Industrial Implementation Research Articles

IMPLEMENTATION OF INNOVATIVE INDUSTRY 4.0 TECHNOLOGIES FOR THE RECONSTRUCTION OF THE ENERGY SECTOR OF UKRAINE

Abstract. The article explores the implementation of innovative Industry 4.0 technologies in the energy sector of Ukraine. It investigates key issues in the energy sector, including the reduction of electricity production capacities due to destruction, capture, and damage to property, low levels of innovation activity in energy enterprises, high energy intensity of production, moral and physical aging of equipment, and a low influx of foreign direct investments into Ukraine's energy sphere. The majority of the analyzed period indicates a relatively low level of interest from foreign investors in the examined sector of the domestic economy. The study identifies the Industry 4.0 toolkit applicable for the restoration of damaged and modernization of outdated infrastructure, enhancing the efficiency of Ukraine's energy sector. It is noted that among the tools, special attention should be paid to monitoring and forecasting systems, the Internet of Things (IoT), big data, integrated digital platforms, smart financial technologies, smart meters, remote monitoring systems, and artificial intelligence. The author emphasizes the peculiarities of applying this toolkit to solve the diagnosed problems in the energy sector of Ukraine. It is noted that the use of Industry 4.0 tools, namely the Internet of Things and big data analytics, can significantly improve the monitoring and management of energy processes. The problem of low innovation activity of Ukrainian energy companies can be solved by creating integrated digital platforms and developing partnerships between companies to share innovations. The use of smart financial technologies can make Ukraine's energy sector more attractive to investors. The high level of electricity losses during generation and distribution can be addressed by introducing smart meters and remote monitoring systems.

Policy innovation and the sustainable quality management 4.0 framework for integrating sustainable services

Over the past twenty years, service organizations have adopted total quality management to enhance their service quality, significantly impacting business performance, customer satisfaction, and profitability. This study delves into policy development of sustainable quality management theory, benefits, and various service components, while reviewing its implementation in services industries and policy innovation. The concept of Sustainable Quality Management 4.0 (SQM 4.0) integrates sustainable management, traditional quality management, and Quality 4.0 principles to optimize resources, reduce environmental impacts, and enhance decision-making through Industry 4.0, IoT, AI, and big data analytics. The findings offer valuable framework and policy insights for managers and practitioners on quality management and service systems, providing an implementation framework for Sustainable Quality Management in the service sector. The paper outlines comprehensive elements and strategies for implementation as a SQM framework for attaining sustainable quality management in the services industry.

A novel FuseDecode Autoencoder for industrial visual inspection: Incremental anomaly detection improvement with gradual transition from unsupervised to mixed-supervision learning with reduced human effort

The industrial implementation of automated visual inspection leveraging deep learning is limited due to the labor-intensive labeling of datasets and the lack of datasets containing images of defects, which is especially the case in high-volume manufacturing with zero defect constraints. In this study, we present the FuseDecode Autoencoder (FuseDecode AE), a novel reconstruction-based anomaly detection model featuring incremental learning. Initially, the FuseDecode AE operates in an unsupervised manner on noisy data containing predominantly normal images and a small number of anomalous images. The predictions generated assist experts in distinguishing between normal and anomalous samples. Later, it adapts to weakly labeled datasets by retraining in a semi-supervised manner on normal data augmented with synthetic anomalies. As more real anomalous samples become available, the model further refines its capabilities through mixed-supervision learning on both normal and anomalous samples. Evaluation on a real industrial dataset of coating defects shows the effectiveness of the incremental learning approach. Furthermore, validation on the publicly accessible MVTec AD dataset demonstrates the FuseDecode AE's superiority over other state-of-the-art reconstruction-based models. These findings underscore its generalizability and effectiveness in automated visual inspection tasks, particularly in industrial settings.

