Product Line Management Research Articles

A quick response data collection and management system for digital twin production line based on cloud-fog-edge computing collaboration

Background A production line is the basic unit of smart factories and smart manufacturing. However, owing to the development of the industrial Internet of Things, sensors, and other technologies, more data are being collected, leading to a data explosion, and the heterogeneous nature of multiple sources makes it difficult to manage data in a unified manner. Methods A production line data collection, storage, and management system based on cloud-fog-edge computing collaboration and a digital twin was designed. Multi-source heterogeneous data were collected and transmitted based on the OPC UA, and an information model of the production line was established. Modules for data mapping, publishing, and receiving were developed to achieve unified data collection and transmission. The data storage and management platform was constructed by front-end and back-end separation technologies. Results The developed data collection and management system was verified for functionality and performance on a digital twin production line. Functional tests show that the system has the functions of data acquisition and transmission, device addition and viewing, device data querying and downloading, data and model visualization, and user rights setting. The average time for edge data collection and transmission is 183.6ms. The average response time of the cloud for fog requests is less than 1s. This shows that the system can satisfy the real-time requirements of a digital twin production line. Conclusions The proposed system is real-time and stable, providing support for big data and virtual-reality interaction in digital twins.

A benchmark dataset with Knowledge Graph generation for Industry 4.0 production lines

Industry 4.0 (I4.0) is a new era in the industrial revolution that emphasizes machine connectivity, automation, and data analytics. The I4.0 pillars such as autonomous robots, cloud computing, horizontal and vertical system integration, and the industrial internet of things have increased the performance and efficiency of production lines in the manufacturing industry. Over the past years, efforts have been made to propose semantic models to represent the manufacturing domain knowledge, one such model is Reference Generalized Ontological Model (RGOM).11 https://w3id.org/rgom However, its adaptability like other models is not ensured due to the lack of manufacturing data. In this paper, we aim to develop a benchmark dataset for knowledge graph generation in Industry 4.0 production lines and to show the benefits of using ontologies and semantic annotations of data to showcase how the I4.0 industry can benefit from KGs and semantic datasets. This work is the result of collaboration with the production line managers, supervisors, and engineers in the football industry to acquire realistic production line data22 https://github.com/MuhammadYahta/ManufacturingProductionLineDataSetGeneration-Football,.33 https://zenodo.org/record/7779522 Knowledge Graphs (KGs) or Knowledge Graph (KG) have emerged as a significant technology to store the semantics of the domain entities. KGs have been used in a variety of industries, including banking, the automobile industry, oil and gas, pharmaceutical and health care, publishing, media, etc. The data is mapped and populated to the RGOM classes and relationships using an automated solution based on JenaAPI, producing an I4.0 KG. It contains more than 2.5 million axioms and about 1 million instances. This KG enables us to demonstrate the adaptability and usefulness of the RGOM. Our research helps the production line staff to take timely decisions by exploiting the information embedded in the KG. In relation to this, the RGOM adaptability is demonstrated with the help of a use case scenario to discover required information such as current temperature at a particular time, the status of the motor, tools deployed on the machine, etc.

Rational design and optimization of self-powered instantaneous dual-parameter triboelectric sensor

Wireless sensing networks are essential for Internet of Things, smart city etc., however its development has been obstructed by the lack of suitable energy sources. Triboelectric nanogenerators (TENG) based self-powered instantaneous wireless sensing systems are particularly attractive for real-time applications owing to its high energy output and conversion efficiencies. However, such sensing systems are not fully optimized and have low sensitivity and instability. In this work, through analytic derivation of frequency and attenuation coefficient, rational system design and optimization are carried out for the first time. By optimizing coil inductive sensor and stabilizing capacitor, the stability of the dual-parameter sensor is greatly improved, achieving ∼0.02% and ∼1% fluctuation for frequency and attenuation coefficient, respectively. Owing to the high stability, a high sensitivity self-powered instantaneous film thickness sensor is obtained, which is capable of self-powered micrometer film thickness measurement (< 9 µm). Besides, during dual-parameter sensing, the maximum error of decoupled distance and resistance can be improved from 7.6% to 4.83%, and 46.23% to 9.64% after optimization, respectively. A self-powered weight and distance instantaneous sensing system is also demonstrated which could be used in conveyer and robotic arm for production line management in the future, showing its feasibility and potential for future real-time monitoring applications.

