Shop Floor Research Articles

Mitigation of human factors in aviation maintenance

Human factors during aircraft maintenance have a significant impact on aviation safety. Human factors analysis, therefore, is an essential aspect in aviation. The researchers have analyzed human factors in detail. yet, the existing studies are mostly reactive in nature. In the present study, incidents during aircraft maintenance related to human factors were investigated using the HFACS-ME classification system. Critical human factors and their latent conditions were identified and mapped. Identification of active and latent human factors enables proactive mitigation of weaknesses in the system thus ensuring maintenance safety. Interventions to mitigate human factors and improve the maintenance setup at the shop floor, management, and organization levels were formulated. The proposed model validated that past incidents could be effectively utilized as predictors to identify and mitigate active and latent human factors during aircraft maintenance by implementing proactive interventions.

From CySkin to ProxySKIN: Design, Implementation and Testing of a Multi-Modal Robotic Skin for Human-Robot Interaction.

The Industry 5.0 paradigm has a human-centered vision of the industrial scenario and foresees a close collaboration between humans and robots. Industrial manufacturing environments must be easily adaptable to different task requirements, possibly taking into account the ergonomics and production line flexibility. Therefore, external sensing infrastructures such as cameras and motion capture systems may not be sufficient or suitable as they limit the shop floor reconfigurability and increase setup costs. In this paper, we present the technological advancements leading to the realization of ProxySKIN, a skin-like sensory system based on networks of distributed proximity sensors and tactile sensors. This technology is designed to cover large areas of the robot body and to provide a comprehensive perception of the surrounding space. ProxySKIN architecture is built on top of CySkin, a flexible artificial skin conceived to provide robots with the sense of touch, and arrays of Time-of-Flight (ToF) sensors. We provide a characterization of the arrays of proximity sensors and we motivate the design choices that lead to ProxySKIN, analyzing the effects of light interference on a ToF, due to the activity of other sensing devices. The obtained results show that a large number of proximity sensors can be embedded in our distributed sensing architecture and incorporated onto the body of a robotic platform, opening new scenarios for complex applications.

Optimization design of control charts: A systematic review

AbstractIn today's competitive landscape, fulfilling customer expectations and achieving a competitive edge are crucial for business success. These objectives can be attained by effective monitoring in both manufacturing and service sectors to enhance quality, reduce variation, and augment productivity. The control chart, a widely used tool for this purpose, has attracted significant attention from researchers for its ability to detect anomalies and manage out‐of‐control situations. The optimization of control charts, a central focus of this review, not only enhances the detection effectiveness but also maintains the desired false alarm rate, thus ensuring efficient process control without additional cost, complexity, or operational challenges for shop floor personnel. The optimization process involves adjusting charting parameters like the sample size, sampling interval, and control limits within a hypothesis testing framework, thereby achieving optimal system performance. Numerous optimization models have been developed to enhance control chart performance. This paper introduces a classification scheme to analyze and categorize the existing research on control chart optimization. By conducting a thorough review of more than 240 articles, the study pinpoints research gaps and offers valuable insights, thereby advancing the future research in this domain.

Conceptualising the robotisation of manufacturing work: a thematic analysis of the literature using soft systems thinking as lens

PurposeThe introduction of robots as value-adding “workers” on the shop floor triggers complex changes to manufacturing work. Such changes involve highly entangled relationships between technology, organisation and people. Understanding such entanglements requires a holistic assessment of contemporary robotised manufacturing work, to anticipate the dynamically emerging opportunities and risks of robotised work.Design/methodology/approachA systematic literature review of 87 papers was conducted to capture relevant themes of change in robotised manufacturing work. The literature was analysed using a thematic analysis approach, with Checkland’s soft systems thinking as an analytical framework.FindingsBased on the literature analysis, the authors present a systemic conceptualisation of robotised manufacturing work. Specifically, the conceptualisation highlights four entangled themes of change: work, organisation of labour, workers’ (experiences) and the firm’s environment. Moreover, the authors discuss the complex patterns of interactions between these objects as relationships that defy straightforward cause–effect models.Practical implicationsThe findings draw attention to complex interactions between robotisation and manufacturing work. It can, therefore, inform strategic decisions and support projects for robotisation from a holistic perspective.Originality/valueThe authors present a novel approach to studying and designing robotised manufacturing work as a conceptual system. In particular, the paper shifts the focus towards crucial properties of the system, which are subject to complex changes alongside the introduction of robot technology in manufacturing. Soft systems thinking enables new research avenues to explain complex phenomena at the intersection of robotisation and manufacturing work.

