Robotic Process Automation module 1 notes vtu

Robotic Process Automation Design and
Development
Sub Code:(21CS744)
Module -1 RPA Foundations

RPA stands for Robotic Process Automation.
It is the technology used for software tools that automate human tasks, which are
manual, rule-based, or repetitive.
Typically, it is like a bot that performs such tasks at a much higher rate than
a human alone.
These RPA software bots never sleep and make zero mistakes, and can
interact with in-house applications, websites, user portals, etc.
They can log into applications, enter data, open emails and attachments,
calculate and complete tasks, and then log out.

Why RPA?
Robotic Process Automation is economically capable as
compared to any other automation solutions.
is the new buzz word in the IT industry. It has shifted the
traditional way of doing the business task manually into an
automatic task within an organization.
RPA technology uses bots that interact with web
applications, web sites, excel worksheets, and emails to
automate the tasks just like a human.

BENEFITS OF RPA
1.Cost Savings
RPA helps organizations to save a huge amount of cost as it is typically cheaper than hiring an employee
to perform the same set of tasks.
2.Less Error
RPA works on standard logic and does not get bored, distracted, or tired. Hence, the probability of
making errors reduces to a great extent, which means less re-work and an enhanced reputation for
efficiency.
3.Faster Processing
RPA works faster than human employees as computer software does not need breaks, food, rest, etc.,
and can perform repetitive operations tirelessly. With RPA, processing time becomes predictable and
consistent, which ensures high-quality customer service across the operations.

4.Better Regulatory Compliance
RPA software works on the logic and data fed to it and does what is only needed as per the given instructions.
Hence, there are minimal chances of not complying with the standard regulations.
5.Better Customer Service
When RPA is implemented in a business, it frees many of its employees who can spend their time working on
customer-related services. It is very beneficial for businesses that receive a lot of customer queries. It also leads to
increased productivity for employees.
Low Technical Barrier
RPA does not require any programming skills to configure the software robot. Since it is a code-free technology, any
non-technical person can set up the bot using drag and drop features. It also includes the 'Recorder' to record the
steps of automation.

Flavors of RPA
Attended RPA (which may be referred to as robotic desktop automation or RDA):
 This was the first form of RPA that emerged, back in 2003 or so.
 Attended RPA means that the software provides collaboration with a person for certain tasks.
 Example: would be in the call center, where a rep can have the RPA system handle looking up
information while he or she talks to a customer.
Unattended RPA:
 This technology was the second generation of RPA.
 With unattended RPA, you can automate a process without the need for human involvement –
that is, the bot is triggered when certain events happen,
Example: such as when a customer e-mails an invoice.
 Consider that unattended RPA is generally for back-office functions.
Intelligent process automation or IPA (this may also be referred to as cognitive RPA):
 This is the latest generation of RPA technology, which leverages AI to allow the system to learn over time
Example: would be the interpretation of documents, such as invoices.
 There may be even less human intervention .

History of RPA
Mainframe Era: These were huge machines developed by companies like IBM. They were expensive and mostly available to
large companies (although, innovators like Ross Perot would create outsourcing services to provide affordable options). Yet they
were incredibly useful in helping manage core functions for companies, such as payroll and customer accounts.
PC Revolution: Intel’s development of the microprocessor and Microsoft’s development of its operating system revolutionized
the technology industry. As a result, just about any business could automate processes; say by using word processors and
spreadsheets
But around 2012 or so, the RPA market hit an inflection point. There was a convergence of trends that made this happen, such as
the following:
 In the aftermath of the financial crisis, companies were looking for ways to lower their costs. Simply put, traditional
technologies like ERP were reaching maturation. So, companies needed to look for new drivers.
 Companies also realized they had to find ways to not be disrupted from technology companies. RPA was considered an easier
and more cost-effective way to go digital.
 Some industries like banking were becoming more subject to regulation. In other words, there was a compelling need to find
ways to lessen the paperwork and improve audit, security, and control.
 RPA technology was starting to get more sophisticated and easier to use, allowing for higher ROI (return on investment).

