Engineering Management in Production and Services Feed

Building energy performance and housing market regimes: evidence from EU countries

Thu, 02 Apr 2026 00:00:00 GMT

In assessing developments in the real estate market, the energy efficiency of buildings is increasingly considered important from an environmental and economic perspectives. However, empirical evidence on the relationship between energy performance indicators and house price dynamics at the macro level remains mixed, suggesting that this relationship may vary across countries and contexts. This article examines whether and how building energy performance indicators are related to house price dynamics across European Union countries, focusing on short- and long-term relationships and cross-country heterogeneity. The study is based on panel data for 2010-2023, using house price indices as the dependent variable and several energy efficiency indicators, including final energy consumption in buildings and the share of renewable energy sources. Various econometric methods were applied, including panel cointegration analysis (FMOLS and DOLS), dynamic panel models (Arellano-Bond GMM), Granger causality tests, impulse-response analysis, quantile regression, threshold models, and exploratory country clustering. The results show that energy efficiency indicators are not strongly associated with house prices at the aggregate EU level, and their short-term effects are generally weak. Instead, the findings reveal significant heterogeneity across countries and income regimes, suggesting that the capitalisation of energy efficiency in house prices is highly context-dependent. By combining econometric analysis with exploratory segmentation approaches, the study helps structure heterogeneous energy housing interactions into distinct market environments relevant for investment and policy analysis. These results highlight the limitations of uniform macro-level approaches and underline the importance of differentiated market analysis.

Building business models of high-growth enterprises based on AI systems

Thu, 02 Apr 2026 00:00:00 GMT

To date, the use of artificial intelligence (AI) to shape the business models of high-growth enterprises (HGEs) remains an unexplored research topic. The article aims to examine the use of artificial intelligence to build business models for high-growth enterprises, considering the heterogeneity of these entities. The empirical research aimed to answer the following questions: (1) Does the use of artificial intelligence to build individual components of the business model depend on the size of the HGE? 2. Does the use of artificial intelligence to build individual components of the business model depend on the age of the HGE? The study was conducted in the second half of 2024 on a sample of 200 Polish high-growth enterprises that declared using AI in their business activities. Data were collected through a survey questionnaire. The survey questions were derived from the assumptions of building a business model based on three value components: value proposition, value creation and delivery, and value capture. The chi-square test, the Kruskal-Wallis test, and measures of dependence for immeasurable features were used to address the research questions. It has been shown that statistically significant relationships exist only between individual value components and the size of the enterprise when these components are perceived as the average of the values of the variables that comprise them. It has also been shown that the values of these components are differentiated by enterprise size. The novelty of the article is the research on the use of AI to build business models for high-growth enterprises, accounting for their heterogeneity. The article is addressed to scientific researchers and business practitioners, particularly those dealing with issues related to building business models and using AI to create value.

Evolving AI models: adoption patterns of transformers and diffusers

Thu, 02 Apr 2026 00:00:00 GMT

This study investigates the development, adoption, and implications of artificial intelligence (AI) models by analysing a comprehensive dataset of over 316,000 models hosted on the Hugging Face platform. Focusing on two dominant model architectures - transformers and diffusion models - it examines their distribution across tasks, user engagement patterns, and practical applications in domains such as natural language processing, computer vision, audio processing, and generative media. The research highlights the growing prominence of generative AI, the role of open-source platforms in shaping model accessibility, and the divergence in use trends between foundational and emerging AI tools. Drawing on correlations between downloads, likes, citations, and model size, the paper discusses how each library’s community-driven dynamics shape their respective strengths. Finally, the paper discusses implications for business strategy and adoption, encompassing practical considerations like infrastructure requirements and ethical challenges, and underscores the potential for these evolving model ecosystems to drive innovative, human-centric AI solutions across diverse sectors.