Development of cloud visualization a machining manufacturing system shop floor

<p><span>The industry is currently experiencing the fourth industrial revolution, characterized by the automation of cyber physical systems and advanced connectivity through the internet of things (IoT). This revolution enables real-time monitoring of machines status on the shop floor by leveraging cyber-physical and IoT technologies. This paper describes the results of research that develops IoT and cloud-based visualization for a machining manufacturing system shop floor. Our proposed solution involves an internet of things device equipped with two current sensors to detect machine and spindle current. The sensor connected to an Arduino Nano, which is then connected to Wemos D1 for wireless transmission of data to the cloud. The cloud has been developed to store data and provide visualization applications, in the form of machines layout map to monitor machines conditions in the form of machines ON, machines OFF, spindles ON and spindles OFF in real time.</span></p>

IIoT Low-Cost ZigBee-Based WSN Implementation for Enhanced Production Efficiency in a Solar Protection Curtains Manufacturing Workshop.

Nowadays, the Industry 4.0 concept and the Industrial Internet of Things (IIoT) are considered essential for the implementation of automated manufacturing processes across various industrial settings. In this regard, wireless sensor networks (WSN) are crucial due to their inherent mobility, easy deployment and maintenance, scalability, and low power consumption, among other benefits. In this context, the presented paper proposes an optimized and low-cost WSN based on ZigBee communication technology for the monitoring of a real manufacturing facility. The company designs and manufactures solar protection curtains and aims to integrate the deployed WSN into the Enterprise Resource Planning (ERP) system in order to optimize their production processes and enhance production efficiency and cost estimation capabilities. To achieve this, radio propagation measurements and 3D ray launching simulations were conducted to characterize the wireless channel behavior and facilitate the development of an optimized WSN system that can operate in the complex industrial environment presented and validated through on-site wireless channel measurements, as well as interference analysis. Then, a low-cost WSN was implemented and deployed to acquire real-time data from different machinery and workstations, which will be integrated into the ERP system. Multiple data streams have been collected and processed from the shop floor of the factory by means of the prototype wireless nodes implemented. This integration will enable the company to optimize its production processes, fabricate products more efficiently, and enhance its cost estimation capabilities. Moreover, the proposed system provides a scalable platform, enabling the integration of new sensors as well as information processing capabilities.

Production automation upgrades and the mystery of workers' overwork: Evidence from a manufacturing employer-employee matching survey in China

The automatic production line will alter the workflow and workshop production system, which will significantly impact how the workers perform their jobs. Using employer-employee matching survey data from the manufacturing sector in Guangdong Province, this paper found that production automation upgrades significantly increase working hours. The findings remain robust even after dealing with endogeneity issues. A plausible explanation is that improved general-purpose technology integrated into the production system will damage employees' unique talents by "deskilling". The heterogeneity analysis shows that labor degradation caused by technological change can be effectively mitigated by labor union protection and the accumulation of workers' human capital. Furthermore, production automation upgrades will probably change the way wages are paid, with less use of the piece-rate system. Although workers may receive some wage compensation for overwork, production automation upgrades may have unfavorable consequences (such as mental health, etc.). This study sheds light on how shop floor workers perform their jobs in the face of the wave of automation, offering fresh policy insights for promoting decent work and achieving inclusive growth.

Integration of digital lean principles and line balancing in apparel manufacturing

Purpose: The integration of digitalization into the apparel manufacturing sector has become a strategic imperative, providing companies with a unique competitive edge. This study explores the application of ITEX PMD, an Internet of Things (IoT) data collection device, and evaluates the ITEX digital line balancing program within the context of digital lean management. By leveraging real-time data tracking, big data utilization, and advanced data analysis techniques, companies aim to enhance flexibility, agility, and operational efficiency in their manufacturing processes. Design/methodology/approach: This research delves into operational definitions for men's hoody sweatshirts, detailing the types of machines used, specifying standard task times, and elucidating the precedence relations of operations. The study utilizes ITEX PMD as a real-time production data collection device, generating periodic line reports through the associated software program. Findings: The ITEX digital line balancing program shows promise in assisting shop floor managers to optimize sewing lines with efficiency and seamlessness, contributing to overall operational excellence. The program provides another layer of visibility into the factory, reducing non-value-added activities, and improving efficiency. The ITEX Soft algorithm facilitates an ideal assembly line layout, ensuring a balanced workload among workstations, aligned with the continuous flow principle in the sewing line. Originality/value: This shop floor and software solution not only enhance visibility but also offer a practical means to reduce non-value-added activities, thereby improving overall efficiency. The ITEX Soft algorithm emerges as a valuable tool, contributing to a more balanced workload among workstations and optimizing assembly line layout. This study sheds light on the potential of digital lean principles in reshaping manufacturing processes and fostering operational excellence in the apparel industry.

