22ME926Introduction to Business Intelligence and Analytics, Advanced Integration techniques.pptx

22ME926
- INTRODUCTION TO BUSINESS INTELLIGENCE AND
ANALYTICS, ADVANCED INTEGRATION TECHNIQUES

Introduction to
Industry 4.0
Industry 4.0 represents the fourth industrial revolution,
characterized by the integration of advanced technologies like
automation, data analytics, and cyber-physical systems into
manufacturing and other industries. This transformation is
reshaping how businesses operate and deliver products and
services.
by Sengottaiyan K Assistant Professor

What is Industry 4.0?
Connectivity
The integration of smart,
connected devices and
systems that can
communicate and exchange
data.
Automation
The use of intelligent
machines and systems to
perform tasks with minimal
human intervention.
Data Analytics
The ability to collect,
analyze, and derive insights
from large amounts of data
to improve decision-making.
Cyber-Physical Systems
The merging of the physical
and digital worlds, enabling
real-time monitoring and
control of industrial
processes.

Key Technologies of Industry 4.0
Internet of Things (IoT)
Interconnected devices that can
communicate and exchange
data, enabling real-time
monitoring and optimization.
Artificial Intelligence (AI)
Advanced algorithms and
machine learning that can
analyze data, automate
decision-making, and drive
innovation.
Big Data and Analytics
The ability to collect, process,
and derive insights from large
volumes of structured and
unstructured data.

Benefits of Adopting Industry 4.0
1 Improved Efficiency
Increased productivity,
reduced waste, and
optimized processes
through automation and
data-driven decision-
making.
2 Enhanced Quality
Consistent, reliable, and
high-quality products and
services through real-time
monitoring and control.
3 Cost Savings
Reduced operational costs,
maintenance expenses, and
inventory levels through
predictive analytics and
optimization.
4 Increased Flexibility
Ability to quickly adapt to
changing market demands
and customer preferences
through flexible, agile
production.

Challenges of Implementing Industry 4.0
Technological Complexity
Integrating and managing a
wide range of advanced
technologies, systems, and data
sources.
Cybersecurity Risks
Protecting interconnected
systems and data from cyber
threats and ensuring data
privacy.
Organizational Change
Overcoming resistance to
change and upskilling the
workforce to embrace new
technologies and processes.

Industry 4.0 in Manufacturing
1
Smart Automation
Automated manufacturing processes with real-time
monitoring and control systems.
2
Predictive Maintenance
Leveraging sensor data and analytics to predict and
prevent equipment failures.
3
Customized Production
Flexible, on-demand production to meet individual
customer requirements.

Industry 4.0 in Supply Chain Management
End-to-End Visibility
Real-time tracking and tracing
of products and materials
throughout the supply chain.
Demand Forecasting
Leveraging data analytics to
improve demand forecasting
and inventory optimization.
Logistics Optimization
Automated route planning,
fleet management, and
transportation optimization.

Industry 4.0 in Healthcare
Remote Monitoring
Wearable devices and IoT sensors for real-time patient monitoring and early detection.
Robotic Surgery
Advanced, precision-guided surgical robots for minimally invasive and complex procedures.
Predictive Analytics
Using data-driven insights to predict and prevent health issues, optimize
treatments, and improve patient outcomes.

Preparing Your Organization for Industry 4.0
Assess Readiness
Evaluate your organization's current capabilities, infrastructure, and resources to identify areas for improvement.
Develop a Strategy
Create a clear, comprehensive plan for implementing Industry 4.0 technologies and transforming your operations.
Invest in Talent
Upskill your workforce and hire the necessary talent to support the adoption and integration of new technologies.

The Future of Industry 4.0
1 Increased Automation
Intelligent machines and
robots will take on an even
greater role in
manufacturing and other
industries.
2 Personalized Products
Highly customized and
personalized products will
become the norm,
catering to individual
customer preferences.
3 Sustainable Operations
Industry 4.0 technologies
will drive more eco-
friendly and resource-
efficient production
processes.
4 Predictive Maintenance
Advanced analytics will
enable proactive
maintenance and
minimize unplanned
downtime across
industries.

Introduction to
Lean Production
System
Lean production is a systematic approach to eliminating waste and
improving efficiency in manufacturing and business processes. It
focuses on optimizing workflows, reducing inventory, and
empowering employees to drive continuous improvement.

The Principles of Lean
1 Value
Identify and focus on
creating value for the
customer.
2 Value Stream
Analyze the end-to-end
process to eliminate
waste.
3 Flow
Ensure a smooth,
continuous flow of work.
4 Pull
Produce only what the
customer needs, when
they need it.

Eliminating Waste
Overproduction
Making more than the
customer needs.
Transportation
Unnecessary movement of
materials, information, or
people.
Inventory
Excess raw materials, work-
in-progress, or finished
goods.
Motion
Unnecessary movement of
people or equipment.

Just-in-Time Production
1
Pull System
Products are made only when the customer orders them,
reducing waste and inventory.
2
Small Batches
Producing in small, frequent batches improves responsiveness
and reduces defects.
3
Leveled Production
Maintaining a consistent production pace to avoid bottlenecks
and overloads.

Continuous Improvement (Kaizen)
Incremental Changes
Kaizen focuses on making small,
incremental improvements rather
than large, disruptive changes.
Employee Involvement
Kaizen relies on the collective
knowledge and ideas of all
employees to identify and solve
problems.
Ongoing Process
Kaizen is a never-ending cycle of
continuous improvement, ensuring
the organization is always evolving
and adapting.

5S Workplace Organization
Sort
Eliminate unnecessary items and
organize the work area.
Set in Order
Arrange essential items in a
logical, efficient manner.
Shine
Keep the work area clean and
well-maintained.
Standardize
Establish consistent procedures and best practices.
Sustain
Continuously monitor and improve the 5S system.

Visual Management
Display Key Information
Use visual tools like signs, charts,
and indicators to communicate
important data and metrics.
Identify Problems Quickly
Visual management makes it easier
to spot issues and deviations from
standard processes.
Empower Employees
Visual management gives
employees the information they
need to make informed decisions
and take action.

Standardized Work
1 Documented
Processes
Standardized work
involves creating
detailed, written
procedures for each task
and operation.
2 Consistency and
Quality
Standardized work
ensures a consistent,
high-quality output by
reducing variation in
processes.
3 Continuous Improvement
Standardized work provides a stable foundation for
identifying and implementing improvements.

