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Imagine a bored assembly line worker doomed to keep performing the same menial, repetitive task until retirement. The picture is easy to envision. For a long time, this was the reality that many people lived from day to day. In the more modern business environment, where many tasks and activities are performed in an office, the parody still applies. There are a multitude of repetitive tasks performed on a daily basis that cost companies money instead of adding value to an organization. Luckily, modern business practices are innovating in the same way assembly lines once did, by eliminating the ‘human’ robot.
The first robot to appear in the workplace was called the Unimate in 1956. The purpose was to extract die castings from machines and carry out spot welding on vehicles at General Motors. The Unimate was the first of many robots that changed how businesses operate. While the designs and functions of robots vary as widely as the businesses in which they operate, the fundamental purpose has not changed: to automatically perform complicated, often repetitive tasks.
If you apply this purpose in a literal sense to the modern office worker, there isn’t a giant leap to coin the phrase ‘human robot.’ Employing humans to perform repetitive and redundant tasks, like data entry, data manipulation, and transferring information from one system to another, is not cost effective. Even with tools such as Excel, Access, and various other software available for a multitude of functions, businesses are still very susceptible to ‘human robot’ error. Luckily for companies wanting to stay competitive in today’s fast-paced digital world, a cost-effective solution is available for many scenarios. Robotic Process Automation (RPA) is general terminology used to describe the emerging suite of technological solutions that automate and streamline enterprise operations by executing prescribed steps automatically. In application, RPA is the beginning of the end of the human robot.
Oftentimes, RPA is used to describe processes which are more accurately classified as intelligent automation. Intelligent automation has a similar foundation to RPA while also incorporating the power of machine learning and artificial intelligence into the processing routine. The addition of artificial intelligence enables the software to learn from previous interactions and adapt as transactional counts increase. As the application suite is exposed to increased volumes of data, the rules and processes change to increase performance and adapt to changes systemically. The robot ‘learns’ to further optimize its designated process.
With the evolution of machine learning, artificial intelligence, and cognitive technologies, organizations are able to expand the usage of traditional RPA into more holistic system stacks. These complimentary systems enable the processing of scenarios in which multiple steps are automated. In this situation, the technology is able to execute decisions on how to proceed, thus further eliminating the need for human intervention typically required in robotic processing. According to Gartner, leveraging RPA properly can reduce employee requirements in a shared service environment by up to 65%. This reduction in employee requirements, which can be used in many environments in addition to shared services, enables organizations to reallocate resources to focus on higher value processes.
How You Benefit from Eliminating Human Robots
Organizations have been leveraging RPA in a myriad of ways, from automating intercompany transactions through the use of tools such as BlackLine to invoice processing capabilities provided by Basware. Essentially, RPA can be applied to span into all segments of business operations that regularly process routine transactions with clearly defined process steps. The ability to automate these processes with robots results in a multitude of benefits to the organization, including:
RPA technology enables organizations to develop ‘robots’ that automatically move and transform data within one system or to another, acting as middleware, without the need for human intervention. This streamlined transformation process can expedite downstream workflows that are either being executed by end users or additionally developed robots. For example, RPA can be utilized to transform data within an Oracle database to a SQL-based language; or to improve the processing time associated with various accounting activities. This same technology can also automate more complex processes employing a higher degree of frequency, such as processing repetitive forms, stress testing systems, or updating information between systems. Using robots to enhance a system with data integration and automated updates results in the ability to capitalize on the system’s full potential by ensuring data is updated in a timely manner.
In 2009, the University of Nevada conducted a study to determine the average number of errors made during data entry. This blind study consisted of 215 undergraduate students entering in various data points into an Excel template. Researchers were able to determine that on average, 11.5% of the data entered was incorrectly captured. At first glance, 11.5% might not seem extreme; however, consider the following scenario: if an organization processes 100,000 invoices per month, an error rate of 11.5% means that 11,500 invoices have errors that require human intervention. Even if each invoice only requires three minutes to resolve, this results in an additional 575 hours per month (or 6,900 hours per year). This is a significant financial and opportunity cost for any organization. The elimination of human touch points, and thus human error (e.g. human robots), can significantly lower error ratios.