Automated guided vehicle dispatching and routing integration via digital twin with deep reinforcement learning

The manufacturing industry has witnessed a significant shift towards high flexibility and adaptability, driven by personalized demands. However, automated guided vehicle (AGV) dispatching optimization is still challenging when considering AGV routing with the spatial-temporal and kinematics constraints in intelligent production logistics systems, limiting the evolving industry applications. Against this backdrop, this paper presents a digital twin (DT)-enhanced deep reinforcement learning-based optimization framework to integrate AGV dispatching and routing at both horizontal and vertical levels. First, the proposed framework leverages a digital twin model of the shop floor to provide a simulation environment that closely mimics the actual manufacturing process, enabling the AGV dispatching agent to be trained in a realistic setting, thus reducing the risk of finding unrealistic solutions under specific shop-floor settings and preventing time-consuming trial-and-error processes. Then, the AGV dispatching with the routing problem is modeled as a Markov Decision Process to optimize tardiness and energy consumption. An improved dueling double deep Q network algorithm with count-based exploration is developed to learn a better-dispatching policy by interacting with the high-fidelity DT model that integrates a static path planning agent using A* and a dynamic collision avoidance agent using a deep deterministic policy gradient to prevent the congestion and deadlock. Experimental results show that our method outperforms four state-of-the-art methods with shorter tardiness, lower energy consumption, and better stability. The proposed method provides significant potential to utilize the digital twin and reinforcement learning in the decision-making and optimization of manufacturing processes.

A new description model for enabling more general manufacturing systems representation in digital twin

The demand for high efficiency and flexible production has led to more complex and flexible production organizations in discrete manufacturing systems. How to rapidly construct/reconfigure a workshop digital twin in response to a configurable complex physical production organization becomes a challenging problem. To address this challenge, based on the existing SevenElement description model, we propose an improved Material Nodes-oriented SevenElements (MNOSE) description model. This model is not only capable of describing/expressing inter-device relationships among manufacturing equipment/devices but also intra-device internal relationships between the device and materials handled by it. Its potential impact on shop-floor digital twin is to make an updating of shop-floor digital twin easier and faster because it can easily express and rapidly (re)-construct a shop-floor digital shadow in response to a rapidly physically reconfigured workshop for a different production organization. In addition, the model can drive the logical unification of multiple digital twin services in a plant and improve the efficiency of integration between several information systems. The utility of the description model in describing shop floor is demonstrated by a case study and validated by modeling representations of typical production organizations.

Fitting digital visualization board transitions to shop floor tasks

AbstractThe increased digitalization of shop floors has provided unprecedented opportunities for real‐time sharing of process and resource details. Visualization boards (VBs), which provide ubiquitous technology‐enabled renderings of such details, salient to a local shop‐floor setting, have the potential to play a significant role in this regard. Critical to the effectiveness of this role is the fit between VB design and shop‐floor needs. In our study, we apply task‐technology‐fit theory in case examinations focused on identifying mismatch, implications of mismatch, and putative best practices in designs for future interventions. Our assessment capitalizes on both core operations management design principles as well as technology management design principles. We develop grounded propositions regarding guidelines that should be applied in future VB designs and deployments.

The realities of achieving a Smart, Sustainable, and Inclusive shopfloor in the age of Industry 5.0.

The employment of Industry 4.0 technologies in manufacturing has paved the way to interlinked machinery, shop floor flexibility and personalised production. However, despite the amplified level of productivity and efficiency, such advances may also take a toll on the operators’ skills and well-being. Over and above, worker diversification culminates in the novel ideology of Industry 5.0; where a triad is established between sustainability, human-centricity, and resilience within manufacturing. This broadens the horizon for worker diversification, especially for people with disabilities who may be estranged from the manufacturing shop floor due to an absence of accessibility, stigma, and other lingering challenges. In pursuit of comprehending these hurdles in the form of themes, a set of interviews were conducted with designers and engineers from a range of manufacturing companies, followed by a Reflexive Thematic Analysis Exercise. Three umbrella themes and eight sub-themes revealed an apparent level of friction between manufacturing engineers and the potential of recruiting operators with disabilities, a lack of design knowledge in creating inclusive workstations, and an absence of social sustainability with respect to disability employment in manufacturing. Accordingly, two major areas for future work by engineers, designers and academics are also proposed.

