Smart Industry Research Articles

Machine Learning and IoT-Based Solutions in Industrial Applications for Smart Manufacturing: A Critical Review

The Internet of Things (IoT) has radically changed the industrial world, enabling the integration of numerous systems and devices into the industrial ecosystem. There are many areas of the manufacturing industry in which IoT has contributed, including plants’ remote monitoring and control, energy efficiency, more efficient resources management, and cost reduction, paving the way for smart manufacturing in the framework of Industry 4.0. This review article provides an up-to-date overview of IoT systems and machine learning (ML) algorithms applied to smart manufacturing (SM), analyzing four main application fields: security, predictive maintenance, process control, and additive manufacturing. In addition, the paper presents a descriptive and comparative overview of ML algorithms mainly used in smart manufacturing. Furthermore, for each discussed topic, a deep comparative analysis of the recent IoT solutions reported in the scientific literature is introduced, dwelling on the architectural aspects, sensing solutions, implemented data analysis strategies, communication tools, performance, and other characteristic parameters. This comparison highlights the strengths and weaknesses of each discussed solution. Finally, the presented work outlines the features and functionalities of future IoT-based systems for smart industry applications.

Blockchain and Digital Twins in Smart Industry 4.0: The Use Case of Supply Chain-A Review of Integration Techniques and Applications

The Fourth Industrial Revolution has transformed industries and supply chains by integrating advanced operations, tools, and logistics services. Despite these advancements, challenges persist, particularly in ensuring data dependability, security, and operational efficiency. Digital twins (DTs), which replicate real-world components and processes, have emerged as essential tools for enhancing predictive analytics, simulation, and product lifecycle management in Industry 4.0. However, traditional DT development relies on centralized systems, which are vulnerable to data tampering and security breaches, especially in the management of transaction logs and historical data. To address these challenges, this review provides a comprehensive analysis of the current state of integrating blockchain with DTs. Using a qualitative research methodology, including desk research, case studies, and interviews with industry experts, we analyze various blockchain-based DT applications across industries and specifically in supply chain management. The findings reveal that blockchain-enhanced DTs can significantly improve data integrity, traceability, and security, thus boosting operational efficiency and quality control in supply chains. Additionally, this study identifies key integration techniques and the role of blockchain in automating processes through smart contracts. This review provides insights into the practical implications of blockchain-based DTs, highlighting their potential to enhance the reliability and scalability of Industry 4.0 operations.

SIRT: A distinctive and smart invasion recognition tool (SIRT) for defending IoT integrated ICS from cyber-attacks

With the rise of smart industries, Industrial Control Systems (ICS) has to move from isolated settings to networked environments to meet the objectives of Industry 4.0. Because of the inherent interconnection of these services, systems of this type are more vulnerable to cybersecurity breaches. To protect ICSs from cyberattacks, intrusion detection systems equipped with Artificial Intelligence characteristics have been used to spot unusual system behavior. The main research problem focused on this work is to guarantee ICS security, a variety of security strategies and automated technologies have been established in past literary works. However, the main problems they face include a high proportion of incorrect predictions, longer execution times, more complex system designs, and decreased efficiency. Thus, developing and putting in place a Smart Invasion Recognition Tool (SIRT) to defend critical infrastructure systems against new cyberattacks is the main goal of this project. This system cleans and normalizes the supplied ICS data using a unique preprocessing technique called Variational Data Normalization (VDN). Furthermore, a novel hybrid technique called Frog Leap-based Ant Movement Optimization (FLAMO) is applied to choose the most important and necessary features from normalized industrial data. Furthermore, the methodology of Weighted Bi-directional Gated Recurrent Network (WeBi-GRN) is utilized to precisely distinguish between genuine and malicious samples from information collected by ICS. This work validates and evaluates the performance findings using many assessment indicators and a range of open-source ICS data. According to the study's findings, the proposed SIRT model accurately classifies the different types of assaults from the industrial data with 99 % accuracy.

