Search Results (174)

The decarbonisation of the automotive sector requires product innovation, circular end-of-life management and energy-system transformation to be treated as interdependent strategic choices. This study proposes a decision-oriented life cycle assessment (LCA) framework for evaluating sustainable product innovation in sport utility vehicles (SUVs), focusing on how powertrain selection and post-consumer management support the low-carbon transition. Six SUV powertrain technologies—petrol, diesel and CNG internal combustion engine vehicles (ICEVs), petrol plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs)—were assessed for 2025–2050 using ReCiPe 2016, IPCC 2021, Cumulative Energy Demand, CML-IA and Ecological Scarcity 2021. Landfilling and recycling scenarios were combined with fuel- and energy-cycle modelling, including well-to-tank (WTT) and tank-to-wheel (TTW) emissions and a Paris Agreement-compatible 2050 pathway. Recycling generally outperformed landfilling, reducing greenhouse gas emissions by 26–35%, cumulative energy demand by 28–59%, carcinogenic air emissions by 27–43% and heavy-metal impacts on soil by 62–80%, although eutrophication revealed category-specific trade-offs. BEV and FCEV configurations were particularly sensitive to material recovery and energy-supply decarbonisation, whereas ICEV impacts remained dominated by fuel use. The findings show that sustainable SUV design requires strategic alignment of product architecture, circular supply chains, recycling technologies and low-carbon energy policy. Full article

Background: Manufacturing supply chains must increasingly coordinate cost, environmental impact, and service continuity under demand uncertainty and limited capacity. Methods: This study develops a stochastic mixed-integer linear programming framework for carbon-aware production–distribution planning in an automotive supply chain. The model jointly optimizes production quantities, inventory levels, shipments, truck usage, and lost sales over a multi-period horizon. Demand uncertainty is represented through scenarios, while production- and transportation-related emissions are monetized using an internal carbon price. Lost-sales penalties capture service degradation when demand cannot be fulfilled by the focal plant, and a rolling-horizon analysis evaluates planning responsiveness as demand information is updated. The framework is applied to an industrially inspired, capacity-constrained automotive case with multiple products, production lines, destinations, and demand scenarios. Computational experiments assess carbon pricing, lost-sales penalties, demand volatility, deterministic versus stochastic planning, and rolling-horizon replanning. Results: Results show that carbon pricing mainly acts as an economic valuation mechanism under the studied fixed-structure configuration, whereas lost-sales penalties strongly influence service performance. Demand volatility increases unmet demand, and lower emissions may reflect lower fulfilled demand rather than improved efficiency. Conclusions: The study provides a decision-support framework for evaluating cost–carbon–service trade-offs under stochastic demand while acknowledging single-plant and fixed-routing limitations. Full article

Gigacasting is emerging as a disruptive manufacturing route for automotive body-in-white structures, especially for electric vehicles, by enabling large aluminium alloy components to replace assemblies traditionally produced from stamped and joined sheet-metal parts. This paper presents a technological assessment of aluminium gigacasting against conventional multi-material mix sheet-metal manufacturing. The comparison addresses product architecture, structural performance, manufacturability, factory organisation, cost, repairability, supply chain implications, and sustainability. Gigacasting offers benefits in part consolidation, reduced joining operations, shorter process chains, and potentially lower non-material manufacturing costs, making it attractive for high-volume, low-variant EV platforms and greenfield production. However, these advantages are counterbalanced by challenges, including high capital investment, limited die life, defect sensitivity, dimensional distortion, mechanical-property variation, and reduced repairability. Recent benchmark data also indicate that total part cost and production-phase CO₂ emissions may remain higher than conventional solutions when aluminium material cost, component mass, and aluminium carbon intensity are considered. Conventional sheet-metal architectures retain advantages in modularity, repairability, quality control, tooling flexibility, and lower-risk implementation in brownfield plants. The analysis concludes that gigacasting should not be regarded as a universal replacement for sheet-metal multi-material Body-in-White (BIW) manufacturing but as a platform-dependent technology whose success requires defect control, low-carbon aluminium supply, process-aware simulation and validation, and high and stable production volumes. 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

