Search Results (343)

Background: Reducing production time while making efficient use of resources is a key challenge in modern manufacturing, particularly in environments with high variability in specific components for the automotive industry. Methods: This paper presents a hybrid approach to production scheduling that combines linear programming (LP) principles with heuristic decision-making. A structured literature review is conducted to compare exact methods, heuristics, and metaheuristics in terms of their applicability and limitations. Based on this analysis, a hybrid scheduling method is proposed, where LP defines the objective function and constraints, while heuristic rules enable efficient assignment of operations to workstations under capacity limitations. The approach is validated through a case study involving over 900 product variants in an automotive part production system characterized by interchangeable workstations. The proposed heuristic algorithm was tested in terms of real company daily scheduling performance and compared with former scheduling performance. Results: The results show that the proposed approach achieves better solution quality with significantly lower computational effort, while also improving time utilization and production efficiency. Conclusions: The hybrid LP-heuristic approach provides a computationally efficient and practical tool for real-time production scheduling in high-variability manufacturing environments, effectively balancing solution quality and sub-minute execution speed under strict capacity constraints. Full article

Integration of composites into the fabrication process of structural assemblies within the aerospace, automotive, marine or civil engineering industries represents a rational solution adopted by leading companies which are guided by the necessity for novel low-weight, high-strength, and high-stiffness materials. During the manufacturing process and throughout the service life, fiber-reinforced polymer structures are subjected to impact loading, either accidentally or as an inherent requirement of the operational cycle. Firstly, general aspects regarding impact loading and some parameters used for its characterization are briefly described. Recent progress regarding the influence of the stacking sequence, fiber type, and impactor geometry on the impact performance of Kevlar and glass fiber reinforced composite materials is emphasized. Additionally, the effects of environmental factors (such as temperature, UV radiation, or humidity) on the impact energy absorbed by polymers reinforced with each of the two types of synthetic fibers are presented. Finally, the importance of directing the researcher’s judgment towards improving the characteristics of materials subjected to impact, from a sustainable perspective, is motivated through the presentation of the impact behavior of polymer composites reinforced with Kevlar fibers or glass fibers hybridized with vegetable fibers. Full article

Integrating IT systems in the automotive industry remains a technically complex and costly process, particularly in environments based on legacy ERP and WMS platforms. Existing research discusses QR codes and process automation separately. However, little attention is paid to non-intrusive integration architectures that combine standardized identification and intelligent automation. This study develops and implements a universal logistics integration model based on GS1-compliant QR codes and JSON data structures combined with intelligent process automation (IPA). A case study from an automotive company is presented. Analysis indicates that the main loss factors include misidentification of assets, manual data entry, and lack of feedback on delivery status. The architecture proposed in this manuscript consists of three modules: INTELOGBOT (2.07), IPABOT (1.79), and APIBOT (1.79). A structured QR-JSON identifier schema was also designed to ensure platform-independent data exchange. This eliminated manual data re-entry and enabled real-time inventory and delivery synchronization. Furthermore, it also automated logistics documentation and reduced identification errors in inbound and outbound operations. The research contribution consists of developing a solution that enables the connection of QR codes with intelligent IPA-based bots, providing a repeatable framework for advanced automation of logistics processes in complex supply chains. Full article

This study developed a hybrid fuzzy decision-making algorithm based on the Fuzzy Best-Worst Method (FBWM) and the Fuzzy Square-Root-based Evaluation Method (FSREM). Despite the widespread application of the 8D methodology in engineering practice, the importance of its disciplines has not been sufficiently investigated; therefore, the aim of this study is to determine their significance and priority. The proposed fuzzy algorithm was applied to three companies operating within the automotive supply chain. FBWM was used to determine the criteria weights, while FSREM was applied to rank the 8D disciplines. Sensitivity analysis showed that the expert teams from the three considered companies perceived the problem in a very similar manner. The results of applying the proposed algorithm in all three companies showed that the discipline Identify and Verify Root Cause (D4) has the greatest influence on problem-solving effectiveness. In two of the three companies, Prevent Recurrence (D7) was ranked as the second most influential discipline, while in one company Define Permanent Corrective Actions (D5) was identified as the second most influential discipline. It can be concluded that the results demonstrated a high degree of consistency, while minor ranking deviations can be attributed to different quality management system approaches within each company. Full article

