Quantifying the Impact of Industry 4.0 Technologies on Leather Processing Efficiency: A Meta-Analysis

meta-analysis aims to quantitatively synthesize the existing evidence on the impact of Industry 4.0 technologies on leather processing efficiency. A systematic search was conducted across multiple databases, including Scopus, Web of Science

in efficiency, productivity, and sustainability.
Automation and robotics can streamline various stages of production, reducing reliance on manual labor, minimizing errors, and enhancing product consistency.AI and big data analytics can enable realtime monitoring, predictive maintenance, and datadriven decision-making, leading to process optimization and resource efficiency.[3] The potential benefits of Industry 4.0 technologies for the leather industry are substantial.[6] Nevertheless, the potential benefits of Industry 4.0 technologies outweigh the challenges.The leather industry is at a crossroads, and embracing these technologies is crucial for manufacturers to remain competitive and meet the growing demand for sustainable and efficient production practices.
Numerous studies have investigated the impact of individual Industry 4.0 technologies on various aspects of leather processing, including efficiency, productivity, quality, and sustainability.However, a comprehensive quantitative synthesis of the evidence on the impact of these technologies on leather processing efficiency is lacking.This meta-analysis aims to fill this gap by systematically reviewing and synthesizing the existing literature to quantify the overall effect of Industry 4.0 technologies on leather processing efficiency.

Methods
A comprehensive and systematic search strategy was employed to identify all relevant studies that investigated the impact of Industry 4.0 technologies on leather processing efficiency.This strategy aimed to minimize the risk of bias and ensure the inclusion of a wide range of studies, regardless of their publication status or outcomes.The following electronic databases were systematically searched: Scopus is the largest   The meta-analysis was repeated excluding studies with a low methodological quality rating to assess the impact of study quality on the overall effect size.The influence of individual studies on the overall effect size was assessed by removing each study one at a time and recalculating the pooled effect size.This analysis helped to identify any studies that may have a disproportionate influence on the results.The metaanalysis was repeated using alternative effect size measures, such as Cohen's d or odds ratios, to assess the sensitivity of the findings to the choice of effect size metric.

Results and Discussion
Table 1 provides     Control" suggests a potentially lower impact on efficiency in this stage compared to others.This could be because quality control often relies on subjective assessments and complex decision-making, which may be more challenging to fully automate or enhance through digital technologies alone.The non-significant coefficients for these stages suggest that their efficiency gains are in line with the average effect observed across all stages.Overall, Table 4 provides valuable insights into the factors that may influence the effectiveness of Industry 4.0 technologies in improving leather processing efficiency.5 regarding the assessment of publication bias in the meta-analysis.The analysis did not detect any significant evidence of publication bias.This is based on the results of Egger's Regression Test, which yielded a non-significant p-value (p = 0.40).
Publication bias refers to the tendency for studies with positive or statistically significant results to be more likely to get published than those with null or negative results.If present, this bias can distort the overall findings of a meta-analysis, making the combined effect size appear larger than it truly is.Egger's test is a statistical method to detect this bias.It examines the relationship between the effect size of each study and its standard error (a measure of precision).If smaller studies (with larger standard errors) tend to show larger effects, it hints at the possibility that smaller studies with null results might be missing from the published literature.In this case, the non-significant p-value from Egger's test indicates that such a pattern was not observed in the data.This suggests that the  This suggests that there is still room for further technological advancements in this area.[16] The findings of this meta-analysis paint a compelling picture of the transformative power of The future of leather processing lies in the seamless fusion of traditional craftsmanship and cutting-edge technology.This meta-analysis serves as a clarion call for the industry to embark on this exciting journey, unlocking new levels of efficiency, productivity, and environmental responsibility.Furthermore, the metaanalysis reveals that the impact of Industry 4.0 is not merely confined to efficiency gains. 17[24][25]