Lean Leadership and Culture
Empowerment
Lean leaders empower employees to identify and solve
problems.
Servant Leadership
Lean leaders support and facilitate the work of their
teams.
Continuous Learning
Lean leaders foster a culture of continuous learning
and improvement.

Implementing Lean in Your Organization
1
Assess Readiness
Evaluate your organization's culture, processes, and
willingness to change.
2
Develop a Plan
Create a detailed implementation plan with clear goals,
timelines, and responsibilities.
3
Pilot and Iterate
Start with a pilot project, gather feedback, and continuously
improve the approach.

Introduction to
Smart and
Connected
Business
Smart and connected business leverages the power of
technology, data, and automation to drive innovation, improve
efficiency, and enhance customer experiences. This presentation
will explore the key trends and strategies behind this
transformative approach to modern enterprise.

The Rise of the Internet of Things (IoT)
Sensors
IoT sensors collect real-time data from the physical world.
Connectivity
Devices connect to the cloud, enabling remote monitoring and control.
Analytics
Data is analyzed to uncover insights and drive intelligent decision-making.

Leveraging Data and
Analytics for Competitive
Advantage
Predictive Maintenance
Analyze sensor data to
predict equipment failures
and schedule proactive
maintenance.
Personalized
Customer Experiences
Use customer data to
deliver tailored products,
services, and interactions.
Operational Optimization
Identify inefficiencies and optimize processes to improve
productivity and cost-savings.

Automating Processes for
Increased Efficiency
1 Identify
Analyze business processes to pinpoint repetitive,
rules-based tasks.
2 Automate
Implement robotic process automation (RPA) to
execute these tasks.
3 Scale
Expand automation across the organization to drive
efficiency and cost savings.

Enhancing Customer Experience through
Connectivity
Omnichannel Support
Provide seamless, personalized
experiences across multiple
touchpoints.
Predictive Analytics
Use customer data to anticipate
needs and proactively offer
solutions.
Intelligent Automation
Leverage chatbots and virtual
assistants to streamline
customer interactions.

Optimizing Supply Chain
and Logistics
1 Real-Time Visibility
Track inventory,
shipments, and asset
locations in real-time.
2 Automated
Warehousing
Utilize robotics and AI
to optimize storage,
picking, and packing.
3 Predictive Logistics
Forecast demand and plan routes to minimize delays and
costs.

Cybersecurity Considerations in a Connected World
Secure IoT Devices
Implement robust security
protocols to protect connected
devices.
Data Encryption
Safeguard sensitive
information through advanced
encryption techniques.
Incident Response
Develop comprehensive plans
to detect, respond, and
recover from cyber threats.

Workforce Transformation
and Upskilling
Identify Skills Gap
Assess current employee capabilities and future skill
requirements.
Upskill Workforce
Provide targeted training and development opportunities.
Foster Innovation
Empower employees to embrace new technologies and
processes.

Implementing a Smart and Connected Strategy
Assess Readiness
Evaluate your organization's
digital maturity and
infrastructure capabilities.
Prioritize Initiatives
Identify and focus on the most
impactful smart and connected
use cases.
Manage Change
Develop a comprehensive
change management plan to
drive adoption.

The Future of Smart and
Connected Businesses
1 Hyper-Automation
Integrate AI, machine
learning, and advanced
analytics to automate
end-to-end processes.
2 Intelligent Ecosystems
Foster collaborative
networks of connected
devices, partners, and
customers.
3 Sustainability and Resilience
Leverage smart solutions to reduce environmental impact
and build business continuity.

Smart factories are a key aspect of the Industry 4.0
movement, which seeks to integrate digital technologies
into the manufacturing process to create a more efficient,
autonomous, and connected production system. By
leveraging a range of technologies, including the Internet of
Things (IoT), artificial intelligence (AI), and machine
learning, smart factories are able to collect and analyze
vast amounts of data in real time, allowing manufacturers
to identify inefficiencies and bottlenecks in the production
line and make adjustments on the fly.
Smart factories

https://industry40thinktank.com/what-is-smart-factory-four-zero/

what is smart factory 4.0 FAQs
1. What is a smart factory?
2. What is Industry 4.0?
3. What are the benefits of a smart factory?
4. What are the key technologies used in a smart
factory?
5. How can a company implement a smart
factory?

Benefits-Smart Factory
Smart factories use connected equipment and devices to allow for
evidence-based decision-making to optimise efficiency and productivity
throughout the manufacturing process.
Delivering an agile, iterative production process can extend the
capabilities of both devices and employees, leading to lower costs,
reduced downtimes and less waste in the manufacturing industry.
Identifying and then reducing or eliminating underused or misplaced
production capabilities increases efficiency and output with little
investment in new resources.
The benefits of digitalising a factory include those related to planning,
quality control, product development and logistics as each is assessed and
optimised based on real feedback.
There are also long term benefits to be gained through the introduction
of machine learning to the process. By collecting and analysing data, it is
possible to schedule preventive and predictive maintenance - based on
accurate real-life information - to avoid production line shutdowns.
https://www.twi-global.com/technical-knowledge/faqs/what-is-a-
smart-factory#Benefits

1. Level One: Basic Data Availability
At this level, a factory or facility is not really ‘smart’ at all. There is data available but it
is not easily accessed or analysed. Data analysis, where it is done, is time consuming and
can add inefficiencies to your production process.
2. Level Two: Proactive Data Analysis
At this level, the data can be accessed in a more structured and understandable form.
The data will be centrally available and organised with visualisation and displays assisting
with its processing. This all allows for proactive data analysis, although there will still be
a level of effort involved.
3. Level Three: Active Data
At this level, the data can be analysed with the assistance of machine learning and
artificial intelligence, creating insight without as much human supervision. The system is
more automated than at level two and can predict key issues or anomalies to proactively
predict potential failures.
4. Level Four: Action-Oriented Data
The fourth level builds on the active nature of level three to create solutions to issues
and, in some instances, undertake action to alleviate a problem or improve a process
with no human intervention. At this level, data is collected and analysed for issues before
solutions are generated and, where possible, actioned with very littl e human input
The Four Levels of Smart Factories

What Technologies are used in a Smart Factory
Sensors
Cloud Computing
Big Data Analytics
Virtual and Augmented Reality
Digital Twins

What are the Key Principles of a Smart Factory
The key principles behind the factory of the future are
connectivity alongside data analysis and diagnostics;
leading to less shutdowns, improved processes and
optimised facilities.
A smart factory is based around using the latest
technologies and connectivity to drive improvements to
processes.
Using technologies such as IoT and artificial intelligence
allows for a more responsive, yet also predictive, line;
making the most of the available resources to deliver
cost-effective and efficient manufacturing.

