Manufacturing Management Research Articles

Qualitative analysis about best practices and project management tools in intralogistics

Project management in automotive manufacturing is crucial due to the dynamism in updating operating models and high investment in the supply chain and logistics. This article presents a systematic literature review of 134 articles (2011-2023) from four relevant databases (IEEE, ScienceDirect, Scopus, Taylor & Francis) with the purpose of exploring the best practices in project management applied in intralogistics activities. Analyzing aspects such as country, management methods and techniques, solution levels in internal logistics projects, business line, among other items. The findings will identify opportunities that contribute to academic indicators and the generation of proposals in the automotive industry in Mexico, focused on the fulfillment of projects and management according to their context.

HUB3D: Intelligent Manufacturing HUB System

HUB3D represents a cutting-edge solution for managing and operating a 3D printer farm through the integration of advanced hardware and software. It features intuitive, responsive interfaces that support seamless interaction across various devices. Leveraging cloud services ensures the system’s stability, security, and scalability, enabling users from diverse locations to effortlessly upload and manage their 3D printing projects. The hardware component includes a purpose-built rack capable of housing up to four 3D printers, each synchronized and managed by a manipulator arm controlled via Raspberry Pi technology. This setup facilitates continuous operation and high automation, optimizing production efficiency and reducing downtime significantly. This integrated approach positions HUB3D at the forefront of additive manufacturing management. By combining robust hardware capabilities with sophisticated software functionalities and cloud integration, the system offers unparalleled advantages. It supports continuous manufacturing processes, enhances workflow efficiency, and enables remote monitoring and management of printing operations. Overall, HUB3D’s innovative design and comprehensive features cater to both individual users and businesses seeking to streamline 3D printing workflows. With scalability, automation, and remote accessibility at its core, HUB3D represents a pivotal advancement in modern manufacturing technology, promising increased productivity and operational flexibility in the realm of additive manufacturing.

Supply chain model having stochastic lead time demand with variable production rate and demand dependent on price and advertisement

In this present study, a single-manufacturer and single-retailer supply chain management model are formulated for a single product. This study specifically looks at a supply chain with variable production rate, stochastic lead time demand, and price- and advertisement-dependent demand. By incorporating these complex aspects into a model, which enables to examine their combined effects on supply chain performance, this study adds to the body of knowledge. The study reveals unique insights into the complex interplay between pricing tactics, advertising efforts, production dynamics, and the variability brought on by stochastic lead times through meticulous study and modelling. Finally, the total system profit is calculated and optimized with all the decision variables. A classical approach is performed to obtain the optimized solution of the joint profit function along with the decision variables. Two models are discussed in the study: (1) the model with normally distributed lead time demand and (2) the model with distribution-free lead time demand. The joint profit of the supply chain is found to be lesser by 1% for the normally distributed lead time demand than the distribution free pattern. The comparison of the shipment policies and the safety factors for the different distribution patterns of the lead time demand are shown. Though the huge increment in the safety factor for unknown leadtime demand distribution may help in reducing the uncertainity factor and disruptions in the supply chain, but also it may unnecessarily tie up more capital which can be invested in other sectors of the supply chain.

Magnetite Reduced Graphene Oxide/Ordered Mesoporous Carbon Nanocomposite as Effective Adsorbent for Removal of 2-Naphthol in Wastewater

