=Paper=
{{Paper
|id=Vol-3080/paper4
|storemode=property
|title=Impact and Usability of Artificial Intelligence in Manufacturing workflow to empower Industry 4.0
|pdfUrl=https://ceur-ws.org/Vol-3080/4.pdf
|volume=Vol-3080
|authors=Muskaan Chopra,Sunil K Singh,Sidharth Sharma,Deepak Mahto
}}
==Impact and Usability of Artificial Intelligence in Manufacturing workflow to empower Industry 4.0==
https://ceur-ws.org/Vol-3080/4.pdf
Impact and Usability of Artificial Intelligence in Manufacturing
workflow to empower Industry 4.0
Muskaan Chopra1, Sunil K. Singh2, Sidharth Sharma3, Deepak Mahto4
1, 2, 3, 4
Chandigarh College of Engineering and Technology, Chandigarh, India
Abstract
AI has created a massive revolution in the information and technology sector around the
world. Analyzing the capabilities of AI, it is clear that the manufacturing sector is soon going
to experience a drastic game-changing effect at various levels of production. To take
advantage of Industry 4.0's tremendous potential and capabilities, businesses must begin
focusing on where AI can offer more value and increase efficiency and productivity. This
study analyzes the manufacturing capabilities of Industries powered by Artificial
Intelligence. The authors have broadly discussed the role of GDP in AI for manufacturing
industries globally by 2030. The paper also sheds light on the impact of AI in Manufacturing
on the economy and scaling of the same.
Keywords 1
Artificial Intelligence, Industries, GDP, Analytics, Manufacturing
1. Introduction
Artificial intelligence (AI) is gently but steadily entering practically every area of our life. Its
applications in medicine, geology, consumer data analysis, self-driving cars, and even art are diverse
and ever-changing. However, AI has raised as many questions as it has answered, such as how the
technology is defined and used (for example, assisted vs. augmented vs. autonomous intelligence),
whether computers can think like humans (the so-called Turing test), the broader impact of
automation on society, and the unexpected ethical and moral quandaries it may cause [9][13].
As the digital world grows, assembling will change – and Artificial Intelligence (AI) will
undoubtedly be at the center of this transformation. Artificial intelligence has the potential to provide
a competitive advantage to businesses at every stage of the value chain [15]. Direct automation,
predictive support, reduced vacation, every minute of everyday creation, improved security, lower
functional expenses, higher productivity, quality control, and faster navigation are just a few of the
advantages available to businesses that embrace change and expertly implement AI across their entire
business [9][14].
2. Literary Work
According to a Gartner survey, 79 percent of respondents stated their companies were looking into
or piloting AI projects, but only 21 percent said their AI initiatives were operational in manufacturing.
It also stated that 66 percent of organizations increased or did not change their investments in AI
projects since the onset of Covid-19 [11][16].
The influence of AI on manufacturing GDP is predicted to grow in every area of the globe. AI is
predicted to contribute 24 percent of China's GDP by 2030 to the manufacturing sector [6]. With a
10% contribution from AI in the manufacturing sector, North America comes in second [6].
In terms of AI implementation in manufacturing, the Automotive Industry currently leads [4][17].
1
International Conference on Smart Systems and Advanced Computing (Syscom-2021), December 25–26, 2021
EMAIL: chopramuskaan47@gmail.com (A. 1); sksingh@ccet.ac.in (A. 2); sharmasidharth2001@gmail.com (A. 3);
deepak1202mah@gmail.com (A. 4)
ORCID: 0000-0002-7672-9186 (A. 1); 0000-0003-4876-7190 (A. 2); 0000-0002-0931-8176 (A. 3); 0000-0003-2826-9055 (A. 4)
©️ 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
Despite being a slow-growing sector in many parts of the world, the Automotive Industry has still
emerged as the biggest hub for smart manufacturing using AI models in quality control, product
development, and manufacturing [8]. Motivated by the success of their first AI system now through
its "Dreamcatcher system," GM has used machine learning to build goods that are more cost-effective
and faster.
Figure 1: GDP impact of AI by 2030 around various regions of the world in the manufacturing
industry
Source- Statista
Demand Planning is an important aspect in manufacturing to ensure a sufficient amount of
manufacturing is taking place at any given point of time. By reducing stockouts and waste, AI helps
businesses improve product availability. AI can be used to assist in bettering our understanding of
sales patterns [11].
3. Benefits Of Deploying AI Models
Deploying AI models for smart manufacturing has led to such a huge difference in not only the
revenues for the companies but also the GDP of the nation’s contributing towards the development of
AI technologies for smart manufacturing [7][18].
Businesses have been seen expanding their manufacturing capacity to fulfill the increased demand
of clients all around the world. Although bringing AI to the industrial business would need a
considerable financial expenditure, the return on investment will be tremendous [3]. Businesses can
experience much cheaper operational costs as intelligent devices take care of day-to-day tasks.
This has resulted in better utilization of the human resource which earlier did all the labor work
will now be able to focus on sophisticated and inventive jobs as AI takes over the manufacturing
facility and automates mundane and dull human labor. Humans may focus on pushing innovation and
bringing their firm to advanced levels while AI takes care of unskilled tasks [4][19].
Although bringing AI to the industrial business would need a considerable financial expenditure,
the return on investment will be tremendous. Businesses can experience much cheaper operational
costs as intelligent devices take care of day-to-day tasks [12][21].
