Volume 9, Issue 4, April – 2024 International Journal of Innovative Science and Research Technology

ISSN No:-2456-2165 https://doi.org/10.38124/ijisrt/IJISRT24APR1221

Intelligent Engines: Revolutionizing

Manufacturing and Supply Chains with AI
Vishwanadham Mandala1; Manogna Dolu Surabhi2

Abstract:- Artificial intelligence (AI) technologies are

becoming a reality, with intelligent engines that can
learn and simulate human thinking. These engines have
three key features: micro-level intelligence with sensors,
logic-based intelligence with software tools, and the
ability to adapt and learn using algorithms. AI reduces
the need for human intervention and cognitive thinking,
finding more efficient solutions to complex problems in
manufacturing and supply chain industries. AI simulates
human cognition using software tools, allowing for the
automation of tasks and analysis of complex systems.
However, it raises questions about whether problems can
be solved differently and the limitations of explicit
algorithms.
Fig 2: The Industrial Revolution
Keywords:- Intelligent Engines, Supply Chain, Industry 4.0,
Internet of Things (IoT), Artificial Intelligence (AI), A. Background
AI is a technology that imitates intelligent behavior
Machine Learning (ML), Smart Manufacturing (SM).
and has widespread applications in manufacturing. It
enhances data analysis and reduces machinery upkeep costs.
I. INTRODUCTION
AI can be used for preventative maintenance and developing
systems that maximize goal achievements. Like analyzing
This book focuses on the design of 'intelligent' engines,
using modern Information Technology (IT) to automate vehicle downtime statistics, intelligent engines can
revolutionize manufacturing and supply chains. Caterpillar's
tasks currently done by humans. The goal is to have a cost
research showed that AI can be used to configure machine
or performance advantage over alternative automation
systems for more reliable products. This is advantageous for
methods. The field chosen for examination is optimization,
which involves finding the best solution to a problem by reconfiguring production machinery and improving end-
minimizing a cost function. The book aims to develop product quality and reliability.
general methods applicable to specific problems, with a
single application example being manufacturing process
automation. This choice allows for using proven methods
that are familiar to most readers.

Fig 1: The Industrial Revolution – Scientific View

Fig 3: Supply Chain Starts With Shop Floor

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B. Purpose of the Research C. Scope and Limitations

A theoretical module is proposed to facilitate more This paper will focus specifically on artificial narrow
proactive and autonomous decision-making by individual intelligence (ANI) to ensure an accurate understanding of
factories and their network partners in supply network AI's capabilities and potential impacts. ANI has already
design and planning. This module leverages artificial revolutionized the manufacturing industry, as seen in the
intelligence planning methods to provide a detailed network robotic automation of assembly lines. The scope of this
configuration and the associated operational plan. Past work paper will be confined to AI's impacts on improving or
on supply network design and planning has laid the restructuring the current processes of manufacturing
foundation for this research. However, a significant gap still consumer goods, utilizing the example of an intelligent
needs to be found between the complexity of real-world engine developed by a fictional company.
supply networks and the decision-making capabilities of
managers. This research takes a first step towards providing Table 1: AI and Prior AI Trends/Key
tools to automate the more routine aspects of supply Improvement in Timelines
network decision-making, freeing human managers to focus 1900-1950 Electrification & Industrial Controllers
on more strategic and creative problem-solving. Said 1970-1980 Hardware explosion key to point
another way, the goal is not to replace human decision- 1990-2000 Internet
makers but to make them more effective by automating 2010-2020 Manufacturing Automation, AI, ML
routine decisions and providing intelligent decision support 2020-2024 Rapid Growth – LLM’s
at all levels of the supply network. In pursuing this goal,
specific scope and limitations must be set. II. THE ROLE OF AI IN MANUFACTURING

The manufacturing environment is a perfect platform

for understanding how AI can add learning and decision-
making capabilities to complex tasks. There are many well-
developed AI techniques that have the potential to be
integrated into the manufacturing system. These techniques
include:

 Case-based reasoning for new product design and

recycling knowledge from past cases.
 Intelligent scheduling optimizes resource use and
minimizes make pan under production environment
constraints. Expert planning and rescheduling perform
production activities in a more complex environment.
 Monitoring and diagnostics are used to detect the
tolerance process, and facility monitoring is used to
anticipate system failure.
 Autonomous design to self-develop software design
Fig 4: Industrial AI is Revolutionizing Manufacturing tools that are designed to improve software weaknesses
and many more.