Combining RPA with tools such as Optical Character Recognition (OCR) software, or more recently, Cognitive Machine Reading software, even higher degrees of accuracy can be achieved. For example, one company benefited from the automation of the intake of customer warranty cards. On average, a staff of 20 data entry clerks would process 15,000 product warranty cards by entering 50 data points per card into an internal database. After a significant increase in customer service related issues, an internal audit was conducted on the process. This audit revealed that data entered from the warranty cards had an error rate of 28%. RPA technologies were applied to automatically scan warranty cards using OCR technology, and the information captured was automatically transferred to various databases. Subsequent internal audits revealed that the error rate on warranty cards was reduced by 26.25%, and customer service-related phone calls dropped by 32%.
Customer satisfaction will remain high despite issues with a product or service when customer service is quick, genuine, and focused on the resolution. However, if processing or resolution takes too long, customers will move on. By implementing RPA as part of the customer service life cycle for systematic problem resolution, organizations can benefit from reductions in processing and resolution time.
In the previous example, the organization experienced declining customer service satisfaction scores as a result of poor data integrity. The volume of issues associated with data integrity had concealed other issues associated with product reliability, as well as consumers’ ability to self-diagnose remedial issues with the product. Upon increasing the integrity of the warranty card data, the organization was able to pinpoint five common issues that consumers were experiencing with the product, all of which could be easily resolved by the consumer with the proper guidance from the manufacturer. The organization used RPA to develop automated self-service phone and internet portals that provided step-by-step instructions on how to remedy these common issues. Three months after the launch of the self-service portals, the organization had realized a 32% increase in customer satisfaction related to the products that previously had the highest dissatisfaction rates. Additionally, the automation of product-related claims increased the capacity of the customer service professionals to focus on problems that required more human intervention, which further increased customer satisfaction.
All organizations have internal policies and regulatory guidelines that must be followed. Unfortunately, there are countless reasons why policies may be overlooked, forgotten, or ignored. For example, a new employee might be unaware of policies within the organization, or an existing employee may not be aware of a change to a regulatory guideline. Regardless of the reason, failure to comply can put an organization at risk. Internal audit departments can use RPA to ensure compliance with existing and newly introduced requirements by enforcing systemic rules and logic-based processing. The technology can also be configured to eliminate the need for human intervention while reviewing sensitive documents for classification and production purposes. For example, an organization that is subject to HIPPA regulations can use RPA to automatically sort and file medical documentation and forward to the appropriate parties when and if necessary. Another practical application is compliance with Payment Card Industry Data Security Standard requirements. Securing credit card information to ensure that only qualified individuals are able to access account information can be difficult in a customer service environment. Providing refunds or account adjustments often requires access to the original payment information. By using RPA, refunds and adjustments can be automatically generated without providing this access.
RPA can also support internal audit departments by expanding the scope of examinations from a standard sampling to a review of all data points. For example, one company benefited by using RPA during a goods receipt audit for an organization that processed over 132,000 invoices weekly. RPA with OCR/Machine Reading was implemented to identify receipts that failed to have proper recipient acknowledgement and supporting documentation. Previous audits only identified 1.5% non-compliance that would result in a chargeback to the vendor; but upon implementing RPA technologies to conduct a full receipt analysis, the chargeback rate increased to 7.8% which equated to almost $1 million of unrealized opportunity on a monthly basis.
Data governance is a critical aspect of running an organization efficiently. The old adage “garbage in, garbage out” is still true, and it has endured for a reason. Without accurate data that is clear, concise, and relevant, organizations can make ill-informed decisions resulting in sub-standard products or services. With RPA, data can be automatically validated based upon predetermined rules programed within the system. Data can also be automatically transformed into different formats and loaded into other systems for additional processing and/or analysis. In addition, analytics can be performed throughout the entire process to increase transparency and provide a foundation for more accurate business decisions. If any of these processes are performed by human robots, the risk of subpar quality data increases exponentially. Implementing RPA allows organizations to automate the data quality process to ensure information that is being retained for analysis is complete and accurate. In one instance, an organization used RPA to perform standardized data cleansing to generate forecasting reports which originally required significant human intervention to remove outlying data elements. Multiple full-time employees scoured various databases to remove outlier data. On average, the data cleansing process required 50 hours to complete. Utilizing RPA, the data sets were analyzed and outlier data was removed in four hours which resulted in expedited report production that led to a decrease of overstocked materials in excess of $5.2M.
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