Investigation of Innovative High-Response Piezoelectric Actuator Used as Smart Actuator–Sensor System

This paper presents an experimental analysis of a high-response piezoelectric actuator system for the modular design of hydraulic digital fluid control units. It focuses on determining static and dynamic characteristics, forming the basis for developing a smart Industry 4.0 component that incorporates both actuator and sensor function. The design process examines the main challenges, advantages, disadvantages, and working principles to define parameters that impact the actuator’s behaviour and performance. The new piezoelectric actuator system features three piezoelectric stack actuators in series, enabling simultaneous actuation and sensing by applying and measuring the electrical voltage at each piezo element. The experimental setup and test methodology are explained in detail, revealing that the new design, combined with an appropriate open-loop or closed-loop control method, offers superior actuator stroke control, high stroke resolution, and a high-dynamic step response. This paper proposes a concept of a smart piezo actuator system focused on I4.0 and an actuator administration shell, integrated with 5G and RFID technology, which will allow automatic plug-and-play functionality and efficient interconnection, communication, and data transfer between the hydraulic valve and the piezoelectric actuator system.

Retraction Note: Optoelectronic sensor fault detection based predictive maintenance smart industry 4.0 using machine learning techniques

Retraction Note: Optoelectronic sensor fault detection based predictive maintenance smart industry 4.0 using machine learning techniques

Intrusion Detection for Blockchain‐Based Internet of Things Using Gaussian Mixture–Fully Convolutional Variational Autoencoder Model

ABSTRACTThe Internet of Things (IoT) is an evolving paradigm that has dramatically transformed the traditional style of living into a smart lifestyle. IoT devices have recently attained great attention due to their wide range of applications in various sectors, such as healthcare, smart home devices, smart industries, smart cities, and so forth. However, security is still a challenging issue in the IoT environment. Because of the disparate nature of IoT devices, it is hard to detect the different kinds of attacks available in IoT. Various existing works aim to provide a reliable intrusion detection system (IDS) technique. But they failed to work because of several security issues. Thus, the proposed study presents a blockchain‐based deep learning model for IDS. Initially, the input data are preprocessed using min‐max normalization, converting the raw input data into improved quality. In order to detect the presented attacks in the provided dataset, the proposed work introduced Gaussian mixture–fully convolutional variational autoencoder (GM‐FCVAE) model. The implementation is performed in Python, and the performance of the proposed GM‐FCVAE model is analyzed by evaluating several metrics. The proposed GM‐FCVAE model is tested on three datasets and attained superior accuracy of 99.18%, 98.81%, and 98.4% with UNSW‐NB15, CICIDS 2019, and N_BaIoT datasets, respectively. The comparison reveals that the proposed GM‐FCVAE model obtained higher results than the other deep learning techniques. The outperformance shows the efficacy of the proposed study in identifying security attacks.

분석기반 안전보건관리체계 구축을 위한 건설업 스마트안전기술의 연구동향 고찰

This study aims to propose the research direction for establishing an analysis-based safety management system. As for a study method, it collected academic papers related to smart safety in the construction industry and analyzed their research trends. For the data analysis, 1,326 papers were collected from the Research Information Sharing Service (RISS) after the keywords “smart safety” were searched for and the filters 'full text provided' and 'KCI indexed' were applied. Duplicate papers were checked, and the scope was narrowed down to those papers containing the term “construction.” With the exclusion of the papers unrelated to the research topic, 48 papers were finally selected. Morphological analysis was conducted on the titles, keywords, and abstracts to extract nouns, followed by topic modeling and semantic network analysis. Topic modeling and semantic network analysis are techniques of text mining used for analyzing unstructured big data. Textom was used as the tool topic modeling and semantic network analysis. The study results are as follows: First, according to the word-frequency based keyword analysis, the top 10 keywords in academic papers on smart safety were 'Internet of Things', 'sensor', 'accident', 'bridge', 'prevention', 'environment', 'drone', 'equipment', 'law', and 'ICT'. Second, based on the keywords derived from the word frequency analysis, 10 topics were extracted through topic analysis (LDA topic modeling), and the occurrence ratio of keywords per topic was calculated. Third, the text relationships between topics were clustered by keyword through centrality analysis and CONCOR analysis, and their characteristics were categorized. Implications from this study are as follows: First, all keywords in the field of construction industry smart safety that have been researched so far were analyzed, and it was possible to understand research trends through the extraction of keywords covered in existing studies. Second, the keyword analysis revealed that existing research has been biased towards hardware-centric studies, such as IoT-centric sensor networking and the integration of construction machinery or devices. This indicates a relative lack of research based on software and databases. Third, even though it is crucial to establish a management system for short-term achievements focusing on sensing and monitoring, it is necessary to research and discuss the application of smart safety technologies to build a long-term, analysis-based safety and health management system. To build an analysis-based safety and health management system, the following suggestions are made: First, a safety and health management database should be established. It is necessary to build an integrated database to manage all data generated from systems operated in the field. Second, it should be possible to analyze the data accumulated in the integrated database. For the analysis, analytical tools and methodologies are needed. Third, the analyzed results should be reflected in the safety and health management system. This management system should be established to be reflected throughout the entire lifecycle (PDCA) of the field. It is considered that only when such a management system is built, it is possible to establish an analysis-based safety management system and apply it to accident prediction and prevention.

