scholarly journals DATA-DRIVEN CREATIVITY: COMPUTATIONAL PROBLEM-EXPLORING IN ENGINEERING DESIGN

2021 ◽  
Vol 1 ◽  
pp. 831-840
Author(s):  
Chijioke C. Obieke ◽  
Jelena Milisavljevic-Syed ◽  
Ji Han
Keyword(s):  
Engineering Design ◽  
Industrial Revolution ◽  
Early Stage ◽  
Data Retrieval ◽  
Data Driven ◽  
Markovian Model ◽  
Design Problems ◽  
Computational Problem ◽  
Fourth Industrial Revolution ◽  
Design Engineers

AbstractCreativity is required in engineering design. It is required in the aspects of problem-solving - conceptualizing a new solution to a problem, and problem-exploring - conceptualizing a new problem. Studies show that, in both aspects, creativity is a difficult task in practice. The aim of this study is to support the engineering design community by easing the difficulty in the problem-exploring practice. To achieve this, a computational problem-exploring (CPE) model is developed to mimic how design engineers identify a valid design problem. Consequently, a CPE tool - Pro-Explora V1 is developed based on the CPE model. The CPE model consists of a synergy of emergent computational technologies including data retrieval and machine learning. A Markovian model is employed in the CPE model to enable a data-driven random process for exploring design problems. In pilot test, Pro-Explora V1 generated some engineering design-related problems which are meaningful, unique, and could not be distinguished from naturally generated ones. It provides support to design engineers in problem-exploring at the early stage in engineering design. This study contributes to the global effort towards data-driven processes in the fourth industrial revolution.

Global Diffusion of Innovation during the Fourth Industrial Revolution: The Case of Additive Manufacturing or 3D Printing

2020 ◽  
Vol 17 (01) ◽  
pp. 2050005 ◽  
Author(s):  
Harm-Jan Steenhuis ◽  
Xin Fang ◽  
Tolga Ulusemre
Keyword(s):  
Additive Manufacturing ◽  
3D Printing ◽  
Diffusion Of Innovation ◽  
Industrial Revolution ◽  
Expert Knowledge ◽  
Early Stage ◽  
High Technology ◽  
Fourth Industrial Revolution ◽  
Advanced Economies ◽  
3D Printers

Additive manufacturing can be considered an innovative and high-technology and one of its characteristics is that it has limited dependency on the location. The purpose of this study is to examine this aspect by investigation how additive manufacturing is spreading globally. The focus is on established manufacturers of industrial additive manufacturing machines. It was found that the early-stage diffusion of this technology is primarily in advanced economies. Furthermore, many of the currently established companies that manufacture industrial 3D printers come from already existing companies that expanded into AM or that led to spin-off companies. The complexity of AM which requires expert knowledge across a range of fields may be the key reason for this finding. Recommendations for further research are provided.

scholarly journals The roles of data management and analytics in industry 4.0 ecosystems

2020 ◽  
Author(s):  
Hendro Wicaksono
Keyword(s):  
Artificial Intelligence ◽  
Industry 4.0 ◽  
Linked Data ◽  
Data Analytics ◽  
Industrial Revolution ◽  
Data Driven ◽  
Knowledge Graph ◽  
Fourth Industrial Revolution ◽  
Global Access ◽  
Integration Data

The presentation introduces the technologies associated with the fourth industrial revolution which rely on the concept of artificial intelligence. Data is the basis of functioning artificial intelligence technologies. The presentation also explains how data can revolutionize the business by providing global access to physical products through an industry 4.0 ecosystem. The ecosystem contains four pillars: smart product, smart process, smart resources (smart PPR), and data-driven services. Through these four pillars, the industry 4.0 can be implemented in different sectors. The presentation also provides some insights on the roles of linked data (knowledge graph) for data integration, data analytics, and machine learning in industry 4.0 ecosystem. Project examples in smart city, healthcare, and agriculture sectors are also described. Finally, the presentation discusses the implications of the introduced concepts on the Indonesian context.

scholarly journals A14D

Federalism-E ◽  
2020 ◽  
Vol 21 (2) ◽  
pp. 68-79
Author(s):  
Mayowa Oluwasanmi
Keyword(s):  
Artificial Intelligence ◽  
Human Rights ◽  
International Development ◽  
Industrial Revolution ◽  
Institutional Capacity ◽  
Data Driven ◽  
Democratic Process ◽  
Fourth Industrial Revolution ◽  
Digital World ◽  
Economic Dependency

In the forefront of the fourth industrial revolution is Artificial intelligence, better known as “AI.”  As a frontier technology, AI is implementing deep and far-reaching changes into the way we work, play and live. These tools present numerous opportunities in solving issues of international development. Yet in spite of its infallible potential,  the negative repercussions of AI driven change have become abundantly clear. These consequences will only be exacerbated in the Global South where there is a greater tendency for weak institutional capacity and governance. AI has the potential to threaten employment, human rights, democratic process and worsen economic dependency. The very nature of these tools--the ability to codify and reproduce patterns--must be met with responsible, ethical actors who ensure developmental goals will be met. Is AI4D the answer? This paper will illustrate the opportunities and risks of AI-driven development. I argue that technology can no longer be considered an inherent equalizer, and that the responsibility for fairness in the digital world must be championed by the international community. Finally, I will present possible steps policymakers can take to ensure true development in our data-driven future. 

