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Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper introduces a new approach to design and model shop floor control techniques for wafer fabs in the very large-scale integrated circuit manufacturing industry. The new model suggests the scheduling system should be selected by considering the type of work centres – whether they are sequential or batch – and the relevance of set-up times and capacity utilisation rate. The proposed scheduling model has been tested via a simulation model of the existing system, and by using data coming from the field, which leads to consider real-life performance to be close to the output of the simulation campaign. The main results point out that the performance of batch work centres – even though they represent a reduced portion of the whole set of work centres – can remarkably affect the performance of the whole fab. In addition, relevant improvements in term of throughput rate and capital productivity can be obtained also at the shop floor level via slack-based dispatching procedures. Results are particularly appealing because they can be implemented in a very short period and without significant infrastructural investments. [ABSTRACT FROM AUTHOR]

In the rapidly evolving realm of the Industrial Internet of Things (IIoT), securing shop floor operations, especially in audit processes, is of critical importance. This paper confronts the challenge of ensuring data integrity and trust in IIoT systems by leveraging the capabilities of blockchain technology. The unique characteristics of blockchain, such as its immutable and decentralized ledger, establish a solid and transparent foundation for verifying shop floor transactions and activities. We introduce a privacy-centric approach, meticulously designed to comply with stringent data privacy regulations. This method allows auditors to authenticate both IIoT data and devices, ensuring confidentiality and adhering to regulatory standards. Our practical implementation strategy, tailored for shop floor environments, not only enhances the security of device and data integrity but also showcases robustness against specific adversarial threats, including network intrusion, data tampering, and unauthorized access. The findings indicate that our approach not only strengthens security protocols but also integrates effortlessly with existing IIoT infrastructures. It presents an efficient, scalable solution that elevates the safety and reliability of IIoT ecosystems, making it a significant step forward in the quest for secure and compliant industrial operations.

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. [ABSTRACT FROM AUTHOR]

Copyright of e & i Elektrotechnik und Informationstechnik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper describes a framework for implementing Smart manufacturing Shop floor systems based on the Ubiquitous Augmented Reality technology (SSUAR). The proposed system makes use of data sharing between shop floor resources and a sensor network in order to optimise the production schedules for carrying out projects. The optimisation is performed in real-time and the production scheduling responds to new projects as well as the changing status of resources, such as machines and workers. Ubiquitous augmented reality interface has been developed and utilised as a user interface for the shop floor workers to receive information, instructions and guidance from the experts and manufacturing systems, and to update the systems on task parameters, such as estimated completion times, progress and machine status. A review of related work, methodology and implementation of the proposed system, and a case study are presented in this paper. Using this architecture, real-time scheduling of tasks in the smart shop floor can be achieved. The case study demonstrated the ability of SSUAR to integrate task scheduling with two-way communication between the system and the users. [ABSTRACT FROM AUTHOR]

Augmented Reality (AR) is a pillar of the transition to Industry 4.0 and smart manufacturing. It can facilitate training, maintenance, assembly, quality control, remote collaboration and other tasks. AR has the potential to revolutionize the way information is accessed, used and exchanged, extending user's perception and improving their performance. This work proposes a Pervasive AR tool, created with partners from the industry sector, to support the training of logistics operators on industrial shop floors. A Human-Centered Design (HCD) methodology was used to identify operators difficulties, challenges, and define requirements. After initial meetings with stakeholders, two distinct methods were considered to configure and visualize AR content on the shop floor: Head-Mounted Display (HMD) and Handheld Device (HHD). A first (preliminary) user study with 26 participants was conducted to collect qualitative data regarding the use of AR in logistics, from individuals with different levels of expertise. The feedback obtained was used to improve the proposed AR application. A second user study was realized, in which 10 participants used different conditions to fulfill distinct logistics tasks: C1 — paper; C2 — HMD; C3 — HHD. Results emphasize the potential of Pervasive AR in the operators' workspace, in particular for training of operators not familiar with the tasks. Condition C2 was preferred by all participants and considered more useful and efficient in supporting the operators activities on the shop floor. [ABSTRACT FROM AUTHOR]

