Hasil untuk "Manufacturing industries"

George R. G. Clarke

Many developing country governments would like to attract investment and create jobs in manufacturing and high-tech industries. Heavy and unpredictable laws and regulations, frequent demands for bribes, high taxes, poor-quality roads, slow and inefficient ports, and unreliable power, however, deter private investors. Moreover, political opposition and fiscal constraints prevent governments from resolving the numerous issues. Rather than attempting to solve everything everywhere, many governments have tried to fix problems in only small regions. These special economic zones (SEZs) often have lower taxes, more liberal regulation, and better infrastructure. This paper asks whether firms located in African and South Asian SEZs report less regulation and corruption than other firms in the same countries. We find, on average, being located in an SEZ is associated with lower burdens due to corruption and regulation. Firms in the zones are less likely to report paying bribes than firms outside the zones and report spending less time dealing with inspections and regulations. However, this is not true in Africa; firms in African zones report that corruption and regulation are as troublesome as for similar firms outside the zones.

Zane Mebruer, Wan Shou

In-space manufacturing is essential for achieving long-term planetary colonization, particularly on Mars, where material transport from Earth is both costly and logistically restrictive. Traditional subtractive manufacturing methods are highly equipment-, energy-, and material-intensive, making additive manufacturing (AM) a more practical and sustainable alternative for extraterrestrial production. Among various AM technologies, selective laser melting (SLM) stands out due to its exceptional versatility, precision, and capability to produce dense metallic parts with complex geometries. However, conventional SLM processes rely heavily on inert argon environments to prevent oxidation and ensure high-quality part formation, conditions that are difficult to reproduce on Mars. This study investigates the feasibility of using carbon dioxide (CO2), which makes up over 95% of the Martian atmosphere, as a potential substitute for argon in SLM. Single-track and two-dimensional 316L stainless steel specimens were fabricated under argon, CO2, and ambient air environments with a wide range of laser parameters to evaluate the influence of atmospheric composition on surface morphology, microstructural cohesion, and oxidation behavior. The results reveal that no single parameter controls the overall part quality; rather, a balance of parameters is essential to maintain thermal equilibrium during fabrication. Although parts produced in CO2 exhibited slightly inferior surface finish, cohesion, and oxidation resistance compared to argon, they performed significantly better than those fabricated in ambient air. These findings suggest that CO2-assisted SLM could enable sustainable in situ manufacturing on Mars and may also serve as a cost-effective alternative shielding gas for terrestrial applications.

Kyung Lim Oh, Ju Chan Yuk, Suk Hee Park

Overhang structures are essential geometries in metal additive manufacturing for realizing complex shapes. However, achieving stable, support-free overhang structures requires precise control of process parameters, and securing shape fidelity becomes particularly challenging as overhang length increases due to thermal deformation. To address this challenge, this study proposed a Bayesian optimization framework for efficiently identifying optimal process parameters to fabricate high-difficulty overhang structures. An image-based scoring method was developed to quantitatively evaluate shape defects. Experimental data were collected by fabricating 3, 6, and 9 mm overhang structures with various process parameters. Based on collected data, Gaussian Process Regression (GPR) models were trained. A physics-informed soft penalty term based on energy density was incorporated to construct a surrogate model capable of making physically plausible predictions even in extrapolated regions. Using this model, Bayesian optimization was applied to overhang lengths of 12, 15, and 18 mm, for which no prior experimental data existed. Recommended parameters enabled stable, support-free fabrication of overhang structures. This study demonstrates that reliable optimization of process parameters for complex geometries can be achieved by combining minimal experimental data with physics-informed modeling, highlighting the framework’s potential extension to a wider range of geometries and processes.

Hasan Emin Gurler, Ahmet Kaya, Ayşe Nur Soysal Bilmiş et al.