Enhancing flexibility and safety: collaborative robotics for material handling in end-of-line industrial operations

The manufacturing landscape is rapidly evolving, being characterized by the emergence of Industry 5.0, leading to a growing recognition of the importance of a human-centered, sustainable and resilient approach. In response to these needs, collaborative robotics has emerged as a promising solution to enhance flexibility in industrial operations, while ensuring a higher level of safety. Cobots enable human-robot collaboration without the need for physical barriers, reduce the footprint of the robotic station, and foster a closer working relationship between humans and machines.This article focuses on an application of collaborative robotics in end-of-line industrial operations, specifically in the unloading of products from an automatic machine. By managing repetitive tasks, such as placing the trays near the end of the line of the automatic machine, waiting for them to be filled, and placing them on a trolley, which will then be moved by the human operator, the robot significantly improves efficiency. The proposed solution involves alternating the placement of trays in two rack trolleys, allowing the worker to replace the full trolleys with empty ones at his own timing. The paper examines the potential advantages of using a human-centric approach that emphasizes operator empowerment and human oversight: the collaborative robotic cell and the automation implemented free the workers from the low value-added operations and allow them to oversee and manage multiple machines simultaneously, thus enhancing productivity. The approach behind this proposal is to keep robotic tasks simple, easily reprogrammable and modular, while empowering the human operator to engage in supervision, decision-making, and problem-solving tasks. This collaboration allows the operator to assume a supervisory role, organizing production based on identified needs, and elevating their involvement to production line management tasks.

Product Line Management with Graphical MBSE Views

Product Line Management with Graphical MBSE Views

Construction of a heuristic architecture of a production line management system in the JSW SA Mining Group in the context of output stabilization, quality improvement and the maximization of economic effects

Construction of a heuristic architecture of a production line management system in the JSW SA Mining Group in the context of output stabilization, quality improvement and the maximization of economic effects

Decision support system for power cable insulation vulcanization production line management

The paper proposes a structural model of a decision support system for controlling a cable insulation continuous vulcanization line. The proposed decision support system is based on a mathematical model of the cable insulation continuous vulcanization technological process, a database, and a mode correction algorithm. The DSS will make it possible to quickly develop a new regime in case of using new materials or structures, as well as to correct the current regime in case of unplanned changes during the production process. The core of the DSS is the proposed mathematical model, which is based on conservation laws and is presented as a system of differential equations closed by boundary conditions. The vulcanization process is described taking into account the temperature-time dependence of the kinetic parameters, which made it possible to take into account the uneven heating of the cable, both in the radial direction and along the length. The numerical implementation of the differential mathematical model made it possible to carry out a systematic analysis of the nature of the processes in the vulcanization pipe and to evaluate the influence of various technological, structural and material parameters on the cable insulation vulcanization completion degree. As a result of the analysis, significant parameters were identified that significantly affect the vulcanization process completion degree. Based on the obtained numerical results, technological parameters dependence technological surfaces on the geometry of the product and the properties of the materials used were constructed, a regression mathematical model was proposed that allows determining the values of the process control parameters without resorting to the use of a differential mathematical model. Based on the results of the analysis, an algorithm for correcting the technological regime is proposed, taking into account only significant process parameters. The proposed algorithm allows you to adjust the value of the isolation rate, depending on external influences and deviations from the specified parameters. The results of the study can be used in the production of cable and wire products with vulcanizable insulation, when it is necessary to quickly select the optimal technological mode, take into account changes in the cable design, properties of the insulation material, as well as possible pressure deviations inside the vulcanization pipe.