Smart Factories and Cybersecurity
Since smart factories are reliant on computing and digital
systems, cyber security needs a special mention.
Data protection and privacy are vital for any business and, as
soon as industry is digitised, cybersecurity needs to be
addressed. In some instances, industry will share data with
other companies for the benefit of everybody, for example,
with safety issues. However, your components, processes and
other data need to be protected from accidental error or even
deliberate hacking.
Cybersecurity issues may create a further cost that needs to
be considered when deciding if your smart factory benefits are
worth the expense in setting up.

Creating a Smart Factory
Upgrading a factory so that is ‘smart’ can seem like it would be an expensive
exercise, but you can make fast and effective changes without having to replace
every machine in your manufacturing chain.
If you assess your manufacturing chain and pick out the most important parts, you
can quickly make changes that will benefit the entire process. Analysing these key
areas may then provide information as to what should be improved next.
This analysis should be undertaken with a diverse team driving it, including
specialists in different areas of the business. The more you can involve the
workforce in the improvements, the more effective the changes will be.
Employees may also need training to ensure they can use any new equipment.
Indeed, rather than needing fewer people in the workforce, the skills your
employees require will change as they monitor systems, collate data and action
improvements, inspections or repairs.
Engineers will need to work with management and I.T. systems specialists to find
areas for upgrading, and a plan should be drawn up to look into optimising
processes, increasing sales, reducing costs and saving time across the whole
manufacturing process.

Introduction to Business Intelligence and Analytics,
Advanced Integration techniques,
Business intelligence integration incorporates data
mining, data visualization, business analytics,
infrastructure, data tools, and best practices to
assist businesses and organizations. Speed to
insight, flexible self-service analysis, and
empowered business users are prioritized by BI
integration.
https://www.domo.com/learn/article/an-intro-to-business-
intelligence-integration

BI integration stages
The utilization of data in routine business operations is a comprehensive endeavor. Business intelligence
integration comprises five critical stages, which are as follows:
1. Data gathering
Business intelligence is the process of obtaining information from various data sources, and then using that
information to inform business decisions. This information could come from a variety of on-premise or cloud
sources, such as databases, data warehouses, or other business automation apps.
2. Data evaluation
Business intelligence integration aims to create meaningful knowledge from data sets that exist within the
systems of an organization. It entails assessing present trends, forecasting future trends, and integrating and
summarizing different types of information in order to improve business processes.
3. Situation awareness
Business intelligence helps contextualize data for the business and its happenings. Understanding the context of
data and making judgments is known as situation awareness.
4. Risk evaluation
Business intelligence aims to assist you in evaluating any present potential risks, benefits, and costs of one
course of action over another. It involves digging into the data that influences your decisions and helps you
understand the associated risks.
5. Supporting decisions
Using information wisely is the goal of business intelligence integration. For you to take preemptive action, it
tries to alert you to significant occurrences like subpar employee performance or market shifts, depending on
the metrics you track. It helps you make the right decisions and analysis to increase customer satisfaction,
employee morale, and sales, for example. It provides you with the information you require at the appropriate
time.

Advantages of BI integration
1. Governed and trusted data
BI systems improve data analysis and data organization. When using traditional data analysis, users must visit many databases to find
the answers to their questions. These internal databases can now be combined with external data sources using modern BI
integration to create a single data warehouse. Every department in a business has access to the same data at once, which eliminates
data silos.
2. Increased organizational effectiveness
By utilizing BI integration, leaders may gain access to data, acquire a thorough picture of their operational processes, and evaluate
the performance of specific departments against that of the larger organization. Taking a broad view of the company can help
leaders spot possibilities.
BI integration frees up more time for enterprises to use data to develop new products and programs for their business because they
spend less time gathering reports and conducting data analysis.
3. Increased employee satisfaction
Thanks to BI solutions, IT organizations and analysts can respond more quickly to business user requirements. With little training,
departments that previously couldn’t access their data without contacting analysts or IT professionals may now conduct in-depth
data analysis.
4. Quicker analysis and clear dashboards
Data analysis is made simple and intuitive through business intelligence integration, enabling non-technical individuals to tell stories
with data without learning any programming languages. Dashboards composed of simple, effective visualizations help business users
of all skill levels comprehend critical insights.
5. Better decision making
Better business decisions are made possible by accurate data and quick reporting capabilities. Leaders are no longer required to
wait days, or even months, for reports. When using BI tools that provide access to real-time data, users don’t have to deal with the
possibility of obsolete information.
6. Competitive advantage
When businesses are aware of their performance in the market, they can be more competitive. Companies can use BI to stay ahead
of sectoral changes, track seasonal market shifts, and foresee clients’ wants.
7. Enhanced customer experience
Integration of business intelligence can directly impact customer happiness and experience. By effectively analyzing pertinent data,

Best practices for streamlining BI integration
An efficient integration plan is essential for successful BI integration in an organization.
A successful integration is made possible by the following best practices:
Consider data security
Security measures are essential to have in place while implementing BI in order to
safeguard your information assets from data breaches. Secure your organization’s data
by configuring authentication or authorization protocols and establishing protocols for
secure data processing.
Develop a BI strategy
There is a reporting procedure in place for every business. Before establishing a new BI
technology, it’s crucial to assess current systems and determine which areas could be
improved. Based on that data, you can create a unique design for your
BI integration strategy depending on your particular business structure.
Install data integration software
The complete integration process will take some time. Choose a business intelligence
solution according to your specific business characteristics and needs.
You may want to purchase a business-ready BI solution. Full-stack BI tools are a great
option. These solutions use built-in connectors to take data from many sources, convert
it into the necessary format, and load it into the target system linked to BI tools.
Therefore, investing in them would accomplish both integration and reporting goals.