This study investigates the role of symmetric probabilistic models in predicting financial distress in the automotive industry, with a focus on companies operating in the Slovak Republic. Financial distress prediction represents a binary classification problem characterized by an inherent symmetry between healthy and distressed firms. To capture this structure, two widely used symmetric models—logit and probit—are applied and systematically compared. The modeling framework incorporates LASSO regression for variable selection, enabling dimensionality reduction while preserving the most informative financial indicators. The empirical analysis is conducted on a dataset of 351 manufacturing enterprises. The results indicate that both models achieve comparable predictive performance, with the logit model reaching an accuracy of 78.9% and the probit model 77.8%. The area under the ROC curve further confirms the strong discriminatory power of both approaches. The findings highlight that the symmetric nature of the applied link functions contributes to model stability, interpretability, and balanced classification behavior. This study extends existing research by explicitly linking symmetry concepts with financial distress prediction in a sector-specific context. The proposed approach provides a transparent and practically applicable framework for early risk identification in industrial enterprises. Full article

Effective project selection is a critical determinant of success for Design for Six Sigma (DFSS), particularly in automotive environments defined by high technical complexity and constrained resources. Because these selection tasks involve competing priorities, they are fundamentally multi-criteria decision-making (MCDA) problems that directly impact a company’s economic performance. This paper proposes a hybrid decision-support framework that integrates the Analytic Hierarchy Process (AHP) with a normalized scoring model. In this approach, classical AHP pairwise comparisons are used to derive consistent criteria weights, while project alternatives are evaluated on a 1–10 normalized scale to ensure the model remains scalable and practical for an industrial setting. The framework was empirically validated through a case study in an automotive company evaluating twelve DFSS project concepts. The results reveal that experts prioritize Product Quality (33%) and Cost/Functionality (33%) above all other factors, with these two criteria accounting for 66% of the total decision weight. Furthermore, the study established classification rules where projects scoring above 7.2 showed high implementation potential, while those below 5.2 were frequently discontinued. This structured approach enables a transparent and justifiable prioritization process that supports economic and operational sustainability by significantly reducing wasted engineering hours and prototype costs. Full article

The transition towards a circular economy (CE) has emerged as a key strategy for promoting sustainable development, particularly in resource-intensive industries. Representing such an industry, the automotive sector offers substantial CE potential. However, its practical implementation remains fragmented, and the theoretical discourse lacks consistency. This study addresses these gaps through a scoping review. The analysis first identifies key industry-specific research gaps in the CE transition. A subsequent evaluation of practical case studies reveals significant heterogeneity in the implementation of circular practices across companies and value chain positions. In addition, the summary of recommendations from the existing literature provides a structured overview of necessary measures in the areas of management, research, and policy. The results indicate a strong concentration on two CE-related areas: electric vehicle (EV) batteries and recycling strategies, while higher-value circular strategies remain underrepresented. Moreover, the maturity of circular practices varies considerably across value chain actors, with suppliers in particular lagging behind OEMs and downstream actors. Based on these findings, the study critically discusses the roles of industry, research institutions, and policymakers in enabling a more comprehensive and systemic transition towards circularity in the automotive sector. By systematically linking theoretical developments, empirical evidence, and stakeholder-specific implications, the study advances the field of automotive-related CE research. Full article

Digital transformation (DT) has become a core strategic priority for major economies, with global investments exceeding $2 trillion worldwide and $0.55 trillion in China alone in 2025. As DT reshapes the norms of international competitiveness and sustainable development, experts frequently emphasize the need for innovative cross-domain frameworks to decode the mechanisms of DT success. Even though public economists view government attention to digital talent (GADT) as a key driver of DT, there is an acute shortage of empirical models that explain how it affects firm-level DT directly or indirectly through intermediary mechanisms, e.g., talent agglomeration, absorptive capacity, and subsidies. Thus, exploring this relationship empirically holds significant theoretical and practical value. Based on the latest keyword frequency data from government policies and annual reports from 2008 to 2022 for 3952 A-share listed companies across 243 cities in 31 provinces, this study constructs an interactive two-way fixed-effects panel regression model with 35,058 valid observations. The empirical results show that GADT significantly promotes the digital transformation of enterprises (EDT), supported by enterprise talent agglomeration, absorptive capacity, and government digital talent subsidies. Notably, the effects of GADT on EDT were heterogeneous, with a significant positive impact observed in labor-intensive enterprises, peripheral cities, and enterprises in non-digital-economy pilot areas. Moreover, the effects of GADT on EDT were less pronounced among technology-intensive enterprises (e.g., automotive, pharmaceutical, and manufacturing), central cities (e.g., Chengdu, Fuzhou), and those in digital economy pilot areas (e.g., Xinjiang, Ningxia). This study aims to examine the impact mechanism of GADT on EDT, thereby providing theoretical support and practical implications for more targeted and effective digital talent policies. Full article

