Manufacturing Management Research Articles

Multimodal sensor-to-machined surface image diffusion for defect detection in industrial processes

Generative models, particularly diffusion-based approaches, have gained significant attention recently due to their ability to create realistic outputs. Despite their potential, the application of these models in manufacturing remains largely unexplored. This work presents a framework that addresses this gap by generating machined surface images guided by multiple sensor inputs in manufacturing. The proposed model integrates information from multiple sensors with varying sampling rates using multimodal embedding and employs a latent diffusion model to translate the fused sensor embedding into an image embedding, which is then converted into a machined surface image. The effectiveness of the framework is validated using real-world time-series data, including force, torque, acceleration, and sound, collected from various industrial processes, such as a carbon-fiber-reinforced plastic drilling process. The results demonstrate the model’s ability to predict defects from the generated machined surface images. The proposed approach can potentially revolutionize prognostics and health management (PHM) in smart manufacturing by enabling sensor-guided visual inspection, defect detection, process monitoring, and predictive maintenance.

Additive Manufacturing Applicability for Efficient Solid Waste Management

Objective: This study intends to perform a theoretical analysis of Additive Manufacturing applicability and integration with Solid Waste Management practices. Theoretical Framework: The main concepts and theories in Industry 4.0, Additive Manufacturing, Solid Waste Management, Cleaner Production, Reserve Logistics stand out, providing a solid basis for understanding the investigation context. Method: This study investigated two of the largest sustainable companies that develop green products and resources for Additive Manufacturing uses operating in the Brazilian market. The information was collected from portals, reports of results, processes, and green manufactured products, according to literature. Results and Discussion: The study results that Additive Manufacturing implementation generated significantly less waste due to technology applied and its usage of green and efficient inputs. Research Implications: This research provides insights into how the results can be applied in Additive Manufacturing Field. These implications could encompass the waste management area and the recycling methods for polypropylene residues. Originality/Value: This study contributes to the literature by analyzing the relationship between additive manufacturing and waste management with the Industry 4.0 concepts, showing additive manufacturing as an alternative way of more sustainable production.

The Convergence of SAP PP, RPA, and IBP: A Comprehensive Analysis of Integration Benefits and Challenges in Modern Manufacturing

The integration of SAP Production Planning (PP) with Robotic Process Automation (RPA) and SAP Integrated Business Planning (IBP) represents a significant advancement in manufacturing and supply chain management. This article examines the synergies, challenges, and potential benefits of combining these technologies in modern production environments. Through a comprehensive analysis of existing literature, case studies, and industry best practices, we propose a framework for seamless integration that enhances operational efficiency, improves forecast accuracy, and enables real-time decision-making. Our findings reveal that successfully implementing this integrated approach can substantially improve production planning, resource allocation, and overall supply chain performance. However, organizations face notable challenges regarding technical complexity, data synchronization, and change management. This article contributes to the growing body of knowledge on digital transformation in manufacturing by providing actionable insights and recommendations for practitioners. Furthermore, it identifies key areas for future research, including the role of artificial intelligence and machine learning in further optimizing these integrated systems.

Analysis of the Use of IoT in Attendance Management and Productivity Monitoring on Employee Performance and Operational Efficiency in Central Java Manufacturing Industry

This study investigates the impact of Internet of Things (IoT) usage in attendance management and productivity monitoring on employee performance and operational efficiency in the manufacturing industry of Central Java. Using a quantitative approach, data were collected from 270 respondents and analyzed with Structural Equation Modeling-Partial Least Squares (SEM-PLS 3). The results indicate that IoT-driven systems in both attendance management and productivity monitoring have a significant positive effect on employee performance and operational efficiency. Specifically, IoT-enabled attendance management improves workforce accountability and discipline, while productivity monitoring enhances real-time feedback and workflow optimization. These findings highlight the critical role of IoT technology in transforming workforce and operational management in manufacturing, contributing to improved performance and efficiency. The study provides valuable insights for decision-makers in the manufacturing industry regarding the adoption of IoT technologies to enhance competitiveness and operational outcomes.