Smart Industrial Internet of Things Framework for Composites Manufacturing.

Composite materials are increasingly important in making high-performance products. However, contemporary composites manufacturing processes still encounter significant challenges that range from inherent material stochasticity to manufacturing process variabilities. This paper proposes a novel smart Industrial Internet of Things framework, which is also referred to as an Artificial Intelligence of Things (AIoT) framework for composites manufacturing. This framework improves production performance through real-time process monitoring and AI-based forecasting. It comprises three main components: (i) an array of temperature, heat flux, dielectric, and flow sensors for data acquisition from production machines and products being made, (ii) an IoT-based platform for instantaneous sensor data integration and visualisation, and (iii) an AI-based model for production process forecasting. Via these components, the framework performs real-time production process monitoring, visualisation, and prediction of future process states. This paper also presents a proof-of-concept implementation of the framework and a real-world composites manufacturing case study that showcases its benefits.

Utilizing improved YOLOv8 based on SPD-BRSA-AFPN for ultrasonic phased array non-destructive testing

Non-destructive testing (NDT) is a technique for inspecting materials and their defects without causing damage to the tested components. Phased array ultrasonic testing (PAUT) has emerged as a hot topic in industrial NDT applications. Currently, the collection of ultrasound data is mostly automated, while the analysis of the data is still predominantly carried out manually. Manual analysis of scan image defects is inefficient and prone to instability, prompting the need for computer-based solutions. Deep learning-based object detection methods have shown promise in addressing such challenges recently. This approach typically demands a substantial amount of high-resolution, well-annotated training data, which is challenging to obtain in NDT. Consequently, it becomes difficult to detect low-resolution images and defects with varying positional sizes. This work proposes improvements based on the state-of-the-art YOLOv8 algorithm to enhance the accuracy and efficiency of defect detection in phased-array ultrasonic testing. The space-to-depth convolution (SPD-Conv) is imported to replace strided convolution, mitigating information loss during convolution operations and improving detection performance on low-resolution images. Additionally, this paper constructs and incorporates the bi-level routing and spatial attention module (BRSA) into the backbone, generating multiscale feature maps with richer details. In the neck section, the original structure is replaced by the asymptotic feature pyramid network (AFPN) to reduce model parameters and computational complexity. After testing on public datasets, in comparison to YOLOv8 (the baseline), this algorithm achieves high-quality detection of flat bottom holes (FBH) and aluminium blocks on the simulated dataset. More importantly, for the challenging-to-detect defect side-drilled holes (SDH), it achieves F1 scores (weighted average of precision and recall) of 82.50% and intersection over union (IOU) of 65.96%, representing an improvement of 17.56% and 0.43%. On the experimental dataset, the F1 score and IOU for FBH reach 75.68% (an increase of 9.01%) and 83.79%, respectively. Simultaneously, the proposed algorithm demonstrates robust performance in the presence of external noise, while maintaining exceptionally high computational efficiency and inference speed. These experimental results validate the high detection performance of the proposed intelligent defect detection algorithm for ultrasonic images, which contributes to the advancement of the smart industry.

ChatGPT and similar generative artificial intelligence (AI) for smart industry: role, challenges, and opportunities for Industry 4.0, Industry 5.0, and Society 5.0

ChatGPT and similar generative artificial intelligence (AI) for smart industry: role, challenges, and opportunities for Industry 4.0, Industry 5.0, and Society 5.0

A machine learning and CFD modeling hybrid approach for predicting real-time heat transfer during cokemaking processes