This study investigates the application of the SMED (Single-Minute Exchange of Die) methodology to improve operational efficiency and support energy- and resource-efficient manufacturing systems. The research is based on a case study conducted in an automotive company producing electrical harnesses, where SMED was implemented to optimize changeover processes and reduce process-related inefficiencies. The methodological approach follows an AS-IS to TO-BE framework, incorporating direct observation, time measurements, and the classification of activities into internal and external operations. In addition to operational indicators, selected energy- and resource-related aspects such as energy consumption during changeovers, material usage, and waste generation were evaluated based on process observation and indirect estimation. The results indicate a significant reduction in changeover time, along with improvements in machine availability and production flow. Furthermore, the study suggests a reduction in process-related energy consumption and material intensity associated with improved organization and reduced downtime, although these effects are partially indirect. The findings demonstrate that SMED can enhance operational efficiency and indicate its potential to improve energy performance in manufacturing systems, primarily through reduced machine downtime and more stable production flows. However, the results are case-specific, and further research based on direct energy measurements and broader industrial applications is required to confirm their generalizability. Full article

Manufacturing enterprises face increasing complexity in managing the complete life cycle of production systems, requiring integration of information from diverse sources to support timely maintenance, diagnostics, and operational decisions. This paper presents a comprehensive service-oriented architecture (SOA) for decision support in industrial life-cycle management, integrating real-time monitoring, predictive maintenance, and collaborative problem-solving across extended manufacturing enterprises. The architecture implements a three-layer service model comprising eight core collaborative services, three application services, and six life-cycle management services, orchestrated through a risk assessment module that monitors life-cycle parameters and triggers appropriate maintenance, diagnostics, or hazard prevention actions. The system was developed in the context of a European research project and validated in two industrial settings: automotive assembly lines at a German SME and air conditioning manufacturing at a Portuguese company. Results demonstrated substantial operational improvements, including reduced problem resolution time, lower diagnostic travel requirements, reduced spare-parts consumption, and increased structured problem registration. The original SOAP-based web-services implementation is further contextualized within the contemporary Industry 4.0 landscape through comparison with microservices architectures and discussion of integration paths involving OPC UA, Asset Administration Shells, and digital twins. The paper contributes a validated reference architecture for service-based industrial life-cycle management and clarifies its relevance as an early precursor of contemporary smart manufacturing approaches. Full article

Embracing environmental, social, and governance (ESG) principles is essential for the automotive industry to align with the global low-carbon trend, and carbon emission intensity serves as the core metric for evaluating the sector’s emission reduction effectiveness. We construct a two-way fixed-effects model to assess the influence of ESG performance on carbon emission intensity in the automotive manufacturing sector of Chinese A-share-listed companies from 2009 to 2022 and arrive at the following conclusions: There is a significantly negative relationship between the ESG performance and carbon emission intensity of automotive manufacturing firms, and the finding remains valid according to a series of robustness and endogeneity tests. In addition, small-scale and non-state-owned enterprises appear to focus more on managing carbon emission intensity than their large-scale and state-owned firms do. We also find that ESG performance impacts the carbon emission intensity of automotive manufacturing companies through increased executive compensation incentives, while financing constraints have enhanced the influence of ESG performance on the sector’s carbon emission intensity. Full article

As a crucial pillar industry in the country, the automotive industry continues to evolve with the increasing number of vehicles in operation, leading to a continual rise in the need for aftermarket parts and repair services. Fluctuations in automotive spare part requirements are influenced by various complex factors, which significantly impact production costs. The intermittent distribution of such requirements and strict limitations highlights the importance of automotive spare part management to enhance production efficiency and reduce costs. To improve demand forecasting accuracy, this study summarizes and synthesizes trends in automotive spare parts; proposes a tree-based machine learning forecasting model, based on a two-stage random forest (RF) structure that separately models demand occurrence probability and conditional demand size; and compares the outcomes with benchmarks to validate model effectiveness. The empirical study is conducted using an industrial dataset consisting of monthly demand records for approximately 2500 spare parts over a four-year period. This forecasting approach enables companies to rationalize inventory storage, ensure the quality of automotive repairs, and elevate service standards. Simultaneously, by improving the efficiency of inventory planning and allocation decisions, companies can enhance the quality of after-sales services, reduce inventory costs, and maximize the value of the automotive industry chain. Through reducing spare parts wastage and further lowering enterprise costs and industrial emissions, companies can achieve the goals of automotive supply chain resilience. Notably, this study focuses on automotive spare parts management and provides a feasible, reliable, and interpretable forecasting solution for automotive manufacturers to address intermittent demand challenges in spare parts management. Full article