Impact of Reynolds and Prandtl Numbers Coupled with Viscous Dissipation on Mixed Convection in A Square Cavity

Efficient thermal management in industrial manufacturing and electronic cooling systems can be achieved by comprehending characteristics of Reynolds and Prandtl numbers in mixed convection scenarios, which aids in the optimization of heat transfer systems through the utilization of forced and natural convection effects. The impact of Reynolds and Prandtl numbers on mixed convection in a square cavity connected to a moving heated horizontal plate is investigated in this research paper. The convective behavior under different conditions was evaluated by discretizing the flow governing equations, which included the momentum and energy equations, using the finite difference method. The research assessed various fluids, including air (Pr = 0.7), liquid metal (Pr = 0.01), and oil (Pr = 10). The Reynolds number ranged from 0.001 to 100, the Eckert number ranged from 0.01 to 40, and a constant Richardson number (Ri = 1) was maintained throughout. The results revealed that the Reynolds number substantially impacts the velocity and temperature characteristics, especially when coupled with a Prandtl number over one and when viscous dissipation remains constant. The utmost velocity that can be attained within the cavity is significantly diminished as the Reynolds number rises, underscoring the critical importance of dynamic fluid properties in determining heat transfer efficiency and fluid flow characteristics. The research unveiled the critical importance of Reynolds and Prandtl numbers in the field of fluid dynamics concerning enhancing heat transfer attributes for engineering purposes, thereby guaranteeing the effectiveness of thermal systems.

Interior Profile Accuracy Assessment Method of Deep-Hole Parts Based on Servo Drive System.

Dimensional and profile measurements of deep-hole parts are key processes both in manufacturing and product lifecycle management. Due to the particularity of the space conditions of deep-hole parts, the existing measurement instruments and methods exhibit some limitations. Based on the multi-axis, highly precise servo drive system, a novel measuring device is developed. The laser displacement sensors are fed by the flux-switching permanent magnet linear motor, and the part is rotated by the servo motor. On this basis, the assessment methods of roundness, straightness, and cylindricity are proposed by employing the least square method (LSM). Additionally, considering the axial center deviation between the sensors and the part, the rotating center coordinate is optimized by the gradient descent algorithm (GDM). Then, the measurement system is constructed and the experiment study is conducted. The results indicate favorable evaluation error of the LSM fitting and GDM iteration. Compared with the coordinate measuring machine (CMM), the measured results show good consistency. In the error analysis, the angle positioning error of measured point is less than 0.01°, and the axial positioning error is less than 0.05 mm. The proposed system and assessment method are regarded as a feasible and promising solution for deep-hole part measurements.

MediGate: a MedTech product innovation development process from university research to successful commercialization within emerging markets

This study proposes a comprehensive MedTech product innovation development framework tailored for university research commercialization within emerging markets. The MediGate framework, built on the Augmented Stage-Gate model, addresses the unique challenges of MedTech innovation, including regulatory compliance, stakeholder engagement, and market dynamics. The framework integrates critical decision-making criteria for different types of inventions to drive academic research toward commercialization in clinical settings. Through detailed case studies, including innovations like albumin strip test, 3D-printed patient-specific implant, COVID-19 nasal spray, and AI platform for depression detection, and iterative refinement, the framework provides actionable guidelines for navigating the complexities of product development. These guidelines ensure alignment with clinical needs, regulatory requirements, and market strategies. The research highlights the importance of early-stage valuation, reimbursement strategies, legal and IP considerations, and manufacturing and quality management. By offering a structured pathway, this research contributes to the theoretical and practical understanding of MedTech commercialization, aiming to enhance innovation success and healthcare impact in emerging markets.