BI integration stages
Data gathering. Business intelligence is the process of obtaining
information from various data sources, and then using that
information to inform business decisions.
•Data evaluation
•Situation awareness
•Risk evaluation
•Supporting decisions.
https://open.ocolearnok.org/informationsystems/chapter/chapter-
6-introduction-to-business-intelligence-and-analytics/

EXAMPLE: HOW TESLA USES BIG DATA
EXAMPLE: DATA MINING AT NETFLIX
https://open.ocolearnok.org/informationsystems/chapter/chapter-
6-introduction-to-business-intelligence-and-analytics/

EXAMPLE: THE “BALANCED SCORECARD”

DISCUSSION QUESTIONS:
1. What is business intelligence and how can it be used to improve organizational
performance?
2. What is big data and how has it changed the way companies approach decision-
making?
3. How does data mining differ from traditional statistical analysis?
4. How can visualization be used to communicate insights from data?
5. What are the ethical considerations of using data analytics in business?
6. What is machine learning and how can it be used in business intelligence?
7. How can companies maximize the value of their data assets?
8. What are some common challenges companies face when implementing business
intelligence and analytics solutions?
9. How can businesses use predictive analytics to identify patterns and trends?

Industrial internet of things (IIoT)
https://www.techtarget.com/iotagenda/definition/Industrial-
Internet-of-Things-IIoT
The industrial internet of things (IIoT) is the use of smart sensors, actuators
and other devices, such as radio frequency identification tags, to enhance
manufacturing and industrial processes. These devices are networked
together to provide data collection, exchange and analysis. Insights gained
from this process aid in more efficiency and reliability. Also known as the
industrial internet, IIoT is used in many industries, including manufacturing,
energy management, utilities, oil and gas.
IIoT uses the power of smart machines and real-time analytics to take
advantage of the data that dumb machines have produced in industrial
settings for years. The driving philosophy behind IIoT is that smart machines
aren't only better than humans at capturing and analyzing data in real time,
but they're also better at communicating important information that can be
used to drive business decisions faster and more accurately.

Which industries are using IIoT?
Numerous industries use IIoT, including the following:
The automotive industry. This industry uses industrial robots, and IIoT can help
proactively maintain these systems and spot potential problems before they can
disrupt production. The automotive industry also uses IIoT devices to collect data
from customer systems, sending it to the company's systems. That data is then
used to identify potential maintenance issues.
The agriculture industry. Industrial sensors collect data about soil nutrients,
moisture and other variables, enabling farmers to produce an optimal crop.
The oil and gas industry. Some oil companies maintain a fleet of autonomous
aircraft that use visual and thermal imaging to detect potential problems in
pipelines. This information is combined with data from other types of sensors to
ensure safe operations.
Utilities. IIoT is used in electric, water and gas metering, as well as for the
remote monitoring of industrial utilities equipment such as transformers.

What are the benefits of IIoT?
IIoT devices used in the manufacturing industry offer the following benefits:
Predictive maintenance. Organizations can use real-time data generated from IIoT systems to predict when a
machine needs to be serviced. That way, the necessary maintenance can be performed before a failure
occurs. This can be especially beneficial on a production line, where the failure of a machine might result in a
work stoppage and huge costs. By proactively addressing maintenance issues, an organization can achieve
better operational efficiency.
More efficient field service. IIoT technologies help field service technicians identify potential issues in
customer equipment before they become major issues, enabling techs to fix the problems before they affect
customers. These technologies also provide field service technicians with information about which parts they
need to make a repair. This ensures technicians have the necessary parts with them when making a service
call.
Asset tracking. Suppliers, manufacturers and customers can use asset management systems to track the
location, status and condition of products throughout the supply chain. The system sends instant alerts to
stakeholders if the goods are damaged or at risk of being damaged, giving them a chance to take immediate
or preventive action to remedy the situation.
Increased customer satisfaction. When products are connected to IoT, the manufacturer can capture and
analyze data about how customers use their products, enabling manufacturers and product designers to build
more customer-centric product roadmaps.
Improved facility management. Manufacturing equipment is susceptible to wear and tear, which can be
exacerbated by certain conditions in a factory. Sensors can monitor vibrations, temperature and other factors
that could lead to suboptimal operating conditions.

Who are IIoT vendors?
There are numerous vendors that offer IIoT platforms, including the following examples:
ABB Ability. ABB specializes in connectivity, software and machine intelligence.
Aveva. Acquired by Schneider Electric in early 2023, Aveva develops AI, digital
transformation, IIoT and IoT edge platforms for original equipment manufacturers and
end users.
Cisco IoT. Cisco offers platforms for network connectivity, connectivity management,
data control and exchange as well as edge computing.
Fanuc. Fanuc combines robotics, automation and advanced analytics to provide
industrial IoT offerings.
GE Predix Platform. This IIoT software platform helps connect, optimize and scale
digital industrial applications.
Plataine. Plataine specializes in using AI to generate actionable insights in
manufacturing.
Siemens Insights Hub. Insights Hub offers industrial IoT based on AI and advanced
analytics.

Business Systems Integration: A Definition
Business systems integration is the process of connecting
different parts of an enterprise system to enable
efficient and effective communication and data flow
between them. It involves combining various applications
such as customer relationship management (CRM),
financials, enterprise resource planning (ERP), human
resources, and more.
The main goal is to ensure that all the systems are
working together in harmony, with no duplication or gaps
in data storage or retrieval.
https://www.clarity-ventures.com/how-to-guides/step-by-step-
guide-to-business-system-integration

Advanced Integration techniques
https://estuary.dev/data-integration-strategy/

Cloud Integration
Cloud-based Integration uses cloud services for data storage, processing, and integration. One
of the key benefits of cloud-based integration is scalability. With cloud services, you can easily
scale your data storage up or down and process resources based on your needs. This scalability
eliminates the need for you to invest in additional hardware or infrastructure, saving you both
time and money.
Cloud services also allow you to access your data anytime, anywhere, and from any device as
long as you have an internet connection. They can seamlessly integrate with other cloud-based
applications and services for a connected and cohesive workflow.

Extract, Transform, Load is a 3-step process where you first pull data
from various sources, then transform it into a format that fits your
needs, and finally load it into a unified database. By extracting and
combining data from multiple sources, you can create a centralized
and unified view of your data.

Change Data Capture (CDC) is a technique that identifies and
captures changes made in a database and applies them to another
data repository. Its primary purpose is to enable real-time data
integration by efficiently identifying and transferring only the modified
data.