Synergistic Alkaline Hydrogen Evolution Catalysis over MoC Triggered by Doping Single Ru Atoms

AbstractThe rational design of single atom‐based catalysts and precise elucidation of the synergistic interaction between the metal site and substrate are pivotal to identifying the real active sites and explicating the catalytic mechanisms at the atomic scale, thus contributing to the development of high‐performance catalysts for diverse industrial implementations. Herein, a Ru single‐atom doping strategy is developed to activate the MoC substrate with superior hydrogen evolution reaction (HER) activity in an alkaline medium. The atomically dispersed Ru sites are elaborately doped into MoC nanoparticles loaded on 3D N‐doped carbon nanoflowers (Ru‐SAs@MoC/NCFs hereafter). The experimental results and theoretical calculations manifest that the atomically isolated Ru dopants can effectively trigger the Mo sites with thermodynamically favorable water adsorption/dissociation energies and facilitate the OH− desorption on Ru sites and H adsorption on N sites, thus synergistically expediating the overall alkaline HER kinetics. As such, the well‐designed Ru‐SAs@MoC/NCFs demonstrate extraordinary HER activity with a low overpotential of 16 mV at 10 mA cm−2 in 1.0 m KOH electrolyte, outperforming Pt/C benchmark and a vast of molybdenum/ruthenium‐based HER catalysts reported to date. These findings disclose the alkaline HER mechanistic induced by the atomic doping modulation and suggest a design principle of high‐efficiency electrocatalysts via the atomic‐level manipulation leverage.

Enhancing Selenium Removal Using Pre-Treated Natural Clay: Experimental Investigation and Predictive Modeling

Abstract A heat treatment methodology was adopted as a pretreatment strategy, altering the porous structure of the clay to minimize leaching for selenium adsorption in an aqueous system. Rigorous experiments were carried out in batch mode to determine optimal parameters across various variables, including contact time, adsorbent dosages, selenium concentrations, pH, temperature, and stirring speed during selenium removal using natural clay. Investigating several kinetic and isotherm models revealed the best fitting for the pseudo-second-order and the Langmuir isotherm. Endothermic and spontaneous characteristics of the adsorption process were shown during thermodynamic analysis. In this study, a predictive model for the efficiency of selenium separation was established using Response Surface Methodology (RSM). Additionally, an Artificial Neural Network (ANN), a data-driven model, was employed for comparative analysis. The predictive model exhibited a high degree of agreement with experimental data, demonstrated by a low relative error of <0.10, a high regression coefficient of >0.97), and a substantial Willmott-d index of >0.95. Moreover, the efficacy of pre-activated clay in selenium removal was assessed, revealing the superior performance of ANN models over RSM models in forecasting the efficiency of the adsorption process. This research significantly advances an effective and sustainable material for selenium removal, providing valuable insights into predictive modeling techniques applicable to similar contexts to boost scale-up confidence during industrial implementation in affected regions.

Tomato Waste as a Sustainable Source of Antioxidants and Pectins: Processing, Pretreatment and Extraction Challenges

Tomato processing waste (TPW), a byproduct of the tomato processing industry, is generated in significant quantities globally, presenting a challenge for sustainable waste management. While traditionally used as animal feed or fertilizer, TPW is increasingly recognized for its potential as a valuable raw material due to its high content of bioactive compounds, such as carotenoids, polyphenols and pectin. These compounds have significant health benefits and are in growing demand in the pharmaceutical and cosmetic industries. Despite this potential, the broader industrial utilization of TPW remains limited. This review explores the influence of various processing, pretreatment and extraction methods on the concentration and stability of the bioactive compounds found in TPW. By analyzing the effects of these methodologies, we provide insights into optimizing processes for maximum recovery and sustainable utilization of TPW. Additionally, we address the major challenges in scaling up these processes for industrial application, including the assessment of their ecological footprint through life cycle analysis (LCA). This comprehensive approach aims to bridge the gap between scientific research and industrial implementation, facilitating the valorization of TPW in line with circular economy principles.

A comprehensive framework for examining the influence of tower crane safe operations on sustainable practices in modular integrated construction

