An Integrated approach to energy efficiency in automotive manufacturing systems: quantitative analysis and optimisation

Summary This paper presents a mathematical model to estimate both production frontier and energy demand frontier, and evaluate the energy efficiency in the automotive manufacturing sector using plant-level production and utility consumption data. By using the stochastic frontier analysis, two models—an output efficiency model (production frontier) and an input efficiency model (energy demand frontier)—are developed to analyze the plant's energy efficiency performance relative to the ‘best practice’ among peers. Both the structure and parameters of two stochastic frontier functions are identified to understand the relationship between production inputs (e.g., utilities and plant-specific variables) and the overall productivity. A case study of a set of automotive engine manufacturing plants is conducted and shows that electricity has higher efficiency than other forms of utility in those production-related activities, and the regional and climate factors have significant impacts on energy efficiency. The models provide a way to measure how far from the production frontier as well as how far from the energy demand frontier the plant is. The results also provide useful information about the inefficient energy components in manufacturing facilities. The opportunities of improving plant-level efficiency in automotive manufacturing plants can be revealed by the results. Copyright © 2015 John Wiley & Sons, Ltd.

Automatic sensing devices and computer systems have been widely adopted by the automotive manufacturing industry, which are capable to record machine status and process parameters nonstop. While a manufacturing process always has natural variations, it is crucial to detect significant changes to the process for quality control, as such changes may be the early signs of machine faults. This motivates our study on change-point detection methods for automotive manufacturing. We aim at developing a systematic approach for detecting process changes retrospectively in complex, nonstationary data. The proposed approach consists of nonparametric change-point detection, alarm generation based on change-point estimations, and performance evaluation against historical maintenance records. For change-point detection, three nonparametric methods are suggested—least absolute shrinkage and selection operator (LASSO), thresholded LASSO, and wild binary segmentation (WBS). Multiple decision rules are proposed to determine how to generate alarms from change-point estimations. Numerical studies are conducted to demonstrate the performance of the proposed systematic approach. The different change-point detection methods and different decision rules are evaluated and compared, with scenarios for choosing one set of change-point detection method and decision rule over another combination identified. It is shown that LASSO and thresholded-LASSO outperform WBS when the shift size is small, but WBS produces a smaller false alarm rate and handles the clustering of changes better than LASSO or thresholded LASSO. Data from an automotive manufacturing plant are used in the case study to demonstrate the proposed approach. Guidelines for implementation are also provided.

Automotive manufacturing is energy-intensive. The consumed energy contributes to the generation of significant amounts of greenhouse gas (GHG) emissions by the automotive manufacturing industry. In this paper, a study is conducted on assessing the application potential of such clean energy power systems as solar PV, wind and fuel cells in reducing the GHG emissions of the global auto manufacturing industry. The study is conducted on the representative solar PV, wind and fuel cell clean energy systems available on the commercial market in six representative locations of GM’s global facilities, including the United States, Mexico, Brazil, China, Egypt and Germany. The results demonstrate that wind power is superior to other two clean energy technologies in the economic performance of the GHG mitigation effect. Among these six selected countries, the highest GHG emission mitigation potential is in China, through wind power supply. The maximum GHG reduction could be up to 60 tons per $1,000 economic investment on wind energy supply in China. The application of wind power systems in the United States and Germany could also obtain relatively high GHG reductions of between 40–50 tons per $1,000 economic input. When compared with wind energy, the use of solar and fuel cell power systems have much less potential for GHG mitigation in the six countries selected. The range of median GHG mitigation values resulting from solar and wind power supply are almost at the same level.