Artificial Intelligence Adoption and Project Success: A Mixed-Method Study

AI's growing acceptance is changing project management's human-centric approach. Project management is using AI to automate and support duties. This change could improve workflows, decision-making, and project efficiency. The full influence of AI on project success is unknown. There is little empirical evidence linking AI use to project outcomes. This ignorance highlights the necessity to study AI's impact on project management. The project management AI industry is expected to expand 38% annually. Since the late 1980s, AI has improved project management by providing more intelligent and autonomous help. Data privacy, accountability, strategic leadership, communication, innovation, and emotional intelligence are important ethical issues. This study examines how AI adoption affects project success through communication and feedback. This mixed-method study examines how AI adoption affects project success. The quantitative phase measured AI communication, feedback, and project progress via a predefined questionnaire. The sample includes construction, IT, manufacturing, healthcare, and finance project managers and team members. Multiple regression analysis and structural equation modelling were employed in IBM SPSS AMOS to examine AI adoption and project success measures. A qualitative phase of semi-structured interviews with respondents contextualised the quantitative data. Thematic analysis gleaned insights from interview transcripts. AI's impact on project success was examined using integrated data, with ethics in mind. The study examined AI tool-project success relationships using a structural equation model. Communication mode, feedback style, and frequency explained 3% of project success variance. Quantitative research showed that AI communication frequency improves project success, whereas mode and style negatively impact it. Participants' qualitative comments indicated six themes that match quantitative findings, and their replies enhance quantitative results and recommend improvements. The study concluded that AI communication frequency positively increases project success, while mode and style negatively affect it. The mixed-methods approach showed that AI tools alone cannot ensure project success; communication style and frequency are. The study recommended among others that organisations should integrate AI tools into project management systems, match AI communication modes to project team preferences, optimise feedback styles, and provide regular updates to improve AI communication. Project teams need ongoing training.

Linking manufacturing firms with environment: role of green manufacturing and environmental management on firm’s environmental performance with moderating effect of external environmental regulations

PurposeThe purpose of this study is to examine the vital link between manufacturing firms and the environment, delving into the intricate connections among factors affecting these firms. Specifically, it investigates how the environmental performance of manufacturing firms is shaped by their adoption of environmental management practices and the regulatory environment in which they operate.Design/methodology/approachData are currently being collected through a structured questionnaire from employees working in manufacturing firms in Pakistan. Random sampling was used to select the participants. The hypotheses were tested using PLS-SEM analysis.FindingsThe study reveals a positive correlation between green manufacturing practices and superior environmental performance. Effective environmental management systems further help firms reduce their environmental footprint. External environmental regulations play a significant role as moderators, influencing the strength and direction of the relationship between green manufacturing, environmental management and environmental performance.Practical implicationsThe practical implications offer valuable insights and guidance for manufacturing companies seeking to improve their environmental responsibility and performance. Additionally, policymakers gain insights into how regulatory frameworks can be designed or modified to better support sustainability efforts within the manufacturing sector.Originality/valueThis study offers timely insights for sustainable business practices, aligning with corporate responsibility efforts. It contributes to both academic knowledge and provides actionable guidance for fostering environmentally responsible practices in the manufacturing sector.

Operational Efficiency and Business Organization: A Case Study on Mumbai Dabbawalas

Operational effectiveness is essential for organizational success in the complicated world of contemporary business. This study explores the complex factors that support operational efficiency, with a focus on the Mumbai Dabbawalas—a distinctive, long-standing service that is a prime example of great dependability and efficiency. By looking at the Mumbai Dabbawalas, we may learn about the values and methods that allow them to continue performing brilliantly even under trying circumstances. Operational efficiency is the capacity of a business to provide clients with goods or services in the most economical way possible while maintaining a high standard of quality. In order to increase production and profitability, it entails simplifying procedures, cutting waste, and maximizing resources. The planned arrangement of resources, procedures, and practices inside a business with the goal of accomplishing particular goals is known as business organization. These ideas work together to directly affect customer happiness, market competitiveness, and overall sustainability, making them the cornerstone of any successful business. Understanding and putting into practice basic management concepts like Six Sigma, lean manufacturing, and total quality management (TQM) are key to operational efficiency. While Six Sigma focuses on lowering variability and errors in processes, lean manufacturing concentrates on removing waste and enhancing workflow. TQM places a strong emphasis on customer satisfaction and ongoing improvement. When combined, these approaches have the power to completely change an organization's processes, resulting in increased efficacy and efficiency.