PurposeThis study aims to explore the tower crane safety factors (TCSFs) that influence tower crane safe operations (TCSOs) in modular integrated construction (MiC). It evaluates how the adoption of these factors contributes to achieving TCSOs and promoting sustainable practices (SPs) within MiC.Design/methodology/approachTo achieve this aim, the study employed a systematic search to ensure a comprehensive collection of variables. Additionally, it conducted a questionnaire survey involving professionals and utilized a brainstorming technique to categorize the different variables. Finally, partial least squares structural equation modeling (PLS-SEM) was employed to test the relationship between TCSOs and SPs.FindingsThe results of measurement models indicated strong convergent and discriminant validity, with each observed variable correlating well with its latent variable. Moreover, a significant positive correlation between TCSOs and SPs was evidenced by a path coefficient (β = 0.755) and a p-value of <0.05. Lastly, the structural model revealed that the independent variables strongly influence the dependent variable (i.e. SPs) by 57%, underscoring safety's pivotal role in advancing sustainability within MiC projects. These findings provide empirical evidence that improving tower crane safety can directly enhance sustainable practices, offering a dual benefit of increased safety and sustainability for the construction sector.Originality/valueThis study makes a unique and previously undiscovered contribution to the field by identifying the TCSFs in MiC and employing a novel approach by utilizing PLS-SEM to create a unique mathematical model. It offers valuable insights into the relationship between TCSFs, TCSOs and SPs, thus contributing to methodological advancements within Safety Science and providing a foundation for future research and practical implementation in the construction industry.

Determination of steady-state for continuous powder mixing: Analysis of mixture properties and signal processing

Continuous powder mixing offers many advantages over batch processes, but its industrial implementation requires control of transient phases due to startup or variations in operating conditions. Detecting the steady-state by observing the mixture properties, obtained through in-line analysis of the mixture composition, is challenging due to the significant fluctuations in these properties. However, there is no quantitative method for identifying the steady-state for continuous powder mixing. We therefore propose in this work a methodology for steady-state detection by comparing several methods of mixture property analysis and signal processing. We show that conventional methods of mixture property analysis cannot reliably and accurately detect steady-state. We conclude that the best methodology is to perform signal processing on the content of the key component in the mixture. To do this, the signal of the content of the key component is first smoothed by the Savitzky-Golay filter. Then, the standard deviation of the filtered signal is compared with a tuning parameter to detect the beginning of steady-state. This method could be used to determine the mixing conditions leading to the smallest startup time, hence avoiding costly waste.

Nanofluid Minimum Quantity Lubrication (NMQL): Overview of Nanoparticle Toxicity and Safer-Design Guidelines

Minimum Quantity Lubrication (MQL) has received much attention from the research community as a potential lubricating system to reduce environmental hazards and health issues that can be commonly found in flood cooling/lubricating systems based on metalworking fluids. The addition of nanoparticles in MQL systems (NMQL) has led to improved machining performance, increasing the cooling capability and reducing friction and tool wear, and some researchers have proved the applicability of this type of system for difficult-to-cut materials. However, the mist generated by MQL systems due to both the MQL system itself and the machining operation may pose an additional hazard to operators which is being overlooked by the research community. These hazards become more severe when using nanoparticles, but unfortunately very few works have paid attention to nanoparticle toxicity as applied in MQL systems, and this issue should be clearly understood before encouraging its implementation in industry. Furthermore, current legislation does not help since regulation of permissible exposure limits when dealing with nanoparticles is still ongoing in most cases. In this work, the toxicity of nanoparticles applied in MQL systems is analyzed, and recent research on studies of nanoparticle toxicity both in vitro and in vivo is presented. A relative comparison of toxicity is provided for those nanoparticles that have been reported in the literature as potential additives for MQL. The review is focused on analyzing the main factors of toxicity of nanoparticles which are identified as size, shape, surface properties, agglomeration and solubility. This review presents guidelines for safer nanolubricant formulations, guiding practitioners towards proper NMQL implementations in industry. Furthermore, current occupational exposure limits and recommendations are provided for all the nanoparticles potentially used in MQL systems, which is of interest in terms of work safety.

2D layered halide perovskite for field-effect transistors

Field-effect transistors are crucial components for modern electronics, generating significant research and profitable interest. Metal halide perovskites have recently emerged as a pioneering active material in solar cells, generating interest in their potential use in other electronic and (opto)electronic devices, including field-effect transistors and phototransistors. However, before they can be commercialized, they still face significant challenges owing to their immanent instabilities with respect to heat, moisture, and light. In contrast, due to their exceptional environmental stability, the newly emerging two-dimensional Ruddlesden–Popper type perovskites have garnered significant recognition. The current state of the field is covered in this review article, as are the problems, and a perspective for the scenarios of perovskite field-effect transistors. The effects of temperature, light, and measurement conditions are taken into account, as well as the physics of the device and the fundamental mechanisms that drive these devices, such as ion migration and ionic defects. Subsequently, the performance of perovskite transistors and phototransistors described so far is analyzed and critically evaluated. Finally, the major roadblocks to perovskite transistor advancement are identified and explored. The lessons learned from other perovskite optoelectronic devices are investigated in order to address these obstacles and bring these devices closer to industrial implementation.