IoT Platform
An IoT platform serves as a mediator between the
world of physical objects and the world of actionable
insights. By combining numerous tools and
functionalities, Internet of Things platforms empower
you to develop unique hardware and software
products for gathering, storing, analyzing, and
managing the abundance of data generated by your
connected devices and assets

Amazon Web Services (AWS) IoT
Microsoft Azure IoT
Oracle IoT
Particle
IRI Voracity
ThingWorx IIoT Platform
Google Cloud IoT
Cisco IoT Cloud Connect
Salesforce IoT Cloud
IBM Watson IoT
Most Popular IoT Platforms
https://www.sam-solutions.com/blog/top-iot-platforms/

UNIT II
INTEGRATION OF THE PLM SYSTEM
WITH OTHER APPLICATIONS

https://www.arenasolutions.com/what-is-plm/plm-integrations/
Disconnected Systems Result in Errors and Inefficient
Workflows
Today’s manufacturing ecosystem
consists of a diverse mix of product
teams and supply chain partners from
across the globe. They often rely on
multiple, disjointed software
applications to design, develop, and
maintain a product. Teams must switch
from one system to another and
manually re-enter data to keep product
information up to date and move
processes along. This can be a labor-
intensive task that results in errors and
costly delays.
Enterprise PLM solutions serve as the single source of truth for managing the product
bill of materials (BOM) and associated quality and change processes. Integrating PLM with
upstream and downstream enterprise systems ensures a smooth handoff of product
information between design, quality, and production teams. Because data is automatically
updated and transmitted across all systems, everyone has full visibility into any changes or
issues. Ultimately, they can work more efficiently to get high-quality products to market on
time and on budget without any surprises.

Upstream Systems:
Engineering Design Solutions: Integration with design solutions such as
mechanical CAD (MCAD) and electronic design automation (EDA) software facilitates
earlier design reviews and feedback from downstream product teams and supply chain
partners. This ensures design for manufacturability (DFM) and reduces costs associated
with scrap and rework.
Downstream Systems:
Enterprise Resource Planning (ERP): Integration with ERP systems ensures that manufacturing receives the
latest released design information, eliminating production errors and resulting scrap or rework. In addition,
teams can easily monitor inventory, supply chain activities, and financial operations.
Manufacturing Execution Systems (MES): Integration with MES enables real-time collection of data on events
that take place on the factory floor. This data can be used to help manufacturing teams optimize their
efficiency.
Electronic Component Databases: Integration with component databases like SiliconExpert and Octopart
provides greater visibility into critical component details such as market availability, cost, and
environmental compliance information. This helps organizations mitigate supply chain risks, avoid product
delays, and meet regulatory requirements.
Customer Relationship Management (CRM) Systems: Integration with CRM systems like Salesforce,
ServiceMax, and Zendesk provides product teams centralized access to critical customer feedback and
data. This can help further inform product development and quality processes and improve customer
satisfaction.

Companies that integrate PLM with ERP and other enterprise systems are
more likely to realize these benefits:
Enhanced collaboration across the organization
Decreased product scrap and rework
Cost reduction
Greater efficiency/streamlined operations
Improved accuracy/product quality
Better inventory tracking
Improved cost analysis
Streamlined environmental, export control, and quality
compliance
Accelerated time to market
Increased customer satisfaction
Greater profitability

DIFFERENT APPLICATION INTEGRATION APPROACHES
There are various methods that can be used to connect enterprise systems. Some require minimal IT
resources whereas others may involve more extensive coding or customization for deployment.
Application Programming Interface (API)
An API is an intermediary that is used for point-to-point integrations, enabling two different software
systems to talk directly to each other and securely exchange data and functionality. Because APIs
have a defined architecture and protocol for sharing information, they simplify the integration
process. Typically, SaaS PLM vendors will provide customers their own APIs for integration. REST APIs
are well-documented, fast, and can be implemented as needed to support greater scalability.
Electronic Data Interchange (EDI)
An It automates and replaces paper-based transactions such as invoices and purchase orders. EDEDI is
a standard electronic format that is used to move business documents from one organization’s
computer system to another. Is can be challenging to implement and maintain due to the unique and
ever-changing requirements of each business.
Middleware
Middleware is software that lies between the applications and their operating system. It functions as a
“hidden” translation layer between systems, enabling communication and data management
capabilities beyond what is provided in the operating system. This approach is used when more
complex business rules are needed, or a large volume of data is transmitted between systems.
Middleware requires extensive programming and can be costly to configure and maintain. It does not
allow for scalability.

A PRODUCT STRUCTURE CONTAINS DOCUMENTS AND
MATERIALS TO COMPLETELY DESCRIBE A PRODUCT
https://help.sap.com/docs/SAP_S4HANA_ON-PREMISE/add30a34af294d51a0ccd83db28791f9/
c3c16f6e86fa49a68cb1d19c9f29b4b9.html

MODELING OF PRODUCT STRUCTURES
https://help.sap.com/docs/SAP_S4HANA_ON-PREMISE/
add30a34af294d51a0ccd83db28791f9/4deb2d635c4945b7b1eff40c7616cd4b.html

TRANSFER FILE
 Basic of PLMXML Export/Import
 PLMXML is a very powerful tools for importing/exporting data and file from Teamcenter to external
system and vice-verse. PLMXML is Siemens PLM sponsors XML schema for exporting/importing metadata as
well files from Teamcenter to other system,
 The advantage of PLMXML is its flexibility of defining rule for data extraction and import make it one of
the widely use tool for integration and data exchange. It is also widely used in teamcenter other module
like report builder and integration. For defining the rules, admin module is present where new rules can
be created or modified existing rules. This rules are called Transfermode. In this blog we will discuss in
detail the transfermode and its child rules.
 Transfer Mode:
 Transfermode encapsulate the rules which defines import/export data from teamcenter. It basically govern
the Export/Import rules and meta data which required to be extracted from Teamcenter. Transfer mode
mainly consist of
 · Closure Rule
 · Filter Rule
 · Property Set
ref: https://teamcenterplm.blogspot.com/2012/08/basic-of-plmxml-exportimport.html

ENTERPRISE RESOURCE PLANNING (ERP)
 Enterprise resource planning (ERP) is a software
system that helps you run your entire business,
supporting automation and processes in finance,
human resources, manufacturing, supply chain,
services, procurement, and more.
Ref:https://www.investopedia.com/terms/e/erp.asp

TYPES OF ERP SYSTEM
 On-Premise ERP
 Cloud ERP
 Industry-Specific ERP
 Open-Source ERP
 Small Business ERP
 Tiered ERP

ERP SOLUTIONS PROVIDERS
 Oracle Corp. (ORCL)
 SAP (SAP)
 Microsoft (MSFT)

SIX KEY BENEFITS OF ERP
https://www.sap.com/products/erp/what-is-erp.html#:~:text=In%20accounting%2C
%20the%20acronym%20ERP,reporting%20and%20analysis%2C%20and%20more.

