Pub Date : 2021-03-01DOI: 10.1177/1063293X21994356
K. Vijayakumar
The network of interconnected and synchronized machines, instruments, and other such devices in the industrial sphere is known as the Industrial Internet of Things. Smart sensors and actuators are integrated into industrial machines to enhance industrial activities and business-related applications with little to no human input. The analysis of the real-time data that is obtained from this vast internetwork of machinery allows for greater streamlining in the industrial processes and thereby provides an even greater benefit to businesses which adopt the IIoT framework. This special edition focuses on analyzing the interdependence and unavoidable overlap of big data analytics and IIoT. Businesses and industrial pursuits are often shaped by dynamic demands, changing environments, and even socio-political flux. In the rapidly evolving world of today, these catalysts of change may make it difficult for businesses to keep pace. As a solution to this problem, IIoT effectively facilitates intelligent industrial and customer-level operations by using advanced data analytics to positively transform business outcomes. With the accelerated advancements in IIoT, we can soon expect billions of interconnected machines to stream unprecedented volumes of sensor data at remarkable speeds. According to a report by the International Data Corporation (IDC), the big data and analytics market, which reached $60 billion worldwide in 2018, is expected to grow at a 5-year compound annual growth rate of 12.5%. An incline of this magnitude can be attributed at large to the growing importance of automation in industrial enterprises. This explosive growth in the number devices in IIoT networks and the consequential rise in the amount of data produced and consumed is an apt reflection of how the growth of big data and IIoT are mutually beneficial to one another. Businesses are benefitted by IIoT in terms of increased revenue, reduced costs, and increased efficiency. However, merely generating a large amount of data is not the end goal. The data streamed from IIoT sensors only become useful if the data is appropriately analyzed. Considering the sheer volume of the influx of data, storing, processing, and analyzing this data is prone to become problematic due to limitations in computational power, inadequate networking capacities, and insufficient storage. Security concerns also pose a large threat to the convergence of IIoT and data analytics. Securely handling data, maintaining it, and extracting the necessary insights from it require a robust security framework to prevent mismanagement and fraudulent use. Implementing such a framework successfully has been a challenge as data analytics in the IIoT context is still at its infancy. IIoT has taken a stronghold in the industrial paradigm with the intention to simplify, streamline, and automate industrial activities to achieve maximum output. Overcoming issues regarding efficient data storage, optimized data processing and analysi
{"title":"Concurrent Engineering: Research and Applications (CERA)– An international journal: Special issue on “Data Analytics in Industrial Internet of Things (IIoT)”","authors":"K. Vijayakumar","doi":"10.1177/1063293X21994356","DOIUrl":"https://doi.org/10.1177/1063293X21994356","url":null,"abstract":"The network of interconnected and synchronized machines, instruments, and other such devices in the industrial sphere is known as the Industrial Internet of Things. Smart sensors and actuators are integrated into industrial machines to enhance industrial activities and business-related applications with little to no human input. The analysis of the real-time data that is obtained from this vast internetwork of machinery allows for greater streamlining in the industrial processes and thereby provides an even greater benefit to businesses which adopt the IIoT framework. This special edition focuses on analyzing the interdependence and unavoidable overlap of big data analytics and IIoT. Businesses and industrial pursuits are often shaped by dynamic demands, changing environments, and even socio-political flux. In the rapidly evolving world of today, these catalysts of change may make it difficult for businesses to keep pace. As a solution to this problem, IIoT effectively facilitates intelligent industrial and customer-level operations by using advanced data analytics to positively transform business outcomes. With the accelerated advancements in IIoT, we can soon expect billions of interconnected machines to stream unprecedented volumes of sensor data at remarkable speeds. According to a report by the International Data Corporation (IDC), the big data and analytics market, which reached $60 billion worldwide in 2018, is expected to grow at a 5-year compound annual growth rate of 12.5%. An incline of this magnitude can be attributed at large to the growing importance of automation in industrial enterprises. This explosive growth in the number devices in IIoT networks and the consequential rise in the amount of data produced and consumed is an apt reflection of how the growth of big data and IIoT are mutually beneficial to one another. Businesses are benefitted by IIoT in terms of increased revenue, reduced costs, and increased efficiency. However, merely generating a large amount of data is not the end goal. The data streamed from IIoT sensors only become useful if the data is appropriately analyzed. Considering the sheer volume of the influx of data, storing, processing, and