe coal mine production.

Oleksandr Korystin, Igor Tsiupryk, Oleksandr Nikolaiev

The forestry sector in Ukraine is currently confronted with a multitude of challenges, including the repercussions of climate change, ecological issues, economic challenges, and the consequences of military actions initiated by the Russian Federation, which have caused extensive damage to forests across the country. This highlights the necessity for research aimed at assessing threats and risks in the forestry sector, as well as evaluating the institutional capacity to ensure the sustainable development of the industry. The research was conducted in accordance with the mandate of the Temporary Investigative Commission of the Verkhovna Rada of Ukraine, which was established to examine instances of malfeasance and non-compliance with environmental safety standards in the domain of environmental protection. The methodology of this study is based on a risk-based approach that involves a systematic analysis of threats affecting the forest sector in Ukraine and an assessment of their impact on environmental safety. The principal instrument for data collection was an online survey of experts drawn from a range of sectors, including government agencies, local communities, research institutes, non-governmental organisations and businesses. The data was subjected to statistical analysis, including correlation and regression analysis, which enabled an assessment of the relationship between the level of threats and institutional capacity. The aim of this article is twofold: firstly, to identify the principal threats and risks facing Ukraine's forestry sector; and secondly, to evaluate the extent of the institutional capacity to mitigate these threats. The study identified 153 indicators that characterise threats and 102 indicators that describe the institutional capacity of the sector. Following preliminary analysis and data cleansing, a high-quality sample was constructed based on expert assessments, which helped to avoid logical errors and enhance the reliability of the results. The results of the study indicate that uncontrolled mass deforestation represents one of the most significant environmental threats, resulting in a reduction in the population of flora and fauna. The probability of this threat materialising was calculated to be 60.89%. The correlation and regression analysis showed that out of 102 indicators of institutional capacity, only 14 have a significant correlation with the threat of uncontrolled logging, and all 11 vulnerability indicators showed a statistically significant relationship with this threat. The key factors affecting the reduction of the risk associated with these threats are the level of bureaucratic obstacles in the performance of official duties by forestry employees and the level of bureaucracy in the provision of services to the public. The findings of the study indicate a relatively low level of institutional capacity within Ukraine's forestry sector. This suggests a need to improve management processes in order to reduce risks in this area. The recommendations developed based on the obtained data can be employed to devise strategic measures to guarantee environmental safety and the sustainable development of Ukraine's forestry sector.

Hossein Firouzi, Javad Rezaeian, Mohammad Mehdi Movahedi et al.

