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Evolution Of Automation From What It Was To What It Is

Automation is a very common term known across sectors today, yet it still remains among the big unknowns. Most people relate it to machines and its ability to replace human involvement at work. Terminologies such as robotic automation and artificial intelligence, many a times, are loosely or interchangeably used. This Supply WisdomSM whitepaper attempts to shed light on and clearly define some of those confusing automation concepts that we have heard of so far, keeping the evolutionary timeframe in mind.

 

First Wave Of Automation

The concept of Automation dates back several centuries. While its origins are debatable, many trace it back to 1620, when Dutch scientist Cornelius Drebbel invented the Thermostat.

The term was not very common too, until Ford developed such a programme in the 1900s and its assembly line concept led to significant reduction in the turnaround time for the production of one car, from 12 hours to a mere 1.5 hours.

The concept travelled further and by the 1950s, Japan had become the global leader in automobile automation with brands like Toyota, Honda, and Nissan, all vouching of high standards and quality.

What is interesting is that it did not stop there. The progression continued and the next big change coincided with the third and fourth generation of computers. In the 1980s, computers had slowly entered homes and by the 1990s, the ability of personal systems to create networks brought about the biggest phenomenon ever - the internet.

Automation underwent a major change at that precise point in time, and since then, its advancement has been faster than ever before. Here are some concepts that are closely related to the first wave.

 

1. Computerisation:

Computerisation should not be interchangeably used with automation simply because doing one is not equal to doing the other. But automation can and does involve computerisation.

As per Merriam-Webster, to computerise means “to use a computer to make, do, or control (something)”. Computerisation, therefore, means that a function (which could be a process or operation) is integrated with a computer system and performed by someone who has been trained. In the present scenario, automation can be viewed from a digital transformation perspective, which is nothing but computerised change.

As per CIO Magazine, it means “the acceleration of business activities, processes, competencies, and models to fully leverage the changes and opportunities of digital technologies and their impact in a strategic and prioritised way.” Historically, computerisation was largely applied to bank transactions, billing counters, etc.

 

2. Data Center Automation:

Another commonly heard of terminology is Data Center Automation, which simply put, is the way in which workflow and processes in a data center facility are managed through automation. This involves automation across computing, network, and storage layers in both physical and virtual environments, thus bringing down high human dependency in data centers.

It is chiefly achieved through a data center automation software solution (therefore also called Software Defined Data Centers or SDDCs) that provides centralised access to all or most data center resources. How does it exactly help:

•          It helps with tasks related to scheduling and monitoring

•          It provides insights into server nodes and their configurations

•          It helps in running routine processes including patching, updating, reporting etc. automatically

•          It ensures that processes and controls are compliant with standards, policies, and procedures.

Some well-known data center automation tools include OpenStack, Puppet, CloudStack, Microsoft System Center, OpenNebula, HPE Helion Eucalyptus, Chef, Ansible Tower, and Git. It is interesting to note that in 2016, the Global Data Center Automation Market was valued at nearly US$4.18 B. Already growing at a significant rate, the projections for 2022 amount to US$18.33 B, at a CAGR of 23.5%.

 

3. Robotic Desktop Automation (RDA):

Often confused with Robotic Process Automation or RPA (they only sound similar), RDA is a “form of RPA software deployed locally on a user’s desktop or laptop, whereby the software is initiated on demand or against a schedule to carry out an automated action.“

This is easy to install due to its relatively lower cost and high efficiency in automating routine tasks. However, it is limited to a single operating system or user account.

Desktop automation tools can be window handle-based or based on properties of controls, and even record and playback-based. Some of the known desktop automation tools include Sikuli, AutoIT, TestComplete, Winium, PyWinAuto, and Microsoft UI Automation library, among others.

 

4. Business Process Automation (BPA):

Often mistaken with Business Process Improvement (BPI) or Business Process Management (BPM), BPA refers to the technology-enabled automation of complex business processes and functions that looks at activities that can accomplish a specific workflow or function.

Generally, BPA is mostly applied by organisations to automate less complex processes or single processes in extensive workflows, but again it is not limited to only simple, linear processes. According to Gartner, BPA “focuses on ‘run the business’ as opposed to ‘count the business’ types of automation efforts and often deals with event-driven, mission-critical, core processes.”

However, it is important to understand the feasibility of BPA before using it, because there are times it may fail. For instance, the decision to automate a company’s customer-interaction processes may not always work or for that matter where the human decision-making factor is high.

But BPA has been known to produce some great results, especially when it comes to routine tasks, difficult decisions that can be addressed by machines, research, self-service portals, risky or hazardous manual tasks, sensor-based tracking and alerts, IT back-office processes, document management etc.

 

5. Current Wave Of Automation:

Through the previous sections about the first wave of automation technologies, we can easily say that automation has been around for a long time Recently, however, with the latest technologies trying to take human efforts out of any possible work, we can safely say we are in a new era of robotics automation.

To an extent, ‘automation’ has always been a centrepiece for innovation. Starting from calculators to today’s autonomous driving car, technological advancements have concentrated heavily on making human work non-mundane. While the first wave of automation described here involved the transitioning of automation. In current terms, this means more on reducing time spent on computers to get the work done. Let us take a look at the technologies that are currently doing rounds in the market.