Impact of human resource training on supply chain efficiency in Guiyang’s enterprises: a structural equation modelling (SEM) analysis

Thu, 02 Apr 2026 00:00:00 GMT

This study investigates the impact of human resource (HR) training on supply chain efficiency (SCE) among enterprises in Guiyang, China. A quantitative approach was used to analyse data from 316 respondents across ten key industrial sectors. Structural equation modelling (SEM) was applied to analyse the relationships between HR training, organisational culture, technological adaptability, and supply chain efficiency. The measurement model demonstrated adequate convergent validity (average variance extracted > 0.50) and internal consistency (composite reliability > 0.70). Discriminant validity was confirmed through the Fornell-Larcker criterion and HTMT ratio (< 0.85). The structural model revealed a positive influence of HR training on organisational culture (β = 0.664, p < 0.01), technological adaptability (β = 0.399, p < 0.01), and SCE made directly (β = 0.262, p < 0.05) and indirectly through organisational culture and technological adaptability (β = 0.272, p < 0.05). The model fit indices (χ²/df = 1.295; CFI = 0.984; TLI = 0.976; RMSEA = 0.031) confirmed its robustness. The findings suggest that HR training enhances SCE by improving employee skills, fostering technological integration, and cultivating an adaptive organisational culture. This research contributes to the theoretical understanding of HR development in supply chain management. It provides policymakers and business leaders with practical insights on leveraging workforce training as a strategic tool to enhance supply chain performance in regions such as Guiyang.

Strategic competency development for Industry 5.0 leaders: perspectives from the manufacturing sector

Thu, 02 Apr 2026 00:00:00 GMT

This paper aims to identify and define key leadership competencies required for Industry 5.0 in the manufacturing sector, emphasising the integration of advanced technologies with human-centric and sustainability values across different organisational levels. A two-round Delphi study was conducted with 30 manufacturing experts from multiple priority sub-sectors in Indonesia. Quantitative consensus was assessed using median ≥ 4.0 and IQR ≤ 1.0, complemented by Kendall’s W to measure agreement strength. The study generated a comprehensive Industry 5.0 Leadership Competency Framework consisting of five core dimensions: technical mastery, strategic leadership, people management, business acumen, and sustainability, supported by 24 validated competencies prioritised across senior, middle, and entry-level leadership roles. This study advances leadership theory by proposing a multilevel (macro-meso-micro) human-centric leadership model that integrates sustainability and ethical technological implementation, addressing a missing linkage in current Industry 5.0 literature. The framework guides manufacturing organisations in designing tiered leadership development pathways, performance evaluation instruments, and succession strategies aligned with Industry 5.0 transformation and sustainable operational excellence.

Empirical insights into supply chain management integration with product lifecycle management: benefits and challenges

Thu, 02 Apr 2026 00:00:00 GMT

Integration of product lifecycle management systems with supply chain management processes has proven challenging for companies that attempt it, with limited empirical research available on this topic. Consequently, this study aimed to explore whether implementing product lifecycle management systems can enhance supply chain management within an organisation. To obtain relevant data, this study employed a single qualitative case study design, using in-depth semi-structured interviews and on-site observations conducted at ABC TECH to examine how Sovelia PLM supports supply chain processes. The data was collected from a company in Northern Europe that uses Sovelia lifecycle management software. Findings showed that this software generally aids in enhancing supply chain process integration, traceability, and structure. However, it is design-centric, which makes it more difficult to adequately address the supply chain management department’s operational requirements. These insights may enhance the decision-making processes and guide actionable strategies for seamless integration. The findings call for a more holistic product lifecycle management system that encapsulates both design and supply chain functionalities, thereby ensuring cohesive operations and improved communication within the company’s supply chain network.

Building safe organisations: using machine learning to decode safety habits of blue-collar workers in the construction industry

Thu, 02 Apr 2026 00:00:00 GMT

This study aims to provide a framework for categorising safety behaviours of construction workers, recognising the importance of employee safety in the competitive business environment. Employee safety is crucial to overall efficiency, productivity, and well-being, and the study seeks to contribute to understanding and managing workplace safety in the construction industry. This study utilises machine learning (ML) algorithms, like logistic regression, support vector machine, and decision trees, to develop a categorisation framework for the safety behaviours of construction workers. The framework is validated using frequent safety behaviours observed in a random sample of construction professionals. The study finds that workplace safety behaviours (WSB) are primarily influenced by supervisor support, reckless habits, and safety motivation. Limiting workplace accidents, enforcing safety laws, properly documenting safety processes, and organising sessions to educate staff are identified as critical sub-factors. Advancements in technology have resulted in significant improvements across construction organisations in allied domains. Additional considerations include education, preempting the possibility of accidents in different workplace situations, and enforcing strong disciplinary measures. The framework proposed can serve as a valuable tool for organisations to tailor safety interventions. By recognising the diverse influences on safety behaviours, companies can implement targeted measures to address specific root causes of unsafe practices. The practical implications of these findings for safety management in the construction industry are noteworthy.