The palladium market represents a critical role in supporting key industrial sectors and facilitating the energy transition, as it is widely used in the automotive industry, electronics, chemical manufacturing, and hydrogen energy. These sectors influence a steady demand amid tightening environmental regulations and the development of green technologies. The aim of this study is to assess the structural transformation of the global palladium market through 2030 and to project Russian palladium production for 2026–2028 amid the energy transition by applying economic-mathematical methods, including linear regression, the Grey forecasting model, exponential smoothing, and Autoregressive Integrated Moving Average (ARIMA) time-series modeling. Particular attention is paid to the analysis of factors influencing the dynamics of the global palladium market, including the electrification of transportation, the substitution of palladium with alternative materials, and changes in global supply chains. The simulation results showed that the exponential smoothing model possesses the highest predictive accuracy, enabling it to estimate future palladium production volumes. The market is undergoing a structural transformation: declining demand from the traditional automotive sector is partially offset by the development of new applications in hydrogen energy, electronics, and advanced materials, suggesting that technological improvements can compensate for the loss of conventional demand segments. The key findings are (1) exponential smoothing (R² = 0.9812) outperforms linear regression, Grey model, and ARIMA; (2) Russian palladium production is projected at 74–130 tonnes (2026), 63–141 tonnes (2027), and 54–150 tonnes (2028); and (3) the decline in automotive demand is partially offset by new applications. Our findings confirm the need for Russian producers to adapt their strategies to the structural transformation of global demand, deepen domestic processing, and develop new high-tech applications for palladium to maintain their competitive positions amid the energy transition. 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

As the automotive industry undergoes a green transformation, digital upgrading, and increasingly intensive supply chain collaboration, the supply chain finance credit risks faced by small and medium-sized enterprises (SMEs) in the sector exhibit characteristics such as multi-source interaction, nonlinear transmission, and class imbalance. This study uses 210 SMEs in China’s A-share automotive sector from 2020 to 2024 and constructs a credit risk evaluation system covering 56 indicators across the macro environment, financing enterprises, supply chain characteristics, and core enterprise credit support. Methodologically, DE-LightGBM is employed for feature selection to reduce redundancy and noise, while TabPFGen is introduced to generate synthetic risk-class samples. Business logic constraints and a Nearest Neighbor Distance Ratio filtering mechanism are further applied to improve the plausibility and fidelity of generated samples. Empirical results show that the TabPFN model achieves superior predictive performance after feature selection and data augmentation, and the Wilcoxon signed-rank test confirms the effectiveness and stability of sample augmentation. In addition, the ablation experiment demonstrates that green-related features provide significant incremental predictive value for supply chain finance credit risk identification. The proposed framework provides a useful reference for SME credit assessment, risk early warning, and green financial resource allocation in the automotive industry. Full article

Platinum group metals (PGMs: Pt, Pd, Rh, Ru, Os, Ir) are critical strategic metals. Spent automotive catalysts (SACs) represent one of the most significant secondary sources of PGMs, and their recovery is essential for alleviating the supply–demand imbalance. In the recycling chain, pyrometallurgical processing of SACs generates Fe-Si-based alloy concentrates (termed Fe−Si−PGMs), serving as an important yet challenging intermediate resource for PGM recovery. This review first summarizes the pyrometallurgical and hydrometallurgical processes used for recovering PGMs from SACs, before shifting its focus to the treatment technologies for PGMs in Fe–Si–PGMs alloy. These techniques, including direct extraction, extraction following desilication (via alkaline roasting, slagging, or hydrometallurgical routes), and in situ mechanochemical extraction, are critically evaluated in terms of their advantages and limitations. Furthermore, given that the accurate quantification of trace-level yet high-value PGMs represents another key challenge in the recovery chain due to complex sample matrices, this work systematically outlines and compares the analytical methods commonly employed, such as fire assay, spectroscopic and mass spectrometric techniques, electrochemical methods, and alkali fusion. Finally, several recommendations are provided regarding PGM recovery from SACs, with emphasis on Fe−Si−PGMs alloy processing and analytical methods for PGMs. Full article

The automotive industry in Mexico faces significant logistical challenges in optimizing distribution routes, particularly in border regions, where traffic variability directly affects operational performance. This study proposes a multiperiod route optimization approach for a Tier 1 automotive supplier by applying the Floyd–Warshall algorithm to a cross-border transportation network. Distance matrices are constructed for multiple time windows to capture traffic-related variations in route efficiency. The algorithm is applied independently to each scenario, enabling the identification of time-dependent optimal routes and the development of alternative routing strategies. The results show that optimal routes vary across different periods of the day, leading to measurable improvements in routing efficiency and enhanced decision-making flexibility. The proposed approach supports more realistic logistics planning in congested urban environments and improves operational performance in cross-border automotive supply chains. Full article