Balancing economic efficiency and national security: Industrial policy at the nexus of geopolitics and globalisation

This article examines the resurgence of industrial policy in the EU, with a particular focus on its evolving rationale in the context of geopolitical fragmentation and economic security concerns. It explores how recent global events, including the Covid-19 pandemic and Russia’s invasion of Ukraine, have intensified debates about government intervention in the economy and led to a renewed focus on strategic autonomy. The paper analyses the key EU industrial policy initiatives and their implications, particularly in sectors such as energy, defence and critical technologies. Special attention is given to the challenges and opportunities presented by Ukraine’s reconstruction, including potential adaptations to EU state aid rules. The article concludes by proposing principles for effective industrial policy design and implementation, emphasising the importance of maintaining single-market integrity, ensuring transparency and accountability, and balancing interventions with sound macroeconomic policies.

A maturity assessment methodology for ISO 13485 implementation in the medical devices industry

PurposeThis study aims to develop a novel diagnostic methodology for implementing ISO13485:2016 and test its applicability to improve quality management systems (QMS) in the medical devices industry context.Design/methodology/approachFirst, a literature review on the topic was conducted. Second, insights gained from the literature and expert interviews were employed to develop the new maturity assessment methodology. Subsequently, the methodology was tested on a medical device manufacturer. Next, based on the evaluation of the intervention, actions were recommended to improve the QMS.FindingsResearch findings have developed a maturity assessment methodology comprising 52 certifiable requirements structured into four macro-requirements derived from ISO 13485:2016. Findings show that the methodology is valuable for aiding QMS implementation, and the diagnosed maturity levels corresponded with the company’s empirical perceptions of the requirement’s maturity.Practical implicationsEmpirical evidence validates the significance and practical utility of the proposed methodology, as evidenced by the company’s attainment of FDA (US Food and Drug Administration) approval after the intervention. Findings suggest that the methodology could be replicated within the medical products industry or adapted to assess other QMS, leveraging the organizational alignment with the international regulations of the sector and the ISO 9000 requirements.Originality/valueThe developed methodology fills existing gaps in both literature and practice within the medical devices industry, providing a valuable contribution by addressing the limited research on diagnostic methodologies designed for ISO 13485:2016 implementation. The article assists medical device enterprises in addressing QMS maturity levels as a metric for evaluating QMS requirements, which is an underexplored avenue in existing QMS evaluation approaches.

Разработка и внедрение многопоточного сервиса распознавания речи с помощью связки C#, C++ и Python

<p>This article discusses specificity of multithread industrial systems implementation using Python and its computation libraries. Article contains description of theory, including working principle of the Python’s Global Interpreter Locker (GIL), dependency management, multiprocessing library. The practical part of the article devoted to speech recognition service implementation. The implementation uses interprocess communication through shared memory based on "boost.interprocess" library. The result of integration architecture described by the article is reducing of CPU loading, in that case.</p>

Integrating Kubernetes Autoscaling for Cost Efficiency in Cloud Services

Kubernetes Autoscaling Mechanism for Integration into Cloud Services to Achieve Cost Efficiency Organizations have turned towards containerized applications and microservices architecture. Optimizing and using resources appropriately as per the expected operational cost becomes the need of the hour. There are several autoscaling mechanisms within Kubernetes, that include Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and Cluster Autoscaler, working towards cost optimization. We study predictive scaling algorithms, multi-dimensional autoscaling strategies, and machine learning-based approaches for resource allocation. Among the new challenges of implementing the solution are the methodologies followed in evaluating the research, which also involves complex advanced optimization techniques: from integrating serverless, towards multicloud autoscaling. Our findings will give an understanding of the status quo of Kubernetes autoscaling towards cost efficiency and recommendations for future research and industrial implementation.