We examine the effect of plant managers on productivity using unique matched manager-plant panel data on U.S. auto-assembly plants during 1993–2007. Our econometric approach is two-pronged. Our first approach relies on using the panel nature of our data to measure variation in productivity due to managerial influence. We estimate the interquartile range of the effect of individual plant managers on average hours-per-vehicle to be about 30%. Further, we find that plant managers' experience with the models that are in production ameliorates the negative impact of new model introductions on productivity. We also observe evidence that managers' plant-specific tenure has a positive impact on productivity. In our second approach, we use high-frequency time-series data, along with structural-break tests and machine-learning methodologies, to predict variation in production using plant-manager switches. We find that a plant manager's identity is predictive of changes in both the mean and variance of production, further highlighting their channels of managerial influence. These findings are robust to narrowing the sample to focus on retirements as an exogenous source of managerial switches. This paper was accepted by Serguei Netessine, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.4427. [ABSTRACT FROM AUTHOR]

Accident prevention on the manufacturing shop floor is essential. This paper aims to develop a proximity warning system capable of reducing accidents on the manufacturing shop floor, by notifying employers of any possible danger within the workplace. The system consists of an ultrasonic sensor, Arduino microcontroller, alarm system and an easy to understand user interface that can detect and alert workers within the danger zone. The system was developed using the Unified Modelling Language, python programming language and the Arduino software. Preliminary test conducted on volunteer participants revealed that the system is easy to use and provides real-time easy-to-understand audio and visual feedback to workers. The system is useful for reducing accidents on shop floors.

Offsite construction (OSC) is an innovative approach where building components (e.g., panels or modules) are manufactured in a shop floor environment, then transported to, and installed at the site. Although there are numerous benefits inherent to the OSC approach, practitioners still struggle to provide tailored projects to their clients due to the low level of flexibility in production caused by uncertainty, multiple projects, and variable market demands. Indeed, the lack of production flexibility limits shop floors to manufacture projects efficiently in an ever-changing environment, especially when processes are still labor-intensive and are not leveraged by autonomous systems, such as a digital twin (DT). Hence, this paper proposes the use of a DT to improve production on OSC shop floors by increasing flexibility, i.e., the ability to adapt to uncertainty, through the automated reassignment of multiskilled workers based on data pertaining to production status that are updated in near real-time. The present study presents key metrics adopting a lean thinking approach for waste identification that quantifies the improved production performance attributable to the proposed DT. Using simulation as a surrogate system, this research evaluates the production performance on the shop floor according to different simulated scenarios varying the number of interventions made by the DT and multiskilling configurations. Moreover, this research considers significant aspects of multiskilling such as reduced productivity, increased cost, and the time spent when moving between workstations during reassignment. The primary findings from the system's practical application indicate a significant improvement in production due to the reduction of waiting waste, total production duration, and total production cost being reduced by 62%, 40%, and 25%, respectively. Finally, the present study presents a novel approach to increasing flexibility on shop floors while also demonstrating the benefits attributable to the use of a DT to manage multiskilled workers in OSC. [ABSTRACT FROM AUTHOR]

The industrial domain is experiencing a so-called fourth industrial revolution in which the evergrowing complexity of manufacturing information, the increasing amount of knowledge and the use of web-oriented techniques, represent three crucial factors that are accelerating the growth of complexity of industrial systems. On the other hand, continuous-evolving requirements in industrial environments, due to technology outbreaks and a new global marketplace, have led to an on-going evolution of human resource management through the creation and adoption of alternative business models. In the past decade, semantic models such as ontologies have been proven to be effective for many knowledge-intensive applications, since they provide formal models of domain knowledge that can be exploited in different ways. For all these reasons, an innovative human resource optimisation (HRO) engine is introduced, which employs semantically enhanced information and conditional random field (CRFs) probabilistic models with knowledge derived from industrial shop floor level, and proposes the right person for the right job in real-time shop floor operations towards optimising decisions on how to implement and schedule either repeatedly or non-occurring tasks. Industrial information data flow and semantic enrichment were ensured through the combined use of a common interface data exchange model (CIDEM) and ontologies, after which a feasibility study at a chemical plant presented interesting preliminary results. [ABSTRACT FROM AUTHOR]

Accident prevention on the manufacturing shop floor is essential. This paper aims to develop a proximity warning system capable of reducing accidents on the manufacturing shop floor, by notifying employers of any possible danger within the workplace. The system consists of an ultrasonic sensor, Arduino microcontroller, alarm system and an easy to understand user interface that can detect and alert workers within the danger zone. The system was developed using the Unified Modelling Language, python programming language and the Arduino software. Preliminary test conducted on volunteer participants revealed that the system is easy to use and provides real-time easy-to-understand audio and visual feedback to workers. The system is useful for reducing accidents on shop floors. [ABSTRACT FROM AUTHOR]