analyzing this data is prone to become problematic due to limitations in computational power, inadequate networking capacities, and insufficient storage. Security concerns also pose a large threat to the convergence of IIoT and data analytics. Securely handling data, maintaining it, and extracting the necessary insights from it require a robust security framework to prevent mismanagement and fraudulent use. Implementing such a framework successfully has been a challenge as data analytics in the IIoT context is still at its infancy. IIoT has taken a stronghold in the industrial paradigm with the intention to simplify, streamline, and automate industrial activities to achieve maximum output. Overcoming issues regarding efficient data storage, optimized data processing and analysi","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"10 1","pages":"82 - 83"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72673152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-01DOI: 10.1177/1063293X21998087
Kai Qu, Chanjie Li, Feiyu Zhang
A kind of Keller Segel chemotaxis model has a wide range of applications, but its coupling relationship is very complex. The commonly used method of constructing the upper and lower solutions is no longer suitable for the model solution, which results in a long time for its analysis. In this paper, we propose a method to analyze the asymptotic behavior and stability of a Keller Segel chemotaxis model. The previous methods of first formally and then rigorously, the asymptotic expansion of these monotone steady states, and then we use this fine information on the spike to prove its local asymptotic stability. Moreover, we obtain the uniqueness of such steady states. The asymptotic behavior of the solution of a Keller Segel chemotaxis model is analyzed, and the asymptotic rate is calculated; According to the limitation of Neumann boundary condition, the complete blow up of chemotaxis model solution and the stability of the initial value of the complete blow up time are studied, and the asymptotic and stability analysis of a kind of Keller Segel chemotaxis model solution is completed. The experimental results show that the proposed method takes less time to solve a kind of Keller Segel chemotaxis model, improves the efficiency of the solution, and the accuracy of the solution is higher.
{"title":"Asymptotic and stability analysis of solutions for a Keller Segel chemotaxis model","authors":"Kai Qu, Chanjie Li, Feiyu Zhang","doi":"10.1177/1063293X21998087","DOIUrl":"https://doi.org/10.1177/1063293X21998087","url":null,"abstract":"A kind of Keller Segel chemotaxis model has a wide range of applications, but its coupling relationship is very complex. The commonly used method of constructing the upper and lower solutions is no longer suitable for the model solution, which results in a long time for its analysis. In this paper, we propose a method to analyze the asymptotic behavior and stability of a Keller Segel chemotaxis model. The previous methods of first formally and then rigorously, the asymptotic expansion of these monotone steady states, and then we use this fine information on the spike to prove its local asymptotic stability. Moreover, we obtain the uniqueness of such steady states. The asymptotic behavior of the solution of a Keller Segel chemotaxis model is analyzed, and the asymptotic rate is calculated; According to the limitation of Neumann boundary condition, the complete blow up of chemotaxis model solution and the stability of the initial value of the complete blow up time are studied, and the asymptotic and stability analysis of a kind of Keller Segel chemotaxis model solution is completed. The experimental results show that the proposed method takes less time to solve a kind of Keller Segel chemotaxis model, improves the efficiency of the solution, and the accuracy of the solution is higher.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"362 1","pages":"75 - 81"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76507161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-01DOI: 10.1177/1063293X21992014
M. Praveena, B. Bharathi
Duplication of data in an application will become an expensive factor. These replication of data need to be checked and if it is needed it has to be removed from the dataset as it occupies huge volume of data in the storage space. The cloud is the main source of data storage and all organizations are already started to move their dataset into the cloud since it is cost effective, storage space, data security and data Privacy. In the healthcare sector, storing the duplicated records leads to wrong prediction. Also uploading same files by many users, data storage demand will be occurred. To address those issues, this paper proposes an Optimal Removal of Deduplication (ORD) in heart disease data using hybrid trust based neural network algorithm. In ORD scheme, the Chaotic Whale Optimization (CWO) algorithm is used for trust computation of data using multiple decision metrics. The computed trust values and the nature of the data’s are sequentially applied to the training process by the Mimic Deep Neural Network (MDNN). It classify the data is a duplicate or not. Hence the duplicates files are identified and they were removed from the data storage. Finally, the simulation evaluates to examine the proposed MDNN based model and simulation results show the effectiveness of ORD scheme in terms of data duplication removal. From the simulation result it is found that the model’s accuracy, sensitivity and specificity was good.