Conclusion
This meta-analysis provides compelling evidence that Industry 4.
efficiency and productivity can lead to increased output, reduced costs, and improved competitiveness.Improved product quality can result in higher customer satisfaction and brand reputation.Reduced costs can make leather products more affordable and accessible to a wider consumer base.Enhanced worker safety can create a more positive and productive work environment.And improved sustainability can contribute to a cleaner and more environmentally responsible leather industry.Despite the promising potential, the adoption of Industry 4.0 technologies in the leather industry is not without challenges.The implementation of these technologies often requires significant upfront investment, which can be a barrier for small and medium-sized enterprises (SMEs).There is also a need for upskilling and reskilling of the workforce to operate and maintain these advanced systems.Data security and privacy concerns arise with the increased connectivity and abstract and citation database of peer-reviewed literature, offering comprehensive coverage across various scientific disciplines.Its inclusion ensured access to a broad range of relevant studies from diverse sources; Web of Science is another prominent citation index, providing access to high-quality, peerreviewed research across multiple fields.Its inclusion further broadened the scope of the search and increased the likelihood of identifying relevant studies; Google Scholar is a freely accessible search engine that indexes scholarly literature across various disciplines and sources.Its inclusion allowed for the identification of potentially relevant studies that may not have been indexed in the other databases.A combination of keywords and Boolean operators was used to construct the search queries.The following search terms were employed: Leather processing; Industry 4.0; Efficiency; Productivity; Automation; Robotics; Artificial intelligence; Internet of Things; Big Data analytics.These terms were combined using Boolean operators (AND, OR) to create search strings that captured the relevant concepts, "leather processing" OR "leather manufacturing") AND ("Industry 4.0" OR "digital technologies") AND ("efficiency" OR "productivity".The search strategy was adapted for each database to ensure optimal retrieval of relevant studies.Additionally, the reference lists of included studies were manually screened to identify any additional relevant articles that may have been missed by the electronic searches.The search was limited to studies published between 2018 and 2024.This timeframe was selected to capture the most recent advancements in Industry 4.0 technologies and their applications in the leather processing industry.The search was restricted to studies published in English.This limitation was imposed due to resource constraints and the need to ensure consistency in data extraction and analysis.In addition to the electronic database searches, efforts were made to identify relevant grey literature, such as conference proceedings, technical reports, and dissertations.This was done by searching relevant websites and contacting experts in the field.Key journals in the field of leather science and technology were hand-searched to identify any potentially relevant studies that may have been missed by the electronic searches.Clear and explicit inclusion and exclusion criteria were established to ensure the selection of studies that were directly relevant to the research question and met the methodological requirements for inclusion in the meta-analysis.Inclusion Criteria: Study Design: Studies employing a quantitative research design were included.This included experimental, quasiexperimental, and observational studies that reported quantitative data on the impact of Industry 4.0 technologies on leather processing efficiency; Population: Studies focusing on leather processing facilities or operations were included.This included studies conducted in various geographical locations and across different scales of leather processing operations; Intervention: Studies investigating the implementation or adoption of one or more Industry 4.0 technologies in leather processing were included. The statistical analysis aimed to synthesize the extracted data and quantify the overall effect of Industry 4.0 technologies on leather processing efficiency.The following statistical methods were employed: Effect Size Calculation: Hedges' g, a standardized mean difference that accounts for differences in sample