Fig 5: Significance of AI in Manufacturing

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Table 2: AI-Enhanced/Enabled Supply Chain Management Process

Technology Role in SCM Benefits in SCM
Cloud Computing and Storage Enables integrated and seamless data storage Improves operational efficiency and
and access speed of data access and analysis
Inventory and Facilitates efficient management and Reduces inventory costs, improves
Network Optimization Tools distribution of inventory. customer service
Used for tracking and identification of goods
within the supply chain
Sensors and Automatic Automates tasks such as demand planning, Improves tracking accuracy and security
Identification inventory management, and product in the supply chain
development.
Used in warehousing operations, production
lines, and transport.
Enables real-time visibility across the supply
chain
Artificial Intelligence Allows for anticipation of demand fluctuations Improves efficiency, accuracy, and cost
Robotics and Automation and optimization of resources. savings.
Provides opportunities for onsite production, Increases productivity, reduces labor
reducing the need for transportation and storage costs,improves decision-making,
increases the speed of delivery
Industrial Internet-of-Things (IoT) Used for transportation of goods within and Reduces transportation and storage costs
Predictive and between facilities. andallows for customization of products.
Prescriptive Analytics, Enhances real-time communication and Improves delivery speed andreduces
Autonomous Vehicles and Drones information access in the supply chain transportation costs and human errors.

A. Automation and Efficiency Machine shops and manufacturing businesses may still
Once a business decides to manufacture, its primary need to start using outdated processes and methods.
goal is to stay competitive. Growth and profit drive the However, this may not continue due to the retiring skilled
business, leading to increased pressure for better and workers and the fewer younger workers interested in the
cheaper products. Small and medium-sized businesses need field. Increased competition and material costs also make
help investing in large-scale production lines to meet current processes less effective. Intelligent software and AI
production demands. However, computer-aided can offer a solution by optimizing processes like high-speed
manufacturing and intelligent software have overcome this machine-making, reducing cycle time without requiring
issue and provide efficient solutions. These tools are crucial costly changes in the entire process. Due to limited time and
for thriving businesses and avoiding closure due to market resources, this ability to improve existing processes is highly
decline. sought after.

Table 3: Automation Advantages

B. Predictive Maintenance the aerospace and defense industries, resulting in significant

Intelligent AI engines increase downtime intervals by savings and prevention of engine removals. Companies like
implementing predictive maintenance strategies. AI uses Delta Airlines and American Airlines use predictive
statistical analysis, supervised learning, and unsupervised maintenance systems to improve operational efficiencies.
learning methods to forecast system failures. This approach Predictive maintenance's success and cost efficiency will
differs from preventive maintenance and saves on costs. make current manufacturing methods appear outdated.
Implementing predictive maintenance has been prevalent in

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Fig 6: Predictive Maintenance

C. Quality Control
The second most significant cited problem was
manufacturers' inability to utilize the information available
to them to make informative decisions, with a third of
manufacturers saying data needed to be more bulky and
complex to manage. AI can help manufacturers sift through
this data to detect patterns, diagnose issues, and recommend
action.

Fig 7: Quality Control Equation Fig 8: Quality Control

This has the potential to improve quality, prevent D. Supply Chain Optimization
costly defects, and reduce rework, an area where Supply chains are vital but complex and fragile. AI can
manufacturers currently need help to make informed predict and solve problems, like disruptions and risks. IBM's
decisions using inductive problem-solving. 30% of AI early Watson Supply Chain and UPS's ORION platform are
adopters in manufacturing are using or planning to use AI examples. They optimize routes and offer responsive
for predictive maintenance, compared to only 6% of systems. AI can also simulate and predict cash flow impacts.
manufacturers overall, which tells of AI's potential to AmBev and AB InBev's case showed the risks of extending
improve maintenance activities and shift away from the payment terms. AI estimated a cost of US$200 million for
current cost-intensive run-to-fail maintenance strategy. AB InBev and risk to relationships with small suppliers and
farmers.