In search of factors reinforcing the relationship between organisational resilience and organisational performance: a conceptual framework and research results

Wed, 31 Dec 2025 00:00:00 GMT

The study aims to explore how external support and the ability to understand systemic linkages (organisation’s capability for systems thinking) influence the relationship between organisational resilience and organisational performance. The empirical research was conducted to verify whether external support and systems thinking have the capacity to moderate the relationship between organisational resilience and organisational performance. A set of hypotheses was developed based on the theoretical research and subsequently tested on a sample of 268 organisations operating in Poland. Two alternative testing methods were used: stepwise regression analysis with a moderator and Hayes’ PROCESS macro technique (also based on regression analysis). The Johnson-Neyman technique was used to identify regions within the range of the moderator variable, where the effect of the focal predictor on the outcome was statistically significant or non-significant. The obtained results clearly show that the external support and systems thinking, when considered separately, act as moderators of the analysed relationship. The model with two moderators is also statistically significant, and the simultaneous inclusion of both moderators significantly increases the percentage of explained variance. However, an interesting phenomenon can be observed here. The analysis of conditional effects reveals that at a low level of systems thinking, and across all levels of external support, the moderation effect is not statistically significant. It becomes statistically significant only at the average and high levels of systems thinking, and at both of these levels, the effect increases with the rise in external support. This study provides important insights into the factors influencing the relationship between organisational resilience and organisational performance. It emphasises the importance of systemic linkages and, above all, the understanding of the context in which organisations operate.

Navigating servitisation in the GCC fashion sector: a comprehensive assessment of status and barriers

Wed, 31 Dec 2025 00:00:00 GMT

This study aims to evaluate the current level of servitisation in the Gulf Cooperation Council (GCC) markets in the fashion sector and identify various internal and external obstacles that may hinder fashion organisations in the GCC region from fully adopting the servitisation strategy. An exploratory methodology was employed, using a qualitative approach with semi-structured interviews on a purposive sample. The study reveals that the implementation of the servitisation strategy in GCC is in its initial stages. While evidence of the dimensions underlying such a strategy was found, they were not employed and linked as suggested in the literature to generate the required results. Additionally, non-transparent and limited relationships with partners and unskilled employees were identified as the main barriers preventing fashion agents from fully embracing servitisation in the GCC fashion sector. This study uniquely explores servitisation in the GCC fashion sector, filling a significant gap in existing research that has largely overlooked this region and industry. Unlike previous works that broadly address servitisation in manufacturing, this paper delves into the specific challenges and adoption levels within the GCC’s culturally and economically distinct context. By offering nuanced insights from senior managers in leading fashion organisations, it provides valuable empirical evidence and practical implications for both academia and industry, marking a notable contribution to the literature on servitisation strategies in emerging markets.

Sustainability in digital transformation: towards an integrated framework

Wed, 31 Dec 2025 00:00:00 GMT

This paper investigates how existing literature has approached the concept of sustainability in the context of digital transformation. It adopts a three-step procedure for a systematic literature review (SLR), including planning, conducting, and reporting the review to synthesise current knowledge, classify key thematic areas, and establish a foundation for further empirical research. The findings emphasise the need for a structured and sustainable approach to digital transformation to enhance business resilience and effectiveness. Although this is a relatively new and emerging topic, and there is a lack of a solid conceptualisation of the role of sustainability in digital transformation, several important theoretical implications can still be identified. By addressing the fragmented nature of the literature, this study contributes to the field by developing a structured framework for classifying key areas of sustainability in digital transformation. The systematic literature review revealed an absence of a unified conceptualisation and a lack of consistent understanding of sustainability in digital transformation across the analysed publications. Nevertheless, the study successfully identified 24 areas related to the sustainability of digital transformation, which were categorised into a framework comprising four fields: people, management, technology, and environment. The study plays a crucial role in enhancing the understanding of the sustainability of the digital transformation process by using a classified set of sustainability areas within it. The study results provide practical information for managers leading digital transformation processes in companies.

