scholarly journals Recent automation trends in Portugal: implications on industrial productivity and employment in automotive sector

2021 ◽  
Author(s):  
Nuno Boavida ◽  
Marta Candeias
Keyword(s):  
Manufacturing Industry ◽  
Organizational Design ◽  
Autonomous Systems ◽  
Automotive Sector ◽  
Learning Collaborative ◽  
Human Machine Interaction ◽  
Machine Age ◽  
Structural Funds ◽  
Collaborative Tools ◽  
Industrial Productivity

Artificial Intelligence (AI) is an automation mechanism that runs in a computer system performing tasks that normally require human intelligence, such as visual perception, speech recognition, decision making or translation [1]. Some authors argue that recent developments in AI are leading to a wave of innovation in organizational design and changes to institutionalized norms of the workplace [2]. Techno-optimists even named this present phase the ‘second machine age’, arguing that it now involves the substitution of the human brain (Brynjolfsson and McAfee 2014). Potentially, the ability to apply AI in a generalized way can produce significant technical, economic and social effects in firms. But how many of these AI applications are ready and how far can they be from reaching the manufacturing industry market? The paper will answer the question: what are the implications on industrial productivity and employment in the automotive sector with the recent automation trends in Portugal? We will focus on AI as the most relevant emergent technology to understand the development of automation in areas related to robotics, software, and data communications in Europe (Moniz 2018). R&D investments in industrial processes in general may reflect productivity improvements derived from the increased automation process. Our results will be based on case studies from the automotive and components sector combined with database search by keywords that signal intelligence automation developments and AI applications selected from national R&D projects (on robotics, machine learning, collaborative tools, human-machine interaction, autonomous systems, etc) supported by European structural funds. The implications on industrial productivity and employment will be discussed in relation to automation trends in the automotive sector.

scholarly journals Recent Automation Trends in Portugal: Implications on Industrial Productivity and Employment in Automotive Sector

Societies ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 101
Author(s):  
Nuno Boavida ◽  
Marta Candeias
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
Organizational Design ◽  
Work Organization ◽  
Automotive Sector ◽  
Negative Effects ◽  
Short Term ◽  
Machine Age ◽  
Recent Developments ◽  
Industrial Productivity

Recent developments in automation and artificial intelligence (AI) are leading to a wave of innovation in organizational design and changes in the workplace. Techno-optimists even named it the “second machine age,” arguing that it now involves the substitution of the human brain. Other authors see this as just a continuation of previous ICT developments. Potentially, automation and AI can have significant technical, economic, and social implications in firms. This paper will answer the following question: What are the implications on industrial productivity and employment in the automotive sector with the recent automation trends, including AI, in Portugal? Our approach used mixed methods to conduct statistical analyses of relevant databases and interviews with experts on R&D projects related to automation and AI implementation. Results suggest that automation can have widespread adoption in the short term in the automotive sector, but AI technologies will take more time to be adopted. The findings show that adoption of automation and AI increases productivity in firms and is dephased in time with employment implications. Investments in automation are not substituting operators but rather changing work organization. Thus, negative effects of technology and unemployment were not substantiated by our results.

scholarly journals Design and Digital Manufacturing: an ergonomic approach for Industry 4.0

2016 ◽  
Author(s):  
Elena Laudante ◽  
Francesco Caputo
Keyword(s):  
Production Process ◽  
Virtual Environment ◽  
Production Line ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Continuous Process ◽  
Design Environment ◽  
Human Machine Interaction ◽  
Holistic View ◽  
Assembly Tasks

The contribution proposes innovative methods for design and ergonomic configuration of tools, equipment and manual workplaces for automobile assembly tasks, in order to increase the worker’s welfare and the system’s performance by improving general safety conditions. Developed activities are part of the research project “DEWO – Design Environment for WorkPlace Optimization”, financed by Italian Government to the Second University of Naples. The aim of this project is to identify new methods for optimization of assembly tasks  in a virtual environment in terms of overall integration among materials management, working tasks organization and layout, starting from the principles of "WorkPlace Organization" and the modern theories of "Lean Production ". The manufacturing industry is heading to the ever more pushed use of digital technologies in order to achieve very dynamic production environments and to be able to develop continuous process and product innovations to fit into the so called Fourth Industrial Revolution, Industry 4.0. The main goal of Industry 4.0 is to “rethink” companies through the use of digital, to reconsider the design approach and to monitor the production process in real time. The research addresses the evolution of innovation 4.0 in relation to the discipline of design, where the management of knowledge in the production process has led to the strengthening and improvement of tangible goods. Starting by current ergonomic analysis models and innovative approaches to the process of industrial production line, the manufacturing processes in the virtual environment were defined and optimized with the use of innovative 3D enjoyment technologies. The constant interaction among the different disciplines of design, engineering and occupational medicine, enables the creation of advanced systems for simulating production processes based on virtual reality and augmented reality, mainly focused on the needs and requirements of the workers on a production line where it is possible to bring out the interaction between real and virtual factory (Cyber-Physical System). The objective is to define new models of analysis, of development and testing for the configuration of ergonomic processes that improve and facilitate the human-machine interaction in a holistic view, in order to protect and enhance human capital, transferring the experiences and knowledge in the factory system, key factors for the company and for the sustainability of workers welfare levels.DOI: http://dx.doi.org/10.4995/IFDP.2016.3297