 Fast forward to today, RPA is the fastest growing part of the software industry. According to Gartner,
the spending on this technology jumped by 63% to $850 million in 2018 and is forecasted to reach $1.3
billion by 2019. Or consider the findings from Transparency Market Research. The firm projects that
the global market for RPA will soar to $5 billion by 2020.

The Downsides of RPA
 Cost of Ownership: The business models vary. Some have a subscription or
multiyear license. Other vendors may charge based on the number of bots.
 But there is more to the costs. There is the need for some level of training and
ongoing maintenance. Depending on the circumstances, there may be requirements
for buying other types of software and hardware. Oh, and it is common to retain
third-party consultants to help with the implementation process.
 Technical Debt: This is an issue with RPA. As a company’s processes change, the
bots may not work properly. This is why RPA does require ongoing attention.
 Security: This is a growing risk with RPA implementations, especially as the
technology covers more mission-critical areas of a company’s processes. Let’s face
it, if there is a breach, then highly sensitive information could easily be obtained.
Actually as RPA gets more pervasive in manufacturing, there may even be risks of
property damage and bodily harm. This would likely be the case with attended
RPA.

 Expectations: According to a survey from PEGA, the average time it takes to
develop a quality bot was 18 months, with only 39% being deployed on time.
 Preparation: You need to do a deep dive in how your current tasks work. If not,
you may be automating bad approaches.
 Limits: RPA technology is somewhat constrained. For the most part, it works
primarily for tasks that are routine and repetitive. If there is a need for
judgment – say to approve a payment or to verify a document – then there
should be human intervention. Although, as AI gets more pervasive, the issues
are likely to fade away.
 Virtualized Environments: This is where a desktop accesses applications
remotely, such as through a platform. However, some of the latest RPA
offerings, such as from UiPath, are solving the problem.

RPA Compared to BPO, BPM, and BPA
Business process management (BPM)
Business process outsourcing (BPO)
Business process automation (BPA)

BPM
For example, FileNet introduced a digital workflow management system to help better
handle documents (the company would eventually be purchased by IBM). Then there would
come onto the scene ERP vendors, such as PeopleSoft
 All of this would converge into a major wave called BPM.
 For the most part, the focus was on having a comprehensive improvement on business
processes. This would encompass both optimizing systems for employees but also IT
assets.
 There were also various business process management software (BPMS) solutions to
help implement BPM.
 One was Laserfiche. Nien-Ling Wacker founded the company in 1987, when she saw
the opportunity to use OCR (optical character recognition) technology to allow users to
search huge volumes of text.

So then how is BPM different from RPA?
 With BPM, it requires much more time and effort with the implementation because
it is about changing extensive processes, not tasks.
 There also needs to be detailed documentation and training. Because of this
rigorous approach, BPM is often attractive to industries that are heavily regulated,
such as financial services and healthcare.
 However, the risk is that there may be too much structure, which can stifle
innovation and agility.
 On the other hand, RPA can be complementary to BPM. That is, you can first
undergo a BPM implementation to greatly improve core processes. Then you can
look to RPA to fill in the gaps.

BPO
This is when a company outsources a business service function like payroll, customer support, procurement, and
HR.
 The market is massive, with revenues forecasted to reach $343.2 billion by 2025 (according to Grand
View Research). Some of the top players in the industry include ADP, Accenture, Infosys, IBM, TCS,
and Cognizant.
BPO will have three types of strategies:
 Offshore: This is where the employees are in another country, usually far away.
 Nearshore: This is when the BPO is in a neighboring country. True, there are usually higher costs but there
is the benefit of being able to conveniently visit the vendor. This can greatly help with the collaboration.
 Onshore: The vendor is in the same country. For example, there can be wide differences in wages in the
United States.