sizes, was used to calculate the effect size for each study.Hedges' g was chosen due to its robustness and applicability to a wide range of study designs; Pooling of Effect Sizes: Random-effects models were used to pool the effect sizes across studies.Random-effects models were chosen to account for the anticipated heterogeneity between studies due to differences in study designs, populations, interventions, and outcome measures; Subgroup Analyses: Subgroup analyses were conducted to explore the impact of different Industry 4.0 technologies and stages of leather processing on efficiency.These analyses aimed to identify potential sources of heterogeneity and provide insights into the specific contexts in which Industry 4.0 technologies may have a greater or lesser impact on efficiency; Meta-Regression: Meta-regression analyses were performed to investigate potential moderators of the effect sizes.Moderators are variables that may influence the relationship between the intervention (Industry 4.0 technologies) and the outcome (efficiency).The moderators examined in this metaanalysis included study year, country of origin, sample size, type of Industry 4.0 technology, and stage of leather processing; Publication Bias Assessment:Funnel plots and Egger's regression test were used to assess the presence of publication bias.Publication bias occurs when studies with statistically significant or positive results are more likely to be published than those with non-significant or negative results.The presence of publication bias can distort the findings of a meta-analysis, leading to an overestimation of the true effect size.All statistical analyses were performed using the 'metafor' package in R, a powerful and flexible tool for conducting meta-analyses.The analyses were conducted with a 95% confidence level, and p-values less than 0.05 were considered statistically significant.Sensitivity analyses were conducted to assess the robustness of the findings to various methodological decisions and assumptions.
a summary of the key characteristics of the 25 studies included in the metaanalysis, highlighting the diversity of research conducted on the impact of Industry 4.0 technologies on leather processing efficiency.The included studies were published between 2018 and 2024, reflecting the recent surge of interest in Industry 4.0 applications within the leather industry.This indicates a growing recognition of the potential benefits these technologies offer.The studies originated from various countries, with a notable concentration in China, India, and Italy.This distribution suggests that these countries are at the forefront of adopting and researching Industry 4.0 technologies in leather processing.The sample sizes ranged from 20 to 250 leather processing facilities, showcasing a mix of smaller-scale investigations and larger, potentially more generalizable studies.Industry 4.0 Technologies: The studies encompassed a range of Industry 4.0 technologies, including: Automation & Robotics: The application of automated machinery and robots to streamline and optimize various leather processing tasks; Artificial Intelligence: The use of AI algorithms and machine learning models to analyze data, make predictions, and improve decision-making; Internet of Things (IoT): The network of interconnected devices and sensors that collect and exchange data, enabling real-time monitoring and control of processes; Big Data Analytics: The analysis of large and complex datasets to uncover patterns, trends, and insights that can inform process optimization.Stages of Processing: The studies investigated the impact of Industry 4.0 technologies on different stages of leather processing, including: Hide Preparation: The initial stages of processing raw hides, including cleaning, fleshing, and liming; Tanning: The core process of converting raw hides into leather through the use of tanning agents; Finishing: The final stages of leather production, involving coloring, coating, and embossing; Quality Control: The inspection and assessment of leather products to ensure they meet quality standards.Efficiency Outcome Measures: The studies utilized various efficiency outcome measures, commonly including: Production Time: The time taken to complete a specific processing step or the overall production cycle; Throughput: The quantity of leather processed within a given timeframe; Resource Utilization: The efficiency of water, energy, and chemical usage during processing; Defect Rates: The percentage of leather products with quality defects.