Barriers to integrated lean management-industry 4.0 technologies

Lean management is a well-known manufacturing and service supply chain management philosophy. The various industry 4.0 technologies have also received enough attention because of their capabilities and initiated a debate among practitioners and academicians on integrating both paradigms. Despite the potential benefits estimated in many research studies and reports, the implementation status could be more encouraging in organisations. Therefore, in this study, all potential barriers are identified and analysed. These barriers were identified from existing literature and finalised with the help of experts. Further, ‘Interpretive Structure Modeling (ISM)’ was used to reveal their contextual relationship. Finally, the ‘Analytic Network Process (ANP)’ ranked the key barriers based on their importance. The results revealed that ‘lack of skilled and experienced workers’ and ‘lack of government policies’ are the most critical barriers. Based on the study’s outcomes, policymakers could make a more effective strategy to address these critical barriers. Additionally, organizations can adapt the proposed hybrid approach to address the barriers more effectively.

Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review

Energy efficiency in production systems and processes is a key global research topic, especially in light of the Green Deal, Industry 4.0/5.0 paradigms, and rising energy prices. Research on improving the energy efficiency of production based on artificial intelligence (AI) analysis brings promising solutions, and the digital transformation of industry towards green energy is slowly becoming a reality. New production planning rules, the optimization of the use of the Industrial Internet of Things (IIoT), industrial cyber-physical systems (ICPSs), and the effective use of production data and their optimization with AI bring further opportunities for sustainable, energy-efficient production. The aim of this study is to systematically evaluate and quantify the research results, trends, and research impact on energy management in production based on AI-based demand forecasting. The value of the research includes the broader use of AI which will reduce the impact of the observed environmental and economic problems in the areas of reducing energy consumption, forecasting accuracy, and production efficiency. In addition, the demand for Green AI technologies in creating sustainable solutions, reducing the impact of AI on the environment, and improving the accuracy of forecasts, including in the area of optimization of electricity storage, will increase. A key emerging research trend in green energy management in manufacturing is the use of AI-based demand forecasting to optimize energy consumption, reduce waste, and increase sustainability. An innovative perspective that leverages AI’s ability to accurately forecast energy demand allows manufacturers to align energy consumption with production schedules, minimizing excess energy consumption and emissions. Advanced machine learning (ML) algorithms can integrate real-time data from various sources, such as weather patterns and market demand, to improve forecast accuracy. This supports both sustainability and economic efficiency. In addition, AI-based demand forecasting can enable more dynamic and responsive energy management systems, paving the way for smarter, more resilient manufacturing processes. The paper’s contribution goes beyond mere description, making analyses, comparisons, and generalizations based on the leading current literature, logical conclusions from the state-of-the-art, and the authors’ knowledge and experience in renewable energy, AI, and mechatronics.

Development of a Quality Deterioration Index for Sustainable Quality Management in High-Tech Electronics Manufacturing

In high-tech electronics manufacturing, non-quality costs significantly impact organizational profitability and competitiveness. This case study introduces a novel Quality Deterioration Index (QDI) to systematically identify and prioritize root causes of non-quality costs within a leading electronics manufacturer. The primary objective is to integrate sustainable quality management practices that align with green sustainability objectives, such as reducing electronic waste, improving energy efficiency, and minimizing hazardous materials usage. Our comprehensive methodology encompasses a literature review, interviews, document analysis, and statistical analysis of survey data to uncover the influence of procedural, cultural, and environmental factors on quality deviations. The key findings reveal critical areas for improvement, particularly in supply chain inefficiencies, workforce challenges, and procedural gaps. By employing the QDI, we provide a structured framework that enhances both operational efficiency and environmental performance. The novelty of this research lies in its dual approach to simultaneously address economic and environmental performance, offering actionable insights for manufacturers aiming to integrate robust quality management systems with sustainability objectives. This study contributes to the ongoing dialogue on sustainable manufacturing strategies, underscoring the pivotal role of quality management in achieving both economic viability and environmental stewardship. Future research should expand this approach across various industries and global contexts to validate and refine the integration of quality management and sustainability.

