scholarly journals Industrial Artificial Intelligence, Sustainable Product Lifecycle Management, and Internet of Things Sensing Networks in Cyber-Physical Smart Manufacturing Systems

2020 ◽  
Vol 8 (4) ◽  
pp. 19
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Manufacturing Systems ◽  
Product Lifecycle Management ◽  
Smart Manufacturing ◽  
Product Lifecycle ◽  
Sustainable Product ◽  
Lifecycle Management ◽  
Smart Manufacturing Systems

scholarly journals Requirements for a Product Lifecycle Management System Using Internet of Things and Big Data Analytics for Product-as-a-Service

2021 ◽  
Vol 2 ◽  
Author(s):  
Tomohiko Sakao ◽  
Alex Kim Nordholm
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Product Lifecycle Management ◽  
Product Lifecycle ◽  
Use Phase ◽  
Lifecycle Management ◽  
Original Equipment Manufacturers ◽  
The Internet Of Things

Product-as-a-service (PaaS) offerings have advantages and potential for transforming societies to a circular economy and for improving environmental performance. Original equipment manufacturers providing PaaS offerings take higher responsibility for product performances in the use phase than those selling products. This responsibility can be supported by digital technologies such as the Internet of Things (IoT) and big data analytics (BDA). However, insights on how data of product designs and in-use services are managed for PaaS offerings in product lifecycle management (PLM) software are scarce. This mini-review first gives an account of extant major research works that successfully applied BDA, a specific technique of artificial intelligence (AI), to cases in industry through a systematic literature review. Then, these works are analyzed to capture requirements for a PLM system that will exploit the IoT and BDA for PaaS offerings. The captured requirements are summarized as (1) facilitate product and service integration, (2) address multiple lifecycles, (3) adopt an ontology approach encompassing several product standards, and (4) include reading data to process in an interoperation layer.

Elements of managerial integration for sustainable product lifecycle management

2016 ◽  
Vol 9 (2) ◽  
pp. 87 ◽  
Author(s):  
Darli Rodrigues Vieira ◽  
Raimundo Kennedy Vieira ◽  
Milena Chang Chain
Keyword(s):  
Product Lifecycle Management ◽  
Product Lifecycle ◽  
Sustainable Product ◽  
Lifecycle Management

scholarly journals An Approach to Sustainable Product Lifecycle Management (Green PLM)

2015 ◽  
Vol 132 ◽  
pp. 585-592 ◽  
Author(s):  
C. Vila ◽  
J.V. Abellán-Nebot ◽  
J.C. Albiñana ◽  
G. Hernández
Keyword(s):  
Product Lifecycle Management ◽  
Product Lifecycle ◽  
Sustainable Product ◽  
Lifecycle Management

scholarly journals Product Lifecycle Management as Data Repository for Manufacturing Problem Solving

Materials ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1469 ◽  
Author(s):  
Alvaro Camarillo ◽  
José Ríos ◽  
Klaus-Dieter Althoff
Keyword(s):  
Fault Diagnosis ◽  
Problem Solving ◽  
Manufacturing Systems ◽  
Product Lifecycle Management ◽  
Shop Floor ◽  
Case Based Reasoning ◽  
Product Lifecycle ◽  
Considerable Difficulty ◽  
Lifecycle Management ◽  
Manufacturing Problems

Fault diagnosis presents a considerable difficulty to human operators in supervisory control of manufacturing systems. Implementing Internet of Things (IoT) technologies in existing manufacturing facilities implies an investment, since it requires upgrading them with sensors, connectivity capabilities, and IoT software platforms. Aligned with the technological vision of Industry 4.0 and based on currently existing information databases in the industry, this work proposes a lower-investment alternative solution for fault diagnosis and problem solving. This paper presents the details of the information and communication models of an application prototype oriented to production. It aims at assisting shop-floor actors during a Manufacturing Problem Solving (MPS) process. It captures and shares knowledge, taking existing Process Failure Mode and Effect Analysis (PFMEA) documents as an initial source of information related to potential manufacturing problems. It uses a Product Lifecycle Management (PLM) system as source of manufacturing context information related to the problems under investigation and integrates Case-Based Reasoning (CBR) technology to provide information about similar manufacturing problems.

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