 Enterprise resource planning systems include a variety
of different modules. Each ERP module supports
specific business processes – like finance, procurement,
or manufacturing – and provides employees in that
department with the transactions and insight they need
do their jobs. Every module connects to the ERP
system, which delivers a single source of truth and
accurate, shared data across departments.
COMMON ERP MODULES

COMPONENTS OF AN ENTERPRISE RESOURCE
PLANNING SYSTEM

ERP HISTORY: THE RAPID EVOLUTION OF ERP

ERP AT ANY SIZE: WHAT ARE MY OPTIONS
 ERP isn’t just for global enterprises. ERP solutions are designed for businesses of all sizes – small, midsize, and large.
You can also get industry- and company-specific functionality to meet unique business needs. Regardless of your
business sector and size, you’ll want to plan your ERP implementation project carefully, following best practices.

 Small business ERP
 ERP software for small businesses can help you move beyond spreadsheets and efficiently manage every aspect of your
growing company – from sales and customer relationships to financials and operations. Small business ERP tools are
typically in the cloud, quick to install, and designed to grow with you.

 Mid-Market ERP
 Today, ERP software designed for mid-market companies and subsidiaries benefit from built-in analytics, rapid
deployment, and best practices for dozens of different business processes – financials, HR, supply chain management,
and more. Midsize ERP tools help growing businesses scale and compete, even with limited resources. Modular,
cloud-based enterprise ERP systems are also a popular choice for midmarket companies with complex processes or
plans for rapid growth.

 Enterprise ERP
 Large companies with global or subsidiary operations need a robust, market-leading ERP system with embedded AI,
machine learning, and analytics – and intelligent automation to transform business models and processes. ERP systems
can be deployed on premise, in the cloud, or in a hybrid scenario depending on business need. They can integrate with
existing databases or, ideally, run on newer, powerful in-memory databases.

 Many companies are modernizing and upgrading their on-premise ERP systems to cloud deployments. This requires
careful planning of your ERP upgrade, as well as an ERP evaluation and review of your deployment options.

ERP FAQS
 What is an ERP software system?
 What is ERP cloud software?
 What is ERP in accounting?
 How do I know I'm ready for an ERP system?

THE MOST WIDELY USED ERP MODULES INCLUDE:
 Finance: The finance and accounting module is the backbone of most ERP systems. In addition
to managing the general ledger and automating key financial tasks, it helps businesses track
accounts payable (AP) and receivable (AR), close the books efficiently, generate financial
reports, comply with revenue recognition standards, mitigate financial risk, and more.
 Human resources management: Most ERP systems include an HR module that provides core
capabilities such as time and attendance and payroll. Add-ons, or even entire
human capital management (HCM) suites, can connect to the ERP and deliver more robust HR
functionality – everything from workforce analytics to employee experience management.
 Sourcing and procurement: The sourcing and procurement module helps businesses procure
the materials and services they need to manufacture their goods – or the items they want to
resell. The module centralizes and automates purchasing, including requests for quotes,
contract creation, and approvals. It can minimize underbuying and overbuying, improve
supplier negotiations with AI-powered analytics, and even seamlessly connect with buyer
networks.
 Sales: The sales module keeps track of communications with prospects and customers – and
helps reps use data-driven insights to increase sales and target leads with the right
promotions and upsell opportunities. It includes functionality for the order-to-cash process,
including order management, contracts, billing, sales performance management, and sales
force support

THE MOST WIDELY USED ERP MODULES INCLUDE(CONT..)
 Manufacturing: The manufacturing module is a key planning and execution component of ERP
software. It helps companies simplify complex manufacturing processes and ensure production is in
line with demand. This module typically includes functionality for material requirements planning
(MRP), production scheduling, manufacturing execution, quality management, and more.
 Logistics and supply chain management: Another key component of ERP systems, the
supply chain module tracks the movement of goods and supplies throughout an organization’s supply
chain. The module provides tools for real-time inventory management, warehousing operations,
transportation, and logistics – and can help increase supply chain visibility and resilience.
 Service: In an ERP, the service module helps companies deliver the reliable, personalized service
customers have come to expect. The module can include tools for in-house repairs, spare parts, field
service management, and service-based revenue streams. It also provides analytics to help service
reps and technicians rapidly solve customer issues and improve loyalty.
 R&D and engineering: Feature-rich ERP systems include an R&D and engineering module. This module
provides tools for product design and development, product lifecycle management (PLM), product
compliance, and more – so companies can quickly and cost-effectively create new innovations.
 Enterprise asset management: Robust ERP systems can include an EAM module – which helps asset-
intensive businesses minimize downtime and keep their machines and equipment running at peak
efficiency. This module includes functionality for predictive maintenance, scheduling, asset operations
and planning, environment, health and safety (EHS), and more.

UNIT III DIGITAL TWIN BASICS
 Introduction, Industrial Revolution Facts, Industry
4.0 Environment, Technologies Transforming
Industry 4.0. Basic Concepts of Digital Twin:
Evolution of Pairing, Definition and Features of
Digital Twins, Digital Twin Timeline.
UNIT IV DIGITAL TWIN
Features and Implementation of Digital Twin: Digital Twin Terminologies &
Essentials, Working of Digital Twins. Building Blocks of Digital Twin: Digital
Twin Building Blocks, Digital Twin Technology Drivers & Enablers. Types of
Digital Twin: Based on Product, Process, Based on Functionality, Based on
Maturity, Characteristics of a Good Digital Twin Platform. Digital Twin:
Benefits, Impacts and Challenges: Barriers of Digital Twin Implementation

 What is a Digital Twin?
 A Digital Twin of any device/system is a working model of all
components (at micro level or macro level or both) integrated
and mapped together using physical data, virtual data, and
interaction data between them to make a fully functional
replica of the device/system and that too on a digital medium.
 This digital twin of the physical system is not intended to
outplace the physical system but to test its optimality and
predict the physical counterparts’ performance characteristics.
 You can know of the system’s operational life course, the
implication of design changes, the impact of environmental
alters and a lot more variables using this concept. Talking about
life course, it invites me to aromatize your awareness of the
concept with its origin.
REF:https://www.geeksforgeeks.org/introduction-to-digital-twin/

Brief History of Digital Twin
The concept and model of the Digital Twin was officially put forward in 2002 by Dr.
Michael Grieves as the conceptual model underlying Product Lifecycle
Management (PLM). The concept was being practiced since the 1960s by NASA.
They used basic twinning ideas for space programming at that time. They did this
by creating physically duplicated systems at ground level to match the systems in
space.
Example : When NASA developed a digital twin to assess and simulate conditions
on board Apollo 13. The efforts were made keeping in mind only a particular
mission and because of that, this concept didn’t gain recognition until 2002
after Dr. Grieves presented it with all the elements including real space, virtual
space and the spreading of data and information flow between real and virtual
space.
The concept of integrating the digital and physical parts as one entity has
remained the same since its emergence. Although the terminology has changed
over the years till 2010 when it was subsequently called ‘Digital Twin’ by John
Vickers of NASA in a 2010 Roadmap Report.