This paper presents a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic, incorporating dimensions of sustainability. The objectives of the model are as follows: 1) Minimizing the costs associated with building facilities and waste treatment centers, vehicle fuel costs, and environmental costs due to pollutant emissions; 2) Maximizing the energy generated from the waste combustion process; 3) Minimizing the risk of virus transmission resulting from inadequate waste management; and 4) Maximizing the number of job opportunities in the established centers. It is important to note that existing uncertainties are addressed through the application of fuzzy set theory. Given the multi-objective nature of the model, two multi-objective algorithms, namely the Pareto archive-based Krill Herd Algorithm and Non-dominated Sorting Genetic Algorithm II (NSGA-II), are employed to solve the defined problem. The results indicate that the proposed Krill Herd Algorithm converges to a solution with higher quality and dispersion compared to NSGA-II. Additionally, through a comparison of the spacing index and running time of the two algorithms, it is observed that NSGA-II explores the solution space with higher uniformity and solves the model in less time.IntroductionHospital waste encompasses a broad spectrum of both hazardous and non-hazardous materials. The management of hospital waste involves the development of a suitable supply chain network for handling waste generated in the healthcare sector. Improper disposal or mishandling of contaminated waste not only contributes to environmental pollution but also poses a risk of transferring viral pathogens to healthcare and recycling personnel. Research has shown that inadequate disposal of medical waste can lead to the transmission of up to 30% of hepatitis B, 1-3% of hepatitis C, and 0.3% of HIV infections from patients to healthcare workers. This paper aims to design a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic while considering the dimensions of sustainability.Literatur ReviewIn recent years, various studies have delved into the complexities of medical and hospital waste management, proposing mathematical models to address this intricate issue. The current study is built upon the work of Valizadeh et al. (2021). In their paper, a hybrid mathematical modeling approach was introduced, featuring a Bi-level programming model specifically tailored for infectious waste management during the COVID-19 pandemic. The outcomes revealed that, at the higher level of the model, governmental decisions aiming to minimize total costs associated with infectious waste management were crucial. This involved the conversion of collected infectious waste into energy, with the generated revenue being reinvested back into the system. The findings indicated that, through energy production from waste during the COVID-19 pandemic, approximately 34% of the total costs related to waste collection and transportation could be offset. The uniqueness of this study lies in its consideration of three sustainability dimensions: risk, vehicle routing, energy production, employment, and emission of polluting gases. Consequently, the novelty of this research, when compared to previous studies and the article by Valizadeh et al. (2021), is evident in several aspects. It introduces an integrated multi-objective positioning-routing model for the supply chain of waste management under pandemic conditions, taking into account sustainability dimensions, notably the economic aspect, and employs meta-heuristic algorithms for model resolution.MethdologyTo ensure the proper management of hospital waste, the waste is categorized into two groups: infectious and non-infectious waste. It is assumed that waste in hospitals and health centers is segregated and placed in infectious and non-infectious waste bins. The collected waste undergoes further processing: infectious waste is transported to incineration centers, where it is burned and converted into electrical energy, while non-infectious waste is sent to waste recycling centers, where it is reprocessed and returned to the production cycle in the industry. A multi-objective mathematical model is presented to integrate location-routing decisions in the supply chain of hospital waste management, with the following modeling assumptions:Waste segregation at the source helps prevent all waste from becoming viral, reducing the spread of viruses through waste.The risk of spreading viruses is assumed to be relatively equal for each type of waste.Two types of vehicles are considered for transporting waste: the first type carries non-infectious waste, while the second type carries infectious waste.The number of cars, waste collectors, and the capacity of waste incinerators are considered constant in this study.The mathematical model is multi-objective, with the objectives being to optimize the three dimensions of sustainability (economic, social, and environmental).The economic goal is to minimize system costs, including the cost of site location, recycling, collection, segregation of non-infectious waste, and incineration.The environmental goal is to minimize the emission of pollutants in the transportation and processing system in various facilities, as well as to maximize the production of electrical energy.The social goal is to minimize the risk of virus transmission and maximize the employment rate.Results and DiscussionThis research presents a multi-objective mathematical model for the reverse supply chain of hospital waste management during the COVID-19 pandemic in Iran and solves it. The pandemic period is considered a time of maximum utilization of health centers and waste disposal. In this context, a three-objective mathematical model was initially introduced. To solve the model, the krill herd optimization algorithm was employed. The performance of the krill herd optimization algorithm was scientifically and practically evaluated by comparing it with the well-known NSGA-II algorithm. After designing the model, both the multi-objective krill herd algorithm based on Pareto Archive and the NSGA-II algorithm were utilized to solve the model. The results of solving the model demonstrated that the proposed krill herd algorithm, designed in combination with VNS, effectively solved the model and determined the optimal solution within a boundary. Comparing the results of this algorithm with those obtained by the renowned NSGA-II algorithm revealed that the krill herd algorithm produced solutions of much higher quality.ConclusionThe comparison of the Index of dispersion between the two algorithms indicates that the krill herd optimization algorithm explores more points in the solution space, leading to a lower probability of getting stuck in local optima compared to the NSGA-II algorithm. On the other hand, the index of uniformity for the NSGA-II algorithm is lower than that of the krill herd algorithm (lower values are better), suggesting that the multi-objective genetic algorithm explores the solution space more uniformly. Considering the execution time of the two algorithms, it was observed that the NSGA-II algorithm solved the model in less time. Additionally, the increasing trend of execution time in both algorithms confirms the NP-HARD nature of the hospital waste management problem. According to the output of the MATLAB software, considering the presented model, the results affirm the capability to optimally select hospital waste recycling centers.

Fereshteh F. Amini, Alireza A. Hidarnia, Fazlollah F. Ghofranipour et al.