应用程序中的重复数据将成为一个昂贵的因素。需要检查这些数据的复制，如果需要，则必须从数据集中删除，因为它占用了存储空间中的大量数据。云是数据存储的主要来源，所有组织都已经开始将他们的数据集迁移到云中，因为它具有成本效益，存储空间，数据安全和数据隐私。在医疗保健领域，存储重复的记录会导致错误的预测。同时很多用户上传相同的文件，会产生数据存储需求。为了解决这些问题，本文提出了一种基于混合信任的神经网络算法的心脏病数据中重复数据删除(ORD)的优化方法。在ORD方案中，采用混沌鲸优化(混沌鲸优化)算法对包含多个决策指标的数据进行信任计算。模拟深度神经网络(Mimic Deep Neural Network, mnn)将计算得到的信任值和数据的性质依次应用到训练过程中。它对数据是否重复进行分类。因此，可以识别重复文件，并从数据存储中删除它们。最后，通过仿真验证了所提出的基于MDNN的模型，仿真结果表明了ORD方案在消除重复数据方面的有效性。仿真结果表明，该模型具有较好的准确性、灵敏度和特异性。
{"title":"An approach to remove duplication records in healthcare dataset based on Mimic Deep Neural Network (MDNN) and Chaotic Whale Optimization (CWO)","authors":"M. Praveena, B. Bharathi","doi":"10.1177/1063293X21992014","DOIUrl":"https://doi.org/10.1177/1063293X21992014","url":null,"abstract":"Duplication of data in an application will become an expensive factor. These replication of data need to be checked and if it is needed it has to be removed from the dataset as it occupies huge volume of data in the storage space. The cloud is the main source of data storage and all organizations are already started to move their dataset into the cloud since it is cost effective, storage space, data security and data Privacy. In the healthcare sector, storing the duplicated records leads to wrong prediction. Also uploading same files by many users, data storage demand will be occurred. To address those issues, this paper proposes an Optimal Removal of Deduplication (ORD) in heart disease data using hybrid trust based neural network algorithm. In ORD scheme, the Chaotic Whale Optimization (CWO) algorithm is used for trust computation of data using multiple decision metrics. The computed trust values and the nature of the data’s are sequentially applied to the training process by the Mimic Deep Neural Network (MDNN). It classify the data is a duplicate or not. Hence the duplicates files are identified and they were removed from the data storage. Finally, the simulation evaluates to examine the proposed MDNN based model and simulation results show the effectiveness of ORD scheme in terms of data duplication removal. From the simulation result it is found that the model’s accuracy, sensitivity and specificity was good.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"57 1","pages":"58 - 67"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89403320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-08DOI: 10.1177/1063293X21991806
Claudio Sassanelli, Sânia da Costa Fernandes, H. Rozenfeld, J. Mascarenhas, S. Terzi
Most methodologies developed to support the Product-Service System (PSS) design consider the integration of service features into the product design from a high-level of abstraction and are usually focused on the conceptual phase, neglecting the detailed level of design. Besides, the Knowledge Management perspective is not considered in those methodologies, also affecting how new design knowledge is created, formalized, and shared across the company’s organization. The PSS Design GuRu Methodology, grounded on Concurrent Engineering and Design for X approaches, was developed to fill these issues. This study presents how the PSS Design GuRu Methodology can be incorporated into a PSS detailed design process in a B2B company operating in the food and bakery machinery sector, focusing the analysis on its contribution to promoting Knowledge Management. In particular, a detailed case of development and integration of a service feature—the installation service—to a product in the PSS scope is conducted. The PSS Design GuRu Methodology proves to be effective in supporting the generation, management, use, sharing, and reuse of new knowledge in the shape of design guidelines and rules.