scholarly journals Turn-Taking and Coordination in Human-Machine Interaction

AI Magazine ◽  
2017 ◽  
Vol 37 (4) ◽  
pp. 5-6 ◽  
Author(s):  
Sean Andrist ◽  
Dan Bohus ◽  
Bilge Mutlu ◽  
David Schlangen
Keyword(s):  
Autonomous Systems ◽  
Research Area ◽  
Human Robot Interaction ◽  
Research Progress ◽  
Virtual Agents ◽  
Human Machine Interaction ◽  
Dialog Systems ◽  
Turn Taking ◽  
Core Concepts ◽  
Machine Interaction

This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines. The contributing authors work in interrelated fields of spoken dialog systems, intelligent virtual agents, human-computer interaction, human-robot interaction, and semiautonomous collaborative systems and explore core concepts in coordinating speech and actions with virtual agents, robots, and other autonomous systems. Several of the contributors participated in the AAAI Spring Symposium on Turn-Taking and Coordination in Human-Machine Interaction, held in March 2015, and several articles in this issue are extensions of work presented at that symposium. The articles in the collection address key modeling, methodological, and computational challenges in achieving effective coordination with machines, propose solutions that overcome these challenges under sensory, cognitive, and resource restrictions, and illustrate how such solutions can facilitate coordination across diverse and challenging domains. The contributions highlight turn-taking and coordination in human-machine interaction as an emerging and evolving research area with important implications for future applications of AI.

scholarly journals Identify eight aspects of ergonomics to determine the improvement of human-machine interaction work (case studies in manufacturing industry)

2018 ◽  
Vol 218 ◽  
pp. 04018
Author(s):  
Wahyu Susihono ◽  
Tania Anggi Saputri
Keyword(s):  
Work Performance ◽  
Manufacturing Industry ◽  
Human Performance ◽  
Muscle Power ◽  
Raw Materials ◽  
Appropriate Technology ◽  
Human Machine Interaction ◽  
Cross Sectional ◽  
Variable Effect ◽  
Machine Interaction

Manufacturing Industry is one of the industrial activities in Indonesia, manufacturing industry is an industry with main activities is to change raw materials, components, or other parts into goods which is according to company specifications standards. In the production floor, activity in the manufacturing industry, the workers have different job specifications with each other. Some works consist of human-machine interaction is found by the activity between workers and lathe machine, welding maching, milling machine, frais machine, and others. The manufacturing industry will increase its ability to serve a variety of better quality products caused by the desire or encouragement of the customers. In general, according to the increase of corporate targets, its also need improvement from the aspect of work performance. To obtain a description of the proposed improvement based on human performance, it is necessary to identify the eight aspects of ergonomics include the consumption of nutritionalfor workers (energy), muscle power, body posture, environment, time conditions, social conditions, information conditions, and human machine interaction. This research use cross sectional method approach that is research done at one time, no follw up, to find the correlation between independent variable (risk factor) with dependent variable (effect). The conclusion of this research is needed nutrition intake or nutrition to recover the workers, it is necessary to design facilities such as manufacturing aids to reduce the use of excess muscle or appropriate technology (TTG). After the application of TTG (Appropriate Technology) to reduce the excessive use of muscle to the workers, the company should provide the nutritional intake accordance with workload of employees in the manufacturing industry

Transportation Infrastructure, Industrial Productivity, and Return on Investment

2013 ◽  
Vol 2345 (1) ◽  
pp. 109-116 ◽  
Author(s):  
Jeffrey J. Eloff ◽  
Oleg A. Smirnov ◽  
Peter S. Lindquist
Keyword(s):  
Manufacturing Industry ◽  
State Level ◽  
Transportation Infrastructure ◽  
Spatial Econometric ◽  
Spatial Spillovers ◽  
The North ◽  
Spatial Econometric Models ◽  
Alternative Approach ◽  
Industrial Productivity ◽  
Function Models

This study examined the North American Industrial Classification System–based manufacturing industry (NAICS 31-33) from 1997 to 2010 in a cost-based framework. First, both profit and production function models were constructed and estimated for the U.S. manufacturing industry at the state level to allow for spatial spillovers and interactions. A model based on profit and production provided an alternative approach to the dual-cost function. Elasticities associated with infrastructure investment and industry total costs were determined by the inclusion of data on transportation infrastructure spending. Results of the spatial econometric models and the computed elasticities were then delivered in a geographic information system.

