scholarly journals New Business and Operating Models. Optimization of a Blast Furnace in the Steel Industry. Machine Learning as a Process Optimization

2021 ◽  
Author(s):  
Andrés Redchuk ◽  
Federico Walas Mateo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Blast Furnace ◽  
Conceptual Framework ◽  
Manufacturing Process ◽  
Smart Manufacturing ◽  
Key Factors ◽  
Steel Company ◽  
Steel Manufacturing ◽  
New Business

The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Method: The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Results: This case is relevant for the authors by the way the business model proposed by the startup attempts to democratize Artificial Intelligence and Machine Learning in industrial environments. This way the startup delivers value to facilitate traditional industries to obtain better operational results, and contribute to a better use of resources. Conclusion: This work is focused on opportunities that arise around Artificial Intelligence as a driver for new business and operating models. Besides the paper looks into the framework of the adoption of Artificial Intelligence and Machine Learning in a traditional industrial environment towards a smart manufacturing approach.

scholarly journals New Business Models on Artificial Intelligence—The Case of the Optimization of a Blast Furnace in the Steel Industry by a Machine Learning Solution

2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Andrés Redchuk ◽  
Federico Walas Mateo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Business Model ◽  
Steel Industry ◽  
Business Models ◽  
State Of The Art ◽  
The State ◽  
Steel Manufacturing ◽  
New Business ◽  
New Business Models

This article took the case of the adoption of a Machine Learning (ML) solution in a steel manufacturing process through a platform provided by a Canadian startup, Canvass Analytics. The content of the paper includes a study around the state of the art of AI/ML adoption in steel manufacturing industries to optimize processes. The work aimed to highlight the opportunities that bring new business models based on AI/ML to improve processes in traditional industries. Methodologically, bibliographic research in the Scopus database was performed to establish the conceptual framework and the state of the art in the steel industry, then the case was presented and analyzed, to finally evaluate the impact of the new business model on the operation of the steel mill. The results of the case highlighted the way the innovative business model, based on a No-Code/Low-Code solution, achieved results in less time than conventional approaches of analytics solutions, and the way it is possible to democratize artificial intelligence and machine learning in traditional industrial environments. This work was focused on opportunities that arise around new business models linked to AI. In addition, the study looked into the framework of the adoption of AI/ML in a traditional industrial environment toward a smart manufacturing approach. The contribution of this article was the proposal of an innovative methodology to put AI/ML in the hands of process operators. It aimed to show how it was possible to achieve better results in a less complex and time-consuming adoption process. The work also highlighted the need for an important quantity of data from the process to approach this kind of solution.

scholarly journals Empowering IT Operations through Artificial Intelligence – A New Business Perspective

2020 ◽  
Author(s):  
Logica Banica ◽  
Persefoni Polychronidou ◽  
Cristian Stefan ◽  
Alina Hagiu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Business Performance ◽  
Business Environment ◽  
Infrastructure Monitoring ◽  
Parallel Data ◽  
New Business ◽  
Main Components ◽  
It Operations

This paper aims to describe the concept of applying Artificial Intelligence to IT Operations (AIOps) and its main components, Big Data, Machine Learning and Trend Analysis. The concept was implemented by developing a multi-layered fusion of the technologies that powers the components in AIOps platforms present on the IT market. The core of an AIOps platform is represented by the Big Data organization structure and by a massive parallel data processing platform like Apache Hadoop. The ML component of the platform is able to infer the future behaviour and the regular operations that are performed from the large volume of collected data, in order to develop the ability to automate the activities. AIOps platforms find their place especially in very complex IT infrastructures, ones that require constant monitoring and quick decisions in case of failures. The case study is based on the Moogsoft AIOps platform, and its features are presented in detail, using the Cloud trial version, clearly showing the potential of such an advanced tool for infrastructure monitoring and reporting. The experiment was focused on the way Moogsoft is monitoring computing resources,    is handling events and records alerts for the defined timespan, alerts grouped by category (like web services, social media, networking). The platform is also able to display at any given moment the unresolved situations and their type of origin, and includes automated remediation tools. The study presents the features of this software category, consisting in benefits for the business environment and their integration into the Internet-of-Things model. Keywords: Big Data, Machine Learning, AIOps, business performance.

scholarly journals Is Artificial Intelligence (A.I.) Ready to Run a Factory?