Both natural and man-made dangers cause bodily harm, as well as social, economic and environmental harm. In order to minimise the complications of these threats, proper training and preparedness are crucial. The purpose of this study was to look at the factors that affect how well-trained healthcare volunteers are for natural hazards in Iran. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach, a systematic review of literature on the factors influencing the training of healthcare volunteers in natural hazard published between 2010 and 2020 was conducted. The Google Scholar search engine, PubMed (Medline and Central), Science Direct and Web of Science databases were searched using both individual and combined key phrases. The Strengthening the Reporting of Observational studies in Epidemiology checklist was used to select and evaluate 592 observational and quasi-experimental articles. Finally, the study comprised 24 papers that satisfied the research criteria and made good use of good technique, sample size and adequate tools for validity and reliability. The most useful variables for disaster preparedness were job self-efficacy, strategic decision-making and quality of work-life, job performance, job motivation, knowledge, awareness and health literacy. Contribution: To avoid calamity, a thorough training program is required. Therefore, the most crucial objectives for health education specialists are to identify the factors that determine disaster preparedness, train volunteers and provide fundamental techniques to reduce natural dangers.

Bikram Bhusal, Muhammad Aaqib, Satish Paudel et al.

This study was carried out to estimate the seismic requirement for analyzing and designing the monumental structure, Dharahara, in Kathmandu, Nepal. A probabilistic seismic hazard analysis of the study region was performed to develop the elastic response spectrum for the bedrock site. The obtained spectrum was compared with the existing seismic codes, NBC 105:2020 and IS 1893:2016, to estimate the target response spectrum. One-dimensional equivalent linear (EQL) and non-linear (NL) ground response analyses were performed using DEEPSOIL v7, incorporating a suite of ground motions. The three borehole logs exhibited very soft soil in the region. Due to the presence of soft soil strata in the region under study, strength-corrected modulus reduction and damping curves were adopted in the current study to overcome the effect of larger shear strain. The average response spectra from EQL analyses predicted higher values than NL analyses. Finally, a comparison of the proposed design response spectrum with the code spectra was performed. Comparisons revealed that the mean EQL and NL outputs fall in between the design spectra of similar soil classes in NBC 105:2020 and IS 1893:2016 seismic codes.

A. V. Trachuk, N. V. Linder

This paper is devoted to the study of the correlation between the perceived significance of indicators of innovation activity and their actual use at enterprises of the Russian manufacturing industry. A sample of 132 manufacturing enterprises in Russia was used for the analysis. It was found that the recognition of the significance and the actual use of financial and non-financial indicators varies significantly depending on the affiliation of companies to a particular innovation regime: radical innovators, technological innovators, effective producers, creators of value innovations and imitators. Three key performance indicators (KPIs) reflecting the company's focus on the introduction of technological innovations (the share of modern equipment in the company's technology park (taking into account the technological features of industries); the average time to adapt the acquired innovative product, days; the share of implemented patents from the total number of patents received by the organization) were recognized as important managers of companies belonging to technological and radical innovators (74.5, 76.9, 78.1%, respectively). Three key performance indicators reflecting customer orientation (the number of new categories of products or services introduced in the reporting year; the share of products certified according to international standards in the total production of the company; the percentage of innovative expenditures on the modernization of existing products/processes/business models in relation to the total innovative expenditures on products/processes/business models) were recognized as important companies classified as effective producers and creators of value innovations (83.4, 81.9, 76.8%, respectively).But at the same time, the study showed that the most commonly used indicators are sales growth from new products (88.7%); the share of patents implemented (74.3%); total R&D expenses per 1 thousand dollars of revenue in the current reporting period (89.2%). In summary, conclusions are drawn about the actual application of key performance indicators of innovation activity by companies.

Yu. Yu. Kostyukhin

The subject of the study is Russian metallurgical companies, which in the context of the transformation of the economy, taking into account the difficult geopolitical situation, need to move from a recovery strategy to a strategy of progressive growth. The latter, taking into account the factors that ensure the success of Russian companies in the metal market, should become a tool for effective management, as well as for predicting business risks. The article presents the riskdominating elements and macroeconomic indicators for building an effective strategy for the development of enterprises in the metallurgical industry, which will allow for a comprehensive assessment of the situation and making rational and effective management decisions to increase the competitiveness of companies, their adaptability to modern factors of the external environment, the growth of economic potential and capitalization. The peculiarity of the study is to identify the dominant factors of entrepreneurial risk and improve ways to increase the investment attractiveness of the company.