Objective: To systematically evaluate the incidence of low back pain (LBP) and analyze its main influencing factors among automobile manufacturing workers in China. Methods: In March 2024, literatures related to LBP of workers in the automotive manufacturing industry were retrieved from China National Knowledge Infrastructure (CNKI) , VIP China Science and Technology Journal Database, Wanfang Database, PubMed, and Web of Science Database. The search time range was from the establishment of the database to March 2024, and the literature was screened according to the inclusion and exclusion criteria. After evaluating the quality of the article using the quality evaluation criteria recommended by the Agency for Healthcare Research and Quality in the United States, Stata 17.0 software was used for analysis. Random effects models or fixed effects models were selected based on the degree of heterogeneity to calculate the combined effect size, and subgroup analysis and analysis of influencing factors of LBP were conducted. Results: A total of 16 articles were included, with a total sample size of 22245 people. The literature quality score ranged from 6 to 8 points. The results of the Meta-analysis showed that the incidence of LBP among automobile manufacturing workers in China was 32% (95%CI: 22%, 42%) . The results of subgroup analysis showed that the incidence of LBP among automotive manufacturing workers aged ≥30 years was 39% (95%CI: 22%, 57%) , which was higher than that among automotive manufacturing workers aged <30 years (24%, 95%CI: 17%, 32%) . The incidence of LBP among automotive manufacturing workers with a length of service of ≥5 years was 40% (95%CI: 23%, 56%) , which was higher than that among automotive manufacturing workers with a length of service of <5 years (24%, 95%CI: 16%, 33%) . The incidence of LBP reported from 2011 to 2017 (39%, 95%CI: 18%, 60%) was higher than that from 2018 to 2023 (28%, 95%CI: 20%, 36%) . Working in an uncomfortable posture (OR=3.72, 95%CI: 2.05, 6.77) , standing for a long time while working (OR=1.97, 95%CI: 1.61, 2.42) , carrying heavy objects (OR=1.93, 95%CI: 1.63, 2.30) , bending over while working (OR=1.86, 95%CI: 1.60, 2.17) and frequent overtime work (OR=2.38, 95%CI: 1.44, 3.92) were both risk factors for LBP among workers in the automotive manufacturing industry (P<0.05) , while sufficient rest time (OR=0.55, 95%CI: 0.48, 0.63) was a protective factor (P<0.05) . Conclusion: The incidence of LBP among workers in China's automotive manufacturing industry is relatively high. Working in an uncomfortable posture, standing for a long time, carrying heavy objects, bending over for work, frequent overtime work and sufficient rest time are the influencing factors of LBP among workers in the automotive manufacturing industry. Preventive measures should be actively taken in response to the above influencing factors to effectively reduce the incidence of LBP among workers in China's automotive manufacturing industry.

An intelligent real-time cyber-physical toolset for energy and process prediction and optimisation in the future industrial Internet of Things

This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates with the plant to obtain new information and update the database in order to improve its training results. Finally the obtained results of the algorithm are used in order to provide a shortterm load forecasting for the different modeled consumption processes.

The automotive manufacturing industry has transitioned in the past 20 years from a central technical focus to an integrated and globally distributed supply chain. As car makers outsource not only a greater portion of their manufacturing, but also their technical design responsibility, a more thorough understanding of both design and manufacturing changes' effect on total vehicle and total production system performance and cost is critical. The distribution of technical responsibility in automotive manufacturing has motivated the development of a specific curriculum in Automotive Engineering at Clemson University in South Carolina, USA, with core focus on the interaction between systems, both in design and manufacturing. In this development, a detailed survey of automotive Original Equipment Manufacturers and major suppliers was carried out. The differences in perceived need between these organization types is explored, and the incorporation of these perceived needs to a new Automotive Engineering curriculum is presented.

The automotive manufacturing industry plays a pivotal role in global economic development, providing transportation solutions while simultaneously facing multifaceted challenges related to environmental health and safety (EHS) practices. This review investigates the indispensable role of EHS practices within the automotive manufacturing sector, highlighting their significance in mitigating environmental impact, ensuring workplace safety, and complying with regulatory standards. Effective EHS practices are integral to managing environmental sustainability within automotive manufacturing. These practices encompass waste management, emissions reduction, and resource conservation strategies aimed at minimizing the industry's ecological footprint. Additionally, the adoption of eco-friendly technologies and processes, such as renewable energy integration and material recycling, contributes to the industry's overall environmental stewardship. Furthermore, prioritizing workplace safety is imperative in the automotive manufacturing sector due to its inherently hazardous operational environments. EHS initiatives focus on risk assessment, hazard identification, and the implementation of preventive measures to safeguard employees from occupational injuries and illnesses. Moreover, fostering a culture of safety awareness through training programs and regular audits promotes a conducive working environment conducive to employee well-being and productivity. Compliance with regulatory standards is a cornerstone of EHS management in the automotive manufacturing industry. Adherence to local, national, and international regulations ensures operational legality and enhances corporate reputation. Through continuous monitoring and assessment, automotive manufacturers strive to stay abreast of evolving regulatory frameworks, thereby aligning their practices with industry best practices and societal expectations. The integration of robust EHS practices is indispensable for sustainable operations and corporate responsibility within the automotive manufacturing industry. By addressing environmental concerns, ensuring workplace safety, and meeting regulatory requirements, automotive manufacturers can uphold their commitment to environmental stewardship and social accountability while maintaining operational efficiency and competitiveness in the global market. Keywords: Environment, Health, Safety, Automotive, Manufacturing, Industry.