Optimization of reed-based polyol production driven by response surface methodology and machine learning: Process modeling and hydroxyl value prediction

In pursuit of sustainable development goals, the transformation of biomass resources into high-value chemicals has become a focal point within the academic community. Despite being a biomass resource, reed (Phragmites australis) has not yet fully realized its potential in the production of bio-based polyols. This study utilized Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) enhanced by a Genetic Algorithm (GA) to model and optimize the liquefaction process of reeds. Furthermore, this study employed a pre-existing predictive model to estimate the hydroxyl value of reed-derived liquefaction products, thereby aiming to curtail expenses during the biomass chemical development phase. The results demonstrated that both the RSM and GA-ANN models accurately predict bio-polyol yield, with the RSM model outperforming the ANN model. Based on optimal conditions predicted by the RSM model, the best experimental process parameters were established: raw material particle size 2–12 mesh, solid-liquid ratio 5:1, glycerol content 32.5 %, catalyst content 3.6 %, temperature 169 °C, and reaction time 58 minutes. Under these conditions, the polyol yield reached 89.145 %, with a relative deviation of 2.366 %, and the bio-polyol exhibited a viscosity of 0.51 Pa·s. The predicted hydroxyl value for the bio-based polyol was found to be 399.19 mg KOH/g. The liquefied product is rich in hydroxyl groups, indicating its potential application value in preparing bio-based polymer materials. This study introduces innovative approaches to the economical production of bio-based polyols at the laboratory scale, which may pave the way for their broader industrial implementation.

Innovation and competitiveness in the industry 4.0 era: the SYNCHRO framework for digital servitization

PurposeThis study aims to develop a theoretical and practical framework to guide manufacturing companies in their transition towards digital servitization (DS), identifying relevant elements in implementing a digital servitization (DS) strategy.Design/methodology/approachWe use a mixed-methods approach combining qualitative interviews and quantitative surveys. Data were collected from 98 manufacturing companies in Spain to understand their DS strategies and outcomes.FindingsThe SYNCHRO framework, encompassing seven components: Stakeholder environment, Yield through co-creation, New capabilities, Connectivity through Industry 4.0, Handling challenges, Restructuring internally and Organizational adaptation, is presented. The results indicate that companies can advance towards DS autonomously, without necessarily relying on co-creation with customers. However, the implementation of Industry 4.0 technologies and internal adaptation are critical for success.Research limitations/implicationsFuture research should expand the sample size and explore the implementation of the SYNCHRO framework in different industrial and geographical contexts to validate its effectiveness.Practical implicationsThe framework provides a structured approach for companies to integrate digital technologies and reconfigure their business models, ensuring improved competitiveness and adaptability in the Industry 4.0 landscape.Social implicationsBy promoting sustainable business practices and enhancing competitiveness, the SYNCHRO framework can contribute to economic growth and stability within manufacturing sectors.Originality/valueThe SYNCHRO framework offers a comprehensive guide for DS, highlighting the importance of internal innovation and organizational flexibility. This framework contributes to the existing literature and provides a tool for companies to enhance business competitiveness and sustainability.

General Design of Aligned-Channel Porous Carbon Electrodes for Efficient High-Current-Density Gas-Evolving Electrocatalysis.

Gas-evolving reactions (GERs) are important in many electrochemical energy conversion technologies and chemical industries. The operation of GERs at high current densities is critical for their industrial implementation but remains challenging as it poses stringent requirements on the electrodes in terms of reaction kinetics, mass transfer, and electron transport. Here the general and rational design of self-standing carbon electrodes with vertically aligned porous channels, appropriate pore size distribution, and high surface area as supports for loading a variety of catalytic species by facile electrodeposition are reported. These electrodes simultaneously possess high intrinsic activity, large numbers of active sites, and efficient transport highways for ions, gases, and electrons, resulting in significant performance improvements at high current densities in diverse GERs such as urea oxidation, hydrogen evolution, and oxygen evolution reactions, as well as overall urea/water electrolyzers. As an example, the carbon electrode decorated with Ni(OH)2 demonstrates a record-high current density of 1000mAcm-2 at 1.360V versus the reversible hydrogen electrode, largely outperforming the conventional nickel foam-based counterpart and the state-of-the-art electrodes.