As the development of green energy and smart industries progresses, concerted efforts are being directed towards the transformation of coking industries. Effective management of heat transfer during the coking process is essential to fortify energy consumption management and augment the quality of coke produced. However, the process during cokemaking is a complex thermochemical conversion process where even minor deviations in operating conditions can lead to instability. Additionally, due to the environmental and chemical conditions inside the coke oven, temperature measurements during operation are difficult. As the fields of Industry 4.0 continue to evolve, numerous industries have begun to leverage intelligent strategies to enhance advanced process management. The digital twin (DG) technique stands as a pivotal technology in the realm of industrial transformation, paving the way for a smarter future. However, given the inherent difficulty in real-time data collection for process operations, the challenge of achieving synchronization becomes increasingly prevalent in the development of DG for the smart coking process. As a result, predicting changes in coal temperature during the cokemaking process is crucial. Currently, computer technology allows for the simulation of complex chemical processes. While some numerical models describe the cokemaking process, these models are limited by the time-consuming characteristic and computing resources. To address this issue, this study proposed a Machine Learning (ML) and Computational Fluid Dynamics (CFD) hybrid model for real-time prediction of heat transfer during the cokemaking process. A CFD model was built based on an industrial-scale coke oven, with cokemaking process data generated through CFD simulation serving as input for the ML models. This study used a total of nine ML models, including ensemble and deep learning models. The hyperparameters of each model were optimized by Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and JAYA algorithms to identify the optimal model structure. Both simulation and empirical experimentation using a coke oven, coupled with industrial data verification, have substantiated the superiority of the proposed model. It simulates the heat transfer during the coking process with high accuracy and efficiency, thereby demonstrating its capacity to significantly reduce energy consumption within the coking industry.

A Review of Energy-Efficient Technologies and Decarbonating Solutions for Process Heat in the Food Industry

Heat is involved in many processes in the food industry: drying, dissolving, centrifugation, extraction, cleaning, washing, and cooling. Heat generation encompasses nearly all processes. This review first presents two representative case studies in order to identify which processes rely on the major energy consumption and greenhouse gas (GHG) emissions. Energy-saving and decarbonating potential solutions are explored through a thorough review of technologies employed in refrigeration, heat generation, waste heat recovery, and thermal energy storage. Information from industrial plants is collected to show their performance under real conditions. The replacement of high-GWP (global warming potential) refrigerants by natural fluids in the refrigeration sector acts to lower GHG emissions. Being the greatest consumers, the heat generation technologies are compared using the levelized cost of heat (LCOH). This analysis shows that absorption heat transformers and high-temperature heat pumps are the most interesting technologies from the economic and decarbonation points of view, while waste heat recovery technologies present the shortest payback periods. In all sectors, energy efficiency improvements on components, storage technologies, polygeneration systems, the concept of smart industry, and the penetration of renewable energy sources appear as valuable pathways.

Artificial intelligence enabled self-powered wireless sensing for smart industry

Traditional batteries or external supply powered wireless sensing system are needed to be improved for realizing the development of the smart industry with low-carbon, green and sustainable. This paper proposes and develops a self-powered wireless sensing system for smart industry (SPOT), utilizing a triboelectric nanogenerator (TENG) coupled with an artificial intelligence (AI) transformer model. The SPOT system includes the TENG-based self-powered flexible sensor (SWNG), the wireless aggregate node (WAN), the electromagnetic and TENG hybrid generator (ETCG), and the monitoring and management center with an AI model (MACA). The ETCG serves as a power source for the WAN. The SWNG acquires voltage signals from products on the conveyor belt in the smart industry, powered by the TENG, and transmits the sensor data wirelessly to the MACA via the WAN for processing. The MACA processes the data using the transformer AI model, which not only ensures self-sustainability and long-term stability but also enables intelligent recognition and monitoring of industrial products by their packaging materials, thereby providing precise status information and decision support for the smart industry. The transformer model’s deployment in the MACA has demonstrated robustness and a high classification success rate of up to 97.8 %, efficiently categorizing multiple targets. Additionally, the SWNG and WAN exhibit low power consumption of approximately 80 mW, successfully contributing to the realization of green, low-carbon objectives. The SPOT system significantly enhances the efficiency of product transportation and management within the smart industry and contributes to the advancement of a sustainable, low-carbon, and green smart industry, offering novel technological insights and pathways for future development.

Convergence property of Nesterov‐accelerated adaptive moment estimation with safety helmet detection and classification in smart industry application

We propose a technique for first‐order gradient‐based optimization of stochastic objective functions called Nesterov‐accelerated adaptive moment assessment, which makes use of dynamic evaluations of lower‐order moments. The adaptive moment assessment and the Nesterov acceleration gradient are combined. Consequently, it has perks, and this technique is convenient to use, numerically economical, memory‐light, and very well‐suited for challenges with massive amounts of information and characteristics. Additionally, we investigate the algorithm's convergence characteristics and propose a conservative constraint on the convergence rate. Finally, we employ this technique for the detection and classification of safety helmets.