Multi-Robot Collaborative Flexible Manufacturing and Digital Twin System Design of Circuit Breakers

Circuit breakers (CBs) are mainly designed to interrupt current flow when faults are detected and have been widely used in industrial applications. The existing CBs manufacturing method is semi-automatic and requires a lot of labor. To realize flexible manufacturing, a multi-robot cooperative CBs flexible manufacturing system (CBFMS) is presented in this study. Aiming at the efficiency of the multi-robot cooperative CBFMS key units, a two-arm cooperation robot approach is proposed. The reinforcement learning algorithm is developed to optimize the manufacturing trajectory of the two-arm cooperation robot. To build and optimize the multi-robot cooperative CBFMS, a digital twin (DT) system describing all physical properties of the physical manufacturing plant is constructed for simulation. In the developed DT system, a kinematic control model of the collaboration robot is established. A real-time display of the robot’s trajectory, manufacturing status, and process manufacturing is provided by the data interaction with the physical cell flow between the units. Following this design, a synchronous mapping between the flexible manufacturing DT system of the CBs and the physical workshop is realized, which enables real-time monitoring and management of the physical production line. The experiments’ results show that the manufacturing efficiency, compared with traditional CBs production, is improved by 22%. Moreover, the multi-robot cooperative CBFMS can make process changes according to the production requirements, which can improve the stability of production.

Eden All Natural peanut butter: A small enterprise competing in a saturated South African market

Subject area of the teaching case: The case is particularly well suited to courses in marketing, promotion, integrated marketing communication, or marketing for small and medium enterprises. Student level: This teaching case is aimed at postgraduate students in management or business programmes. Brief overview of the teaching case: This case focusses on the growth direction and product promotion decisions of Debbie Ncube, cofounder and managing director of Eden All Natural (Eden) - an award-winning small enterprise that competes in the peanut butter category - in 2021. Conservative with the use of the company’s financial resources, Ncube has to reconsider Eden’s reliance on word-of-mouth, social media, and network marketing for promoting her range of natural peanut and other nut-based products. The case requires students to identify and evaluate the growth options available to Eden, to consider the strategy decisions around product line management and brand development, and to explore the role of packaging in effective product promotion. Expected learning outcomes: To develop strategic product-market growth options (using the market diversification matrix) for a growing enterprise To recommend what product line management and brand development strategies can be employed as the product mix gets bigger To explore how packaging could continue to be leveraged to grow sales

Modeling the product line management process at the level of a large pharmaceutical distributor

Background. Socio-economic, geographical, logistic and other features of the Russian pharmaceutical market determine a number of requirements for its wholesale segment. The growth of competition, product saturation and information availability for consumers of the necessary information about the characteristics of goods, their advantages and disadvantages directs the activities of pharmaceutical distributors to search for reserves to increase their competitiveness. One of these areas is the optimization of product line management. Objective. The purpose of the study was to develop a model for managing the assortment policy at the level of a large pharmaceutical distributor, based on the results of analyzing the influence of internal and external factors in the Russian pharmaceutical market and the dynamics of the structure of the product line. Methods. The work used the methods of bibliographic description, content analysis, documentary observation, data copying, SWOT analysis and ABC analysis, the method of expert assessments, modeling. Findings. The SWOT analysis of the operating conditions of a large Russian pharmaceutical distributor revealed a number of external and internal factors affecting economic results.

Optimization and Design of General Machinery Production Line Management Process Based on Intelligent Computing Model

Machinery is the most important basic tool for modern times, and it is also an indispensable sharp weapon for creating big country projects. The production line management of general machinery is particularly important. Traditional production line management causes a lot of labor waste, time waste, and low production efficiency, so the efficiency of production line management also determines the quality and output of general machinery. Therefore, it has become an industry consensus to realize intelligent management in the manufacturing process of general machinery. General machinery is not only a complex industrial structure but also has a series of preconditions that are not conducive to production, such as the diversity of parts and the low precision of preparation in the early stage. Therefore, to realize automation in the field of construction machinery manufacturing, it usually faces more challenges. Whether it is traditional production process or automatic production process, the realization of intelligent production line is the primary problem, because both production efficiency and product quality are determined by the efficient production line management efficiency and exquisite process. The workload of production line management is heavy, which is time-consuming, labor-consuming, and expensive. Adhere to the goal of quality first, and how to improve the efficiency of the production line has become the biggest problem at present. Based on the above problems, this paper adopts intelligent computing model to improve the efficiency of production line management and optimize the process. A series of tedious processes from product adoption to final shipment of production lines need intelligent technology to simplify the process, which can effectively improve the production efficiency of general machinery, reduce production costs, and improve the production quality of machinery. Supply chain management theory is used to manage suppliers’ production behavior, so as to reduce costs and improve quality and service, thus improving the competitiveness of batch production lines and enterprises. Advanced manufacturing technology is used to realize automation and flexible production of batch production lines, thus improving the rapid response ability of production lines to market demand.