Shirin AYANI, Mahsa MIRZAEI, Nasibeh ABBASI et al.

By growing the interest of health organizations in providing quality services and the need for quick access of physicians to quality health information, the implementation of ERP in the health industry has been considered. The purpose of this study is to investigate the effect of application and exploitation problems of the enterprise resource planning system in the health industry based on the extent of the countries' development. systematic review was reported according to PRISMA Guidelines. Four scientific databases of Scopus, PubMed, Web of Science and IEEE were searched with no time limitation using keywords related to ERP, health care and challenges and benefits. The selection of articles and data extraction were carried out by three researchers and disagreements were discussed with fourth researcher. A total of 1206 articles were retrieved, after removing duplicate articles and applying inclusion criteria, 24 articles remained for further review. The findings of this study show that the severity of ERP implementation problems was different in the three categories of the studied countries. The main problems of developing countries was found in the group of the cultural and technological problems, the semi-developed countries was in the group of the economic and technological problems, and the main problems of the developed countries was referred to the problems of the service quality received from the software. Although in many cases implementing and applying ERP fails, awareness about different challenges that countries are faced based on their development level and paying attention to the appropriate strategies to each challenge and its risk management leads to successful ERP implementation.

Iman Shahidi Kaviani, parvaneh paykanpoufard

Background and purpose: Population growth leads to the expansion of industries and improper management of industries leads to land pollution and irreparable damage to nature and living organisms. Therefore, studying the role of industrial centers in environmental pollution, including soil, is one of the most important measures in the field of pollution control. The aim of this study was to evaluate the rate of surface soil contamination with heavy metals exposed to the oil industry. Materials and methods: In this study, to measure the rate of heavy metals, a total of 15 soil samples were taken from 5 stations with 3 replications and from depth of 0-30 cm. Heavy element measurements were performed by induction coupled plasma spectroscopy. Results: Based on the results the average amount of Cadmium, Lead and Copper in the soil were 2.40 ± 1, 8.89 ± 5.91 and 55.83 ± 52.88 mg / kg respectively. Conclusion: The average values of Cadmium and Copper were higher and the average values of lead were lower than the global average. Also, due to the high degree of toxicity of Cadmium, the highest risk of soil contamination can be attributed to Cadmium. The measurement of pollution of each of the three elements and for five sampling stations showed that the soil of the oil region was more polluted than Cadmium than the other two elements and the oil industry was more effective in Cadmium pollution than other elements. By measurement and comparison, the rate of soil pollution indices in the sampled zones, it showed that, as expected, the soil of the processing areas and the well head zones had more severe pollution than other areas and adopted more strict environmental control measures are essential on these areas.

Hailin Wang, Lie Nie, Jing Li et al.

E. Krahmann

ABSTRACT Life in the European Union (EU) has never been as safe as it is today. Nevertheless, EU citizens express widespread anxiety about new risks, such as internal and external migration, transnational crime and terrorism, economic and fiscal uncertainty. One actor which has profited from this development is the security industry. Across Europe there are now nearly as many private security guards employed as public police forces. This article draws on the concept of ontological security to understand the discrepancy between safety and anxiety which underpins the expansion of private security services in Europe. It argues that Private Security Companies (PSCs) are involved in the construction and provision of ontological security through three mechanisms: risk identification, risk profiling and risk management. These mechanisms not only offer physical security, they also reduce existential anxieties by contributing to stable self-identities through personalised risk profiles, commodified lifestyle choices and security routines. Nevertheless, the effects are not only positive. In addition to individualisation and the responsibilization of European citizens for their own physical and ontological security these mechanisms increase societal reliance on commercial expert systems, while reinforcing the perceived failure of the EU as a political and collective security community.