Valuation of Internet of Energy (IoE) Platforms in Smart Cities: A Hybrid Multi-Criteria Decision Making Approach

A smart city is a place where people live using computing, sensing, and networking technology in a variety of ways, including smart buildings, smart homes, smart transportation, smart workplaces, smart offices, smart industries, and smart agriculture. These smart technologies need to connect so; we need to use the Internet of Things (IoT) that has applications in many areas of our smart lives. IoT has been used due to the growing usage of renewable energy sources and the challenges of network management. Internet of Energy (IoE) integrates the IoT and smart grid capabilities. The basic role of smart grids or IoE is an essential element in creating smart cities. In this work, we present some IoE platforms that represent the alternatives and their criteria. The proposed study treats the uncertainty of multi-criteria decision-making (MCDM) modified by single value triangular neutrosophic number (SVTrN) by choosing the optimal IoE platform based on the criteria importance through inter-criteria correlation (CRITIC) method and multi-attributive ideal-real comparative analysis (MAIRCA). The proposed model proved its effectiveness by applying it to an experimental case. The results show that the DCC (A3) platform is the best one.

SmAuto: A domain-specific-language for application development in smart environments

A common problem in the development of Internet-of-Things (IoT) and Cyber-Physical System (CPS) applications is the complexity of these domains, due to their hybrid and distributed nature in multiple layers (hardware, network, communication, application etc.). Apart from other issues, this inherent complexity often gives room for implementation errors, which can be in many cases fatal and drive the application and/or the system to undesired states. The current work aspires to alleviate this problem by introducing a low-code approach for building IoT and CPS applications. We argue that, through the proposed approach it is possible to lower development time and risk (errors/bug-related ones) and allow a wide range of end-users to build and monitor applications for state-of-the-art domains, such as smart home and smart industry. In this context, Model-Driven Engineering (MDE) approaches are explored and SmAuto, a Domain-specific Language (DSL) is proposed for creating and executing automation tasks for smart environments. Through SmAuto it is possible to handle the heterogeneity and complexity issues of the IoT and CPS domains, this way allowing end-users are non-technical application experts to build well-designed and properly functioning smart applications. The proposed DSL implements a Sense-Think-Act-Communicate model for smart environments and enables the creation, validation, and dynamic execution of composite automation models in physical, virtual and hybrid environments, while it also enables automated code generation of virtual entities for verification purposes. By using layered abstractions to automate the development process, end-users can concentrate on the real problem instead of dwelling into technical details, thus increasing their productivity. The results of the empirical evaluation and the comparison to existing approaches show that SmAuto can make application development more rigorous, improves productivity of end-users including non-experts, i.e. citizen developers and satisfies several functional and non-functional requirements of modern DSLs, such as tool support, modular deployment, reusability, availability and extensibility.

Adaptive TS-ANFIS neuro-fuzzy controller based single phase shunt active power filter to mitigate sensitive power quality issues in IoT devices

The emergence of Internet of Things (IoT) offers numerous functions, such as intelligent sensor integration, remote sensing, and high-speed data transmission, which have found widespread applications in the smart industry and commercial applications. The associative nonlinear effects of a variety of undesirable power quality concerns were resolved by using harmonic mitigation in nonlinear loads and high-performance converters were built on power electronics in conventional systems. Among other performance objectives, total harmonic (THD) distortion analysis and higher order harmonics mitigation is given main concern in smart electronics equipment. This paper proposes an approach to minimize higher-order harmonics due to nonlinear load disturbances in smart IoT devices using the Takagi–Sugeno (TS) Neuro Fuzzy (TS-ANFIS) supervised shunt APF. The proposed work elicits the novel adaptive harmonic mitigation technique in hybrid IoT embedded systems to protect from malfunctioning and to deal with the uncertainty of harmonic signal stimuli in sensitive sensors-based IoT systems. Hysteresis current control is used for the ignition of reference signal under uncertainty of harmonic stimuli. The single phase shunt APF is used to mitigate higher-order harmonics from supply mains, while the implementation of TS-ANFIS supervises the controller action to generate a trigger signal for adequate single phase APF gate excitation. The higher order harmonic current data set is used for error deviation for training neural networks and adaptive control. The estimated adoption of the neuron's empirical weight reduces the total THD to a significant reduction rate of 72.8 % to 0.78 %. The control mechanism is feasible for a range of smart IoT systems to adhere to the standard of 519 (IEEE).