{"title":"Enhancing knowledge management in the PSS detailed design: a case study in a food and bakery machinery company","authors":"Claudio Sassanelli, Sânia da Costa Fernandes, H. Rozenfeld, J. Mascarenhas, S. Terzi","doi":"10.1177/1063293X21991806","DOIUrl":"https://doi.org/10.1177/1063293X21991806","url":null,"abstract":"Most methodologies developed to support the Product-Service System (PSS) design consider the integration of service features into the product design from a high-level of abstraction and are usually focused on the conceptual phase, neglecting the detailed level of design. Besides, the Knowledge Management perspective is not considered in those methodologies, also affecting how new design knowledge is created, formalized, and shared across the company’s organization. The PSS Design GuRu Methodology, grounded on Concurrent Engineering and Design for X approaches, was developed to fill these issues. This study presents how the PSS Design GuRu Methodology can be incorporated into a PSS detailed design process in a B2B company operating in the food and bakery machinery sector, focusing the analysis on its contribution to promoting Knowledge Management. In particular, a detailed case of development and integration of a service feature—the installation service—to a product in the PSS scope is conducted. The PSS Design GuRu Methodology proves to be effective in supporting the generation, management, use, sharing, and reuse of new knowledge in the shape of design guidelines and rules.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"80 1","pages":"295 - 308"},"PeriodicalIF":0.0,"publicationDate":"2021-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91024843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-03DOI: 10.1177/1063293X20988395
S. Behera, Dr. Prabira Kumar Sethy, S. Sahoo, S. Panigrahi, Sharad Chandra Rajpoot
On-tree fruit monitoring is an important practice to provide the exact status of the fruits concerning its quality, quantity and degree of maturity in the farm. In large farm, it is difficult to look over the individual tree manually to acquire the knowledge about the fruits. Again, the manual inspection method is time-consuming, labor intensive and erroneous. The image processing and IoT are the advance techniques applied in diverse field individually. In agriculture sector, image processing is applied for diagnosis of crops. With help of sensors, the IoT based system able to monitor the condition of field remotely. This paper suggests a frame work, which is the combination of image processing and IoT for on-tree fruit monitoring. İn addition, the on-tree counting and size estimation in terms of coefficient of correlation (R2) are 0.994 and 0.997 respectively.
{"title":"On-tree fruit monitoring system using IoT and image analysis","authors":"S. Behera, Dr. Prabira Kumar Sethy, S. Sahoo, S. Panigrahi, Sharad Chandra Rajpoot","doi":"10.1177/1063293X20988395","DOIUrl":"https://doi.org/10.1177/1063293X20988395","url":null,"abstract":"On-tree fruit monitoring is an important practice to provide the exact status of the fruits concerning its quality, quantity and degree of maturity in the farm. In large farm, it is difficult to look over the individual tree manually to acquire the knowledge about the fruits. Again, the manual inspection method is time-consuming, labor intensive and erroneous. The image processing and IoT are the advance techniques applied in diverse field individually. In agriculture sector, image processing is applied for diagnosis of crops. With help of sensors, the IoT based system able to monitor the condition of field remotely. This paper suggests a frame work, which is the combination of image processing and IoT for on-tree fruit monitoring. İn addition, the on-tree counting and size estimation in terms of coefficient of correlation (R2) are 0.994 and 0.997 respectively.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"115 1","pages":"6 - 15"},"PeriodicalIF":0.0,"publicationDate":"2021-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79379451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-28DOI: 10.1177/1063293X21988944
Dr. Prabira Kumar Sethy, S. Behera, Nithiyakanthan Kannan, Sridevi Narayanan, Chanki Pandey
Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained network is considered for the identification of paddy leaf diseases and nitrogen status estimation. Here, two strategies are carried out to identify images: transfer learning and deep feature extraction. The deep feature extraction approach is combined with a support vector machine (SVM) to classify images. The transfer learning approach of vgg16 for identifying four types of leaf diseases and prediction of nitrogen status results in 79.86% and 84.88% accuracy. Again, the deep features of Vgg16 and SVM results for identifying four types of leaf diseases and prediction of nitrogen status have achieved an accuracy of 97.31% and 99.02%, respectively. Besides, a framework is suggested for monitoring of paddy field remotely based on IoT and deep learning. The suggested prototype’s superiority is that it controls temperature and humidity like the state-of-the-art and can monitor the additional two aspects, such as detecting nitrogen status and diseases.