The Effect of Occupational Health Risk Perception on Job Satisfaction.

This study explored the relationship between occupational health risk perception and job satisfaction. Based on the job demand-resources model and resource conservation theory, eight hypotheses were proposed in this study. In a survey of 237 production line workers and managers, we found that perceived occupational health risks significantly negatively affected job satisfaction. Both work stress and organizational commitment mediate the relationships between perceived occupational health risks and job satisfaction. We also examined whether safety culture could weaken the negative impact of perceived occupational health risks on job satisfaction. However, the results of our study did not support this hypothesis. This study not only helped managers to realize the hazards of occupational health risks, but also encouraged employees to actively participate in safety construction and pay attention to their own health. In addition, we also put forward some targeted intervention measures to reduce the negative impact of perceived occupational health risks on job satisfaction. Therefore, this study had certain practical implications.

Optimization of Costs and Production Process of Fire Dampers

Currently, companies are trying to optimize production, even more so as the economic situation is gradually deteriorating in many countries around the world, which is currently caused mainly by the COVID 19 pandemic. A specific type of production is that on production lines, which are individual workplaces where various production and assembly operations take place. The aim of the article is to analyze the impact of the choice of the control method for the production process on the optimization of costs and the process. The subject of the research is the production of fire-fighting throttles on a production line that has three workplaces. The problem of waste on the production line was identified by direct measurements of time consumption at the individual workplaces and by using images of the working day schedule. The article compares three approaches to production line management and waste reduction. The first approach was based on counting the number of finished products on the production line. The second approach analyzed in detail the duration of the individual operations into which the workplaces were divided. In the third approach, a method of balancing workplaces was used, which brought time and cost savings. The achieved results, using different methods of production management, are analyzed in detail, and the conclusions evaluated. The article contributes a comparison of the achieved results of cost optimization through the course of production of fire dampers. The research results were applied in practice. The research used methods of industrial engineering in the field of ergonomics, technical and economic analysis, and production management. Visualization of hourly performances, downtimes and failures, execution of work procedures, Shop Floor Management and many others, are recommended as ways to implement waste elimination when using the methods of industrial engineering.

Self-powered pumping switched TENG enabled real-time wireless metal tin height and position recognition and counting for production line management

Harvesting high entropy energy using triboelectric nanogenerator (TENG) has attracted more and more attentions. For normal self-powered wireless sensing applications, a significant portion of harvested energy will be consumed by the subsequent power management and wireless communication units, leading to the incapability of real-time wireless sensing. In this paper, we proposed a pumping switched TENG (PSW-TENG) for real-time wireless sensing. An inductor magnetic sensor coil integrated with the TENG forms a LC resonant circuit, converts the pulsed output of TENG into resonant signal with the encoded sensing information，which is significantly boosted using an integrated microswitch and a charge pump, and then transmitted via a laser diode up to 2 m. The sensor system has been utilized to detect metallic components passing through. The self-powered metal component monitoring system has a height recognition sensitivity of 11.7 kHz/mm and a horizontal position recognition sensitivity of 5.6 kHz/mm with the largest error of 8.5 kHz, showing good sensitivity and stability. Based on the sensing system, the types and passing number of tins on a conveyor can be simultaneously identified and counted. All the results have demonstrated the great potential of the real-time self-powered wireless sensing system for production line management.