Christian A. Rudolf, S. Spinler

Purpose Large-scale projects are the typical delivery model in the engineering and construction industry, with their very own characteristics. Even though well established, only 1 in 1,000 large-scale projects is successful (Flyvbjerg, 2011). A lack of effective supply chain risk management (SCRM) has repeatedly been identified as one of the main causes. While the SCRM body of knowledge seems increasingly well established, a lack of effective methods meeting the specific requirements of large-scale projects can be observed. Design/methodology/approach This paper presents a structured and prioritized view on the supply chain risk portfolio in this sector: first, the authors identified and categorized the key supply chain risks in the recent literature. Next, the authors surveyed large-scale project managers across multiple industries, mainly coming from the domains of supply chain management and project management. Finally, the authors provide a contextualized risk taxonomy for engineering, procurement and construction (EPC) projects. Findings The identified risk portfolio deviates from generic projects significantly and shows a very high inherent risk exposure of large-scale projects. In particular, behavioral risks are identified as crucial. Additionally, a bias to considerably underestimate risks at project beginning is found. Originality/value The contextualized SCRM taxonomy offers a systematic and structured view on the key supply chain risks in EPC large-scale projects. The identified risks are considerably different in their characteristics compared to generic projects or classical SCRM approaches. The authors thus provide a new perspective on SCRM in this specific setting and complement traditional risk and project risk management techniques.

Ji Zhonghui, Liu Xueqin

As one of the most vital pointers of the air quality, PM2.5 has a great influence on the sustainable development of the environment and economy in China. To evaluate the PM2.5 pollution risk (PPR), we considered the danger of the hazard (H), the exposure (E), and vulnerability (V) of the hazard-affected body from the modern disaster comprehensive risk perspective. We tried to introduce the three-dimensional Archimedean copula method to assess the PPR from the Eight Comprehensive Economic (ECE) zones to the whole region in China. The analysis results show that: (i) the PPR is monotone increasing with the H, E, and V; (ii) the H and V have more direct impact on the PPR than the E, and the three aspects need to be considered simultaneously in the risk assessment; (iii) although all regions can achieve the daily average concentration limit (H < 35) except the middle reaches of the Yangtze River, it is difficult for the half of the ECE zones to maintain good air quality (H < 15) since the high probability risk.

Karabegovic I., Karabegovic E., Mahmic M. et al.

In the world of global competition, customers have increasing demands that companies must meet in order to remain active in the global market. For this reason, it is necessary to use new technologies in the production processes, i.e. to implement Industry 4.0. In other words, we need to create a connected company through the digital transformation that enables production processes to discover new ways to increase productivity and improve overall business performance. Companies need to get involved and start a digital system, and from supplier to customer. It is a key to the hidden value that can contribute to the company’s productivity, compliance, profitability, as well as the quality of the finished product, and eventually the introduction of flexible industrial automation of production processes. The aforementioned technologies and Internet of Things connect the physical and virtual world with a purpose to better collect and analyze data, transforming them into information that reaches decision-makers. To do this, it is necessary to implement smart sensors that provide information at all times. The implementation of Industry 4.0 in production processes is unthinkable without smart sensors and provides the following: faster product development time, lower overall costs, improved use of production processes and their optimization, as well as company risk management. The paper will outline the motives for the implementation of smart sensors and applications of smart sensors in production processes.

Borkovskaya Victoria, Lyapuntsova Elena, Nogovitsyn Maxim

This study focuses on enhancing the effectiveness of investment in construction given the constant threat of new risks. We investigate the types and causes of risks of firms working in the construction industry, draw conclusions about the importance of risks associated with the investment attractiveness of projects, as well as responsibility for the environmental safety. Urbanization and changes in the surrounding area are the dominant trend in the development of modern society. The constant demand of business for commercial real estate, as well as the increase in population growth demands enhanced effectiveness in the construction market, where supply in construction market depends on the economic conditions. The relevance of the research topic is due to the fact that the increasing capital flows in the construction industry pose new challenges for forecasting, statistics and risk control, and a focus on the rates of return mechanisms to hedge against the uncertainty of the future. The aim of the study is a systematic integrated approach to solving problems to improve the efficiency and effectiveness of investments and their protection using a methodological systematic approach, which considers the components of an integrated investment process. We propose a model of risk hedging management, and focus on an analytical methodology of the most relevant risks in modern construction.

J. Hamm, Kristie Sullivan, A. Clippinger et al.

William G. Heninger