The development of new energy technologies is crucial for the future competitiveness of the automotive industry. Green innovation is a key driver of industrial transformation and advancement. Companies in the new energy vehicle (NEV) sector play a critical role in the automotive supply chain and demonstrate their green innovation capabilities across the industry. The dual-credit policy, a major governmental regulatory incentive, has a significant impact on the innovation performance of NEVs. Therefore, it is important to examine its influence on green innovation outcomes. This study is grounded in institutional theory and the resource-based view, and informed by the TOE analytical framework. It aims to develop a theoretical model to investigate the interplay among technological, organizational, and environmental factors in fostering green innovation. Using panel data from 21 NEV companies spanning the period 2014–2023, the research employs the dynamic fuzzy-set Qualitative Comparative Analysis (fsQCA) method to identify causal configurations associated with high green innovation performance. The results show that no single factor is necessary for achieving superior outcomes. Configuration analysis reveals 3 dominant pathways: “Technology-driven + Environment-pulled” pathway, “Technology-driven + organizational collaboration” pathway and the “Tripartite linkage” pathway. This study advances theoretical understanding by moving beyond unidimensional analyses and offering a holistic perspective on the multiple equifinal paths to high green innovation performance. It also provides practical insights for NEV firms to strategically align their technological, organizational, and environmental resources to enhance green innovation performance. Full article

Improving energy efficiency in industrial manufacturing remains challenging despite substantial technical potential. This has resulted in a persistent energy efficiency gap, which is increasingly understood as a socio-technical issue driven by not only technology limitations but also organizational and informational barriers. This study investigates how energy waste is targeted in practice through an in-depth single case study of an automotive company. Fifteen energy efficiency measures (EEMs) were analyzed and classified by type of energy waste addressed, digital technologies applied, and organizational knowledge required. The results show that industrial efforts primarily focus on reducing idling energy losses, while fewer measures address more complex forms of energy waste, such as over-processing losses. Digital technologies are mainly applied and rolled out at lower maturity levels, emphasizing energy monitoring and visualization. Further, different types of organizational knowledge are associated with targeting energy waste: technical knowledge dominates isolated interventions, process knowledge supports standardized technology diffusion, and leadership knowledge is required for cross-functional coordination. The findings highlight that bridging the energy efficiency gap requires the alignment of technological solutions with organizational knowledge and routines. This study contributes empirical insights into how manufacturing companies can structure and prioritize energy efficiency efforts and provides a framework to support the implementation of energy efficiency measures in practice. Full article

Manufacturing companies face significant challenges in leveraging artificial intelligence for equipment management due to high infrastructure costs and limited availability of labeled data for failures. While most manufacturing AI applications focus on structured sensor data, vast amounts of unstructured textual information containing valuable maintenance knowledge remain underutilized. This study presents a practical generative AI-based framework for structured information extraction that automatically converts unstructured equipment maintenance texts into predefined semantic fields to support predictive maintenance in manufacturing environments. We adopted and evaluated three representative generative models—Bidirectional and Auto-Regressive Transformers (BART) with KoBART, Text-to-Text Transfer Transformer (T5) with pko-t5-base, and the large language model Qwen—to generate structured outputs by extracting three predefined fields: failed components, failure types, and corrective actions. The framework enables the structuring of equipment management text data from Manufacturing Execution Systems (MES) to build predictive maintenance support systems. We validated the approach using a large-scale MES dataset consisting of 29,736 equipment maintenance records from a major automotive parts manufacturer, from which curated subsets were used for model training and evaluation. Our methodology employs Generative Pre-trained Transformer 4 (GPT-4) for initial dataset construction, followed by domain expert validation to ensure data quality. The trained models achieved promising performance when evaluated using extraction-aligned metrics, including exact match (EM) and token-level precision, recall, and F1-score, which directly assess field-level extraction correctness. ROUGE scores are additionally reported as a supplementary indicator of lexical overlap. Among the evaluated models, Qwen consistently outperformed BART and T5 across all extracted fields. The structured outputs are further processed through domain-specific dictionaries and regular expressions to create a comprehensive analytical database supporting predictive maintenance strategies. We implemented a web-based analytics platform enabling time-series analysis, correlation analysis, frequency analysis, and anomaly detection for equipment maintenance optimization. The proposed system converts tacit knowledge embedded in maintenance texts into explicit, actionable insights without requiring additional sensor installations or infrastructure investments. This research contributes to the manufacturing AI field by demonstrating a comprehensive application of generative language models to equipment maintenance text analysis, providing a cost-effective approach for digital transformation in manufacturing environments. The framework’s scalability and cloud-based deployment model present significant opportunities for widespread adoption in the manufacturing sector, supporting the transition from reactive to predictive maintenance strategies. Full article