ABSTRACT Purpose Stakeholder pressure is widely recognized as a key driver of green innovation. However, existing research often overlooks how this relationship may differ across various industry settings and national contexts. Understanding these contextual differences is essential for businesses aiming to implement effective and responsive environmental strategies. Methods: This study synthesizes findings from 23 independent investigations conducted across multiple industries and countries. The analysis aims to uncover patterns in how stakeholder pressure influences green innovation, with a particular focus on variations between sectors and regional settings. Findings The influence of stakeholder pressure on green innovation is generally positive but varies significantly by context. In manufacturing industries, the relationship is weaker, likely due to technical, operational, or financial constraints that hinder firms from fully meeting stakeholder expectations. The effect is also less pronounced in China, potentially due to country‐specific stakeholder norms, regulatory frameworks, or socio‐economic factors that shape corporate environmental responses. Conclusions Both industry characteristics and national contexts significantly shape the effectiveness of stakeholder‐driven green innovation. Firms need to consider these contextual nuances when developing environmental strategies. Tailoring approaches to fit specific industrial and regional conditions can improve alignment with stakeholder expectations and enhance the success of green initiatives.

Sainath K, Karuppasamy R, Prabagaran S

The functionally graded materials (FGMs) have been realised to be potential candidates when it comes to high-pressure projects and applications where thermal and mechanical stability is to be ensured in extreme environments. In the research, the drawback of the widely used stainless steel SS316L facing high-stress conditions in the thermal environment will be overcome by the innovation of two new FGMs composed of SS316L and Inconel 625, Ti6Al4V, and Inconel 718. The aim was to conduct the fabrication and testing of a multi-phase FGM with the help of advanced techniques of manufacturing namely additive manufacturing and powder metallurgy, with the strict control of layer thickness of 0.2 mm and contents of its materials (60% SS316L, 20% Inconel 625 or Ti6Al4V, and 20% Inconel 718). Tensile testing, yield testing, fatigue and creep behaviour were performed at temperatures of −20°C and +60°C. The findings indicated that the FGM containing SS316L, Inconel 625, and Inconel 718 proved to be superior to SS316L at every point where its tensile strength is 992 MPa and its yield strength is 602 MPa, also at a temperature of +60 C versus 460 MPa and 186 MPa tensile and yield strengths in SS316L. The advanced fatigue performance and creep resistance were also indicated because of the better qualities of the alloys Inconel. Such results are indicative of gradient composition and layer formation in augmenting thermal and mechanical capabilities. The research ends up with a conclusion that these FGMs can be considered as excellent prospects in terms of the aerospace and power generation industries where strength and thermal endurance are of essence to the next generation of the industry.

Pu Tingqian, Zulkafli Abdul Hadi

Background: In the contemporary business environment, corporate research and development (R&D) expenditure is pivotal for fostering technological innovation and advancing technological progress. While much research has focused on the influence of boards of directors on corporate innovation, the role of foreign directors in shaping corporate R&D expenditure, particularly in developing countries, remains underexplored. Purpose: The aim of this paper is to investigate the pivotal role of foreign directors in corporate R&D expenditure within Chinese listed manufacturing firms. It also provides micro-level evidence of the economic consequences of foreign directors, considering heterogeneity across property rights, industry, regional dimensions, and board positions. Study design/methodology/approach: This study utilizes the largest and most detailed dataset of Chinese listed manufacturing firms in the CSMAR database, offering comprehensive proxy variables. The sample encompasses 18,273 observations from 2008 to 2021. Multivariate regression models, employing static two-way fixed effects models with clustered robust standard errors and dynamic generalized method of moment (GMM) models, were established to investigate the relationship between foreign directors and corporate R&D expenditure. Sensitivity tests involve the substitution of dependent and core explanatory variables. Moreover, heterogeneity test and situational analysis are conducted. Findings/conclusions: The results confirmed a significant augmentation in corporate R&D expenditure attributable to foreign directors. Heterogeneity analysis reveals that the positive impact of foreign directors on R&D expenditure is more pronounced in private-owned enterprises, high-tech industries, and economically developed regions of China. Situational analysis further confirms that foreign independent directors are the main driving force behind this effect. Limitations/future research: This research is confined to a single-country and single-industry sample, without a comprehensive consideration of the individual traits of foreign directors. Future research avenues could involve cross-national comparisons and a more nuanced categorization of foreign directors.