 

6. Robotic Process Automation:

According to Leslie Willcocks, professor of technology, work, and globalisation at the London School of Economics’ department of management, RPA can be described as something which takes the robot out of the human.                  

He further explains that it is any software that can do repetitive work more quickly, more accurately, and tirelessly, than humans, freeing them to do other tasks requiring human strengths such as emotional intelligence, reasoning, judgment, and interaction with the customer.                   

There are various types of automation that currently fall into the ‘RPA’ framework. It can range from something as simple as everyday data manipulation to something that can be scaled up to be re-used enterprise-wide. Sometimes, some of the RPA tools concentrate only on certain functions, like Blue Prism, which focuses mainly on the financial and banking industry.            

To further differentiate traditional process automation from our current RPA processes, it can be said that tradition automation programmes were part of IT programs, whereas robotic operation is a sourcing decision, run and operated by the business operations team, and not just one IT programmer.

Initially, cost was the main reason companies began exploring RPA. However, companies now have moved beyond cost efficiency and are looking at RPAs that provide them with job accuracy and help them reach their goals faster. In addition, it also boosts employee morale by helping them work in tasks that are more meaningful.

 

7. Machine Learning:

“Machine Learning is based on algorithms that can learn from data without relying on rules-based programming”, says a McKinsey article. Machine Learning understands data pattern from past experiences to extrapolate it for future predictions. However, Machine Learning is unable to reason data or understand motive behind drastic changes.

Facebook has mastered the art of displaying relevant content through Machine Learning using user’s’ historic activities. For instance, in Facebook, news feeds from friends that have been previously liked extensively appear before news feeds from other friends.

 Machine Learning is more complex than Robotic Process Automation because Machine Learning is involved in analysing data and finding a structured pattern rather than repeating the same job at regular intervals. However, RPA is more popular in today’s world than Machine Learning and more complex technologies have not yet been commercialised to the extent of RPA due to their higher costs.

With the ever increasing availability of data and the complexity involved around making sense of it, Machine Learning has found applications in almost all fields. Face detection and recognition, financial risk profiling, medical diagnosis, target marketing, identifying genetic changes due to diseases, etc. Machine Learning - coupled with Natural Language Processing (NLP) - is being widely used to decrease the distance between human talk and machine language.

 

8. Cognitive Computing:

Cognitive Computing is the simulation of human thought process using advanced technologies like Machine Learning, data mining and natural language processing. Cognitive Computing has been closely associated with IBM Watson for example.                      

Cognitive computing helps humans in making a decision. For example, IBM Watson is being used in healthcare to aid doctors with information to evaluate treatments for patients. It takes into consideration various factors like development of the disease, strength of the medicine, body vital signs, patient’s age, patient’s level of physical activity, etc. to suggest four or five medicines/dosages for the disease. And the doctors can choose the best one among these medicines.

An important factor differentiating Cognitive Computing from Machine Learning is its understanding of ‘context’. According to the requirement of the process, cognitive computing analyses the given data based on time, place and tone of the user (for voice recognition software).

 

9. Artificial Intelligence:

Artificial Intelligence is a step further ahead of Cognitive Computing and is still under study. Instead of helping humans make a decision, artificial intelligence makes a decision based on the given inputs. The goal of artificial intelligence is to mimic a human and thus, take out any human intervention in a given process.

In the previous example of cognitive computing, it was evident that IBM Watson helps doctors come to a decision by suggesting medicines. If we were to use artificial intelligence technology in the place of IBM Watson (cognitive computing), the equipment could have suggested the medicine for the patient, thus eliminating the need for a doctor!

 

10. Autonomic Computing:

Autonomic Computing stems from the human ‘autonomic nervous system’ – to have a machine that does not require any action from humans. According to IRPAAI (Institute for Robotic Process Automation and Artificial Intelligence), an autonomic system is identified by eight characteristics:

Artificial Intelligence is like the backbone to an autonomic computing system. An autonomic computing system uses artificial intelligence, data mining, and natural language processing to achieve its process without any human interference.

 

11. Automation And The Future:

 

How will automation be perceived in the days to come – merely a conflict of man vs machine? Or will it become part of a new futuristic business model that is more centered on creativity and collaboration with machines and digital technology?

Whatever the perception may be, with changing times and technological evolution, automation is creating a workforce which is dynamic, and constantly learning and training itself to keep up with the increasing competition and demand. It is leading to new innovations at a much faster pace and some dramatic transformations that the workplace had not experienced before.

Therefore, those who did not see it coming or saw it coming and did not prepare themselves have a task at hand. But at the same time, it is also an opportunity to explore the unexplored and become part of a trend that is revolutionary, advanced, and truly modern.

Automation concepts will continue to evolve in the coming years and one thing is certain – automation as a phenomenon is a big part of the future, if not the future itself. The extent of its impact may be difficult to predict, but it is definitely going to be a big disruptor.

It is already changing the nature of several blue-collar and even white-collar job profiles, with many perceiving automation as a threat and the voices against it growing higher in pitch. However, the reality is also is that not everything can be automated or taken over by a robot. The prospective application of automation differs from industry to industry and activity to activity.

 

By: Swathi Sarma is the Senior Marketing & Social Media Specialist of Neo Group.

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