Achieving an Information System's Capability through C2

2015 ◽  
Vol 7 (1) ◽  
pp. 80-96
Author(s):  
Ana C. Calderon ◽  
Peter Johnson
Keyword(s):  
Autonomous Agents ◽  
Autonomous Systems ◽  
Academic Research ◽  
Command And Control ◽  
Human Machine Interaction ◽  
Military Research ◽  
Task Modeling ◽  
General Aspects ◽  
And Control ◽  
Machine Interaction

The authors present a literature review of command and control, linking sociological elements of academic research to military research in a novel way. They will discuss task modeling literature (seen in human machine interaction studies), general aspects of collectives and military and academic research on command and control, studies of autonomous systems and considerations of interactions between humans and autonomous agents. Based on the survey and associations between aspects from these fields, the authors compose a recommendation list for aspects crucial to building of information systems capable of achieving their true capability, through command and control.

Additive Manufacturing and Its Need, Role, Applications in the Automotive Industry

2021 ◽  
pp. 358-367
Author(s):  
Dhinakaran V. ◽  
Varsha Shree M. ◽  
Swapna Sai M. ◽  
Rishiekesh Ramgopal
Keyword(s):  
Additive Manufacturing ◽  
Automotive Industry ◽  
Industrial Production ◽  
Manufacturing Industry ◽  
Research Work ◽  
High Strength ◽  
Automotive Sector ◽  
Automotive Manufacturing ◽  
Additive Manufacturing Technology

Additive manufacturing (AM) emerged from rapid prototyping to relinquish sustainable industrial production. The role of AM in the industrial field is to diminish manufacturing pace, functioning cost, and assembling the AM lightweight particles together, which enhances the malleable fabrication of personalized user defined components without symbolic concussion. The automotive manufacturing industry plays a chief role in the aggressive trade field where time to market declines. The engraving design structures with weight reduction materials are the demands faced by the automotive industry that can be ominously resolved by additive manufacturing technology. This research work provides a better understanding of AM technology and its role in automotive sector to enhance modern vehicle designs and enduring features and augments the knowledge of both researchers and industrialists to overcome the efficacy in manufacturing process by fabricating relatively high strength geometries with reduced weight.

Learning-structure fit part I

2018 ◽  
Vol 25 (5) ◽  
pp. 294-304 ◽  
Author(s):  
Aleša Saša Sitar ◽  
Miha Škerlavaj
Keyword(s):  
Learning Styles ◽  
Organizational Design ◽  
Organization Design ◽  
Independent Learning ◽  
Learning Behavior ◽  
Learning Collaborative ◽  
Content Type ◽  
Double Loop Learning ◽  
The Impact ◽  
The Relationship

Purpose The purpose of this study, which consists of two parts, is to bring together literature on organizational design and learning of individuals in organizational settings. The literature suggests that learning takes place in organic and less-structured organizational designs, whereas empirical research provides conflicting evidence. This first part theorizes about the influence of mechanistic vs organic designs on three different aspects of employees’ learning behavior: knowledge sourcing, learning styles and learning loops. Design/methodology/approach This paper is built on previous research on the impact of structure on learning and theorizes about the relationship between mechanistic/organic design and specific learning behavior at work. Findings Four propositions are developed in this paper, regarding how a different structure leads to a different learning behavior. Mechanistic structure is associated with internal learning, independent learning and single-loop learning, whereas organic design leads to external learning, collaborative learning and double-loop learning. Research limitations/implications Because the paper is conceptual in nature, the propositions are in need of empirical validation. Some directions for empirical testing are proposed. Practical/implications For an organization design practice, managers should be aware of the distinct impact different structures have on individual learning at work. Furthermore, the appropriate organizational structure for learning must be considered in the broader context of contingencies. Originality/value This paper contributes to the organizational design literature and to the organizational learning theory by conceptualizing the relationship between structure and learning of individuals at work.

scholarly journals Design of Customized Intelligent Manufacturing Information Interaction System Based on Virtual Technology

2021 ◽  
Vol 2074 (1) ◽  
pp. 012043
Author(s):  
Xi Wang
Keyword(s):  
Manufacturing Industry ◽  
Industrial Revolution ◽  
Intelligent Manufacturing ◽  
Virtual Technology ◽  
Technology System ◽  
Information Interaction ◽  
Information And Communication ◽  
Industrial Productivity ◽  
System Intelligence ◽  
Production Resources

Abstract With the continuous development of information technology, system intelligence is leading the next round of “industrial revolution”, especially the intelligent manufacturing industry has become the core of improving industrial productivity. Intelligent manufacturing involves each link in the manufacturing industry, which is the most critical part of intelligent manufacturing is intelligent production, through intelligent manufacturing related technology to optimize the production mode of manufacturing to promote the production state more flexible and integrated. Intelligent manufacturing is based on computer simulation technology and information and communication technology, optimize the production design of the factory and simplify the production process of the factory, the purpose is to reduce the waste of resources and improve the reasonable allocation of production resources.

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