Review of sentiment analysis in new product development: text, audio, visual, and multimodal data

Wed, 31 Dec 2025 00:00:00 GMT

This research provides a literature review on the application of sentiment analysis (SA) in the new product development (NPD) process. The literature review employs a systematic literature review methodology. The steps include selecting a review topic, searching and selecting relevant articles, assessing and synthesising the literature, and organising the writing of the review. Sentiment analysis is a subdomain of natural language processing (NLP) that examines user opinions. The sentiment analysis methodology has been employed in the new product development process to replace traditional methods. Sentiment analysis can be conducted across various modalities, including text, audio, image, and multimodal formats. Text modality for sentiment analysis has been used to enhance the lifetime of products and services. Audio data and image modalities represent alternative modalities; however, they receive significantly less attention. The limitation is that these modalities are predominantly executed in controlled environments, utilising open-source or benchmark datasets, and some still employ text modality sentiment analysis methods or lexicons. Multimodal data, conversely, aims to augment the informational dimension of the text modality and is typically executed using deep learning models. This modality encompasses numerous combinations with the primary objective of enhancing the performance of sentiment analysis, hence reducing bias. The findings suggest that future research in this domain should focus on improving multimodal sentiment analysis to improve the new product development process.

Impact of AI capability, digital strategy, and digital maturity on organisational performance

Wed, 31 Dec 2025 00:00:00 GMT

In the face of the inevitable digitalisation of enterprises, limited research has investigated the impact of digital strategy, digital maturity, and AI capability on organisational performance. Drawing on the resource-based theory and recent work on AI in the organisational context, this research aims to uncover the configurations under which a firm’s digital strategy, digital maturity, and AI capability would jointly lead to higher performance. This study uses a unique fuzzy-set qualitative comparative analysis methodology to analyse data collected from 56 SMEs to investigate three domains of AI capability, along with digital strategy and digital maturity. The results suggest that high organisational performance does not depend on a single condition but rather on complex synergistic interactions among the studied conditions. The results indicate that three equifinal configurations lead to high performance of SMEs. The study suggests that AI technical resources are mandatory for any viable solution. This study provides pioneering insights into the empirical contributions of AI capability, digital strategy and digital maturity and their relationships to organisational performance in SMEs, by using a configurational approach. The adopted theoretical perspective addresses the need for a holistic approach to uncover the mechanisms underlying digital strategy and digital maturity in relation to AI capabilities in SMEs, and their mutual impact on organisational performance. These results have practical implications for decision-makers and owners of SMEs, providing new insights into the combination of factors that drive high performance.

Convergence of marketing technologies and artificial intelligence: a bibliometric review (1987-2025)

Wed, 31 Dec 2025 00:00:00 GMT

This article explores the intersection of artificial intelligence (AI) and marketing technologies (MarTech) by conducting a comprehensive bibliometric analysis. The aim is to identify dominant research themes, key contributors, and major gaps in the existing literature. MarTech is conceptualised as a system of digital tools that enables marketing transformation. Scopus and Web of Science were used to collect records. Following a preliminary comparison, the final analytical corpus of peer-reviewed scientific publications (n=492) was drawn solely from Scopus. The study is based on a dataset from 1987 to 2025. Using Biblioshiny, the analysis examined publication dynamics, citation patterns, co-authorship networks, and thematic clusters. The results indicate consistent growth in scholarly attention, with an annual publication increase of 7.39 % across the full period and 36.53 % between 2015 and 2025. Five primary thematic clusters were identified: (1) AI-Marketing Core and Innovation, this cluster acts as a motor theme, integrating innovation, AI applications, and marketing outcomes, and providing conceptual and methodological scaffolding for the field; (2) Technology Adoption, functioning as a basic theme, it connects sources of innovation with market outcomes; (3) Market Applications and Digital Commerce, this cluster reflects the operationalisation of value in commerce and digital marketing, exhibiting high centrality and moving towards motor-theme status; (4) Perception and Human-Centred Factors, representing a niche but strategically important human perspective; it moderates the relationship between adoption and outcomes. (5) Generative Artificial Intelligence (e.g., ChatGPT), this is the most emerging stream, acting as an accelerator for innovation, adoption, and applications, while simultaneously elevating the importance of quality, safety, and ethics. The United States, India, and China lead in publication volume, while the United Kingdom, France, and Australia demonstrate the highest citation impact. Despite the growing literature base, theoretical fragmentation persists, and limited studies address the ethical, social, and emotional implications of AI in marketing.