Problem statement: The study investigated the prevalence of Musculosk eletal Disorders (MSD) among workers who perform the Manual Material Handling (MMH) task in an automotive manufacturing plant in Malaysia. A cross sectional study was carried out among the workers in an automotive manufacturing plant. Approach: Respondents were selected through random sampling. Then, a Body Parts Symptoms Survey (BPSS) data sheets were given to the workers to obtain the prevalence of MSD among them. Later, a few workers were selected based on the In-House Clinic (IHC) visitations data for interviews. Results: Five hundreds workers showed that the highest prevalence of MSD was Low Back Pain (LBP). It was found from interviews of 17 workers who had 3 or more visitation times to the IHC that the main r eason of the ergonomic risk comes from the task tha t they perform. It was found that the highest prevale nce of MSD was lower back pain, followed by pain at feet/ankle and pain at upper back regions. Almos t one third of the study populations claimed to fee l uncomfortable to their upper back and lower back. Conclusion: It was concluded that the back pain the workers are experiencing may be a result of the ir ignorance in the correct and ergonomic techniques in materials handling.

At present, the digital economy is developing rapidly worldwide, and the global economic system is undergoing profound changes. As an important pillar of traditional industries, the transformation and upgrading of the automotive manufacturing industry's supply chain have a profound impact on the reshaping of the global industrial chain. Although China's automotive manufacturing industry has significant advantages in terms of the length of the industrial chain and the degree of technological integration, its position in the global value chain still faces many challenges, such as insufficient international competitiveness of domestic brands, lagging core technological innovation capabilities, and the immaturity of the development of the new energy vehicle field. This study is based on the data of A-share automotive manufacturing enterprises from 2009 to 2022. It employs a two-way fixed effects model for empirical analysis, aiming to comprehensively evaluate the mechanism of the digital economy's impact on the automotive manufacturing supply chain in terms of efficiency improvement, cost optimization, technological innovation, and green sustainable development. At the same time, the differences in supply chain development of enterprises with different property rights and scales under the background of the digital economy were also explored. The aim is to provide solid academic support and practical suggestions for the high-quality development of supply chains in China's automotive manufacturing industry in the digital economy era, and thereby enhance the competitiveness and status of China's automotive manufacturing industry in the global industrial chain.

Currently, studies on the energy efficiency of manufacturing systems usually lack synthetic and systematic techniques. In this paper, a holistic framework is demonstrated in order to achieve more sustainable manufacturing, which covers machine-level, system-level and life-cycle-level energy efficiency techniques. Based on these, the mechanism of how energy consumption is affected by machining processes and system operation is analyzed to achieve a comprehensive decision on energy efficiency optimization. Four main topics are included in this paper: (1) Hierarchical sustainability goals and metrics for energy-efficient manufacturing; (2) Machine-level machining processes optimization for energy efficiency enhancement; (3) System-level innovations for efficient consumption management; (4) Life-cycle level energy flow modeling and energy recycling strategy. An automotive engine manufacturing system is taken as an example to build a concrete understanding of the application of the framework. Moreover, this holistic framework establishes the theoretical basis for promoting the energy efficiency of automotive engine manufacturing systems. Furthermore, the proposed techniques can provide decision-making support for achieving sustainable manufacturing in a wider scope of mechanical manufacturing.

PurposeThe purpose of this paper is to identify the mediating effect of lean management (LM) dimensions on the relationship between flexible manufacturing systems (FMS) dimensions and operational performance metrics.Design/methodology/approachA survey questionnaire was developed based on previous literature and interviews conducted at the automotive facilities visited that identified 28 lean dimensions, 15 flexibility dimensions and 8 operational performance metrics. The survey was presented to 175 North American automotive manufacturing managers through interviews conducted at 64 automotive facilities. A total of 164 usable responses were studied.FindingsLean and FMS are multi-dimensional philosophies, the results demonstrate that the automotive industry in North America has implemented many dimensions of both philosophies. Each dimension is categorized based on specific organizational metrics. The 64 facilities studied use common dimensions identified by this study for lean and flexibility. Data analysis indicates a direct link between lean, flexibility and operational metrics. It shows that LM plays a critical role in improving the impact of flexibility on performance metrics. The results also indicate that specific lean and FMS dimensions or categories have higher significant roles than others in the relationship between LM, FMS and operational performance metrics.Research limitations/implicationsSince the sample size only considered automotive manufacturing facilities, the results need to be considered with caution.Originality/valueThis research empirically develops a framework linking FMS and LM to operational performance metrics. This research addresses an area within which there is little consensus on the relationship between LM, FMS and their impact on operational performance metrics. The study is unique due to the depth and breadth of the investigation that far exceeds previous literature conducted. In addition, this research highlights the LM and FMS practices on which managers should focus on when attempting to improve operational performance metrics at their facility.