Background: The wastewater released from various industries contains substantial amounts of organic compounds such as dyes and naphthols. However, naphthols are toxic to the environment and human health. So, it is essential to eliminate them, which will contribute to manufacturing and environmental management. Methods: In the work, an eco-friendly method is adapted to synthesize reduced graphene oxide (rGO) using Equisetum arvense plant extract as a strong reducing and stabilizing agent. Then, a hybrid nano adsorbent based on rGO and ordered mesoporous carbon (CMK-3) decorated with iron oxide nanoparticles (Fe3O4@rGO/CMK-3) was prepared as an adsorbent. We investigate the performance of Fe3O4@rGO/CMK-3 to remove 2-naphthol (2-NP). Results: The FE-SEM images exhibited spherical magnetite nanoparticles with sizes ranging from 31 to 47 nm on composite. Efficient removal (90%) of 2-NP from aqueous solution is demonstrated using high surface area Fe3O4@rGO/CMK-3 (initial concentration of 2-NP: 10 mg mL-1, pH: 5.0, time: 30 min, and amount of adsorbent dosage: 3 mg mL-1). The high surface area of Fe3O4@rGO/CMK-3, hydrogen binding, π-π stacking interaction between the benzene rings of 2-NP and graphitic skeleton of hybrid adsorbent facilitate the adsorption of 2-NP on the Fe3O4@rGO/CMK-3. The 2NP removal capacity by (Fe3O4@rGO/CMK-3) showed a significant decrease during five successive cycles. Conclusions: These results promise the potential of high surface area (Fe3O4@rGO/CMK-3) for efficient removal of 2-NP for wastewater treatment.

Optimizing supply chain management: strategic business models and solutions using SAP S/4HANA

In today's highly competitive and rapidly evolving business environment, optimizing supply chain management (SCM) has become a critical imperative for organizations aiming to enhance operational efficiency, reduce costs, and improve customer satisfaction. SAP S/4HANA, a next-generation enterprise resource planning (ERP) suite, offers a comprehensive and integrated platform for streamlining and modernizing SCM processes. This review explores the strategic business models and solutions facilitated by SAP S/4HANA, focusing on its impact on various facets of supply chain operations, including procurement, manufacturing, logistics, and inventory management. The implementation of SAP S/4HANA enables organizations to leverage advanced analytics, real-time data processing, and intelligent automation to optimize supply chain functions. By providing a unified view of supply chain activities, SAP S/4HANA enhances visibility, enabling proactive decision-making and fostering a more responsive and agile supply chain. Key features such as predictive analytics and machine learning algorithms facilitate demand forecasting and inventory optimization, reducing the risk of stockouts and overstock situations. Furthermore, SAP S/4HANA supports the integration of Internet of Things (IoT) technologies, allowing for real-time monitoring of assets and operations. This connectivity enhances the ability to manage logistics and transportation more efficiently, reducing lead times and improving delivery performance. The platform's ability to harmonize data from various sources also aids in supplier collaboration, ensuring better alignment with suppliers' capabilities and enhancing the overall supply chain resilience. Strategic business models enabled by SAP S/4HANA include just-in-time (JIT) production, which minimizes inventory costs, and demand-driven replenishment, which aligns production schedules with actual customer demand. Additionally, the adoption of digital twin technology within SAP S/4HANA allows for virtual modeling and simulation of supply chain scenarios, facilitating strategic planning and risk management. In conclusion, SAP S/4HANA provides a robust foundation for optimizing supply chain management through its advanced technological capabilities and integrated approach. Organizations adopting SAP S/4HANA can achieve significant improvements in supply chain efficiency, agility, and resilience, positioning themselves for sustained competitive advantage in the dynamic global market. This exploration highlights the transformative potential of SAP S/4HANA in redefining supply chain strategies and operational excellence.

Effect of lean six sigma and recent technologies on environmentally sustainable manufacturing and financial management practices

Effect of lean six sigma and recent technologies on environmentally sustainable manufacturing and financial management practices

Revolutionizing Business: The Role of AI in Driving Industry 4.0

The urgency of this research lies in understanding the critical role of Artificial Intelligence (AI) in the rapidly evolving landscape of Industry 4.0. As businesses strive to remain competitive, AI's integration into industrial processes has become essential. The primary aim of this study is to examine how AI drives innovation and efficiency within Industry 4.0. Using a literature review methodology, this research synthesizes findings from existing studies to provide a comprehensive overview of AI's impact on various business sectors, including manufacturing, logistics, and supply chain management. The results reveal that AI significantly enhances operational efficiency, predictive maintenance, and decision-making capabilities. Moreover, the study identifies key challenges in AI implementation, such as data privacy and the need for skilled workforce, while also highlighting the vast opportunities for future advancements. This research underscores the importance of AI in achieving higher levels of customization, quality, and competitiveness, paving the way for sustainable business growth in the digital era