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Fig 9: Supply Chain Optimization

III. INTELLIGENT ENGINES: TRANSFORMING path_to_save (str): Local path where the dataset should
MANUFACTURING PROCESSES be saved.
"""
ML techniques are used in manufacturing. IBM's api = KaggleApi()
Watson, a well-known ML application, optimizes its api.authenticate()
function as a Quality Engineer. ML algorithms predict api.dataset_download_files(dataset_name,
machinery failure and reduce costs. Ecolab used ML to path=path_to_save, unzip=True)
analyze soil loading, resulting in reduced costs.
Manufacturers are increasingly using analytics and AI. def load_data(file_path):
CEOs plan to integrate cognitive computing/AI for various """
purposes in the next 3-5 years. Function to load the data from a CSV file.
Args:
Machine learning is AI that improves prediction file_path (str): Path to the CSV file.
accuracy without explicit programming. Algorithms use """
input data and statistical analysis to predict outcomes. There import pandas as pd
are three types of learning: supervised, unsupervised, and return pd.read_csv(file_path)
reinforcement.
if __name__ == '__main__':
Machine learning technologies are already being used # Define the dataset and path
effectively in manufacturing and can only become more dataset = 'username/dataset-name' # Example:
prevalent in the future. The study "The Learning Enterprise" 'username/manufacturing-engine-data'
sponsored by Accenture, and authored by Sam Khan, states save_path = './dataset'
that top executives believe machine learning can generate file_name = 'data.csv' # Adjust based on actual file
substantial value in terms of increased productivity, higher name in the dataset
efficiency, and lower operational costs.
# Download the dataset
A. Machine Learning Algorithms -Data Collection download_dataset(dataset, save_path)
Data Collection Code sudo code snippets are provided
below. # Load the data
data = load_data(os.path.join(save_path, file_name))
import requests print(data.head())
import os
from kaggle.api.kaggle_api_extended import KaggleApi # Author: Vishwanadham Mandala
def download_dataset(dataset_name, path_to_save): # Date: 2024-04-14
""" Code 1.Python data collection program
Function to download dataset using Kaggle API.
Args:
dataset_name (str): Full path of the dataset on Kaggle.

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B. Machine Learning Algorithms – Train the model def main():

Sudo Code for AI Algorithm. api_url = 'http://example.com/api/engine_heartbeat'
# Author: Vishwanadham Mandala data = fetch_data(api_url)
# Date: 2024-04-14 X_train, X_test, y_train, y_test = prepare_data(data)

import requests # Initialize models

import pandas as pd rf_model = RandomForestClassifier(n_estimators=100,
from sklearn.model_selection import train_test_split random_state=42)
from sklearn.ensemble import RandomForestClassifier, gb_model =
GradientBoostingClassifier GradientBoostingClassifier(n_estimators=100,
from sklearn.metrics import accuracy_score, random_state=42)
classification_report
# Train models
def fetch_data(api_url): rf_model = train_model(X_train, y_train, rf_model)
"""Fetch data from API.""" gb_model = train_model(X_train, y_train, gb_model)
response = requests.get(api_url)
data = response.json() # Evaluate models
return pd.DataFrame(data) rf_accuracy, rf_report = evaluate_model(rf_model,
X_test, y_test)
def prepare_data(df): gb_accuracy, gb_report = evaluate_model(gb_model,
"""Preprocess and split the data.""" X_test, y_test)
X = df.drop('target', axis=1) # Assuming 'target' is the
column to predict print("Random Forest Model Accuracy:", rf_accuracy)
y = df['target'] print("Gradient Boosting Model Accuracy:",
X_train, X_test, y_train, y_test = train_test_split(X, y, gb_accuracy)
test_size=0.2, random_state=42) print("\nRandom Forest Classification Report:\n",
return X_train, X_test, y_train, y_test rf_report)
print("\nGradient Boosting Classification Report:\n",
def train_model(X_train, y_train, model): gb_report)
"""Train the model."""
model.fit(X_train, y_train) # Select the best model based on accuracy
return model best_model = rf_model if rf_accuracy > gb_accuracy
else gb_model
def evaluate_model(model, X_test, y_test): print("Best model selected:", "Random Forest" if
"""Evaluate the model.""" best_model == rf_model else "Gradient Boosting")
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred) if __name__ == '__main__':
report = classification_report(y_test, y_pred) main()
return accuracy, report Code 2.Python code for AI Algorithms

