Post by : Akshay Thakur
RPA Implementation and Complexities
Robotic Process Automation (RPA) is an automation of back and front office processes that are largely rules based, structured, and repetitive. The automation takes place when software “robots” (not physical robots) carry out processes or tasks normally completed by humans. The software vendors of RPA have positioned their software as a headcount saving and have priced their software on the higher end of software, but on the lowest end of hiring a human employee.
The technology reviewed can best be broken down into four key categories. Each category measures the software on four key functional dimensions. The key dimensions defining the software vendors is as follows:
- Data: The level of sophistication in dealing with business data (structured or unstructured);
- Type of Tasks predominately performed: tasks are either rules based or require knowledge from multiple sources to complete the process.
- Interoperability: working across multiple applications (single application or multiple applications and platforms), and;
- AI: The level of artificial intelligence provided by the application (none, machine learning based on pattern recognition and statistics, or emerging true AI).
As acceptance of a digital workforce increases, so will the adoption of automation in the organization increase? It’s inevitable that we begin to see level four implementations of RPA making the evolutionary step from a “dumb” software robot to cognitive based computing. This automations will offer levels of artificial intelligence and value-add to the processes by increasing the level of analytics and predicting business outcomes in a more insightful and commercially useful manner.
The Implementation Process for RPA
Some processes will be better suited to RPA than others. The general characteristics of a process which is ready for robotic process automation would be:
- Repetitive and rules based
- Accesses structured data sets
- Utilizes applications on a Windows or Web based platform
- The process is documented and has been standardized in practice
- Three or more staff are hired to complete the process
- Data input is prone to human error
Technology providers are focusing more on back-office processes that have been typically outsourced to offshore locations partially because of the benefits of labor arbitrage and partially because they are considered of low-value. Although of low strategic value they are processes that are necessary in daily operations of the business. Typical processes currently being managed by an RPA solution include:
Finance & Banking
- New account verification
- Data validation
- Customer account management
- Financial claims processing
- Report creation
- Form filling
- Change of address
- Loan application processing
- Claims processing
- New account creation
- Collection & consolidate customer data from client phone systems
- Backing up information from client systems
- Uploading data
- Transferring customer data between applications
- Extracting data about competitor pricing
- Patient data migration and processing
- Reporting for doctors
- Medical bill processing
- Patient record storage
- Automatically updating online inventory & product information
- Importing website orders and email sales into back-end systems
- Verification process
- Populating forms and assigning sub-contractors to jobs
- Integrating legacy systems with newer systems
Internal departments of organizations are also able to benefit from RPA. Some of the key processes well suited for RPA include the following:
- Invoice processing
- Accounts payable and accounts receivables
- Bank reconciliation
- Fixed assets analysis
- Master data management
- Vendor and customer account creation
- ERP logging from another system
- Employee on-boarding
- Leave of absence management
- Populating employee data into multiple systems
- Performance appraisal management
- Creating new accounts
- Software installations and updates
- Batch processing
- Printer set-ups
What are the complexities faced by RPA developers?
- Scaling the image to the correct size
Most of the market OCR engines require a minimum image quality size. This usually ranges from 200–300 dots per inch (DPI). Anything less than the minimum requirement will result in unclear and inaccurate results. On the flip side, having an exceptionally good DPI quality (e.g. 500 DPI) will not help in increasing the quality of the output. Rather, only the image size will increase, which will result in increased storage. If the quality of the image is not good, the OCR engine can get confused. Instead of reading an S, the output can be provided as 5, the number ‘0’ or the letter ‘O’. Hence, the better the quality of the image, the better is the output provided by the OCR engine.
- Handwritten text/ink stamps over printed text
As part of internal procedures (Maker/Checker, audit etc.), people tend to write critical information or use stamps over documents which are then scanned. Such handwritten text usually interferes with the printed text and makes it difficult for the OCR engine to capture the text from the document. Moreover, this reduces the quality of the document.
- Noise/distortion on the scanned image due to bad scanner quality
The presence of noise (distortion) on the scanned document can significantly reduce the output from the OCR engine. Noise usually appears on a document due to improper scanning or a bad scanner. Examples of noise are spots in the background of the document, uneven contrast, etc.
- Scanning an already scanned image
Scanning a printed copy of an already scanned image would definitely impair the quality of the document, thereby influencing the accuracy levels of the extracted data.
- Multiple formats of inputs
A document can be received for further processing in various formats. The multiple formats increase the complexity of implementation. Examples of such formats are TXT, EML, XLSX, VSD, HTML, DOCX, XLS, VSDX, DOC, PPTX, HTM, PPT, RTF, BMP, PCX, DCX, JPEG, TIFF, GIF, PNG, and PDF.