There are drawbacks with a BPO :
 Security: If a BPO company is developing an app with your company’s data, are
there enough precautions in place so there is not a breach? Even if so, it can still
be difficult to enforce and manage.
 Costs: Over the years, countries like China and India have seen rising labor costs.
This has resulted in companies moving to other locations, which can be disruptive
and expensive.
 Politics: This can be a wildcard. Instability can easily mean having to abandon a
BPO operator in a particular country.

BPA
This is the use of technology to automate a complete process. One common use case
is onboarding.
For example, bringing on a new employee involves many steps, which are repeatable
and entail lots of paperwork. For a large organization, the process can be time-
consuming and expensive. But BPA can streamline everything, allowing for the
onboarding at scale.
OK, this kind of sounds like RPA, right? Yes, this is true. But there is a difference in
degree. RPA is really about automating a part of the process, whereas BPA will take
on all the steps.

Consumer Willingness for Automation
The automation of consumer-facing activities, such as with chatbots on a smartphone or web site,
are becoming more ubiquitous.
An AI-based digital customer service platform automating 80% of customer support issues for
huge
D2C (direct-to-consumer) brands including companies like Flipboard, Microsoft, Tradesy, and 60
others. Its report is based on the analysis of 75 million customer service tickets and 71 million bot-
sent messages.
Here are some of the findings:
 A total of 55% of the respondents – and 65% of millennials – prefer chatbots with customer
service so long as it is more efficient and reduces phone time to resolve an issue and explain
a problem.
 A total of 49% say they appreciate the 24/7 availability of chatbots. - Granted, there is much
progress to be made. Chatbot technology is still in the early phases and can be glitchy, if not
downright annoying in certain circumstances. But in the years to come, this form of
automation will likely become more important – and also a part of the RPA roadmap.

The Workforce of the Future
 According to research from the McKinsey Global Institute, white collar workers still spend
60% of their time on manual tasks, such as with answering e-mails, using spreadsheets,
writing notes, and making calls.
 In light of all this, RPA is likely to have a significant impact on the workplace because more
and more of the repetitive processes will be automated away. One potential consequence is
that there may be growing job losses.
 A survey from Forrester predicts that – as of 2025 – software automation will mean the loss of
9% of the world’s jobs or 230 million. Then again, the new technologies and approaches will
open up many new opportunities.

On-Premise Vs. the Cloud
The traditional IT system approach is the use of on-premise technology. This means that a company
purchases and sets up its own hardware and software in its own data center.
Some of the benefits include:
 A company has complete control over everything. This is particularly important for regulated industries
that require high levels of security and privacy.
 With on-premise software, you may have a better ability to customize the solution to your company’s
unique needs and IT policies.
However, the on-premise technology model has serious issues as well. One of the biggest is the cost, which
often involves large up-front capital expenses. Then there is the ongoing need for maintenance, upgrades,
and monitoring. And finally, the use of point applications like Excel can lead to a fragmented
environment, in which it becomes difficult to centralize data because there are so many files spread across
the organization.

 But as the Internet became more robust, there was a move to so-called cloud
computing.
 One of the first business applications in this industry was developed by
Salesforce.com, which made it possible for users to use the software through a
browser.
 Companies could pay per-user, per-month fees for the services they used, and
those services would be delivered to them immediately via the Internet, in the
cloud.

The downsides with cloud software.
 With less control of the platform, there are more vulnerability to security and privacy
lapses.
 Outages do happen and can be extremely disruptive and costly for enterprise that need
reliability.
 Cloud computing is not necessarily cheap. In fact, one of the biggest complaints
against Salesforce.com is the cost.
 Regardless, the fact remains that the technology continues to gain traction.
Besides the impact of Salesforce.com and other cloud applications companies, another
critical development was Amazon.com’s AWS platform.
AWS essentially handles the complex administrative and infrastructure requirements like
storage, security, compute, database access, content delivery, developer tools, deployment,
IoT (Internet of Things), and analytics (there are currently more than 165 services).