Key elements of digital competence in professional sales & service work: Development and evaluation of a self-assessment scale for frontline employees

Wed, 08 Oct 2025 00:00:00 GMT

Modern work in sales & service is increasingly enhanced and supported by digital technologies. As a result, frontline employees’ digital competencies are becoming a key success factor for sales & service work. Nevertheless, especially with regards to professional work, there is still a lack of knowledge about how to measure digital competencies. So far, specific empirical contribution focussing on professional digital work environments being increasingly knowledge-intense, collaborative, and virtualized are still very rare. In this article we seek to make a substantial contribution in that area of research. Based on the state-of-the art literature about digital competence among employees in professional work this article is one of the very few that introduces an empirically evaluated scale of digital competence based on a sample size of N=1,283. We suggest a context-related set of five dimensions of digital competence named (1) effective usage of technologies and tools, (2) farsighted & critical information handling, (3) sustained cooperation & communication, (4) integrative knowledge generation, and (5) co-creative problem solving. Evaluation of these five dimensions is conducted with the help of technostress, virtualization of work, space-time flexibility at work and availability for work-related issues. Finally, we present a critical reflection about the scale’s five dimensions.

Assessment of product quality risks by qualimetric methods using functionally dependent statistics

Wed, 08 Oct 2025 00:00:00 GMT

In modern production systems, ensuring high product quality while minimising risk is a critical challenge. Traditional quality assessment methods often rely on expert judgment or complex models, which may introduce subjectivity or require large datasets. This study aims to develop a universal methodology for assessing product quality risks using a mathematically grounded approach that eliminates the need for expert-based evaluations and can be easily implemented in various industrial contexts. A qualimetric method based on nonlinear mathematical dependence using the error function “erf” is proposed. The method transforms measured quality indicators into a dimensionless scale and derives functionally dependent statistics under the assumption of a uniform distribution. The model is validated through analytical derivations and numerical experiments on piston components in precision mechanical engineering. A new mathematical model was established to calculate the probability density function of transformed quality indicators. The methodology enables the estimation of the probability that a quality indicator will fall within a risky range near tolerance limits. Numerical experiments confirmed the validity of the model, demonstrating its applicability to real-world production scenarios and its alignment with known principles of qualimetry. The proposed method provides a universal, objective, and practical tool for risk-based quality assessment. It can be applied across different industries, integrated into existing quality management systems, and used to support decision-making in production control. Future research should expand the model to accommodate nonuniform distributions and explore its integration with real-time quality monitoring systems.

Foreign direct investment and technology transfer: unlocking Serbia’s growth potential

Wed, 08 Oct 2025 00:00:00 GMT

This paper examines the effects of foreign direct investments (FDIs) on economic development and the transfer of knowledge and technology (technology spillover) in developing countries, specifically focusing on the Republic of Serbia. The analysis is based on a comparative review of EU countries and Serbia in terms of statistical data on R&D activities and patent applications. The study synthesises existing research and proposes a methodological framework for future empirical studies. Findings indicate that technology spillover - horizontal and vertical - has not yet reached the desired levels in Serbia. Despite the presence of FDIs, the anticipated improvements in domestic innovation and technological advancement remain limited. This study contributes to the literature by contextualising technology spillover in Serbia and identifying gaps in its realisation. It also provides a foundation for future research on the mechanisms by which FDIs influence domestic enterprise performance and national economic development. The paper offers insights for policymakers and business leaders seeking to maximise the FDI benefits. It suggests the need for strategic policies to enhance technology transfer, support domestic R&D, and foster stronger linkages between foreign investors and local firms to accelerate economic development.

Organisational identification-based model of job performance in the IT sector: mediating role of work engagement and organisational citizenship behaviour

Wed, 08 Oct 2025 00:00:00 GMT

High employee job performance is considered one of the key factors contributing to a company’s commercial success, especially in such service-oriented sectors as IT. Researchers recognise a significant role of employee organisational identification, work engagement, and organisational citizenship behaviour in improving job performance; however, a complex model showing the relationship between those variables has not been provided so far. Moreover, a discrepancy exists between the theoretical conceptualisation and definition of organisational identification and its empirically proven measurements. In this context, the article aims to develop a holistic measurement for organisational identification and analyse the roles of organisational identification, work engagement, and organisational citizenship behaviour in improving job performance of the IT sector employees. An empirical study was conducted with 246 employees from IT sector organisations in Poland and Germany. The study was performed using the CAWI technique. The research tool was a questionnaire. The gathered data were analysed using IBM SPSS Statistics (descriptive statistics, scale reliability testing, and EFA) and IBM SPSS AMOS (CFA and path analysis) to verify the model. The analysis results show a positive influence of organisational identification on job performance through work engagement and organisational citizenship behaviour, i.e., organisational identification has a strong, positive and statistically significant effect on work engagement; work engagement has a positive and statistically significant impact on job performance and organisational citizenship behaviour; and organisational citizenship behaviour has a positive and statistically significant impact on job performance of the IT sector employees.