Implementing Lean Manufacturing to Optimize Service Level: An Industrial Lubricating Oils Processing and Marketing Company Case Study

This research aims to implement a lean manufacturing management model using tools such as value stream mapping (VSM), single-minute exchange of die (SMED) and autonomous maintenance (AM) to improve the service level within petroleum refining companies. Moreover, its objective is to mitigate the technical gap that leads to monetary losses for the company in question. In terms of methodology, the research used a case study design focused on pre-test and post-test analyses to identify the key improvements. The results revealed a 35 % reduction in company losses and an increase of 19 % in the service level.

Evaluation of critical success determinants to the implementation of additive manufacturing technology in the spare parts supply chain: a grey causal modelling approach

PurposeAdditive Manufacturing technology (AMT) is swiftly gaining prominence to induce automation and innovation in manufacturing systems. It holds immense potential to change supply chain dynamics by providing the possibility of printing objects on demand. This study thus formulates and analyzes the framework to incorporate AMT to handle the spare parts supply chain management (SPSCM) in capital-intensive industries by identifying and assessing the critical success factors (CSFs).Design/methodology/approachAssessment of the CSFs is performed using the novel Grey Causal Modeling method (GCM) with the objective of making SPSCM resilient and efficient. GCM conducts causal analysis by taking into consideration cause, effects, the objectives, and the situations.FindingsFindings indicate that; Logistics Lead Time (SD4), Time to manufacture (SD3), Management Support (SD11), and Risk Management (SD20) are the most prominent causal factor having a maximum impact when incorporating AMT in SPSCM. The results also reveal that the performance of manufacturing organizations that adopt AMT is substantially influenced by internal and external factors such as Management Support (SD11) and Government Regulations (SD16).Research limitations/implicationsThis research provides valuable information for getting the global spare parts supply chain equipped for the post-COVID age, where digital technologies such as AMT will be fundamental for bolstering supply chain resilience and efficiency.Originality/valueThis research proposes a framework for performance assessment when incorporating AMT in SPSCM. Study also demonstrates methodological application of novel Grey Causal Modelling technique using a real case in a spare parts manufacturing industry in India.

Control of Goods Inventory Uses the Accurate Program

In this era of globalization, information systems play an important role in inventory management in trading, manufacturing and service companies. Inventory is an important asset that can generate profits through the production or sales cycle. CV Duta Survey Indonesia, a company engaged in sales, rental, service, and calibration of land measuring instruments, has a variety of products with stock coming from distributors. However, problems often occur related to recording and checking the stock of goods, which results in losses for the company. The system is needed to solve this problem can manage inventory well. CV. Duta Survey Indonesia has used the Accurate program for processing inventory data, but there are still things that need to be corrected in the inventory report. Therefore, the author will try to implement the accounting system with the Accurate program correctly. This community service activity aims to provide outreach and assistance regarding the use of the Accurate program in managing inventory properly, with the hope of improving the inventory administration system in the warehouse. The managerial implication in this research is that Accurate users help ensure accurate inventory data.