Artificial Intelligence in Modernization of Pharmaceutical and Healthcare Industry: A Review

: Artificial intelligence (AI) falls under the purview of computer technology, which analyzes complex data and helps solve problems in different segments. Big Data, Machine Learning, and AI are currently being used by the major pharmaceutical industries to minimize time and costs and increase possibilities. Artificial intelligence is used in the pharmaceutical industry in diverse ways, such as drug discovery and development, clinical trials, disease diagnosis, and different stages in pharmaceutical manufacturing, data analysis, and supply management. Most of the cost and time are involved in drug discovery and clinical trials. Artificial intelligence can minimize human error in data processing, documentation, data integrity issues, and data selection throughout the journey. It works in descriptive, diagnostic, predictive, and prescriptive mode. Major pharmaceutical conglomerates like Pfizer, Roche, Novartis, and Johnson & Johnson have already applied Artificial Intelligence in different segments of pharmaceutical and medicinal science. Tech companies like IBM Watson, Catalia Health, Intel, Microsoft, and Google, in collaboration with pharmaceutical companies, are working in the different areas of drug discovery, early diagnosis, and personalized medicine. Further, AI finds application in the health sector for data management, scanning and evaluation of medical history reports, and finding optimum treatment strategies for chronic care patients. Though lots of research and development are being done on the utilization of artificial intelligence in the pharmaceutical industry, it is still in the nascent stage. This article is our endeavor to study, in detail, the present and future opportunities of machine learning and AI in the pharmaceutical industry as a whole.

Decision-making frameworks in additive manufacturing management: mapping present landscape and establishing future research avenues

PurposeThe study aims to explore the available academic literature on the decision-making frameworks used in additive manufacturing management (AMM).Design/methodology/approachThis research formulates a systematic literature review to determine the research trend of the decision-making framework in AMM. Further, the theory, context, characteristics, and methodology (TCCM) framework is used to identify the research gaps and suggest future research directions.FindingsThe systematic literature review (SLR) delves into overarching research themes within decision-making frameworks in AMM. Additionally, it uncovers trends in article publication, geographical distribution, methodologies utilized, and industry applications. This review not only reveals research gaps but also proposes directions for future exploration.Originality/valueThe key novelty of this research lies in revealing the five most contributing themes of decision-making frameworks in AMM, with the highest contributing theme being AM process selection, followed by part selection for AM. This finding enables decision-makers to make informed decisions to address similar problems while exploring AM technology. Moreover, this research introduces an AM part fabrication roadmap inspired by the literature review. Lastly, the paper highlights key research gaps for future research.

Impact of digital marketing on the growth of SME manufacturing businesses in Kosovo

This paper aims to investigate the impact of digital marketing on the growth of manufacturing SMEs and the impact of business size and managers’ capability of using digital marketing tools as the most effective technique to communicate directly with clients. SPSS statistical software is used to process the data collected via the questionnaire instrument during the interviews with SME manufacturing managers and business owners. For the verification of statistical hypotheses, the respective patterns of linear multiple regression are constructed. The findings show that the use of digital marketing tools has a positive impact on the growth of annual business turnover. The impact of digital marketing on increasing employee numbers and business size is seen as positive, especially when it is associated with increasing levels of education among managers and owners of manufacturing SMEs. These results imply that manufacturing businesses need to use digital marketing tools and develop capabilities that enable them to increase their business in size and be financially competitive. This work extends the development of a different and comprehensive framework to understand how manager competencies and digital marketing tools affect manufacturing companies.

An Introduction to the Impact of Knowledge Management in Lean Manufacturing

An Introduction to the Impact of Knowledge Management in Lean Manufacturing

Assessment of India's Green Hydrogen Mission and environmental impact

To achieve the goal of Green Hydrogen Mission, India needs to work on three fronts concurrently. These are Feedstock Systems, Circular Economy integration, and the Environmental Impact of Hydrogen Emissions. This study addresses a critical research gap by offering a holistic assessment of India's preparedness in these interconnected areas. Prior research has often focused on individual aspects of this challenge. This work examines all three factors simultaneously to gain a clearer picture of India's ability to achieve its ambitious emission reduction goals. It analyzes India's rich feedstock resources, exploring initiatives like methane cracking and green hydrogen blending as emission reduction and sustainable energy solutions. Examining various sectors, including manufacturing and waste management, reveals barriers to circular economy integration, such as economic constraints and lack of awareness, alongside proposed solutions. Furthermore, the environmental impact of hydrogen emissions is scrutinized, stressing the importance of understanding and mitigating hydrogen emissions for a low-carbon economy. The study concludes by advocating for collaborative efforts among policymakers, industry stakeholders, and researchers. This collaboration is crucial to drive India's Green Hydrogen Mission forward through innovative solutions, policy interventions, and technological advancements. By addressing these interconnected areas, India can achieve its Green Hydrogen Mission goals and contribute significantly to sustainable development and climate change mitigation.