Data-driven design paradigm in engineering problems

2016 ◽  
Vol 231 (8) ◽  
pp. 1522-1534 ◽  
Author(s):  
Changqing Liu ◽  
Xiaoqian Chen
Keyword(s):  
Engineering Design ◽  
Data Driven ◽  
Decision Strategy ◽  
Design Problems ◽  
Design Decisions ◽  
Uncertainty Factors ◽  
Satellite Design ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
Design Paradigm

Engineering design problems can, in general, be discussed under the framework of decision making, namely engineering design decisions. Inherently, accounting for uncertainty factors is an indispensable part in these decision processes. In a sense, the goal of design decisions is to control or reduce the variational effect in decision consequences induced by many uncertainty factors, by optimizing an expected utility objective or other preference functions. In this paper, the value of data in facilitating making engineering design decisions is highlighted, and a data-driven design paradigm for practical engineering problems is proposed. The definition of data in this paradigm is elaborated first. Then the data involvement in a whole stage-based design process is investigated. An overall decision strategy for design problems under the data-driven paradigm is proposed. By a concrete satellite design example, the key ideas of the proposed data-driven design paradigm are demonstrated. Future work is also advised.

scholarly journals DEVELOPING A FRAMEWORK TO EVALUATE INDIVIDUAL LEARNING IN ENGINEERING DESIGN PROBLEMS – PART 1: TEACHING THROUGH SIMULACRA OF REAL-WORLD PROBLEMS

2019 ◽  
Author(s):  
Nishant Balakrishnan
Keyword(s):  
Engineering Design ◽  
Real World ◽  
Problem Based Learning ◽  
Design Problems ◽  
Design Engineers ◽  
Engineering Design Problems ◽  
University Of Manitoba ◽  
The University ◽  
Selection Of ◽  
Real World Problems

In the context of teaching design, engineers often have a strong preference for problem-based learning because the skills they are trying to teach are intrinsic to the solving of design problems. The proliferation of problem-based learning (PBL) in capstone and now cornerstone engineering design courses is well supported by industry and faculty and the trend has been towards seeing more PBL in engineering design courses. This paper explores the basic selection of engineering design problems and presents a fairly simple dilemma: the skills that are required to solve a problem are not necessarily the skills that are taught by the problem if the problem is truly open-ended. This paper presents the idea of using engineering problems that are carefully constructed simulacra of real-world problems with built in scaffolding to create PBL experiences for students that are educationally complete and meaningful. This paper presents examples from two courses developed at the University of Manitoba based on this approach, outcomes of and responses to the course layout, and ideas for how this model can be extended to other courses or programs.

scholarly journals The future and the role of human resource management in South Africa during the Fourth Industrial Revolution

2021 ◽  
Vol 19 ◽  
Author(s):  
Cecilia M. Schultz
Keyword(s):  
South Africa ◽  
Resource Management ◽  
Human Resource Management ◽  
South African ◽  
Human Resource ◽  
Industrial Revolution ◽  
Data Driven ◽  
Fourth Industrial Revolution ◽  
The Future

Orientation: The world of work is evolving at an alarming rate, and human resource (HR) practitioners need to familiarise themselves with the future of human resource management (HRM) in order to add value to their organisations.Research purpose: This article presents South African HR practitioners’ views about the future and the role of HRM in the Fourth Industrial Revolution (4IR) from a qualitative perspective.Motivation for the study: Human resource practitioners play a central role in the 4IR, but theories on how their role is enacted remain insufficient.Research approach/design and method: A qualitative survey design was used to study the views of 105 HR practitioners affiliated with the South African Board of People Practices. Three open-ended questions were sent to participants by means of a SurveyMonkey link. Deductive and inductive coding were used to thematically analyse the data.Main findings: The following themes were identified: technology-driven, data-driven, ethically driven, change driven, business-driven, human–machine collaboration and presilience.Practical/managerial implications: South African HR practitioners should be prepared for the future world of work. If these HR practitioners are not technology-driven, data-driven, ethically driven, change driven, business-driven, human–machine collaboration and presilient, they may have difficulty to add value to the organisation in the 4IR.Contribution/value-add: This study extends the body of knowledge about the future world of work and the role of HRM in South Africa by founding that HR practitioners must have presilience and respect ubuntu. The study also extends contemporary scholarship by using an open-ended qualitative review design to investigate the future of HRM in South Africa during the 4IR.

scholarly journals Enhancing maintenance with a data-driven approach

2019 ◽  
Vol 10 (2) ◽  
pp. 135-140
Author(s):  
João Ostrowski ◽  
József Menyhárt
Keyword(s):  
Cost Reduction ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Predictive Analytics ◽  
Point Of View ◽  
Data Driven ◽  
Fourth Industrial Revolution ◽  
Data Driven Approach ◽  
History Of ◽  
Cloud Technologies

Constant stream of data has been generated and stored as more devices are being connected to the internet and supported with cloud technologies. The price drop of such applications along with industry 4.0 trending, triggered an explosive growth and demand for many IT modern solutions. From an industrial point of view, sensorization practices are spreading through factories and warehouses where software is constantly adapting to provide actionable insights in a data-driven configuration. The fourth industrial revolution is empowering the manufacturers with solutions for cost reduction, which translates in competitive advantage. The sector of maintenance operations is leading in engineering innovation, from reactive to planned preventive techniques the next step in history of proactive approaches is Predictive Analytics Maintenance.

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