Olajesu Olanrewaju, Isiaka Oluwole Oladele, Samson Oluwagbenga Adelani

Natural fiber-reinforced composites (NFRCs) have become vital in various engineering applications due to their exceptional properties and ease of manufacturing. Properties such as lightweight, sustainability, design flexibility, microstructure, durability, and advanced fabrication techniques have expanded their use across industries. Also, NFRCs are preferred because the extensive reliance on synthetic fibers presents significant challenges in recycling and waste management. Despite their excellent properties, NFRCs have three main challenges: fiber degradation, water degradation, and weak interfacial strength (incompatibility of fibers with the matrix). Consequently, research efforts have been directed at combating these challenges using different surface treatment techniques. However, research has been skewed towards experimental approaches for improving the interfacial strength in plant fiber polymer matrix composites (PMCs). Hence, there exists a dearth of information on the computational approaches for optimizing the interfacial properties of NFRCs. Hence, this review provides experimental and computational approaches (machine learning) for comprehensive optimizing strategies for different natural fibers (plant, animal, and microorganism) and matrices (polymers, metals, and ceramics). This review also highlights the importance of theoretical approaches and numerical modeling in analyzing and optimizing NFRCs. Finally, the review highlights recent advancements in NFRCs, their mechanical properties, potential applications, and future directions.

Jiange Lin, Weisheng Mao, Xuehan Lin

Industrial Internet Platforms (IIPs) provide critical value for supply chain management. The motivation for value creation and capture by supplier firms increasingly depends on acquiring and processing customer-oriented information, with geographical distance to customers being a key factor in assessing costs and benefits. This study investigates whether firms’ IIP adoption increases or decreases the geographical distance to their customers, utilizing supply chain data and digital transformation metrics from China’s A-share listed firms in the new energy vehicle industry. The study finds that an increased adoption level of IIPs by focal firms leads to a greater average geographical distance to their customers. This conclusion remains valid after addressing endogeneity concerns and performing several robustness checks. Moreover, the effect of IIP adoption on expanding the geographic scope of sales is significant when the focal firm is more specialized in the supply chain, more socially embedded, located in China’s eastern region, or acts as an upstream/downstream complement to complete vehicle manufacturers. No significant difference in the impact of IIP adoption is observed between private and state-owned enterprises. This study combines platform innovation and transaction cost theories, offering new insights and policy implications regarding the spatial impact of digital technology adoption in advanced manufacturing and related service industries.

Angelos Alexopoulos, Agorakis Bompotas, Nikitas Rigas Kalogeropoulos et al.

Modern manufacturing demands high flexibility and reconfigurability to adapt to dynamic production needs. Model-based Engineering (MBE) supports rapid production line design, but final reconfiguration requires simulations and validation. Digital Twins (DTs) streamline this process by enabling real-time monitoring, simulation, and reconfiguration. This paper presents a novel platform that automates DT generation and deployment using AutomationML-based factory plans. The platform closes the loop with a GAI-powered simulation scenario generator and automatic physical line reconfiguration, enhancing efficiency and adaptability in manufacturing.

Himabindu Thogaru, Saisubramaniam Gopalakrishnan, Zishan Ahmad et al.

Manufacturing planners face complex operational challenges that require seamless collaboration between human expertise and intelligent systems to achieve optimal performance in modern production environments. Traditional approaches to analyzing simulation-based manufacturing data often create barriers between human decision-makers and critical operational insights, limiting effective partnership in manufacturing planning. Our framework establishes a collaborative intelligence system integrating Knowledge Graphs and Large Language Model-based agents to bridge this gap, empowering manufacturing professionals through natural language interfaces for complex operational analysis. The system transforms simulation data into semantically rich representations, enabling planners to interact naturally with operational insights without specialized expertise. A collaborative LLM agent works alongside human decision-makers, employing iterative reasoning that mirrors human analytical thinking while generating precise queries for knowledge extraction and providing transparent validation. This partnership approach to manufacturing bottleneck identification, validated through operational scenarios, demonstrates enhanced performance while maintaining human oversight and decision authority. For operational inquiries, the system achieves near-perfect accuracy through natural language interaction. For investigative scenarios requiring collaborative analysis, we demonstrate the framework's effectiveness in supporting human experts to uncover interconnected operational issues that enhance understanding and decision-making. This work advances collaborative manufacturing by creating intuitive methods for actionable insights, reducing cognitive load while amplifying human analytical capabilities in evolving manufacturing ecosystems.

Duaa Abdullah

In this study, we first reviewed the traditional astrolabe design methods and identified potential sources of manufacturing error. We then proposed an analytical approach using computer assistance to develop designs for the astrolabe components. This approach marks a pioneering step toward designing and producing a physical astrolabe model aided by computer technology. Our goal was to revive this significant heritage instrument while leveraging modern techniques and software to produce astrolabe models free from traditional manufacturing inaccuracies.