2021 ◽  
pp. 126-132
Author(s):  
Craig Eric Seidelson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Nonlinear Systems ◽  
Linear Systems ◽  
Chaotic Systems ◽  
Smart Manufacturing ◽  
Total Investment ◽  
Input Variables ◽  
On Time Delivery ◽  
A Company

With smart factory investment expected to increase 20% year-on-year over the next five years and total investment expected to reach $275 billion worldwide by 2027, the use of Artificial Intelligence (A.I.) to manage operations is receiving considerable attention.  This paper takes an in depth look at how factory data is being generated, stored, processed, transferred, trained and ultimately validated using A.I.  The conclusion is that deep machine learning is more than capable of controlling devices.  Yet, research shows only 14% of smart manufactures would describe their A.I. efforts as successful.  The problems are cost and application.  Smart manufacturing is almost exclusively done by multi-billion dollar operations.  Is this money well spent?  Factories aren’t closed, linear systems. In these chaotic systems infinitesimal changes in any one of the myriad of input variables are capable of producing disproportionate changes in output values. As a result, no matter how much scrap, downtime, sales or on-time delivery data a company collects actual values will diverge exponentially from what existing A.I. algorithms are predicting.  Until more research is done predicting dynamic, nonlinear systems A.I. will not be capable of running a factory without human involvement.

scholarly journals A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Dazhong Wu ◽  
Connor Jennings ◽  
Janis Terpenny ◽  
Robert X. Gao ◽  
Soundar Kumara
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Tool Wear ◽  
Random Forests ◽  
Manufacturing Systems ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Tool Wear Prediction

Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closed-form mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR.

scholarly journals The Rise of Deepfakes: A Conceptual Framework and Research Agenda for Marketing

2021 ◽  
pp. 183933492199947
Author(s):  
Lucas Whittaker ◽  
Kate Letheren ◽  
Rory Mulcahy
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Conceptual Framework ◽  
Research Agenda ◽  
Theory And Practice ◽  
Digital Content ◽  
Future Research ◽  
Marketing Theory ◽  
Artificial Intelligence Technology ◽  
The Media

Deepfakes, digital content created via machine learning, a form of artificial intelligence technology, are generating interest among marketers and the general population alike and are often portrayed as a “phantom menace” in the media. Despite relevance to marketing theory and practice, deepfakes—and the opportunities for benefit or deviance they provide—are little understood or discussed. This article introduces deepfakes to the marketing literature and proposes a typology, conceptual framework, and associated research agenda, underpinned by theorizing based on balanced centricity, to guide the future investigation of deepfakes in marketing scholarship. The article makes an argument for balance (i.e., situations where all stakeholders benefit), and it is hoped that this article may provide a foundation for future research and application of deepfakes as “a new hope” for marketing.

scholarly journals IIoT/IoT and Artificial Intelligence/Machine Learning as a Process Optimization driver under industry 4.0 model

2021 ◽  
Vol 21 (2) ◽  
pp. e15
Author(s):  
Federico Walas ◽  
Andrés Redchuk
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Process Optimization ◽  
Industry 4.0 ◽  
Industrial Process ◽  
Value Added ◽  
Smart Manufacturing ◽  
Industrial Plants ◽  
A Value ◽  
Learning As A Process

The advance of digitalization in industry is making possible that connected products and processes help people, industrial plants and equipment to be more productive and efficient, and the results for operative processes should impact throughout the economy and the environment.Connected products and processes generate data that is being seen as a key source of competitive advantage, and the management and processing of that data is generating new challenges in the industrial environment.The article to be presented looks into the framework of the adoption of Artificial Intelligence and Machine Learning and its integration with IIoT or IoT under industry 4.0, or smart manufacturing framework. This work is focused on the discussion around Artificial Intelligence/Machine Learning and IIoT/IoT as driver for Industrial Process optimization.The paper explore some related articles that were find relevant to start the discussion, and includes a bibliometric analysis of the key topics around Artificial Intelligence/Machine Learning as a value added solution for process optimization under Industry 4.0 or Smart Manufacturing paradigm.The main findings are related to the importance that the subject has acquired since 2013 in terms of published articles, and the complexity of the approach of the issue proposed by this work in the industrial environment.

scholarly journals Smart Manufacturing Real-Time Analysis Based on Blockchain and Machine Learning Approaches

2021 ◽  
Vol 11 (8) ◽  
pp. 3535
Author(s):  
Zeinab Shahbazi ◽  
Yung-Cheol Byun
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Prediction Model ◽  
Real Time ◽  
Manufacturing Process ◽  
Manufacturing System ◽  
Environmental Data ◽  
Smart Manufacturing ◽  
Automotive Manufacturing ◽  
Hybrid Prediction

The growth of data production in the manufacturing industry causes the monitoring system to become an essential concept for decision-making and management. The recent powerful technologies, such as the Internet of Things (IoT), which is sensor-based, can process suitable ways to monitor the manufacturing process. The proposed system in this research is the integration of IoT, Machine Learning (ML), and for monitoring the manufacturing system. The environmental data are collected from IoT sensors, including temperature, humidity, gyroscope, and accelerometer. The data types generated from sensors are unstructured, massive, and real-time. Various big data techniques are applied to further process of the data. The hybrid prediction model used in this system uses the Random Forest classification technique to remove the sensor data outliers and donate fault detection through the manufacturing system. The proposed system was evaluated for automotive manufacturing in South Korea. The technique applied in this system is used to secure and improve the data trust to avoid real data changes with fake data and system transactions. The results section provides the effectiveness of the proposed system compared to other approaches. Moreover, the hybrid prediction model provides an acceptable fault prediction than other inputs. The expected process from the proposed method is to enhance decision-making and reduce the faults through the manufacturing process.

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