scholarly journals A DIN Spec 91345 RAMI 4.0 Compliant Data Pipelining Model: An Approach to Support Data Understanding and Data Acquisition in Smart Manufacturing Environments

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 223114-223129
Author(s):  
Kevin Nagorny ◽  
Sebastian Scholze ◽  
Armando Walter Colombo ◽  
Jose Barata Oliveira
Keyword(s):  
Data Acquisition ◽  
Smart Manufacturing ◽  
Manufacturing Environments

Decision Support for Smart Manufacturing

2021 ◽  
pp. 334-347
Author(s):  
Marzieh Khakifirooz ◽  
Mahdi Fathi ◽  
Panos M. Pardalos ◽  
Daniel J. Power
Keyword(s):  
Decision Making ◽  
Optimization Techniques ◽  
Smart Manufacturing ◽  
Concept Generation ◽  
Wide Range ◽  
System Requirements ◽  
Advanced Information Technology ◽  
Traditional Manufacturing ◽  
Manufacturing Technologies ◽  
Manufacturing Environments

This work introduces a formation and variety of decision-making models based on operations research modeling and optimization techniques in smart manufacturing environments. Unlike traditional manufacturing, the goal of Smart manufacturing is to optimizing concept generation, production, and product transaction and enable flexibility in physical processes to address a dynamic, competitive and global supply chains by using intelligent computerized control, advanced information technology, smart manufacturing technologies and high levels of adaptability. While research in the broad area of smart manufacturing and its challenges in decision making encompasses a wide range of topics and methodologies, we believe this chapter provides a good snapshot of current quantitative modeling approaches, issues, and trends within the field. The chapter aims to provide insights into the system engineering design, emphasizing system requirements analysis and specification, the use of alternative analytical methods and how systems can be evaluated.

Making IoT Run

2017 ◽  
Vol 1 (2) ◽  
pp. 26-44
Author(s):  
Peter Schott ◽  
Torben Schaft ◽  
Stefan Thomas ◽  
Freimut Bodendorf
Keyword(s):  
Production Systems ◽  
Smart Manufacturing ◽  
Structural System ◽  
Maturity Level ◽  
Smart Systems ◽  
Industrial Iot ◽  
Increasing Demand ◽  
Manufacturing Environments ◽  
Self Learning

This article describes how today's manufacturing environments are characterized by an increasing demand for individual products and constantly more product variants. Concomitant, developments in the fields of IT, robotics and artificial intelligence allow the realization of smart systems, which means networked, self-learning, self-regulating and versatile production systems to control this complexity. These developments are referred to as industrial IoT that is acknowledged as “next big thing” in production. Firms face the challenge of lacking guidelines for implementing IoT solutions. Neither the technological prerequisites nor generally applicable procedures for realizing an appropriate technological maturity level of the system-to-be exist. Addressing this deficit, a framework is introduced which systematically implements IoT within manufacturing. The framework presents a guideline for the establishment of structural system understanding, the determination of the target system's technological maturity level from a customer's perspective and, building on this, design implications for smart manufacturing.

Making IoT Run

2020 ◽  
pp. 377-397
Author(s):  
Peter Schott ◽  
Torben Schaft ◽  
Stefan Thomas ◽  
Freimut Bodendorf
Keyword(s):  
Artificial Intelligence ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Maturity Level ◽  
Smart Systems ◽  
Industrial Iot ◽  
Increasing Demand ◽  
Manufacturing Environments ◽  
Self Learning

This article describes how today's manufacturing environments are characterized by an increasing demand for individual products and constantly more product variants. Concomitant, developments in the fields of IT, robotics and artificial intelligence allow the realization of smart systems, which means networked, self-learning, self-regulating and versatile production systems to control this complexity. These developments are referred to as industrial IoT that is acknowledged as “next big thing” in production. Firms face the challenge of lacking guidelines for implementing IoT solutions. Neither the technological prerequisites nor generally applicable procedures for realizing an appropriate technological maturity level of the system-to-be exist. Addressing this deficit, a framework is introduced which systematically implements IoT within manufacturing. The framework presents a guideline for the establishment of structural system understanding, the determination of the target system's technological maturity level from a customer's perspective and, building on this, design implications for smart manufacturing.

Cloud robotics in Smart Manufacturing Environments: Challenges and countermeasures

2017 ◽  
Vol 63 ◽  
pp. 56-65 ◽  
Author(s):  
Hehua Yan ◽  
Qingsong Hua ◽  
Yingying Wang ◽  
Wenguo Wei ◽  
Muhammad Imran
Keyword(s):  
Smart Manufacturing ◽  
Cloud Robotics ◽  
Manufacturing Environments

Decision Support for Smart Manufacturing

2021 ◽  
pp. 2352-2364
Author(s):  
Marzieh Khakifirooz ◽  
Mahdi Fathi ◽  
Panos M. Pardalos ◽  
Daniel J. Power
Keyword(s):  
Decision Making ◽  
Optimization Techniques ◽  
Smart Manufacturing ◽  
Concept Generation ◽  
Wide Range ◽  
System Requirements ◽  
Advanced Information Technology ◽  
Traditional Manufacturing ◽  
Manufacturing Technologies ◽  
Manufacturing Environments

This work introduces a formation and variety of decision-making models based on operations research modeling and optimization techniques in smart manufacturing environments. Unlike traditional manufacturing, the goal of Smart manufacturing is to optimizing concept generation, production, and product transaction and enable flexibility in physical processes to address a dynamic, competitive and global supply chains by using intelligent computerized control, advanced information technology, smart manufacturing technologies and high levels of adaptability. While research in the broad area of smart manufacturing and its challenges in decision making encompasses a wide range of topics and methodologies, we believe this chapter provides a good snapshot of current quantitative modeling approaches, issues, and trends within the field. The chapter aims to provide insights into the system engineering design, emphasizing system requirements analysis and specification, the use of alternative analytical methods and how systems can be evaluated.

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