{"title":"Smart paddy field monitoring system using deep learning and IoT","authors":"Dr. Prabira Kumar Sethy, S. Behera, Nithiyakanthan Kannan, Sridevi Narayanan, Chanki Pandey","doi":"10.1177/1063293X21988944","DOIUrl":"https://doi.org/10.1177/1063293X21988944","url":null,"abstract":"Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained network is considered for the identification of paddy leaf diseases and nitrogen status estimation. Here, two strategies are carried out to identify images: transfer learning and deep feature extraction. The deep feature extraction approach is combined with a support vector machine (SVM) to classify images. The transfer learning approach of vgg16 for identifying four types of leaf diseases and prediction of nitrogen status results in 79.86% and 84.88% accuracy. Again, the deep features of Vgg16 and SVM results for identifying four types of leaf diseases and prediction of nitrogen status have achieved an accuracy of 97.31% and 99.02%, respectively. Besides, a framework is suggested for monitoring of paddy field remotely based on IoT and deep learning. The suggested prototype’s superiority is that it controls temperature and humidity like the state-of-the-art and can monitor the additional two aspects, such as detecting nitrogen status and diseases.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"52 1","pages":"16 - 24"},"PeriodicalIF":0.0,"publicationDate":"2021-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83964946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-25DOI: 10.1177/1063293X20987911
M. S. Abd Rahman, E. Mohamad, A. A. Abdul Rahman
For over three decades, production firms have extensively espoused lean manufacturing (LM) approach for constantly enhancing their operations. Of late, due to the fusion of physical and digital systems within the Industry 4.0 evolution, production systems can upgrade by applying both notions and lift operational excellence to a new high. This is primarily the reason why digital business transformation has gained significance. Moreover, Industry 4.0 that is led by data assures huge strides in output. The sheer volume of pertinent data from the production systems employing servers, sensors, and cloud computing have made the data exchange procedure more gigantic and intricate. However, conventional systems do not extensively support LM in the context of Industry 4.0. Moreover, the previous studies by researchers in the same field, shown that there was no standard platform to manage the new technologies in LM. This study presents a discussion on the interrelated framework about the way Industry 4.0 has transformed production into an industry focusing on connective mechanisms and platforms which utilize data analytics from the real world. The theoretical framework proposed in this paper integrates LM, data analytics, and Internet of Things (IoT) to enhance decision support systems in process improvement. Data analytics in simulation is employed through Internet of Things to improve bottleneck problems by maintaining the principle of LM. The main information flow route within LM decision support system is demonstrated in detail to show how the decision-making process is done. The decision support mechanism has undergone up-gradation and the suggested framework has shown that the assimilated components could function together to augment the output.
{"title":"Development of IoT—enabled data analytics enhance decision support system for lean manufacturing process improvement","authors":"M. S. Abd Rahman, E. Mohamad, A. A. Abdul Rahman","doi":"10.1177/1063293X20987911","DOIUrl":"https://doi.org/10.1177/1063293X20987911","url":null,"abstract":"For over three decades, production firms have extensively espoused lean manufacturing (LM) approach for constantly enhancing their operations. Of late, due to the fusion of physical and digital systems within the Industry 4.0 evolution, production systems can upgrade by applying both notions and lift operational excellence to a new high. This is primarily the reason why digital business transformation has gained significance. Moreover, Industry 4.0 that is led by data assures huge strides in output. The sheer volume of pertinent data from the production systems employing servers, sensors, and cloud computing have made the data exchange procedure more gigantic and intricate. However, conventional systems do not extensively support LM in the context of Industry 4.0. Moreover, the previous studies by researchers in the same field, shown that there was no standard platform to manage the new technologies in LM. This study presents a discussion on the interrelated framework about the way Industry 4.0 has transformed production into an industry focusing on connective mechanisms and platforms which utilize data analytics from the real world. The theoretical framework proposed in this paper integrates LM, data analytics, and Internet of Things (IoT) to enhance decision support systems in process improvement. Data analytics in simulation is employed through Internet of Things to improve bottleneck problems by maintaining the principle of LM. The main information flow route within LM decision support system is demonstrated in detail to show how the decision-making process is done. The decision support mechanism has undergone up-gradation and the suggested framework has shown that the assimilated components could function together to augment the output.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"89 1","pages":"208 - 220"},"PeriodicalIF":0.0,"publicationDate":"2021-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91295867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-24DOI: 10.1177/1063293X20985539
Lei Zhang, Shoutian Shao, Suxin Chen, Xinyu Li, Rui Jiang, Ziqi Li
In the conceptual design phase of eco-products, the evaluation of products eco-design is often considered by designers after determining the design scheme. In order to improve the eco-design efficiency, a CAD-based feedback knowledge push method was proposed to meet the designer’s eco-design knowledge requirements. Combine with the designer knowledge background, classified eco-design knowledge, and decomposed design tasks, eco-design knowledge requirements of designers can be obtained. Based on the cosine similarity algorithm of the knowledge vector space model (VSM), the knowledge requirements of the designer and the knowledge in the knowledge database are matched, and the eco-knowledge is actively pushed to the designer. The feedback and evaluation of designers on the initial push of knowledge was recorded by the system. When the designer is assigned relevant eco-design tasks again, the system conducts secondary filtering of eco-design knowledge through previous feedback records and pushes the filtered knowledge to designers, so as to achieve accurate feedback knowledge push. A CAD-based eco-design knowledge push prototype system for automotive products is developed. The eco-design of the front-end module of the automotive is used as an example to verify the effectiveness of the above method.