This is the third in a series of three articles on enterprise energy management (EEM) systems featured in Strategic Planning for Energy and the Environment. The first article described the current state-of-the-art in EEM systems and their associated benefits in controlling energy cost, quality, and reliability (see Vol. 22, #4). The second piece considered energy in terms of managing the associated cost and reliability risks to businesses (Vol. XX, #X). As the field of energy management matures, so do the tools and best practices available to ensure that the energy required by an organization is used in the most efficient way possible. In the past, energy management practices consisted primarily of replacing inefficient equipment and then using any number of methods to estimate the savings gained. Studies performed by the Department of Energy (DOE) and the Texas State Energy Conservation Office (SECO) have shown, however, that energy savings can be dramatically increased and maintained over time by adopting and implementing consistent energy management practices and recognized measurement and verification procedures. As energy management standards and best practices begin to see widespread adoption, the information systems required to support them will play a crucial role in their implementation and success. The enterprise energy management systems described in the previous articles will not only address shorter term cost, quality, and reliability concerns, but can also provide the detailed data and analysis capabilities required to ensure that energy management strategies and conservation measures are on track throughout an organization. Organizations can apply EEM systems to gain a comprehensive understanding of current energy performance, plan and select cost-effective energy conservation measures, track performance of measures that have been implemented, and verify the savings realized. Over the last several decades, there has been increasing interest and activity in the field of energy management. A Lawrence Berkeley National Labs (LBNL) study of energy efficiency projects completed by US energy service companies over a ten-year period shows that total project spending has increased from roughly $500 million in 1990 to more than $2 billion in 2000 [1]. Energy management practice has traditionally focused exclusively on technologies that increase the energy efficiency of key energy-consuming processes and equipment. Rebuild America, a US Department of Energy (DOE) energy efficiency program, lists lighting and HVAC equipment upgrades among the most commonly implemented energy efficiency measures [2]. The US DOE Energy Information Administration (EIA) lists a variety of energy management activities for several industrial sectors, including waste-heat recovery and deployment of variable-speed drives [3]. Although there is little doubt that upgrading equipment and processes is a key ingredient to increased energy efficiency, there have always been concerns that traditional deployment practices have not resulted in consistent (and long-term) energy savings. While the LBNL study mentioned above notes a steady increase in energy efficiency project spending over time, it also acknowledges that there is a wide variation in typical energy savings [1]. There has been considerable effort over the last several years to define standards and best practices that increase the performance of energy efficiency projects and make the savings realized more predictable and repeatable. The International Performance Measurement and Verification Protocol (IPMVP), for example, provides best-practice methods for measuring and verifying the results of energy efficiency projects in commercial and industrial facilities [4]. MSE 2000, an energy management standard developed by the Georgia Institute of Technology and accredited by ANSI, specifies a management infrastructure for increasing energy efficiency and reducing costs [5]. Both of these standards move beyond traditional energy efficiency practices and into the realm of more comprehensive strategic energy management practices that resemble the structure and discipline found in best-practice management systems like ISO 9000 and 14000.

Crushing characteristics of double circular composite tube structures subjected to axial impact loading

This paper focuses on exploring the role of digital finance in corporate technological innovation, how it influences technological innovation, and the manifestation of green development in this process. The study centers on China's listed automotive manufacturing enterprises and is based on data from Chinese A-share automotive manufacturing companies listed from 2011 to 2023, examining the mechanism by which digital finance affects technological innovation and the moderating effect of green development level. The research results obtained in this paper show that: (1) Digital finance can promote technological innovation in enterprises, and the effect is obvious. It achieves this by optimizing the allocation of resources to make them more reasonably distributed and reducing the cost required for enterprise financing. In this way, digital finance can effectively enhance the technological innovation capabilities of automotive manufacturing enterprises. (2) Its core mechanism is mainly to alleviate the constraints that enterprises encounter in financing, provide very crucial financial support for the innovation activities of typical capital-intensive industries such as the automotive manufacturing industry, and enable these innovation activities to proceed smoothly. (3) The level of green development of enterprises will positively regulate this relationship. In the automotive manufacturing industry, where the pressure of green transformation is relatively high, the better the environmental performance of those enterprises, the more obvious the impact of digital finance.