 Output Snippets.

Fig 10: Output Snippets

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Fig 11: Output Snippets

C. Machine Learning Algorithms – Explanation manufacturing processes constantly changing, machine

Machine learning is a process that uses models to learning models can maintain the most relevant information
improve systems and support decision-making. There are and provide the most accurate decisions. This process can
two types of learning: supervised and unsupervised. In help prevent faulty machinery, increase product quality, and
supervised learning, a model is trained using input-output prevent costly downtime by providing useful predictive
pairs, and predictions are corrected by the user until an analytics to influence decision-making.
acceptable level of performance is achieved. Unsupervised
learning trains the model to recognize patterns and make Machine learning (ML) uses statistical models to
decisions without prior information. improve systems and decision-making. It has two types:
supervised learning, where a model is trained using input-
Machine learning can benefit manufacturing because it output pairs, and unsupervised learning, where the model
adapts and learns from real-time data. With modern recognizes patterns and makes decisions based on data.

Fig 12: Machine Learning Algorithms

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Fig 13: Heatmap. Output Snippets

Fig 14: Algorithm Differences -Output Snippets

D. Robotics and Autonomous Systems where robots learn from experts or their own mistakes, is
The popularity of robotics in manufacturing is being researched. Error recovery is crucial for tasks like
increasing due to lower costs and more accessible assembly. Modular robots that can reconfigure themselves
programming. AI systems are being used to coordinate large are another option. AI techniques are used for coordination
numbers of robots, including CNC machines, for complex and planning. Robotics is expected to be a significant area
tasks like aircraft manufacturing. Inflexibility in for AI in manufacturing.
programmed paths has been a barrier, but flexible robotics,

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Fig 15: Robotics Automation

Fig 16: Autonomous System

E. Sensor Integration component failure, reducing downtime. Intelligent engine

Manufacturers using AI and sensors can improve monitoring and diagnosis can be seen with mobile robots
machine understanding and response to system status. This using sensors to compare map states. Diagnosis maneuvers
saves money by identifying and diagnosing problems and are ranked, and decision-making tools aid in cost-effective
avoiding unscheduled downtime. Traditional scheduled repair. AI and sensors prevent costly failures and
maintenance is costly and inefficient, as potential failures maintenance actions.
are not considered. AI and sensor integration predicts

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Fig 17: Sensor Integration

F. Data Analytics and Decision-Making manufacturing sectors use existing data more effectively to
AI can enhance data analysis and decision-making by plan inventory and meet customer demand. Additionally,
providing superior predictive analysis tools and algorithms. AI-based tools can provide intelligence for ad-hoc analysis
These tools simplify complex analytical procedures and and simulation, enabling specific hypotheses testing and
allow all personnel to run complex analyses. AI also helps evaluating the effects of decisions.

Fig 18: Types of Data Analytics

AI allows complex decision-making in data analysis positive impact, high-level discussions should lead to new
and simulation. It can codify human expertise into a laws, company codes, and customs. As intelligent engines
decision support system, offering specific recommendations develop rapidly, regulations specific to their use are
and guiding users with no experience. AI tools store and essential. Flexibility and performance-based regulations are
reuse knowledge, combining specific intelligence about important to accommodate the challenges and changes
analysis techniques with subject knowledge. For instance, brought by intelligent engines. Preemptive health and safety
scheduling algorithms optimize task scheduling while risk assessments will ensure their beneficial introduction in
leveraging knowledge of the task or machine. the workplace.