4. Scaling AI in manufacturing operations
The latest industrial revolution (Industry 4.0) is destined to integrate more automation using
machine learning and AI models. However, currently, manufacturers are facing the issue of integrating
and deploying digital platforms and technologies. In the automotive industry, only 14% of
manufacturers have deployed AI at scale [3][10].
Successful prototype deployment in live industrial situations is a good starting point for AI system
deployment. Prototypes have already been tested and implemented in a sandbox or controlled
environment. As a result, the system is limited in its exposure to real-time data sets and difficulties.
Before deploying a model into a production-ready environment, it must be trained to a level of
accuracy suitable for real-time manufacturing processes. Testing on real-time data will not only
improve development accuracy but will also confirm that the solution meets industrial requirements
[5][20].
Figure 2: AI use cases various sectors of the manufacturing industry
Source- Statista
5. Challenges and Future Opportunities
While AI may appear to be a profitable alternative for manufacturing, it has its own set of hurdles
and restrictions that might cause issues. In the next sections, we will talk about the problems of AI.
5.1.Increase in job displacements and transformation of work
Figure 3: Machine vs Human contribution in various fields of manufacturing
Source- Future of Jobs Survey 2018, World Economic Forum
When it comes to existing job duties, there will be a substantial change in the human-machine
divide, and in organizations that are more advanced in their use of AI, staff resources will be
supplemented rather than displaced.
One of the main fears about AI is that human capital would lose value as technology advances
since some feel that AI's automation will diminish the need for expensive human labor. However, this
assumption is faulty because most organizations will boost work possibilities as their efficiency and
services improve. Employee resources will be supplemented rather than replaced in organizations that
are more advanced in their use of AI [4]. According to the World Economic Forum, a new division of
labor between people and robots would create more than 130 million new jobs by 2022. In reality,
between 2018 and 2022, there will be a dramatic change in the human-machine frontier when it comes
to existing labor duties [2][21].
Before very long, a significant variable affecting the GDP is the number of cases, recuperation
rates, and the speed at which immunization will be finished. Given the current circumstances, India’s
GDP is predicted to rebound quickly, and as per the forecasts as displayed in Figure 4.
5.2.Lack Of Specialized Workforce for AI Systems
Artificial Intelligence is a growing body of knowledge that will need more educated and qualified
personnel to design, manage, and debug systems. Today's manufacturing business is characterized by
ever-shorter cycles of technological advancement, which results in a fast shift like the industrial jobs
that must be performed, and therefore in the workers’ skill sets [12][24]. It's widespread criticism and
concern among manufacturers today that finding personnel with the necessary skills to implement and
maintain these technologies is proving difficult since current workforce training and expertise will
become obsolete as technological sectors will likely evolve at a quicker rate than ever before [10][22].
To fill the consequent skills gap, new jobs requiring higher degrees and technological abilities will
arise.
5.3.Lack Of Trust and Explainability
Explainable AI is critical for providing clear recommendations with transparent information,
evidence, uncertainty, confidence, and risk that people can understand and machines can
comprehend [1]. To that end, people want computer systems to perform as expected and to
provide clear explanations and justifications for their actions [13][23].
However, there are concerns about the human ability to regulate and comprehend the
judgments made by powerful artificial intelligence algorithms. This problem complicates the
application of AI systems in a variety of businesses.
5.4.Unreal Expectations From AI-Enabled the utilized Systems
Explainable AI is critical for providing clear recommendations with transparent information,
evidence, uncertainty, confidence, and risk that people can understand and machines can comprehend
[4]. To that end, people want computer systems to perform as expected and to provide clear
explanations and justifications for their actions [2][23].
However, there are concerns about the human ability to regulate and comprehend the judgments
made by powerful artificial intelligence algorithms. This problem complicates the application of AI
systems in a variety of businesses [4][24].
5.5.The Need for Accuracy in Data for AI Systems
AI systems cover the vast domains of data capture, data storage, data preparation, and
sophisticated data analytics technologies, and are not restricted to a particular component of Data
Management. Data Quality is a major issue in today's Enterprise Data Management since company
data must be thoroughly cleaned and prepared before it can be utilized as input to any Analytics or
Business Intelligence system [10].
Data preparation and exploration require a significant amount of labor, owing to data quality
issues. In this regard, according to a recent Price Waterhouse Coopers poll, most big firms now
recognize that, after years of accumulating company and consumer data, they are significantly
hampered in their ability to exploit sophisticated data technologies owing to low Data Quality
[12][25]. Data silos, poor data, data compliance difficulties, a lack of data professionals, and
inadequate systems were the top reasons given by corporate leaders in the PwC study for failing to
fulfill their data analytics ambitions [8][26].
Figure 4: AI's challenges with data
Source- PwC
6. Conclusion
AI systems in manufacturing can play a big role in Industrial Revolution 4.0 by completely
changing the ways of manufacturing and designing the products for consumers. While big
multinational firms have begun experimenting with AI use cases, large-scale adoption remains
uncommon. It will be difficult for manufacturing to enter into the 4.0 age until more companies move
beyond pilots and proofs-of-concept to scale. The next industrial revolution can be shaped by
manufacturers focusing on AI's most profitable use cases, while also ensuring proper governance,
platform, and talent development.
With proper research and vision, AI models can be integrated into a stepwise process involving the
integration of AI models with the old manufacturing methods and then slowly eliminating the
loopholes of the current model and moving on to a newer and better version until the whole
manufacturing unit becomes AI friendly with better efficiency and greater profits.
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