Lei Li, Jiale Gong, Ziyang Li et al.

Conventional subtractive manufacturing inevitably involves material loss during geometric realization, while additive manufacturing still suffers from limitations in surface quality, process continuity, and productivity when fabricating complex geometries. To address these challenges, this paper proposes a volume-consistent kneading-based forming method for plastic materials, enabling continuous and controllable three-dimensional deformation under mass conservation. An integrated kneading-based manufacturing system is developed, in which geometry-aware kneading command generation, layer-wise kneading execution, and in-process point-cloud scanning are tightly coupled to form a closed-loop workflow of scanning, forming, and feedback compensation. Target geometries are analyzed through layer-wise point-cloud processing and classified into enveloping and non-enveloping types. Accordingly, an Envelope Shaping First strategy and a Similar Gradient Method are adopted to ensure stable material flow and continuous deformation. An RMSE-based compensation scheme is further introduced to correct systematic geometric deviations induced by elastic rebound and material redistribution. Experimental validation on five representative geometries demonstrates high geometric fidelity, with material utilization consistently exceeding 98%. The results indicate that kneading-based forming provides a promising alternative manufacturing paradigm for low-waste, customizable production.

Jannatul Bushra, Md Habibor Rahman, Mohammed Shafae et al.

Reverse engineering can be used to derive a 3D model of an existing physical part when such a model is not readily available. For parts that will be fabricated with subtractive and formative manufacturing processes, existing reverse engineering techniques can be readily applied, but parts produced with additive manufacturing can present new challenges due to the high level of process-induced distortions and unique part attributes. This paper introduces an integrated 3D scanning and process simulation data-driven framework to minimize distortions of reverse-engineered additively manufactured components. This framework employs iterative finite element simulations to predict geometric distortions to minimize errors between the predicted and measured geometrical deviations of the key dimensional characteristics of the part. The effectiveness of this approach is then demonstrated by reverse engineering two Inconel-718 components manufactured using laser powder bed fusion additive manufacturing. This paper presents a remanufacturing process that combines reverse engineering and additive manufacturing, leveraging geometric feature-based part compensation through process simulation. Our approach can generate both compensated STL and parametric CAD models, eliminating laborious experimentation during reverse engineering. We evaluate the merits of STL-based and CAD-based approaches by quantifying the errors induced at the different steps of the proposed approach and analyzing the influence of varying part geometries. Using the proposed CAD-based method, the average absolute percent error between simulation-predicted distorted dimensions and actual measured dimensions of the manufactured parts was 0.087%, with better accuracy than the STL-based method.

Jaehun Choi, Sajan Tamang, Heesung Park

The Glass Molding Process (GMP) produces large quantities of glass optical parts and provides the advantages of high molding accuracy, short production cycle, low cost, and little pollution. Developments in different sectors, such as cameras and telescopes, are prompting studies on the design of aspherical optical components. Modeling heat transfer and deformation at high temperatures are crucial aspects of studying glass because its properties are significantly influenced by temperature-induced phase changes. In this study, temperature changes and geometric deviations of lenses were studied with respect to heating, pressing, and cooling times and the heat capacity of the heater used. A 3D model was designed for the heating, pressing, and cooling steps, and heat transfer was subjected to numerical analysis considering the specific heat of glass and the temperature dependence of thermal conductivity. Lens molding temperature conditions were then analyzed with the heat capacity of the lens molding heating system. Lens molding conditions were derived by analyzing lens temperatures with respect to heating and cooling capacities at each process step.

Fuad Ameen, Mohammad J. Alsarraf, Tarad Abalkhail et al.