DFT and TD‐DFT Study of Many‐Body and Hydrogen Bonding Interactions: Nitrosamine Oligomers

AbstractThis study focuses on the properties and intermolecular interactions of nitrosamine oligomers, which are important in both semiconductor manufacturing and nuclear waste management. The study explores various configurations of nitrosamine oligomers, ranging from monomers to pentamers in linear, ladder, and cyclic clusters, with the finding that dimer cyclic forms and hexamers and above are unstable. Many‐body analysis revealed that two‐body interaction energies made remarkable contributions to the binding energy, while the sum of 3‐, 4‐, and 5‐body energy contributed less, indicating the decrease in stability of nitrosamine from dimer to pentamer clusters. Using advanced computational techniques, such as dispersion‐corrected B3LYP‐D3 and TD‐DFT, the study investigates the vibrational modes and electronic absorption spectra of nitrosamine oligomers. The results reveal that different configurations can significantly affect the vibrational modes and electronic properties of nitrosamine oligomers. The NH2 symmetric and asymmetric modes were particularly intense in various forms of nitrosamine oligomers. The wavelengths of electronic transition, oscillator strength, and HOMO to LUMO gap were reported. Additionally, the study reports on nitrosamine oligomers’ hydrogen‐bonded cooperativity and binding energy contributions. Overall, these findings offer valuable insights into the stability and properties of nitrosamine oligomers, with potential applications in materials science and chemical engineering.

A bibliometric analysis of the Quality Management Journal: 10 years of publications and the pathway forward

The purpose of this paper is to examine the trends in research published by the Quality Management Journal (QMJ) based on Scopus Indexed publications for a period ranging from 2013 to 2023. The paper intends to offer a bibliometric analysis of the journal by highlighting the contributions made by the journal through document citations, prominent authors, most frequent keywords, and publication trends for the last decade. The analysis provides insight into how the QMJ has contributed to the development of the field of quality management. The journal has remained unwavering in its commitment to uphold both quality and comprehensiveness, thereby leading to widespread acclaim and robust indexing As analyzed from the author keyword analysis the contributions made to the journal can be categorized into seven themes, i.e., Leadership Essentials for Quality Management, Six Sigma & Quality, Quality Management in Services, Stakeholder Involvement for Quality Management, TQM & Continuous Improvement in Manufacturing, and Service Quality and Quality Management in Healthcare. A key theme to emerge from this study is that future research and applications must be robustly framed in relevant theory, to recognize, understand, and articulate implications for practice. Experiential quality will move further along the spectrum of socio-technical or socio-materiality.

Managerial ethical decision-making in export-oriented readymade garment units: mediating roles of attitude and moral obligation

Purpose The purpose of this study is to examine the factors that affect managers’ ethical decision-making in export-oriented readymade garments in Bangladesh. Design/methodology/approach This is an empirical study based on the quantitative approach undertaking a cross-sectional survey method where a convenience sampling technique was applied. The analysis was done using partial least square structural equation model applying Smart-PLS version 3.0. Findings This study confirmed that all the components of cognitive appraisal processes, including perceived severity, perceived vulnerability, response efficacy and self-efficacy, have a significant influence on attitude. Attitude, in turn, mediates the relationship between these variables and the behavioural intention of ethical practice, except for perceived vulnerability. Besides, moral obligation is found to mediate the relationship between attitude, self-efficacy and the behavioural intention of ethical decision-making. The study also found that ethical climate and subjective norms have a direct influence on behavioural intention. Furthermore, behavioural intention, ethical climate and self-efficacy are positively related to actual decision-making behaviour. However, this study did not find any direct effect of subjective norms on moral obligation. Practical implications The organization should include an emphasis on building ethical culture and setting an ethical code of conduct within the organization to sustain ethical practice within employees. However, the practitioner should work on enhancing self-efficacy to curb unethical practices by individuals. Originality/value This research contributes to the management of garments manufacturers by a practical and theoretical understanding of what influences the ethical behavioural decision-making process. Valuable guidelines are provided on the ethical decision-making process in the garments manufacturing companies for future researchers.

Robust Thermal Transport across the Surface-Active Bonding SiC-on-SiC.