The cloud also has different approaches, such as the following:
 Public Cloud: The cloud is accessed from remote servers, such as from AWS,
Salesforce.com, and Microsoft. The servers have an architecture known as multitenant
that allows the users to share a large IT infrastructure in a secure manner.
This greatly helps to achieve economies of scale, which would not be possible if a
company created its own cloud.
 Private Cloud: This is when a company owns the data center. True, there are not the
benefits of the economies of scale from a public cloud. But this may not be a key
consideration. Some companies might want a private cloud because of control and
security.
 Hybrid Cloud: This is a blend of the public and private clouds. For example, the public
cloud may handle less mission-critical functions.

Web Technology
The mastermind of the development of the World Wide Web – which involved the use of hyperlinks to
navigate web pages – was a British scientist, Tim Berners-Lee.
At the core of this was HTML or hypertext markup language, which was a set of commands and tags to
display text, show colors, and present graphics. A key was that the system was fairly easy to learn and use,
which helped to accelerate the number of web sites.
For example, many of the commands in HTML involve surrounding content with tags, such as the
following:
<strong>This is a Title</strong>
HTML would ultimately be too simple. So there emerged other systems to provide even richer
experiences, such as with CSS (Cascading Style Sheets, which provides for borders, shadows, and
animations) and JavaScript (this makes it possible to have sophisticated interactivity, say, with the use of
forms or calculations).
RPA must deal with such systems to work effectively. This means it will have to take actions like identify
the commands and tags so as to automate tasks.

Programming Languages and Low Code
 A programming language allows you to instruct a computer to take actions.
 The commands generally use ordinary words like IF, Do, While, and Then. But there can
still be lots of complexity, especially with languages that use advanced concepts like
object-oriented programming.
 Some of the most popular languages today include Python, Java, C++, C#, and Ruby.
 To use an RPA system, you have to use some code – but it’s not particularly difficult. It’s
actually known as low code. As the name implies, it is about using minimal manual
input.
For example, an RPA system has tools like drag-and-drop and visualizations to create
a bot.

OCR (Optical Character Recognition)
 A key feature for an RPA platform is OCR (Optical Character Recognition), a
technology that has actually been around for decades.
 It has two parts:
->Document scanner (which could even be something like your smartphone)
-> software that recognizes text.
In other words, with OCR, you can scan an image, PDF, or even handwritten
documents – and the text will be recognized. This makes it possible to
manipulate the text, such as by transferring it onto a form or updating a
database.

There are definitely many challenges with effective OCR scanning, such as:
 The size of a font
 The shape of the text
 The skewness (is the text rotated or slanted?)
 Blurred or degraded text
 Background noise
 Understanding different languages

Then how does this technology help with RPA?
 One way is with recoding a person’s actions while working on an application. The OCR
can better capture the workflows by recognizing words and other visuals on the screen. So,
even if there is a change of the location of these items, the RPA system can still identify
them.
 Something else: Automation involves large numbers of documents.
 OCR will greatly improve the processing.
Example of this would be processing a loan. With OCR, a document will use OCR to
extract information about a person’s financial background, the information about
the property, and any other financial details.
After this, the RPA system will apply the workflows and tasks to process the
loan, say, with applying various rules and sending documents to different departments and
regulatory agencies.

Databases
 At the heart of most applications is a database, which stores data that can be searched
and updated. This is usually done by putting the information in tables (i.e., rows and
columns of information).
 To interact with this, there is a scripting language called SQL (Structured Query
Language), which was relatively easy to learn.
 It was not until the late 1970s that relational databases were commercialized, led by the
pioneering efforts of Oracle.
 While relational databases proved to be quite effective, there were still some nagging
issues. Perhaps the biggest was data sprawl. Another problem was that relational
databases were not cheap. And as new technologies came on the scene, such as cloud
computing and real-time mobile applications, it became more difficult to process the data.
 well.