4. 0
technologies (like automation & robotics, which served as the baseline in this analysis).This might suggest that their implementation or integration into leather processing workflows could be more complex or challenging, leading to less pronounced efficiency gains in the short term.The non-significant coefficients for AI and Automation & Robotics suggest that their impact on efficiency is comparable to the average effect observed across all technologies.These technologies appear to be well-established and effective in enhancing various aspects of leather processing.The significant positive coefficient for the "Finishing" stage indicates that this stage experiences a greater improvement in efficiency compared to other stages when Industry 4.0 technologies are implemented.This might be due to the labor-intensive and often manual nature of finishing operations, making them particularly amenable to automation and optimization through digital technologies.The near-significant negative coefficient for "Quality 25 studies included in the meta-analysis likely represent a fair and unbiased sample of the research on this topic.The absence of significant publication bias strengthens the confidence we can have in the overall findings of the meta-analysis.It implies that the reported positive impact of Industry 4.0 technologies on leather processing efficiency is likely a genuine effect, not an artifact of biased reporting.This adds to the robustness of the meta-analysis.It indicates that the methodological choices made in the study selection and analysis process have helped to minimize the potential influence of publication bias.

Industry 4 .
0 technologies in the leather industry.The substantial positive impact on efficiency, coupled with the potential for enhanced sustainability, underscores the urgency for leather manufacturers to embrace these advancements.However, the journey towards a fully digitized and automated leather industry is not without its challenges.The initial investment in these technologies can be substantial, and there is a pressing need to upskill the workforce to operate and maintain these sophisticated systems.Data security and privacy concerns also loom large, requiring robust protocols to safeguard sensitive information.Nevertheless, the potential rewards far outweigh the challenges.By strategically adopting and integrating Industry 4.0 technologies, leather manufacturers can position themselves at the forefront of innovation, achieve greater efficiency and sustainability, and thrive in an increasingly competitive global market.

- 19 Industry 4 .
0 technologies, particularly AI-powered quality control systems and real-time monitoring, enable leather manufacturers to achieve unprecedented levels of product consistency and quality.By automating inspection processes and leveraging machine learning algorithms to identify defects, these technologies minimize the likelihood of substandard products reaching the market.This not only enhances customer satisfaction but also reduces waste and the need for rework, contributing to a more sustainable production model.While the initial investment in Industry 4.0 technologies can be substantial, the long-term cost savings they offer are undeniable.Automation reduces labor costs, optimizes resource utilization, and minimizes waste, leading to significant financial benefits for leather manufacturers.Moreover, the improved efficiency and productivity enabled by these technologies can further drive down costs by increasing output and reducing production time.The leather industry has historically been associated with hazardous working conditions, particularly in tasks involving the handling of chemicals and heavy machinery.Industry 4.0 technologies, by automating many of these tasks, can significantly enhance worker safety.Robots can perform dangerous operations, minimizing the risk of accidents and injuries.Additionally, real-time monitoring and predictive maintenance can help identify potential safety hazards before they escalate, creating a safer working environment for all. 19-21Perhaps the most profound impact of Industry 4.0 lies in its potential to transform the leather industry into a more sustainable and environmentally responsible sector.By enabling resource optimization, waste reduction, and traceability, these technologies pave the way for a circular leather economy.Automation, big data analytics, and IoT can help leather manufacturers optimize their use of water, energy, and chemicals.Smart sensors and intelligent algorithms can monitor and control process parameters in real-time, ensuring that resources are used efficiently and waste is minimized.3D printing and other advanced manufacturing techniques can reduce waste generation by enabling on-demand production and customization.Additionally, optimized processes and predictive maintenance can prevent equipment failures and reduce material wastage.Blockchain and IoT technologies can enhance traceability and transparency in the leather supply chain, enabling consumers to make informed choices about the products they purchase.This promotes ethical and sustainable sourcing practices, ensuring that leather products are produced responsibly and with minimal environmental impact.Industry 4.0 technologies facilitate the transition towards a circular leather economy, where resources are kept in use for as long as possible.This involves recycling, upcycling, and the use of sustainable materials and processes.
0 technologies are poised to revolutionize the leather industry.The substantial positive impact on efficiency, coupled with the numerous other benefits, underscores the transformative potential of these technologies.While challenges remain, the leather industry stands to gain immensely by embracing Industry 4.0.By strategically adopting and integrating these technologies, , and competitiveness, ensuring a bright future for this vital sector.The journey towards a fully digitized and automated leather industry has begun, and those who embrace this transformation will undoubtedly reap the rewards.
confident that the true effect size lies.It suggests that even in the worst-case scenario, the effect is still likely to be moderate (0.48), while in the best-case scenario, the effect could be large (0.82).p-value (< 0.001): The very small p-value indicates strong statistical evidence against the null hypothesis of no effect.This reinforces

Table 2 .
The overall effect of Industry 4.0 technologies on leather processing efficiency.

Table 3 .
Subgroup analyses of the impact of Industry 4.0 technologies on leather processing efficiency.

Table 4 .
Meta-regression analyses of moderators of the effect of Industry 4.0 technologies on leather processing efficiency.

Table 5 .
Assessment of publication bias.