Surface-active bonding (SAB) is a promising technique for semiconductors directly bonding. However, the interlayer of the bonding interface and the reduced layer thickness may affect thermal transport. In this study, the temperature-dependent cross-plane thermal conductivity of 4H-SiC thin films and the effective thermal boundary resistance (TBReff) of the bonding SiC-on-SiC are measured by the multiple-probe wavelength nanosecond transient thermoreflectance (MW-TTR). The measured temperature-dependent cross-plane thermal conductivity of the 4H-SiC thin film exhibits good quantitative agreement with calculation by density functional theory (DFT) including higher-order four-phonon (4ph) scattering, especially at high temperatures (>400 K). The theoretical calculations indicate the non-negligible importance of 4ph scattering in 4H-SiC high-temperature applications, due to the significantly increasing 4ph scattering rate at increasing temperature and strong temperature dependence of 4ph scattering. The measured nonzero but small TBReff (2.33 + 0.43/-1.15 m2 K/GW) at the SiC-SiC interface is analyzed with molecular dynamics (MD) simulation, indicating that a strong bonding interface with an extremely thin interlayer is formed by the SAB process. Two-dimensional finite element simulations of the experimental equivalent structures are further investigated, and the significant effects (at least 19 °C) of TBReff on the maximum temperature (Tmax) are confirmed. This study provides insight into the fundamental phonon transport and interface thermal transport mechanism in SAB SiC-on-SiC and paves the way for improved 4H-SiC efficient device manufacturing and thermal management.

Impact on Smart Supply Chain in the Context of Industry 4.0: The Case of BYD

Industry 4.0 is one of the key topics put into research. Researchers have found that Industry 4.0 has a lot of theoretical research on manufacturing and supply chain management. However, there still needs to be a research gap in enterprise management and optimization of smart supply chains thanks to the fourth information revolution. Therefore, this paper uses the actual case of a BYD enterprise to analyze its enterprise's problems and use the intelligent supply chain to improve the problems. The research methodology of this paper is as follows: firstly, collect the data on BYD's R&D investment and supply chain structure, as well as the relevant thesis research on BYD's green development and cost management. Secondly, the enterprise data, supply chain structure, and so on are analyzed. Finally, it gives the corresponding suggestions and solutions in combination with intelligent supply chain management. The study shows that big data, smart data analysis, artificial intelligence, and smart supply chain framework should be used to manage BYD's capital investment, restructure the supply chain, and balance cost and sustainable development.

Theoretical study of extreme ultraviolet pellicles with nanometer thicknesses

Extreme ultraviolet (EUV) pellicles are required for EUV defectivity management in high volume manufacturing (HVM). Theoretical analysis of EUV pellicles of nanometer thickness is helpful for these fabrications. In this paper, for maximum transverse deflection, an analytical–numerical method is contrasted against the finite element method (FEM) due to the ratio of thickness and width length of EUV pellicles. The difference was increased at a thickness of micron unit. Single- and multiple-variable methods of linear regression in deep learning were used to overcome the ANSYS limitation based on FEM, such as the meshing of more than 10 μm thickness, and shear loading, an error in FEM resulting from the impact on the stiffness matrix caused by variations in the length-to-thickness ratio increases in the beam element, respectively.

Improving Equipment Effectiveness through Visual Stream Mapping: Some Exploratory Research Findings in the Ready-Made Garment (RMG) Sector

Improving equipment effectiveness is crucial for flexible manufacturing, ensuring that machines and tools perform their functions efficiently and consistently. Our study aimed to enhance the Overall Equipment Effectiveness (OEE) in Bangladesh’s Ready-Made Garments (RMG) manufacturing system. We used the DMADV methodology incorporating Visual Stream Mapping (VSM) and OEE. By utilising VSM, we identified issues, eliminated them in the design phase, and evaluated performance in the verification phase. We automated the material handling system to reduce handling time, and the result was a significantly improved OEE in the automated manufacturing system compared to the manual one. This study has numerous benefits in flexible manufacturing and operations management, from immediate efficiency improvements to long-lasting organisational cultural transformations. Thus, it’s a noteworthy topic for practical applications and research. Enhancing Equipment Effectiveness through Visual Stream Mapping 4.0 has broad-reaching implications, including improved productivity, reduced waste, increased efficiency, better resource utilisation, and a more agile and responsive manufacturing environment. Although OEE and VSM are frequently used separately in different manufacturing systems, this study’s novelty lies in their combined application within garment manufacturing.