 In the meantime, there have been new approaches that have gone against the model for
relational databases. They include offerings like MySQL (which is now owned by Oracle)
and PostgreSQL. Yet these systems did not get enough traction in the enterprise.
 But there is one next-generation database technology that has done so: NoSQL. It also
began as an open source project and saw tremendous growth. As of now, MongoDB has
14,200 customers across 100 countries and there have been over 70 million downloads.
 Where relational databases are highly structured, a NoSQL system is quite flexible. It’s
based on a document model that can handle huge amounts of data at petabyte scale.
 And going forward, there is likely to be much innovation with database technology. Yet
relational databases will remain the majority of what companies use – which also means
that this will also be what RPA interacts with as well

APIs (Application Programming Interfaces)
 An API – which is the acronym for “application programming interface” – is software that connects
two applications.
For example: let’s say you want to create a weather app. To get access to the data, you can setup an
API, which often is fairly straightforward, such as by putting together a few lines of code to make
data requests (say, for the city). By doing this, you will increase the speed of the development.
 APIs are pervasive in enterprise environments since they are so effective. They also have different
structures. Although, the most common is a REST (representational state transfer) API.
 It’s true that APIs can be used as a form of automation.
 The technology requires having people with technical backgrounds. The development of an API can
take time and require complex integration.

 APIs can still have bugs and glitches, especially when in complex IT environments.
Because of the difficulties, RPA has proven to be a very attractive alternative. Again, the
development is much easier and there is less of a need for integration. But, interestingly enough,
an RPA platform can be a vehicle for delivering advanced APIs within the enterprise.

AI (Artificial Intelligence)
 A typical RPA system does not have much AI (Artificial Intelligence). The main reason is
that there is a literal carrying out of tasks, which does not require any smart system. But as
AI gets more powerful and accessible, RPA will increasingly start to use this powerful
technology – which should greatly enhance the outcomes.
AI: It’s software that ingests large amounts of data that is processed with sophisticated
algorithms that help answer questions, detect patterns, or learn. Interestingly enough, AI is
actually made up of a variety of subcategories
Machine Learning: This is where a computer can learn and improve by processing data
without having to be explicitly programmed. Machine learning is actually one of the oldest
forms of AI and uses traditional statistical methods like k-nearest neighbor (k-NN) and the
naive Bayes classifier.

Deep Learning: Deep learning became a major force in AI. Some of the important factors for this
included the enormous growth in data, the use of GPUs (graphics processing units) that provided
for ultrafast parallel processing, and innovation in techniques like backpropagation.
Deep learning is about using so-called neural networks – such as recurrent neural networks
(RNNs), convolutional neural networks (CNNs), and generative adversarial networks (GANs) – to
find patterns that humans often cannot detect.
NLP (natural language processing): This is AI that helps understand conversations. The most
notable examples of this include Siri, Cortana, and Alexa.
But there are also many chatbots that focus on specific uses cases (say, with providing medical
advice).

Besides, AI has some major issues, such as the following:
Bias: According to IBM: “Bad data can contain implicit racial, gender, or ideological biases.
Many AI systems will continue to be trained using bad data, making this an ongoing problem.
Causation: Humans have a strong grasp of this. We know what will happen if we use a hammer to hit a
glass. It’s pretty much instinctive. But AI is another matter. This technology is really about finding
correlations in data not causation – and this is a major limiting factor.
Common Sense: A human does not have to process many cases to understand certain rules of thumb. We just
naturally understand them. But with AI, common sense has been extremely difficult to code because of the
ambiguity and the lack of useful data for the seemingly infinite use cases.

Black Box : Deep learning can have an enormous number of layers and parameters. This means it can be nearly
impossible for a person to understand why the model is generating certain results. Now there
is more innovation in trying to find ways to understand deep learning outcomes – which is something called
“explainability” – but the efforts are still in the nascent stages.
Comprehension : An AI system cannot truly understand what it is reading or observing.
For example, if it read War and Peace, it would not be able to provide thoughts on the character
development, themes, and so on.
Static: It is also possible to conduct millions of simulations to learn. But of course, the real world is much more
dynamic, open-ended, and chaotic.
Conceptual Thinking: AI cannot understand abstract ideas like justice, misery, or happiness. There is also a lack
of imagination and creativity.