A Digital Twin consists of three distinct parts:
 1. The physical part ,
 2. The Digital Part ,
 3. The Connection Between the Two

 Types of Digital Twins:
 1. Asset Twins
 2. System Twins
 3. Process Twins

ADVANTAGES OF DIGITAL TWINS
 Enhanced Operational Efficiency
 Predictive Maintenance
 Process Optimization
 Data-Drivn Decision-Making
 Rich Insights
 Reduced Uncertainty
 Fostering Innovation and Collaboration
 Sustainability and Environmental Impact

 What Was the Industrial Revolution?
 The Industrial Revolution was a period of major mechanization
and innovation that began in Great Britain during the mid-18th
and early 19th centuries and later spread throughout much of
the world. The British Industrial Revolution was dominated by
the exploitation of coal and iron.
 The American Industrial Revolution, sometimes referred to as
the “Second Industrial Revolution,” began during the
Gilded Age in the 1870s and continued through World War II.
The era saw the mechanization of agriculture and
manufacturing and the introduction of new modes of
transportation, including steamships, the automobile, and
airplanes
https://www.investopedia.com/terms/i/industrial-revolution.asp

EVOLUTION OF PAIRING, DEFINITION AND FEATURES OF DIGITAL TWINS, DIGITAL TWIN TIMELINE
 Digital twin technology evolution stage.
https://www.researchgate.net/publication/
360537125_Digital_Twin_Technology_Evolution_Stages_and_Impleme
ntation_Layers_With_Technology_Elements/figures

DIGITAL TWIN ESSENTIAL TECHNOLOGIES AND
APPLICATION SERVICES.

TRADITIONAL DIGITAL TWIN EVOLUTION
MODEL.

CONCEPTUAL DIAGRAM OF DIGITAL TWIN
EVOLUTION STAGE.

DIGITAL TWIN IMPLEMENTATION LAYERS

DIGITAL TWIN TECHNOLOGY ELEMENTS ACCORDING TO THE IMPLEMENTATION LAYERS.

CHALLENGES OF DIGITAL TWINS
 Data management
 Data security
 IoT development
 System integration.
 Supplier collaboration
 Complexity

 Use cases and examples of digital twins
 The initial deployments of digital twins were
mostly directed at the design, production and
maintenance of extremely high-value,
physically large equipment, such as airplanes,
buildings, bridges and power-generation plants
where mechanical failure can be life
threatening or cause financial losses that
exceed the significant expense and effort of
developing a digital twin.

 The following industries are seeing the most
activity in planning or deploying digital twins:
 Manufacturing.
 Utilities and energy
 Healthcare.
 Urban planning and construction
 Automotive.

Building Blocks of Digital Twin : Digital Twin Building
Blocks, Digital Twin Technology Drivers & Enablers.

 Digital twins are virtual replicas of physical assets, systems, or processes that help in monitoring, analyzing,
and optimizing performance. Here’s a breakdown of the key building blocks, technology drivers, and enablers
for digital twins:
 Building Blocks of Digital Twins
 Data Collection: Gathering data from various sources such as sensors, IoT devices, and existing databases.
 Modeling: Creating a virtual model using CAD files, BIM software, or other modeling tools.
 Integration: Combining real-time data streams with the virtual model for continuous monitoring and analysis.
 Simulation: Running simulations to predict performance and identify potential issues.
 Visualization: Using 3D visualization tools to provide an immersive view of the digital twin.
 Analytics: Applying algorithms and AI for predictive maintenance and performance optimization
 Technology Drivers
 Internet of Things (IoT): IoT devices provide the necessary data for creating and updating digital twins.
 Artificial Intelligence (AI): AI helps in analyzing data and making predictions.
 Cloud Computing: Cloud platforms offer the storage and computational power needed for digital twins.
 Big Data: Handling and processing large volumes of data is crucial for accurate digital twins.
 Edge Computing: Processing data closer to the source reduces latency and improves real-time decision-
making3
 Enablers
 Building Information Modeling (BIM): BIM provides detailed digital representations of physical and functional
characteristics.
 Geographic Information Systems (GIS): GIS helps in integrating spatial data with digital twins.
 Cyber-Physical Systems (CPS): CPS integrates computation, networking, and physical processes.
 Standards and Protocols: Ensuring interoperability and data exchange between different systems and devices.

 Characteristics of a Digital Twin platform include123
:Connectivity: Digital twins are connected to real-world
data sources such as sensors and IoT devices.
 Homogenization: They enable the homogenization of data
from various sources.
 Reprogrammable and smart: Digital twins allow physical
products to be reprogrammed.
 Real-time data integration: Continuously updated with real-
time data.
 Simulation and modeling: Used for modeling and simulating
behavior.