From negative feedback to actionable insights: a computational analysis of service robot adoption challenges in Chinese hotels

Wed, 08 Oct 2025 00:00:00 GMT

With the rapid rise in labour costs in China’s hotel industry, service robots have emerged as a potential solution to enhance service efficiency and reduce operational expenses. However, their adoption rate in Chinese hotels remains low. While prior studies have primarily explored technical performance and costs from a managerial perspective, there is a lack of systematic methodologies examining adoption barriers from the lens of guests’ negative emotions. This study employs web-crawling technology to collect 20,900 low-rated reviews from six major Chinese online travel platforms. Using Latent Dirichlet Allocation (LDA) topic modelling combined with computational grounded theory, the authors identified ten key barriers to the adoption of service robots in hotels. Notably, this study introduces “Cultural Misfit”, “Frequent Malfunctions”, and “Inconvenient Operation” as distinct barriers. It also reveals a cascading effect involving service quality, functional utility, and expectation alignment, highlighting that multidimensional interactions drive technology acceptance. These findings provide theoretical and practical insights for optimising service robot deployment, offering new perspectives to improve service efficiency and user acceptance in China’s hotel industry.

Generative artificial intelligence-driven medical digital twin technologies in blockchain Internet of Things wearable sensor and computer vision-based extended reality healthcare metaverse

Wed, 08 Oct 2025 00:00:00 GMT

The research problem of this paper was whether medical image, behavioral pattern, and physiological data analysis further artificial intelligence-based disease progression prediction, big medical data analysis and processing, and treatment planning optimization, digital twin- and generative artificial intelligence-based disease progression prediction and medical process simulation, patient outcome and pathological condition improvement, and medical service efficiency and resource allocation. We show that physiological measurement indicator modeling and simulation and patient diagnosis and clinical workflow optimization necessitate generative artificial intelligence- and machine learning-based metaverse wearable and implantable medical devices. Our analyses debate on medical metaverse digital twin generative artificial intelligence and machine learning-based big clinical and medical imaging data interoperability and analysis harnessed in remote medical treatment and healthcare practices, healthcare delivery and patient outcome enhancement, real-time medical anomaly detection, timely medical treatment and response prediction, and immersive medical procedure and healthcare delivery simulation in blockchain Internet of Things wearable sensor and computer vision-based extended reality healthcare metaverse. Our results and contributions clarify that clinical decision support systems and generative artificial intelligence-based patient medical disease and health data processing and analysis configure clinical patient care and outcome prediction, health risk forecasting, medical abnormality detection, and remote patient vital sign and health issue monitoring.

Multi-project buffer setting and dynamic monitoring of a critical chain based on comprehensive factors

Thu, 03 Jul 2025 00:00:00 GMT

The paper introduces a new multi-factor critical chain buffer estimation model and designs a dynamic monitoring method based on the project elements. A literature analysis determined a research gap and a research problem. It was found that the existing methods offer scarce collaborative studies on buffer setting and monitoring and insufficient research on buffer setting considering project economic indicators. However, these topics are often given priority consideration in practical engineering applications. Therefore, the study proposes a multi-factor critical chain buffer setting and its dynamic monitoring method. The planning stage analyses the impact of income, resources, and probability of success on buffer size setting and defines the calculation model of capacity constraint buffer. The execution stage dynamically sets buffer monitoring points according to the progress of project implementation, monitors the remaining buffer amount at the completion of each activity on the critical chain, and takes corresponding actions to ensure that the progress is controllable. The method was applied in a multi-project of a Chinese software enterprise. To further verify the effectiveness of this research, the method is compared with the traditional static buffer monitoring method (TBMM) and the relative buffer monitoring method (RBMM), and the construction period of the real project is simulated through the computer program for analysis. Results show that the research method can reduce unreasonable buffer settings, enhance the robustness of a buffer against complex environments, and reduce the probability of false warnings in the monitoring process.