Do artificial intelligence system adoptions foster production management supply chain performance in pharmaceutical manufacturing firms? An empirical exploring study from the MENA region

PurposeThe goal of this study is to better understand the driving force behind the use of artificial intelligence (AI) in pharmaceutical manufacturing firms (PMFs) that are recognized as developing countries in the Middle East and North Africa (MENA) region that are listed by the Chambers of the Industries of Jordan, the Kingdom of Saudi Arabia, Morocco, and Algeria. Furthermore, the effect of adopting and using AI in managing raw materials (RMs), products, parts, and components for PMFs through supply chains (SCs).Design/methodology/approachA self-administrated questionnaire survey was used to gather data from 95 out of 511 participating managers (e.g. manufacturing, supplying, IT, operational, and logistical managers) utilizing a quantitative technique with a random sample size. In fact, 18.8% of the 89 different manufacturing firms (MFs) in the MENA area responded, with five to six managers from each company. The raw data was analyzed using partial least squares structural equation modeling (PLS-SEM).FindingsThe study’s findings show that the readiness to embrace artificial intelligence (AI) in the production management supply chain performance (PMSCP) of pharmaceutical manufacturing firms in the Middle East and North Africa (MENA) is positively and significantly influenced directly and indirectly by sustainable strategic supplier reliability (SSSR), shipping process dependability (SPD), technological factors (TFs), and infrastructure transformational development capability (ITDC).Originality/valueAs the studied countries are growing economies, such study findings might offer insightful consequences for stakeholders and policymakers regarding the significance of using artificial intelligence system adoptions in pharmaceutical manufacturing enterprises in the MENA region. The managers may also concentrate on the strong positive direct and indirect links between SSSR, SPD, TFs, and ITDC preparedness to accept AI adoption and its applications and systems in supply chain and production management departments and the consequences of informational and product delivery.

Approximation of the Pressure-discharge Curve in Inline Drip Irrigation System

The efficiency of drip irrigation systems depends directly on the uniformity of water discharge from emission devices. Ideally, all emitters should discharge equal amounts of water, but variations occur due to hydraulic and manufacturing factors. This study established the pressure-discharge relationship curve and determined emitter flow variation caused by the hydraulic and the manufacturer’s coefficients of variation for 2 and 4 lph inline emitters. The power exponent and constant of the pressure-discharge curve were determined by measuring the emitter flow rates at operating pressures ranging from 0.2 to 3.0 kg/cm². The emitter flow rates of 100 emitters were measured at an operating pressure of 1.0 kg/cm² to determine the manufacturing coefficient (Vm) and emitter flow variation (qvar(m)). The discharge exponent was found to be 0.46 for both emitter flow rates, with proportionality constants of 0.692 for 2 lph and 1.387 for 4 lph emitters respectively. The results showed a strong correlation between pressure and flow rate, with RMSE values of 0.51 and 0.34 lph, and coefficients of determination of 0.988 and 0.991 for 2 and 4 lph emitters, respectively. High manufacturing precision was indicated by low Vm values of 0.0491 for 2 lph and 0.055 for 4 lph emitters, while qvar(m) values were 0.261 for 2 lph and 0.283 for 4 lph emitters. Total coefficient of variation (Vq) values were 0.1 for 2 lph and 0.14 for 4 lph emitters, with total emitter flow variations (qvar) of 0.29 for 2 lph and 0.39 for 4 lph emitters. The study established the pressure-discharge curve for inline drip irrigation systems, emphasizing the critical relationship between pressure and flow rate. The derived chart from pressure discharge relationship is a valuable tool for estimating emitter flow variation due to hydraulic variation within the same subunit. Precise manufacturing and effective management of hydraulic variations are essential for ensuring uniform water distribution, optimizing drip irrigation systems, and ultimately enhancing crop yield and resource utilization.