In the present article, the authors have employed the Value Stream Mapping (VSM) technique for the existing shop floor process in an earthmoving equipment manufacturing unit. Thereafter, an Artificial Neural Network (ANN)-based information processing technique has been used for generating a prediction model of shop floor management. For developing the ANN-based prediction model, the production time involved in different processes has been collected for 31 working days. This collected data has been used for training and testing the ANN model. Thereafter, to validate the developed ANN model, more 7 days data has been collected and compared with the predicted values of model for the same input attributes. From the results, it has been found that the performance of the developed model is highly adequate for the prediction purpose with the MSE and MAE values for training data and testing data as 0.0008105545 and 0.0000008979 and 0.01012315 and 0.0001658978, respectively. Based on the acquired results it is evident that the proposed methodology may be significant in predicting the production time of the anticipated shop floor. [ABSTRACT FROM AUTHOR]

The competition of companies for market share has confronted manufacturing environments with several problems that end up limiting their earnings. In this sense, this work aims to apply the Theory of Constraints in a manufacturing cell using simulation software. The work was carried out in a company in the region that operates in the industrial sector. The work methodology has a quantitative design, with the object of study in a production cell of an agricultural machinery and implements industry, which was intentionally chosen due to the high rate of delivery delays. The computer simulation tool was used to predict bottlenecks within the manufacturing environment, through the application of the five steps of the Theory of Constraints. With the correct insertion of data in the software, indicators were found that enabled the analysis of various scenarios. Among them, the main highlight was the beginning of meeting monthly demand, the reduction in operating costs, the 88.24% reduction in inventory volume, and the 55.51% increase in the production capacity of the initial bottleneck. [ABSTRACT FROM AUTHOR]

The information flow in a company is a whole set of static or dynamic data obtained from communication used for the conception and realization of products; facing competition, every company aims at improving its performance to meet customer needs. The focus on information flow to improve the performances of companies has always been a major preoccupation in the research domain, which always contributes to the evolution of the analysis of information flow for companies to be more efficient. This article presents the state of the art on the analysis of information flow model based on information characteristics in shop floor manufacturing companies (known as advanced in technology) to ameliorate their performance and future research directions for small- and medium-sized manufacturing companies for developing countries to be more competitive and efficient. [ABSTRACT FROM AUTHOR]

Motivated by the increasing demand and highly customized products, accurate and up-to-date information about the manufacturing process become essential to meet these requirements. In manual assembly activities, performing theoretical planning in simulation environments is a crucial procedure to detect and avoid unreasonable assembly operations. However, the deviations between theoretical and actual assembly actions would result in the failure of the manual assembly planning. Therefore, the verification for the manual assembly planning is significant to ensure the correctness of the actual assembly operations, performing a convergence between the cyber and physical world. The challenges involved in retrieving and utilizing the actual data about the manual activities on a shop floor. In this paper, a self-contained wearable tracking system is proposed and applied to collect the shop-floor data during the manual assembly operations. And then, an unsupervised classification method is applied to empower semantic knowledge to the shop-floor data derived from the workplace. Thus, an automatic spatial–temporal verification for manual assembly planning is carried out, providing indicators to optimize the current manual assembly planning. Experimental results illustrate that the proposed work can perform the spatial–temporal verification for manual assembly task and indicate evidence to improve the manual assembly planning objectively. [ABSTRACT FROM AUTHOR]

The development of simulation models for improving performances is trendy in manufacturing industries. This paper presents simulation model for evaluating and improving performances of a utensils manufacturing plant. Internal benchmarking is employed for comparison under exactly similar conditions. The data of all input variables is collected and statistical models for each process is developed using Arena input analyzer. The simulation model is then developed using SIMIO to optimize throughput, work-in-process, and cycle time. The experiments have been performed under various scenarios, i.e., at different values of the input parameters. The areas for improvement have been highlighted on the basis of results. Furthermore, best and worst case scenarios have been discussed in detail to provide insights for managerial performance improvements by (1) optimizing inter-arrival time, (2) increasing production rate, and (3) reducing the number of rejected parts without disturbing the existing setup of the manufacturing facility. [ABSTRACT FROM AUTHOR]