Intelligent sensing technology inspired by cell electrophoresis: Dielectric polarization sensor for monitoring the electrical equipment status of IoT nodes

The rapid development of the Internet of Things (IoT) era has led to the emergence of various types of information sensing devices. The operation and maintenance of IoT devices require a large-scale use of sensors at the expense of high energy consumption. In this work, we develop a self-powered voltage monitoring sensor (VSM-S) based on the principle of dielectric polarization, which is inspired by the cell electrophoresis phenomenon. This sensor imitates the structure of biological cells and takes advantage of the easier polarization characteristics of liquid medium with high dielectric constant in an external electric field. It collects electric field energy to achieve self-powering function and can monitor the position and status of the electric field source in real-time through output signals. Experiments show that the designed VSM-S has the characteristics of fast response (10−9-10−15 s), high sensitivity (0.3 nA/V), and wide application range. Furthermore, its integration with monitoring systems enables applications in smart industries, smart homes, and smart grids. This work has considerable development prospects in the fields of wearable sensors and the IoTs.

Challenges and benefits for small and medium enterprises in the transformation to smart manufacturing: a systematic literature review and framework

PurposeSmart manufacturing is revolutionizing the manufacturing industry by shifting the focus from traditional manufacturing to a more intelligent, interconnected and responsive system. Despite being the backbone of the economy and despite the government’s efforts in supporting and encouraging the transformation to smart manufacturing, small and medium enterprises (SMEs) have been struggling to transform their operations. This study aims to identify the challenges for SMEs’ transformation and the benefits they can get from this transformation, following a systematic review of existing literature.Design/methodology/approachA systematic review of existing literature has been performed to identify the peer-reviewed journal articles that focus on smart manufacturing for SMEs. First, a comprehensive list of keywords relevant to the review questions are identified. Second, Scopus and Web of Science databases were then used to search for articles, applying filters for English language and peer-reviewed status. Third, after manually assessing abstracts for relevance, 175 articles are considered for further review and analysis.FindingsThe benefits and challenges of SMEs’ transformation to smart manufacturing are identified. The identified challenges are categorized using the Smart Industry Readiness Index (SIRI) framework. Further, to address the identified challenges and initiate the SME’s transition toward smart manufacturing, a framework has been proposed that shows how SMEs can start their transition with minimum investment and existing resources.Originality/valueSeveral studies have concentrated on understanding how smart manufacturing enhances sustainability, productivity and preventive maintenance. However, there is a lack of studies comprehensively analyzing the challenges for smart manufacturing adoption for SMEs. The originality of this study lies in identifying the challenges and benefits of smart manufacturing transformation and proposing a framework as a roadmap for SMEs' smart manufacturing adoption.

Analysis of economic and mathematical models of monetary stimulation for the development of smart industry

Abstract The article considers economic and mathematical models in the context of monetary stimulation of financial and economic development of the smart industry. The article analyses AD-AS, DSGE, VAR, CAPM, RBC, Phillips Curve and Cobb-Douglas models. The advantages and disadvantages of the models in the context of monetary stimulation of smart industry development are identified. The analysis has revealed that all the considered models have certain disadvantages, such as complexity of use and lack of clarity in interpreting the results, as well as limited consideration of factors that are important in monetary stimulation of the smart industry. The reviewed VAR model is limited in taking into account all factors affecting economic variables and is sensitive to specification, which leads to significant changes in the final results depending on the included variables and their specification. The CAPM model is based on assumptions about market efficiency that do not always correspond to reality, while the RBC model is characterised by the absence of instability and unrealistic assumptions about market behaviour. The Phillips Curve model shows instability when inflation and unemployment respond to economic shocks in different ways, which is unacceptable in the context of monetary stimulation for the development of the smart industry. The analysis identified the advantages and disadvantages of all models, which allowed us to objectively assess the actual conditions of the models. The analysis has shown that, given the above shortcomings and the specifics of the current economic environment, the Cobb-Douglas model is the most effective for analysing and forecasting the development of the smart industry in Ukraine. The other models considered may also be useful for stimulating the development of the smart industry, but they do not provide the same flexibility and ease of use as the Cobb-Douglas model. Thus, given the specifics of monetary policy, the Cobb-Douglas model seems to be the most appropriate tool for analysing and forecasting monetary stimulus for the development of the smart industry in Ukraine. It allows for a wide range of production factors and is simple to analyse and interpret the final results, making it the most suitable for addressing complex issues of monetary stimulation in the context of smart industry development. Keywords monetary stimulation, smart industry, development, modeling