An In-Depth Exploration of Unconventional Machining Techniques for INCONEL® Alloys.

Build-up-edge (BUE), high-temperature machining and tool wear (TW) are some of the problems associated with difficult-to-machine materials for high-temperature applications, contributing significantly to high-cost manufacturing and poor tool life (TL) management. A detailed review of non-traditional machining processes that ease the machinability of INCONEL®, decrease manufacturing costs and suppress assembly complications is thus of paramount significance. Progress taken within the field of INCONEL® non-conventional processes from 2016 to 2023, the most recent solutions found in the industry, and the prospects from researchers have been analysed and presented. In ensuing research, it was quickly noticeable that some techniques are yet to be intensely exploited. Non-conventional INCONEL® machining processes have characteristics that can effectively increase the mechanical properties of the produced components without tool-workpiece contact, posing significant advantages over traditional manufacturing.

Circular economy as an imperative for sustainable development

The lack of resources in the modern world necessitates the reorganisation of the conventional linear economy into a new, circular model of economic development, and this requirement is even more urgent for a country at war. The purpose of this study was to prove the necessity and possibility of achieving sustainable development goals even in a crisis, using evidence from Ukraine. Using such methods as statistical analysis, comparison, and synthesis, the dynamics of sustainable development in the European Union and Ukraine were investigated. In addition, relevant experts were interviewed via an online survey. The study obtained and analysed statistics on the transition of European countries to clean energy sources, conducted a detailed investigation of the current legislative framework and state institutions of Ukraine related to recycling war waste, strengthening the digitalisation of Ukrainian society, and studied wartime circular recycling measures. Statistics on the factual destruction and pollution of the environment as a result of hostilities and the temporary occupation of some territories were also examined. Furthermore, information was collected and systematised from the management of Ukrainian manufacturing enterprises on the factual implementation of circular economy principles during the war. The analysis of the data obtained led to the conclusion that the crisis only increases the need to save resources. The practical significance of the study lies in proving that there is no alternative to the goals of sustainable development in the future and emphasising Ukraine’s competitive advantages in this area during the post-war reconstruction. These proposals will be useful for private investors who can profitably finance Ukraine’s future recovery in line with the principles of the circular economy

Optimizing Time-Series forecasting using stacked deep learning framework with enhanced adaptive moment estimation and error correction

Time-series forecasting is essential for decision-making activities, and deep learning models have shown promising results in this field. However, existing models face challenges in capturing the complex non-linear patterns and randomness of real-world time-series data. To address this issue, we propose a novel stacked deep learning framework that combines a bi-directional long-short-term memory network, convolutional 1D, and max-pooling-1D layers in each stack. We also introduce an enhanced version of adaptive moment estimation and an error correction method to improve the model's accuracy and convergence speed. We evaluate our model's performance using four real-world datasets (air quality, PM2.5, electricity price, and securities lending) and compare it with existing models based on statistical measures such as mean absolute error, mean squared error, and standard deviation. The results demonstrate that our proposed model outperforms existing models significantly, achieving a ∼ 52 % improvement in mean absolute error for air quality data, a ∼ 36 % improvement in mean squared error for the PM2.5 dataset, and a ∼ 27.5 % improvement in mean squared error for electricity price data. The enhanced adaptive moment estimation and error correction method further improve the model's accuracy and generalization capabilities, making it suitable for multi-domain data sets. Our proposed model has potential applications in various fields, including manufacturing, traffic prediction, weather forecasting, environmental monitoring, finance, and energy management.