IV. CHALLENGES AND FUTURE DIRECTIONS

A. Ethical Considerations
The concept of addressing the ethical aspects of AI and
robotics with robust policy responses and international
frameworks is recommended by various organizations. The
focus should shift towards the potential benefits of these
technologies rather than the perceived "evils." To ensure a

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Fig 20: Change Management

Fig 19: Ethical Considerations
C. Cybersecurity Risks
B. Workforce Adaptation and Training
The latest digital and AI techniques and tactics
Intelligent engines disrupt industries globally,
challenge cybersecurity system developers. Cybersecurity is
changing work nature and workforce. Machine learning
a critical issue for all as the threats can be severe economic
focuses on replacing human workers with robots, bringing
threats to national security, let alone the effect on individual
economic growth but harming the current workforce.
organizations that are hit by security breaches. The security
Intelligent engines, however, augment human intelligence
vulnerabilities of intelligent systems are a significant issue,
and enhance worker effectiveness.
and further work is required to ensure that AI and intelligent
system applications are secure and do not become a new
Our project uses case-based systems to capture and
attack on organizations or society. As intelligent systems
reuse process knowledge in aerospace, explicitly targeting
begin controlling and optimizing more complex physical,
the design and planning phases. By preserving the
biological, and societal systems, their positive or negative
knowledge base and involving human designers, our
impact will increase. AI technologies have the potential to
technology differs from typical automation approaches and
automate a range of tasks, for instance, from everyday daily
ensures job security for designers.
life to mission-critical endeavors on the road and in the
workplace.

Fig 21: Cybersecurity Risks

A security breach in an AI system has the potential to providing end-to-end security and safety assurance for AI
have a severe impact on society's quality of life. Measures to technologies. Additionally, it will require a shift in how
ensure the security and safety of AI systems present a society approaches security and safety as it becomes reliant
significant challenge. They will require more research into on intelligent systems.

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Fig 22: Cybersecurity Algorithm

D. Potential Applications in Other Industries company. The system was demonstrated in a single facility
In the case study, we found the successful in the manufacturing sector, specifically the supply chain for
implementation of the Intelligent Engine within one an automotive parts warehouse.

Fig 23: AI Application

It was found that the intelligent engine improved the The intelligent engine improved the human engineers'
quality of scheduling. A team of six engineers did the schedules in only a fraction of the computational time. It is
original scheduling within the plant. Their schedules were important to note that the operation research team at the
often found to be of poor quality, and the only feasible way warehouse could have been more enthusiastic about the
to get good results was through extensive debugging within simulation result on the first day.
the simulation environment.

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Table 4: AI Application Types

This was because the quality of engine schedules was use the schedules to direct the factory in a way consistent
very high, and the research team did not believe it was with the global optimality of the engine schedules. The
feasible to get such good results. However, once the engine research team relinquished their scheduling control to the
schedules were implemented, the research team found the engine, which was deemed automatic within two weeks.
quality of work to be much improved, and they were able to

Table 5: Industrial Field of Application

V. CONCLUSIONS diagnosis and reconfiguration will provide a unifying

framework for system understanding and control. They will
Looking beyond control, AI methods are starting to be significantly impact the safety of intelligent engines, ensure
applied to system understanding and failure mitigation. that they are robust and reliable, and minimize the life cycle
Machine learning techniques are used to learn complex costs for complex systems. The effective deployment of AI
equipment and systems models from sensor data and for technologies represents a major opportunity to improve
decision support in maintenance and other operations. This manufacturing and supply chains in the global economy.
development is strongly aligned with the evolution of The UK industry needs to stay competitive in the future.
intelligent engines. Model-based learning and reasoning for

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IIoT improves manufacturing and supply chains, [10]. Kapoor, K., Zhou, Y., & Bhattacharya, S. (2019).
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transformation by optimizing system performance, Management & Data Systems, 119(7), 1561-1587.
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computerization of control and automate the automation twin-driven product design, manufacturing and
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Asynchronous Shipments:Integrating AI Predictive
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2023 8th International Conference on
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Coimbatore, India, 2023, pp. 720-727,
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673.

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