Cognitive Automation, Business Process Optimization, and Sustainable Industrial Value Creation in Artificial Intelligence Data-driven Internet of Things Systems

Bell, Edward

doi:10.22381/jsme8320201

Cited by 8 publications

(14 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Smart manufacturing makes cyber-physical production networks more well-organized and sustainable [5,25,26] by use of artificial intelligence-based decision-making algorithms. Big data analytics can reveal undetected knowledge and connections between lifecycle decisions and operational procedures, assisting in making better-grounded business arrangements in intricate management settings.…”

Section: Cyber-physical Production Network In Data-driven Sustainable Smart Manufacturingmentioning

confidence: 99%

“…The procedure of servitization should integrate product design, smart manufacturing, performance and maintenance, refashioning, recycling, and reprocessing phases of product lifecycle management. Implementation of sustainable smart manufacturing may assist in attaining a data-and service-driven product lifecycle management [6,14,[25][26][27] and facilitate the omnipresent connectivity, effective consistency, and cumulative upgrade of development business processes. Sustainable smart manufacturing can assist in decreasing resources/energy waste and in cutting down or removing emissions from big data-driven industrial processing, consequently advancing towards the objectives of automated, sustainable, cleaner production while carrying out various temporary or lasting customer demands.…”

Section: Cyber-physical Production Network In Data-driven Sustainable Smart Manufacturingmentioning

confidence: 99%

“…Sustainable smart manufacturing can assist in decreasing resources/energy waste and in cutting down or removing emissions from big data-driven industrial processing, consequently advancing towards the objectives of automated, sustainable, cleaner production while carrying out various temporary or lasting customer demands. Users of sustainable smart manufacturing aim to further the design and delivery of services, decreasing resource usage, deterioration, and contamination, and enhancing economic and environmental sustainability by harnessing Internet of Things-based real-time production logistics and big data analytics technologies throughout the management operations of the entire lifecycle [5,7,14,25,28,29] in order to optimize the level of automation in artificial intelligence-based decision making. Sustainable smart manufacturing focuses on servitization everywhere in the product value chain by harnessing groundbreaking information, big data analytics technologies, and large-scale upgrade of the entire product lifecycle management to assist in collating the observations obtained from big data usage.…”

Section: Cyber-physical Production Network In Data-driven Sustainable Smart Manufacturingmentioning

confidence: 99%

“…The system architecture of big data in sustainable smart manufacturing can be harnessed to streamline the elaborate network management environment, to set out the intricate processes of lifecycle data sharing and input interoperability, and to regulate the soundness of artificial intelligence-based decision-making algorithms [5,14,17,18,25,[71][72][73][74][75][76] leading to big data-driven innovation in sustainable Industry 4.0. Cutting-edge technologies can be deployed for controlling the coherence and sustainability of the smart manufacturing systems by supplying well-grounded data and significant insights.…”

Section: Sustainable Organizational Performance In Cyber-physical Smart Manufacturing Systemsmentioning

confidence: 99%

“…To thoroughly harness the big data from manufacturing and energy management database to attain a significant level of sustainability, we require procedures for inspecting, assessing, and upgrading sustainability performance standards of production operations and systems. Big data analytics can adequately enable the implementation of the sustainable smart manufacturing paradigm [25,[77][78][79][80], by gaining value from lifecycle big data, through putting into effect servitization approaches during the entire development, and by generating innovative added-value, improving sustainability in production companies. Through big data analytics-enabled cognitive decision-making algorithms, the performance of the entire lifecycle management operations and service supplying developed on sustainable smart manufacturing can be monitored and optimized.…”

Section: Sustainable Organizational Performance In Cyber-physical Smart Manufacturing Systemsmentioning

confidence: 99%

See 4 more Smart Citations

Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

et al. 2021

View full text Add to dashboard Cite

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.

show abstract

Section: Cyber-physical Production Network In Data-driven Sustainable Smart Manufacturingmentioning

confidence: 99%

Section: Cyber-physical Production Network In Data-driven Sustainable Smart Manufacturingmentioning

confidence: 99%

Section: Cyber-physical Production Network In Data-driven Sustainable Smart Manufacturingmentioning

confidence: 99%

Section: Sustainable Organizational Performance In Cyber-physical Smart Manufacturing Systemsmentioning

confidence: 99%

Section: Sustainable Organizational Performance In Cyber-physical Smart Manufacturing Systemsmentioning

confidence: 99%

See 3 more Smart Citations

Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

et al. 2021

View full text Add to dashboard Cite

show abstract

Coaching as a global approach to the development of skills in various fields including HORECA and academics

Seemann

Štofková

2021

SHS Web Conf.

View full text Add to dashboard Cite

Research background: The paper deals with coaching as one of the methods of developing a manager's personality in many fields including the Horeca sector and academics. Academic field also faces challenges like decreasing number of applicants, little application of graduates in the studied fields and quality of education process too. Purpose of the article: The aim of the research was to analyze the awareness and interest in the coaching approach in the HoReCa sector. The purpose was to find out whether managers perceive the existence of coaching as one of the possible approaches to the development of a person's personality. Furthermore we wanted to show the possibilities to embody coaching approach also to academic field. Methods: Within the research for quantitative survey methods, a questionnaire is most often used, in which it is important to obtain the necessary number of answers from respondents. Other methods belonging to this group are experiment or observation. An interview was used in the article as well as analysis of current coaching activities in academia. Findings & Value added: From the achieved results of the questionnaire survey, it is possible to confirm several facts. It was found that the interest in coaching approach among managers in the HoReCa sector is present because more than half of the respondents answered in the affirmative, i.e. that they are interested in coaching. Respondents clearly think that coaching is a suitable method of developing a manager's personality. Some coaching activities e.g., workshops also took place in the academic field.

show abstract

Educational Information System Optimization for Artificial Intelligence Teaching Strategies

2021

View full text Add to dashboard Cite

Under the background of the information age, scientific research and engineering practice have developed vigorously, resulting in many complex optimization problems that are difficult to solve. How to design more effective optimization methods has become the focus of urgent solutions in many academic fields. Under the guidance of such demand, intelligent optimization algorithms have emerged. This article analyzes and optimizes the modern artificial intelligence teaching information system in detail. On the basis of determining the network architecture, a detailed demand analysis was carried out, and the overall structure optimization of the network was given; the business process and data flow of the main modules of the website (resource center module and collaborative learning module) were optimized. In order to further enhance the local search ability of the algorithm, a multiclass interactive optimization algorithm is proposed in combination with the Euclidean distance-based clustering method, which changes the teaching mode from “one-person teaching” to “multiperson teaching.” This clustering method has lower complexity and is beneficial to enhance the utilization of neighborhood information. At the same time, in order to enhance the diversity of the population and strengthen the connection between the subgroups, after the teaching phase, the worst students in each subgroup are allowed to learn from the best teachers of the population, and after the learning phase, individuals in a random subgroup are allowed to learn from other subgroups. The algorithm was tested in the experimental environment of unconstrained, constrained, and an engineering problem. From the test results, it can be seen that the algorithm is not easy to fall into the local optimum. Compared with other algorithms, the solution accuracy is higher and the stability is better. And it performed well in engineering optimization problems, thus verifying the effectiveness of the strategy.

show abstract

Cognitive Automation, Business Process Optimization, and Sustainable Industrial Value Creation in Artificial Intelligence Data-driven Internet of Things Systems

Cited by 8 publications

References 0 publications

Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

Coaching as a global approach to the development of skills in various fields including HORECA and academics

Educational Information System Optimization for Artificial Intelligence Teaching Strategies

Contact Info

Product

Resources

About