BARRIERS OF DIGITAL TWIN
IMPLEMENTATION

https://www.digitaltwinconsortium.org/glossary/glossary/

THIS EFFICIENT PRODUCTION FLOOR UTILIZES DIGITAL TWIN TECHNOLOGY TO CONNECT ALL
OPERATIONS FROM BEGINNING TO END
https://www.digikey.com/en/articles/the-digital-twin-concept-and-
how-it-works

DIGITAL TWINS HELP UNDERSTAND THE PRESENT AND PREDICT THE
FUTURE

SIX-LAYER ARCHITECTURE OF DIGIT
AL TWIN

THE DIGITAL TWIN AUTOMOTIVE RE
VOLUTION

DIGITAL TWINS SIMPLIFY SYSTEM AN
ALYSIS

UNIT V DIGITAL THREAD
 Digital Thread Definition, Data Storage in the
Digital Thread, Data Sharing and The Digital
Thread, Strategic issues in implementing the digital
thread, Technologies used in the Design Process,
Cyber infrastructure Components of the Digital
Thread and Digital Thread on the Shop Floor

DIGITAL THREAD DEFINITION
 A digital thread is a data-driven communication framework
that connects traditionally siloed elements in manufacturing
processes and provides an integrated view of an asset
throughout the manufacturing lifecycle. In addition to
technology, the establishment of a digital thread requires
business processes that help weave data-driven decision
management into the manufacturing culture.
 Digital threads capture and share data across processes using
a range of technologies, such as computer-aided design
software, product lifecycle management systems and
internet of things (IoT) sensors.
https://www.ptc.com/en/industry-insights/digital-thread

DIGITAL THREAD VS. DIGITAL TWIN
 The terms digital thread and digital twin are
related concepts used in the context of
manufacturing systems, but they refer to different
aspects of a product's lifecycle.
 A digital thread functions as a communication
framework that makes it easier for asset and
product data to flow smoothly throughout their
lifecycles. A digital twin is a virtual model or a
digital replica of an actual physical object or
system and offers a more detailed view for analysis
and optimization.

 Digital thread, also known as digital chain is defined
as “the use of digital tools and representations for
design, evaluation, and life cycle management.”.[2]
It
is a data-driven architecture that links data gathered
during a Product lifecycle from all involved and
distributed manufacturing systems.[3]
This data can
come from any part of product's lifecycle, its
transportation, or its supply chain.[3]
Digital thread
"enables the collection, transmission, and sharing of
data and information between systems across the
product lifecycle" to enable real-time decision
making, gather data, and iterate on the product.

https://www.challenge.org/insights/digital-twin-and-digital-thread/

 What are the threats to digital thread adoption?
 Data issues
 It is difficult to gain a holistic view of a product's lifecycle and ensure every
stakeholder has access to the latest data due to information across organisations often
being isolated in separate systems. Inconsistent data formats and a lack of
standardisation, both within an organisation and across industries, can cause
confusion; data interoperability is key to creating a digital thread.
 Security concerns
 An interconnected system reliant on large amounts of data are vulnerable to cyber-
attacks. As with all emerging technologies, unidentified vulnerabilities will be present
for malicious actors to abuse. To combat this, organisations should adopt clear data
governance policies and procedures to manage access and control.
 Cultural and organisational challenges
 Implementing a digital thread often requires significant changes to workflows and
stakeholder mindsets, often leading to resistance to adoption. This can be combated
by properly informing all employees, educating them on the importance of the digital
thread. Additionally, organisations may need to train employees to manage data or
software tools required for digital thread implementation.
https://www.amrc.co.uk/news/digital-thread-frequently-asked-
questions

 What kind of infrastructure is needed to support digital thread implementation?
 A successful digital thread implementation relies on a robust IT infrastructure that can
handle data integration, storage, and analysis across the entire product lifecycle. The
key components include:
 Product Lifecycle Management (PLM) System: This serves as the backbone of the
digital thread, storing and managing all product data throughout its lifecycle, from
design inception to service and end-of-life.
 Enterprise Resource Planning (ERP) System: The ERP system integrates with the PLM to
provide data on manufacturing processes, inventory management, and financial aspects
of production.
 Scalable storage and computing resources: The volume of data generated from various
sources within the digital thread can be significant. Depending on the complexity of
operations, cloud-based storage or high-performance computing capabilities may be
required to handle data processing and analysis.
 Advanced analytics tools: Extracting meaningful insights from the vast amount of data
collected can require advanced analytics tools like machine learning and artificial
intelligence to identify patterns, predict trends, and optimise processes.
 Secure network infrastructure: A secure network infrastructure is crucial to protect
sensitive data within the digital thread from cyberattacks and unauthorised access.

 HIGHLIGHTS-DT
 Digital threads interlink all the data related to a
product throughout its life cycle—data from within
and outside the enterprise.
 Digital threads can deliver significant business
value—improved time to market, enhanced product
quality, increased operational efficiency—by
streamlining complex processes.
 Establishing an enterprise digital thread is a long-
term, incremental, continuous transformation
journey for a company.
https://www.tcs.com/what-we-do/services/iot-digital-
engineering/white-paper/digital-threads-product-data-value

AN ENTERPRISE DIGITAL THREAD CAN HELP OVERCOME THE CHALLENGES AROUND
ACCESSING ACCURATE PRODUCT DATA.

DATA STORAGE IN THE DIGITAL THREAD

DATA SHARING
Data Sharing
What is data sharing?
Data sharing is the process of making the same data resources available to
multiple applications, users, or organizations. It includes technologies,
practices, legal frameworks, and cultural elements that facilitate secure data
access for multiple entities without compromising data integrity. Data sharing
improves efficiency within an organization and fosters collaboration with
vendors and partners. Awareness of the risks and opportunities of shared data
is integral to the process.
https://aws.amazon.com/what-is/data-sharing/
#:~:text=Data%20sharing%20is%20the
%20process,applications%2C%20users%2C%20or
%20organizations.

 What are the risks of data sharing?
 Data disclosure has potential regulatory, competitive, financial, and security risks. We
outline some critical threats below.
 Privacy disclosure
 Every single organization has legal and ethical obligations to safeguard the privacy of
the customer data they own. They have to take appropriate measures to share data
without compromising privacy. Privacy-preserving technologies like encryption and
redaction allow for safe data sharing.
 Data misinterpretation
 Lack of communication between data producers and consumers can result in analytical
misinterpretation. Analysts may make incorrect assumptions when explaining reports
and outcomes. For example, a reduction in customer orders in a particular month may
be attributed to a lower marketing budget, although the real reason could be a delay in
product availability.
 Low data quality
 Data consumers may have limited control over the quality and availability of data. They
may have to deal with missing or duplicate data, questions about validity, lacking data
documentation, and similar issues. Hidden biases against a particular gender, race,
religion, or ethnic group may also be present in the dataset.

CYBER INFRASTRUCTURE COMPONENTS OF THE DIGITAL
THREAD AND DIGITAL THREAD ON THE SHOP FLOOR

22ME926Introduction to Business Intelligence and Analytics, Advanced Integration techniques.pptx

More Related Content

Recently uploaded (20)

Featured (20)

22ME926Introduction to Business Intelligence and Analytics, Advanced Integration techniques.pptx