{"title":"Individualized and accurate eco-design knowledge push for designers: a CAD-based feedback knowledge push method for the eco-design","authors":"Lei Zhang, Shoutian Shao, Suxin Chen, Xinyu Li, Rui Jiang, Ziqi Li","doi":"10.1177/1063293X20985539","DOIUrl":"https://doi.org/10.1177/1063293X20985539","url":null,"abstract":"In the conceptual design phase of eco-products, the evaluation of products eco-design is often considered by designers after determining the design scheme. In order to improve the eco-design efficiency, a CAD-based feedback knowledge push method was proposed to meet the designer’s eco-design knowledge requirements. Combine with the designer knowledge background, classified eco-design knowledge, and decomposed design tasks, eco-design knowledge requirements of designers can be obtained. Based on the cosine similarity algorithm of the knowledge vector space model (VSM), the knowledge requirements of the designer and the knowledge in the knowledge database are matched, and the eco-knowledge is actively pushed to the designer. The feedback and evaluation of designers on the initial push of knowledge was recorded by the system. When the designer is assigned relevant eco-design tasks again, the system conducts secondary filtering of eco-design knowledge through previous feedback records and pushes the filtered knowledge to designers, so as to achieve accurate feedback knowledge push. A CAD-based eco-design knowledge push prototype system for automotive products is developed. The eco-design of the front-end module of the automotive is used as an example to verify the effectiveness of the above method.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"24 1","pages":"153 - 168"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74779279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-12DOI: 10.1177/1063293X20985531
P. H. P. Setti, Osiris Canciglieri Junior, C. Estorilio
Due to the current and highly competitive industrial scenario, the technology-oriented organizations have been making routine adjustments to the conventional IPDP, in order to seek more profitable business models. Identifying the product functions, as well as its importance - perceived by the consumer - and being able to associate this information with manufacturability and assembly aspects, is fundamental to achieve more competitive, low-cost and higher quality products. This article objective is to evaluate this method concept, applying it in an industrial project. In order to assess the method within the complete integrated product development process (IPDP), the activities related to the conceptual and preliminary phases of the project delineated this article limits. This study selected a subgroup of the white goods industry, where first the traditional models of VE were applied in the conceptual design phase. Subsequently, the classic DFA models were applied in the preliminary design phase. Thus, it was possible to apply the proposed iterative method, where the alternatives generated with the DFA were cyclically re-evaluated, function by function, in the previous stage of value analysis. With this, this study came to the method assessment, its gains and limitations. Then, the original design was compared with the solution after the proposal application, without the method used. Finally, this study verified the influence of the method on the balance between the value and the cost of each function, in addition to the direct comparison of the solution final cost with the version without the method application. Among the results, this article presents a report showing the method viability, its particularities, impacts, and limitations.
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Pub Date : 2020-11-03DOI: 10.1177/1063293X20967929
L. Rihar, Tena Žužek, J. Kušar
Today, three conditions are crucial for a company to be competitive on the market: quality, reduced time and low costs for the development of new products. The paper shows how companies developing new products (NPD) for the market can successfully implement concurrent engineering as an improvement of project management, in order to reduce product development time and costs and to ensure the quality expected by customers. The methodology presented in this paper is based on three main pillars of knowledge: project management, teamwork and concurrent engineering. The methodology provides a step-by-step guideline for the introduction of concurrent engineering in a company. This paper also presents the results of 10 Slovenian companies where this methodology has been tested on 20 pilot projects. The results show that managed projects upgraded with the principles of concurrent engineering lead to cost reduction, shorter development time and fewer discrepancies.
{"title":"How to successfully introduce concurrent engineering into new product development?","authors":"L. Rihar, Tena Žužek, J. Kušar","doi":"10.1177/1063293X20967929","DOIUrl":"https://doi.org/10.1177/1063293X20967929","url":null,"abstract":"Today, three conditions are crucial for a company to be competitive on the market: quality, reduced time and low costs for the development of new products. The paper shows how companies developing new products (NPD) for the market can successfully implement concurrent engineering as an improvement of project management, in order to reduce product development time and costs and to ensure the quality expected by customers. The methodology presented in this paper is based on three main pillars of knowledge: project management, teamwork and concurrent engineering. The methodology provides a step-by-step guideline for the introduction of concurrent engineering in a company. This paper also presents the results of 10 Slovenian companies where this methodology has been tested on 20 pilot projects. The results show that managed projects upgraded with the principles of concurrent engineering lead to cost reduction, shorter development time and fewer discrepancies.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"28 5 1","pages":"87 - 101"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89852884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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