Selecting a modular mining laboratory in the design life cycle

Modular laboratories are becoming increasingly popular in mining operations due to their flexibility, mobility and cost-effectiveness. However, selecting a suitable modular laboratory can be a challenging task as many factors need to be considered throughout the project life cycle. This article provides a comprehensive analysis of the design phase of a temporary research facility, as well as consideration of management and organization to identify the optimal design solution. The authors discuss various project management methodologies such as lean manufacturing and quality management that can help optimize processes and improve efficiency. Key factors such as testing requirements, infrastructure availability, mobility, durability, and environmental considerations are examined. In addition, the article explores the benefits of temporary buildings to permanent buildings in a mining environment. Finally, the authors provide recommendations for selecting a modular laboratory that will provide efficiencies on the shop floor.

Authentic leadership and motivation as moderators of the organizational politics–knowledge sharing relationship: A test amidst crisis

Organizational politics are recognized as a ubiquitous and negative threat to knowledge sharing in organizations, yet less research has examined the boundary conditions that affect this relationship, particularly in interdependent manufacturing contexts. This article aims to explore how authentic leadership, intrinsic motivation, and prosocial motivation bound the effects of organizational politics on the knowledge sharing activities of individuals and workgroups in the shop floor of the Brazilian automotive consortium during a political and economic crisis context (2015–2017). This crisis context was marked by presidential impeachment, high inflation, economic instability, and massive plant layoffs. We sampled 144 shop floor workers using a questionnaire-based survey and established scales, and employed multivariate regression analyses to test our hypotheses. Contrary to expectations, we found that organizational politics increase, rather than decrease, knowledge sharing of individuals and groups. Moderation analyses suggest that worker intrinsic motivation and prosocial motivation significantly moderate the positive relationships between organizational politics and knowledge sharing of individuals and groups, while authentic leadership had no significant effect on this relationship. JEL CLASSIFICATION: D2: Production and Organizations

Implementing Swarm Production System with Multi-Robot Simulation

Swarm Production is a novel paradigm promoting a high degree of flexibility in material flow and reconfigurability in shop floor layout, enabling high-mix production in manufacturing through the induction of autonomous robots. The conceptual description promises a leap within smart manufacturing and the Industry 4.0 domain. Implementing this theoretical concept becomes crucial to the self-organising production domain and gradually learning the relevant technological stack, optimisation methods, objective functions, and shop floor constraints needed for Swarm Production. This research paper addresses the challenges of the simulation of autonomous mobile robots in the production environment and concludes with an exemplified case simulation. A simulation testbed visually demonstrates the concept with planning, scheduling and control systems, providing their feasibility and reliability.

Proposal of a human-in-the-loop-based framework for advancing maintenance applications for collaborative robots

Manufacturing companies are improving shop floor tasks, especially related to assembly, thanks to the installation of collaborative robots, shortly cobots. Despite their reliability, they experience intrinsic difficulties in extending the CBM (condition-based maintenance) solutions from traditional machinery. The inherent challenges are due to the cobot flexibility, on-the-job training from operator, and internal control for trajectory optimization in cobot operations. Therefore, this research work tackles the issue of developing CBM by leveraging on the human-machine collaborative setting, proposing a human-in-the-loop (HIL)-based framework. The framework is demonstrated via a laboratory-scale application through induced functional failures. The demonstration shows that, via entropy-controlled prompting, the operator can provide feedback about CBM algorithms’ prediction accuracy. Based on the current outcomes, a roadmap for further improving the HIL-based solution for cobot maintenance is proposed, with the end purpose of promoting complete monitoring of all the equipment on the companies’ shop floor fostering the smart manufacturing paradigm.