Research and Design of MES System for Intelligent Production Line of Prefabricated Building Components

Due to the national mandatory requirements for building assembly rate, more and more production enterprises have invested in prefabricated building component production. Because of the low intelligence of production line and the lack of advanced production management system, the problems of component production in terms of standard, quality, efficiency and energy consumption are gradually revealed. Based on the information management, combined with the new generation of information technology, this paper studies the technology, functional requirements and system architecture of MES system of intelligent production line, in order to promote the fine management and information management and control of PC intelligent production line.

The Dual Impact of Product Line Length on Consumer Choice

Although extant literature has argued for both positive and negative effects of product line length on choice, i.e. a “dual impact,” such a possibility has not been empirically investigated. This is the first study to address this issue, using a multiple discrete choice model for horizontally differentiated goods. The authors argue that the dual impact of product line length is due to competing effects of the two constituent dimensions of product line structure: a positive effect of product line width (total number of product configurations offered) and a negative effect of average line depth (average number of SKUs per product configuration). They also examine the moderating role of choice diversification propensity manifested in multiple discreteness. An empirical application in the potato chip market confirms the expectations regarding the competing effects of the two product line dimensions and hence the dual impact of product line length. Furthermore, the negative effect of average line depth is found to be more pronounced for households with higher choice diversification propensity. These findings are not only novel but also meaningful since simulations show that the corresponding effects influence product line management decisions and new product design.

Improving Production Performance Through Multi-Plant Cross Learning

Abstract The advancement in Web-/Internet-based technologies and applications in manufacturing sector has increased the tilization of cyber workspace to enable more efficient and effective ways of doing manufacturing from distributed locations. This work introduces a novel continuous improvement framework to enhance the performance of production lines through multi-plant comparison and learning among identical or similar production lines in different locations by leveraging the information stored on factory cloud. In this work, production data from multiple identical production lines are collected and analyzed to learn the “best” feasible action on critical machines, which offers a new way to optimize the management of product lines. Machine learning and system model are used to find the relationships between the performance index and the available data. A real case study based on multiple similar automotive plants is provided to demonstrate the method and the increases of throughput are predicted.

“Gut in a Dish” Facilitates Drug Development

Genetic Engineering & Biotechnology NewsVol. 41, No. 5 Drug Discovery Tutorial“Gut in a Dish” Facilitates Drug DevelopmentCorning's intestinal organoids capture the subtleties of human tissue and allow researchers to explore complex disease mechanismsSoumita Das, PhD, Pradipta Ghosh, MD, Elizabeth Abraham, PhD, and Shabana Islam, PhDSoumita Das, PhDSoumita Das, PhD, is an associate professor of pathology and chief scientific director of the HUMANOID Center of Research Excellence at the University of California, San Diego. Pradipta Ghosh, MD, is a professor of medicine and cellular and molecular medicine, the executive director of the HUMANOID Center of Research Excellence, and the director of the Institute for Network Medicine at the University of California, San Diego. Elizabeth Abraham, PhD, is a senior product manager at Corning Life Sciences. Shabana Islam, PhD, is a product line manager at Corning Life Sciences. Websites: https://medschool.ucsd.edu/research/inmandwww.corning.com.Search for more papers by this author, Pradipta Ghosh, MDSoumita Das, PhD, is an associate professor of pathology and chief scientific director of the HUMANOID Center of Research Excellence at the University of California, San Diego. Pradipta Ghosh, MD, is a professor of medicine and cellular and molecular medicine, the executive director of the HUMANOID Center of Research Excellence, and the director of the Institute for Network Medicine at the University of California, San Diego. Elizabeth Abraham, PhD, is a senior product manager at Corning Life Sciences. Shabana Islam, PhD, is a product line manager at Corning Life Sciences. Websites: https://medschool.ucsd.edu/research/inmandwww.corning.com.Search for more papers by this author, Elizabeth Abraham, PhDSoumita Das, PhD, is an associate professor of pathology and chief scientific director of the HUMANOID Center of Research Excellence at the University of California, San Diego. Pradipta Ghosh, MD, is a professor of medicine and cellular and molecular medicine, the executive director of the HUMANOID Center of Research Excellence, and the director of the Institute for Network Medicine at the University of California, San Diego. Elizabeth Abraham, PhD, is a senior product manager at Corning Life Sciences. Shabana Islam, PhD, is a product line manager at Corning Life Sciences. Websites: https://medschool.ucsd.edu/research/inmandwww.corning.com.Search for more papers by this author, and Shabana Islam, PhDSoumita Das, PhD, is an associate professor of pathology and chief scientific director of the HUMANOID Center of Research Excellence at the University of California, San Diego. Pradipta Ghosh, MD, is a professor of medicine and cellular and molecular medicine, the executive director of the HUMANOID Center of Research Excellence, and the director of the Institute for Network Medicine at the University of California, San Diego. Elizabeth Abraham, PhD, is a senior product manager at Corning Life Sciences. Shabana Islam, PhD, is a product line manager at Corning Life Sciences. Websites: https://medschool.ucsd.edu/research/inmandwww.corning.com.Search for more papers by this authorPublished Online:4 May 2021https://doi.org/10.1089/gen.41.05.18AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 41Issue 5May 2021 Information© 2021 by GEN PublishingTo cite this article:Soumita Das, PhD, Pradipta Ghosh, MD, Elizabeth Abraham, PhD, and Shabana Islam, PhD.“Gut in a Dish” Facilitates Drug Development.Genetic Engineering & Biotechnology News.May 2021.60-62.http://doi.org/10.1089/gen.41.05.18Published in Volume: 41 Issue 5: May 4, 2021PDF download