Jazan Industrial Economic City (JIEC) is located on the Red Sea coast in the province of Jazan, southwest of Saudi Arabia anchors diverse heavy and secondary industries in the energy, water desalination, petroleum, aluminum, copper, refineries, pharmaceuticals and food manufacturing fields. These various industries generate a large quantity of industrial wastewaters containing various toxicants. The present work represents ecologically beneficial alternatives for the advancement of environmental biotechnology, which could help mitigate the adverse impacts of environmental pollution resulting from petroleum refining effluents. The mycobiome (32 fungal strains) isolated from the industrial wastewater of the refinery sector in Jazan were belonged to five fungal genera including Fusarium, Verticillium, Purpureocillium, Clavispora and Scedosporium with a distribution percentage of 31.25, 21.88, 15.63, 12.50 and 18.75 %, respectively.These isolates showed multimetals tolerance and bioremoval efficiency against a large number of heavy metals (Fe2+, Ni2+, Cr6+, Zn2+, As3+, Cu2+, Cd2+, Pb2+, Ag+ and Hg2+) along with potent bioremediation activity toward crude oil and the polycyclic aromatic hydrocarbons. Interestingly, the mycobiome resistance patterns obtained against different classes of fungal antibiotics including azole (fluconazole, itraconazole, voriconazole, posaconazole, isavuconazole and ketoconazole), echinocandin (anidulafungin, caspofungin and micafungin) and polyene (amphotericin B) drugs proved the prevalence of antibiotic resistance among the mycobiome of refinery industry in Saudi Arabia is relatively low. The fungal isolate under isolation code JAZ-20 showed the highest bioremoval efficiency against heavy metals (90.8–100.0 %), crude oil (89.50 %), naphthalene (96.7 %), phenanthrene (92.52 %), fluoranthene (100.0 %), anthracene (90.34 %), pyrene (85.60 %) and chrysene (83.4 %). It showed the highest bioremoval capacity ranging from 85.72 % to 100.0 % against numerous pollutants found in a wide array of industrial effluents, including diclofenac, ibuprofen, carbamazepine, acetaminophen, sulfamethoxazole, bisphenol, bleomycin, vincristine, dicofol, methyl parathion, atrazine, diuron, dieldrin, chlorpyrifos, profenofos and phenanthrene. The isolate JAZ-20 was chosen for molecular typing, cytotoxicity assessment, analysis of volatile compounds and optimization investigations. Based on phenotypic, biochemical and phylogenetic analysis, strain JAZ-20 identified as Scedosporium apiospermum JAZ-20. This strain is newly discovered in industrial effluents in Saudi Arabia. Fungal strain JAZ-20 consistently produced various types of saturated and unsaturated fatty acids. the main fatty acids were C14:0 (1.95 %), iso-C14:0 (2.98 %), anteiso-C14:0 (2.13 %), iso-C15:0 (9.16 %), anteiso-C15:0 (11.75 %), C15:0 (7.42 %), C15:1 (2.37 %), anteiso-C16:0 (3.4 %), C16:0 (10.3 %), iso-C16:0 (9.5 %), C17:1 (1.36 %), anteiso-C17:1 (8.64 %), iso-C18:0 (11.0 %), C18:0 (3.63 %), anteiso-C19:0 (3.78 %), anteiso-C20:0 (2.0 %), iso-C21:0 (2.44 %), C23:0 (1.15 %), and C24:0 (2.17 %). These fatty acids serve as natural and eco-friendly antifungal agents, promoting fungal resistance and inhibiting the production of mycotoxins in the environment. Despite being an environmental isolate, its cytotoxicity was assessed against both normal and cancerous human cell lines. The IC50 values of JAZ-20 extract were 8.92, 10.41, 20.0, 16.5, and 40.0 μg/mL against WI38, MRC5, MCF10A, HEK293 and HDFs normal cells and 43.26, 33.75, and 40.0 μg/mL against liver (HepG2), breast (A549) and cervix (HeLa) cancers, respectively. Based on gas chromatography-mass spectrometry (GC-MS), analysis the extract of S. apiospermum JAZ-20 showed 47 known volatile compounds (VOCs) for varied and significant biological activities. Enhancing the bioremoval efficiency of heavy metals from actual refining wastewater involves optimizing process parameters. The parameters optimized were the contact time, the fungal biomass dosage, pH, temperature and agitation rate.