A framework for throughput bottleneck analysis using cloud-based cyber-physical systems in Industry 4.0 and smart manufacturing

The performance of a production system is primarily evaluated by its throughput, which is constrained by throughput bottlenecks. Thus, bottleneck analysis (BA), encompassing bottleneck identification, diagnosis, prediction, and prescription, is a crucial analytical process contributing to the success of manufacturing industries. Nevertheless, BA requires a substantial quantity of information from the manufacturing system, making it a data-intensive task. Based on the dynamic nature of bottlenecks, the optimal strategy for BA entails making well-informed decisions in real-time and executing necessary modifications accordingly. The efficient implementation of BA requires gathering, storing, analyzing, and illustrating data from the shop floor. Utilizing Industry 4.0 technologies, such as cyber-physical systems and cloud technology, facilitates the execution of data-intensive operations for the successful management of BA in real-world settings. The main objective of this study is to establish a framework for BA through the utilization of Cloud-Based Cyber-Physical Systems (CB-CPSs). First, a literature review was conducted to identify relevant research and current applications of CB-CPSs in BA. Using the results of the review, a CB-CPSs framework was subsequently introduced for BA. The application of the framework was assessed via simulation in a real-world manufacturer of marine engines. The findings indicate that the implementation of CB-CPSs can contribute significantly to throughput improvement.

A novel non-intrusive mental workload evaluation concept in human-robot collaboration

The integration of Human-Robot Collaboration (HRC) in industrial robotics introduces challenges, particularly in adapting manufacturing environments to work seamlessly with collaborative robots. A key objective in HRC system optimization is enhancing human acceptance of these robots and improving productivity. Traditionally, the assessment of human mental workload in these settings relies on methods like EEG, fNIRS, and heart rate monitoring, which require direct physical contact and can be impractical in manufacturing environments. To address these issues, we propose an innovative and non-intrusive method that employs cameras to measure mental workload. This technique involves capturing video footage of human operators on the shop floor, focusing specifically on facial expressions. Advanced AI algorithms analyse these videos to predict heart rate ranges, which are then used to estimate mental workload levels in real time. This approach not only circumvents the need for direct contact with measurement devices but also enhances privacy and data security through privacy computing measures. Our proposed method was tested in an HRC experiment to provide preliminary validation. This pioneering use of non-intrusive AI-based vision techniques for real-time mental workload assessment represents a significant advancement in managing human factors in industrial HRC settings.

Generative Shopfloor Layout Design: Challenges and Proposed Modelling Approach

This paper explores the transformative impact of generative design principles on layout design and organization. The paper provides insights into the practical use of generative design in layout environments, emphasizing the optimization of spatial arrangements, workflow efficiency, and human-machine collaboration. The paper presents a novel generative approach proposed for optimizing manufacturing shop floor layouts, utilizing a three-stage process that integrates Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). The first stage focuses on exploration and learning using VAE to generate a diverse pool of solutions, guided by a fitness profile encompassing characteristics such as travel distance, adjacency score, space utilization, ergonomic score, and aesthetics. The second stage employs GANs for intensive layout improvements, introducing a competitive training process where the generator aims to produce layouts surpassing the quality of previous iterations. The third stage fine-tunes the best layouts identified by the GAN.A much more detailed model is required, and a VAE with higher resolution than the one used in the first phase. The manuscript presents a comprehensive methodology, paving the way for future research into dynamic layout adaptation in manufacturing settings.

Five-Stage Framework of Technology Domestication in the Workplace: An Organisational Case Study of Implementing a Mobile Management Software

With the rise of remote working due to the Coronavirus disease 2019 (COVID-19) pandemic, organisations today increasingly adopt mobile collaborative technologies such as instant messaging platforms, enterprise social media, and project management software to facilitate a variety of work and communication practices. While previous studies on workplace digital transformation have surfaced the wide-ranging implications of digital technologies for organisational functioning, the complicated process of integrating new technologies into an organisation’s workflow, especially in Small and Medium Enterprise (SME) settings, is hitherto understudied. Given the pressing need of digitalisation and the myriad challenges faced by SMEs, it is imperative to uncover the complexity of collective decision-making, interaction, and negotiation during the process of implementing digital technologies in the workplace environment of SMEs. As an effort to shed light on this missing link, we conducted an in-depth ethnographic case study on the dynamic process of implementing a self-developed mobile management software in a construction SME in Singapore. Drawing on technology domestication theory, a five-stage workplace technology domestication framework was developed in which various stakeholders, including the company’s CEO, office staff, shop floor workers, and the developer team, engaged in ongoing and multifaceted negotiations revolving around the application of the new technology and the changes it heralded. During the domestication process, the functionality and meanings of the software as well as employee acceptance and user experiences were continuously reconfigured by a constellation of technological, individual, organisational, and sociocultural conditions. This study uncovers the iterative and participatory nature of technology design and implementation in the workplace, thereby opening up a promising approach for SMEs to maximise benefits from digital transformation with limited resources available.