Operational efficiency of tow trucks: a case based evidence from an Indian automobile manufacturer

PurposeThe purpose of this paper is to examine and model the in-plant operational efficiency of tow trucks of an automobile manufacturing plant. Even though, tow trucks contribute toward the improvement of operational performance, little case-based evidence prevail in the literature. For this purpose, a case study has been conducted in an Indian automobile manufacturer to address the prevailing issues in material handling (MH).Design/methodology/approachInitially, this paper focuses on grouping of the sequence parts and finding the shortest path among the groups. To elucidate this, an analytical framework based on the distance and stuffing quantity is proposed. A fuzzy Dijkstra’s algorithm is used to solve the issues in grouping of the sequence parts and shortest path among the groups.FindingsThis study addressed the four aspects of MH: move cost, time, distance and material by integrating the function of grouping, finding the shortest path and communication with low cost devices. The result shows that logistics routes and activities should not be interrupted by any of the external factors. The availability of stock is a key performance variable to attain efficiency. In addition to this, effective communication between the truck operators and the production line managers is key performance indicator.Originality/valueThe paper helps the automobile practitioners on increasing the efficiency of tow truck by systemizing the routes. Logistics routes and activities should not be interrupted by any of the external factors. The availability of stock is a key performance variable to attain efficiency. In addition to this, effective communication between the truck operators and the line managers is key performance indicator.

Application Design of Quality Management of Assembly Workshop Process

China's manufacturing industry is developing rapidly. Quality management has become an important link in industrial production and operation activities. The level of workshop quality management has a profound impact on the improvement of production efficiency. The use of information technology to design the quality management application system of the assembly workshop process and solve the problems exposed in the quality management process is of great significance to improve the assembly efficiency of the workshop. In the previous workshop assembly process, offline information transmission was inefficient and error-prone. In response to this risk, this article develops a process quality management system for the assembly workshop, which enables the assembly work and quality management work in the workshop to work together. so that the information can be quickly transmitted and responded to. Therefore, this system guarantees high-quality and high-efficiency production of products. At the same time, a quality management model framework was built. And functions such as production line management, schedule efficiency analysis, quality inspection traceability were completed. It has realized the organic integration of efficient and coordinated operations of operators, quality management, planning and materials in the workshop assembly process. Based on this, the design of the quality management information system for the assembly workshop was completed, which provided strong support for the digital integration of the system, big data processing and intelligent manufacturing.