Background: A system dynamics model was developed to assess how United States (U.S.) tariff policies impact production, exports, labor, and Gross Domestic Product (GDP) in the Mexican automotive sector. Methods: Beginning with a baseline scenario without tariffs, the model introduces increases of up to 25% in exports to the U.S. Additionally, it analyzes a tariff increase alongside variations in demand elasticity (−0.5 to −1.6) over a 24-month period. Results: The results indicate that a 25% tariff leads to a sustained decrease in production, GDP, and exports, although adjustments in employment occur with some delay. Specifically, production volume contracts by 34.9%, and exports contract by 31.3%. Furthermore, a greater absolute elasticity of demand corresponds to a more significant decrease in production. Conclusions: The findings indicate that geographic diversification is not an immediate substitute for the current export structure, as it requires significant logistical, organizational, and capital adjustments. Reducing dependence on the U.S. market would involve reconfiguring supply chain management, diversifying suppliers, and adapting distribution networks to gain greater flexibility. The sector’s vulnerability therefore lies in its high concentration in a single destination market, which restricts production adaptability in Mexico and weakens the capacity to respond efficiently to disruptions in the supply chain. Full article

The adoption of circular economy (CE) practices and artificial intelligence (AI) in the supply chain (SC) has become extremely significant in manufacturing organizations. The CE seeks to facilitate sustainable growth by managing the flow of materials and energy within closed-loop systems. The CE has resulted in the development of sustainable business models. AI capabilities transform work activities, data flows, and organizational processes. Therefore, the present study aims to develop a framework to improve circular supply chain (CSC) adoption in the automobile manufacturing sector by identifying and analyzing CE practices and AI-enabled digital strategies. The proposed framework was analyzed by employing a hybrid approach of Prioritized Weighted Average–Criteria Importance Through Intercriteria Correlation–Preference Ranking Organization Method for Enrichment Evaluations-II (PWA-CRITIC-PROMETHEE-II) under an Interval-Valued Fermatean Fuzzy (IVFF) environment. IVFF-CRITIC was employed to determine the CE practices’ weights, while IVFF-PROMETHEE-II was utilized to establish the relative index of AI-enabled digital strategies to enhance the CSC adoption. The key findings of the current study indicate that “AI-enabled infrastructure configuration for circular economy adoption in the supply chain”, “AI-integrated equipment to facilitate adaptability and mass personalization”, and “Robotics and AI-driven manufacturing and material reclamation” are the most significant AI-based digital strategies that support CE practices to enhance the adoption of a CSC and encourage case example manufacturing organizations to align their operations with AI and CE. Moreover, the outcomes of the study will deliver a comprehensive evaluation of CE practices and AI-enabled digital strategies for SC managers, based on the relative indexing obtained through the implementation of the hybrid approach. Full article

This study provides a comprehensive assessment of the global implications arising from China’s dominant position in the electric vehicle (EV) transition. By 2030, under current policy trends, China is projected to account for approximately 57% of the global EV stock (238 million vehicles) and 53% of the worldwide EV-driven oil displacement (2.75 million barrels per day). Its demand for automotive batteries will reach 1516 GWh, representing 47% of the global total. Employing LMDI-I decomposition, we find that China’s outsized impact is driven not merely by the scale but by the higher vehicle utilization intensity (contributing 61% of its advantage) and policy support for efficient vehicle types like plug-in hybrids and two/three-wheelers (contributing 31%). The extreme geographic concentration creates a significant systemic risk; our Monte Carlo simulation indicates a 92% probability that a moderate supply shock in China would trigger a severe global battery shortage. Conversely, China stands to gain substantial economic benefits, estimated at USD 117 billion annually by 2030 (90% CI: 78–173 billion) from the avoided oil imports and potential carbon revenues. These findings highlight a central paradox of the energy transition: while China delivers immense climate and energy security benefits, its dominance introduces unprecedented supply chain vulnerabilities and a highly asymmetric distribution of economic gains, necessitating urgent policy responses for diversification and resilience. Full article

This paper focuses on alternatives to the CAFC-NEV credits policy in the automotive industry of China. It considers a dual-channel supply chain consisting of a manufacturer and a retailer that can simultaneously produce and sell new energy vehicles (NEVs) and internal combustion engine vehicles (ICEVs). Differential game theory is employed to explore dynamic optimal decisions under CAFC-NEV credits and carbon credit policies. The results suggest that the strategies combining CAFC-NEV credits and carbon credit policies are equivalent to a single CAFC-NEV credits policy. Therefore, implementing the carbon credit policy on the basis of the CAFC-NEV credits policy does not affect the increase in NEV range. If the NEV credit score is below a certain threshold, the carbon credit policy will result in a higher range increase and brand goodwill of NEV. In the transition process of implementing the carbon credit policy based on CAFC-NEV credits and subsequently canceling the CAFC-NEV credit policy, the profits of supply chain members change slightly. The findings provide a theoretical basis for the timely exit of the CAFC-NEV credits policy. 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