Ahmed Oluwatobi Adekunle

Research on the complex interplay among urbanization and economic growth frequently examines a number of factors, including industrial sectors including manufacturing, high-tech industries, and productive service industries missing aggregate analysis. Sequel to this, this study analyzed how urbanization impacts economic growth in Nigeria. Essentially, the study uses unit root testing, vector error correction model (VECM) and causality method to analyze the data span over 1986-2021. Given that the likelihood is negligible at 0.39%, the null hypothesis—that there is no causal association between jobs and economic development, directed from jobs to economic growth—cannot be rejected at 5%. On the other hand, at 5%, the null hypothesis—that there is no causal relationship between CGDP and employment—can be rejected, with a probability of 0.02%, which is highly significant. Therefore, for the time span covered by the analysis, there is a unidirectional causal relationship in Nigeria between jobs and economic growth, with jobs driving economic expansion. indicating that more jobs can be created in Nigeria as a result of increased economic growth. The use of the empirical framework and data restrictions are two of the study's shortcomings. To further explore this empirical issue, future efforts should concentrate more on the study of panel and quarterly data.

Paula Fraga-Lamas, Tiago M. Fernandez-Carames

In the last century the automotive industry has arguably transformed society, being one of the most complex, sophisticated and technologically advanced industries, with innovations ranging from hybrid, electric and self-driving smart cars to the development of IoT-connected cars. Due to its complexity, it requires the involvement of many Industry 4.0 technologies, like robotics, advanced manufacturing systems, cyber-physical systems or augmented reality. One of the latest technologies that can benefit the automotive industry is blockchain, which can enhance its data security, privacy, anonymity, traceability, accountability, integrity, robustness, transparency, trustworthiness and authentication, as well as provide long-term sustainability and a higher operational efficiency to the whole industry. This review analyzes the great potential of applying blockchain technologies to the automotive industry emphasizing its cybersecurity features. Thus, the applicability of blockchain is evaluated after examining the state-of-the-art and devising the main stakeholders' current challenges. Furthermore, the article describes the most relevant use cases, since the broad adoption of blockchain unlocks a wide area of short- and medium-term promising automotive applications that can create new business models and even disrupt the car-sharing economy as we know it. Finally, after a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, some recommendations are enumerated with the aim of guiding researchers and companies in future cyber-resilient automotive industry developments.

Jinbo Wen, Jiawen Kang, Dusit Niyato et al.

Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs.

Jonghan Lim, Sujani Patel, Alex Evans et al.

The development of human-robot collaboration has the ability to improve manufacturing system performance by leveraging the unique strengths of both humans and robots. On the shop floor, human operators contribute with their adaptability and flexibility in dynamic situations, while robots provide precision and the ability to perform repetitive tasks. However, the communication gap between human operators and robots limits the collaboration and coordination of human-robot teams in manufacturing systems. Our research presents a human-robot collaborative assembly framework that utilizes a large language model for enhancing communication in manufacturing environments. The framework facilitates human-robot communication by integrating voice commands through natural language for task management. A case study for an assembly task demonstrates the framework's ability to process natural language inputs and address real-time assembly challenges, emphasizing adaptability to language variation and efficiency in error resolution. The results suggest that large language models have the potential to improve human-robot interaction for collaborative manufacturing assembly applications.

Juahn Oh, Chaeyeong Park, Dagyeong Ahn et al.

Products introduced as vegan are recognized by consumers as ethical consumption that protects animals and the environment, so they are becoming a trend in recent marketing strategies. However, consumers are confused by the claim that veganism consumption is not environmentally friendly, contrary to consumers' perception of animal protection and environmental protection. Vegan leather, which appeared as an alternative to natural leather, emits harmful substances during the manufacturing and disposal process, is less durable than natural leather, and has a shorter lifespan, resulting in a problem of fast fashion. Substitute meat is emerging as a food of the future due to environmental problems caused by raising livestock. However, considerable carbon is generated during the production of substitute meat, and there is a problem in verifying the safety of fiber materials such as various additives used during the production process. In the case of vegan cosmetics, they use only eco-friendly ingredients derived from nature instead of animal ingredients and do not test on animals, so the impact on the environment is minimal. Overall, bigonomics products generate relatively less environmental pollutants and cause less environmental pollution, but there are still limitations to be overcome. It is thought that the limitations of bigonomics can be overcome through social collective practice such as education and investment in parallel with individual practice in daily life. Furthermore, I think that if there is a correct understanding of vegannomics and communication about its value, the vegan industry will be able to take its place as a major industry in society. In this review, the current status of bigonomics in the food, cosmetics, and fashion industries, which are being developed by consumers' ethical consumption, is reviewed and prospects are presented. This review will help set the direction in each industry pursuing vigenomics.