Brain: It’s really a miracle of evolution. A typical brain has 86 billion neurons
Structured Data: This is data that is formatted (social security numbers, addresses, point of sale
information, etc.) that can be stored in a relational database or spreadsheet.
Unstructured Data: This is data that is unformatted (images, videos, voicemails, PDFs, emails,
and audio files).
 For the most part, RPA uses structured data. However, this represents about 30% of what’s
available in a typical organization. But with AI, an RPA system will likely be much more
effective since it will be better able to process unstructured data.
Furthermore, there are other potential benefits of the technology: judgement, the use of
reasoning, and the detection of highly complex patterns. With these, the automation will be
greatly enhanced, helping with things like detecting fraud

Cognitive Automation
Consider cognitive automation to be an application of AI, actually.
 First of all, it is automation uses a combination of technologies like speech recognition and
NLP. By doing this, the goal is to replicate human actions as best as possible, such as by
analyzing patterns of workers and then finding patterns and correlations.
 Something else: Unlike other forms of AI, cognitive automation is usually effective with the
use of much less data. There may also be not as much reliance on highly technical talent, such
as data scientists.

Agile, Scrum, Kanban, and Waterfall
 In today’s world, software development has become even more difficult because of the emergence
of new platforms like the cloud and the hybrid cloud. This is why it’s important
to look at software management approaches.
 Agile
-One is called Agile, which was created back in the 1990s (a big part of this was the publication of the
Manifesto for Agile Software Development).
-The focus of this was to allow for incremental and iterative development, which begins with a detailed plan.
This also requires much communication across the teams and should involve people from the-business side of
the organization.
Nowadays, Agile has gotten easier because of the emergence of sophisticated technologies like Slack and Zoom
that help with collaboration.

 Scrum:
-This is actually a subset of Agile. But the iterations are done as quick sprints, which may last a week or two. This
can help with the momentum of a project but also make a larger project more manageable (just as a side note:
Scrum was first used for manufacturing but it was later found to work quite well with software development).
Kanban:
-This comes from the Japanese word for visual sign or card (the roots of the system go back to Toyota’s high-
quality manufacturing processes).
So yes, with Kanban, there is the use of visuals to help streamline the process. What’s more, the general approach
is similar to Agile as there is iterative development.
Waterfall
-This is the traditional code development model, which goes back to the 1970s.
-The waterfall model is a structured plan that goes over each step in much detail. To help this along, there may be
the use of a project management tool, say, a Gantt chart.
-While the waterfall approach has its advantages, it has generally fallen out of favor. Some of the reasons are as

DevOps
 DevOps has emerged as a critical part of a company’s digital transformation.
 The “Dev” part of the word is actually more than just about coding software.
 It also refers to the complete application process (such as with project management and quality
assurance or QA). As for “Ops,” it is another broad term, which encompasses system engineers and
administrators as well as database administrators, network engineers, security experts, and
operations staff.
 For the most part, DevOps has come about because of some major trends in IT. One is the use of
agile development approaches. Next is the realization that organizations need to combine technical
and operational staff when introducing new technologies and innovations.
 And finally, DevOps has proven effective in working with cloud computing environments.

Flowcharts
 An essential part of RPA is understanding workflows and systems, the use of flowcharts is
common.
 It’s usually at the core of the software application.
 With a flowchart, you can both sketch out the existing workflows of a department. And then
from here, you can brainstorm ways of improving them. Then you can use the flowchart to
design a bot for the automation.
 The flowchart is relatively simple to use and it also provides a quick visual way to
understand what you are dealing with. As the old saying goes, a picture is worth a thousand
words.
 some of the basics:
Terminator: This is a rectangle with rounded corners and is used to start and
end the process, as seen in Figure

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