A comparative study of the impact of LSSI4.0 implementation on the financial performance of Chinese manufacturing firms: an empirical analysis

Purpose This paper aims to investigate how implementing lean six sigma and Industry 4.0 (LSSI4.0) can influence a company’s financial performance and discusses the current trend involving LSSI4.0 in China. Design/methodology/approach For statistical analysis, financial data was sourced from the China Stock Market and Accounting Research database. Keywords used to assess the implementation status of LSSI4.0 were extracted from the 2007 to 2020 annual reports of A-share manufacturing companies. Regression analysis was applied to the quantitative analyses of 5,041 observational data points from 945A-share manufacturing companies in China. Findings LSSI4.0 implementation in the manufacturing industry boosts the firms’ financial performance. However, the former outperforms the latter in terms of long-term advantages. Meanwhile, incorporating lean six sigma (LSS) into Industry 4.0 (I4.0) can lead to long-term improved financial performance compared to solely implementing the I4.0. Research limitations/implications The findings possess limited international representativeness because all empirical data were derived from Chinese large manufacturing companies. In addition to return on assets and return on equity, financial performance can also be measured using other financial metrics, such as return on investment. In this study, only listed manufacturing companies were considered as research samples. Practical implications Top management must acknowledge the positive impact of LSSI4.0 on financial performance and prioritize implementing I4.0 based on LSS implementation. Originality/value Empirical results concerning the effectiveness of LSS implementation in enhancing financial performance are inconclusive, particularly in China. In addition, most studies collected data through surveys and interviews, so the representativeness of their outcomes is limited. Overall, this study evaluated the impact of LSSI4.0 implementation with large sample size.

Preface

These proceedings contain the peer-reviewed full papers presented at the joint conference ICMASS (the 6th International Conference on Maritime Autonomous Surface Ships) and MTEC (International Maritime Port Technology and Development Conference), held October 29th – 30th, 2024, in Trondheim, Norway. MTEC and ICMASS bring together academia and industry from around the world showcasing cutting-edge research and development within maritime technology, autonomous ships and port operations. https://www.ntnu.edu/mtec-icmass2024/home Trondheim, Norway’s hub for technological innovation, has utilized its rich maritime heritage to position itself at the forefront of maritime innovation. With the world’s first test area for autonomous ships, its strong position within academia and research, Trondheim has gained a leading position in the field of maritime innovation through collaboration between maritime stakeholders. This year’s conference topics include: • Green shipping and ports • Smart and autonomous ship and port operations • Maritime safety • Maritime digitalization, connectivity and cybersecurity The theme of this year’s conference, featuring contributions covering topics such as AI & autonomy, cybersecurity, onboard control, remote & shore control, smart ships as part of smart logistics, green shipping, safety evaluations, collision avoidance, regulatory & legal frameworks, as well as discussions on liability, etc. The papers, published in these proceedings contain theoretical contributions as well as reports on practical implementations in field labs and industry, and the conference will also have oral only presentation. Totally there will be more than 90 presentations, where more than 60 papers are invited. A conference such as this can only succeed as a team effort, and, as such, we thank the International Program Committee and the Reviewers for their excellent work in reviewing the papers as well as their invaluable input and advice. On behalf of the organizing committee, List of MTEC/ICMASS-24 Organizing Committee, International Programme and Management Committee and International Reviewers are available in this Pdf.

Training of Trainers 4.0 Industri Kecil Menengah Furnitur Untuk Mendorong Transformasi Digital Dengan Teknologi Internet of Things

The furniture industry in Central Java still faces various challenges, especially in adopting advanced technologies such as the Internet of Things and Industry 4.0. Many furniture SMEs still rely on manual or semi-manual production methods, which result in suboptimal product quality and production efficiency, making it difficult to compete in the global market. To address these challenges, the Community Service Program carried out by the Furniture Industry Business Management Study Program at Polytechnic of Furniture Industry and Wood Processing actively played a role through the Training of Trainers activity. This program aims to provide intensive training on the implementation of IoT and Industry 4.0 technologies to furniture SMEs in Central Java, focusing on developing practical skills and fostering digital transformation. Training of Trainers training involved Furniture Industry Business Management lecturers who possess specialized expertise in Industry 4.0 technologies. The training covered both theoretical and practical aspects, allowing participants to not only understand the concepts but also implement them in their work environments. Evaluation results showed a significant increase in the participants' understanding and skills in applying Industry 4.0 and Internet of Things technologies. A tangible example of the success of this training is PT. AST Indonesia, which successfully implemented Industry 4.0 and Internet of Things technologies in its production processes after participating in the Training of Trainers program. This success is concrete evidence that the training provided by Furniture Industry Business Management can contribute significantly to enhancing the competitiveness of furniture SMEs in the digital era, in line with the vision of "Making Indonesia 4.0.