Quality has been a buzzword in business for decades. “The quality goes in before the name goes on” was the famous slogan of Zenith, the electronics giant, from the last century. Then, there was TQI (Total Quality Improvement) and, from Japan, Kaisen, and, of course, many other programs—all dedicated to eliminating errors from manufacturing, sales-order entering, inventory, and other corporate processes. Many of these quality programs were quite complex, requiring intensive education and training of executives as well as line managers and workers and often organizational restructuring as well.
Today, the answer is simpler. Build bots (software robots) using Robotic Process Automation (RPA). Not that it’s an either/or proposition between keeping human eyes on things and having bots do the work. But you may well find that deploying RPA takes you much further on your quality journey than you anticipated.
RPA not only accelerates manual processes and frees up human workers, but it also dramatically shrinks your error margins at whatever scale you’re operating at. This, in turn, results in overall higher quality and thus improved business competitiveness of your business.
Eliminates human error
RPA helps complete business processes by automating, step-by-step, the tasks typically done by human workers. And humans can error, especially with repetitive tasks such as data entry that bots are so good at performing or reconciliation, checking, and record-matching. A bot will work to specifications. It never gets tired and never gets sick and can work 24-hour shifts with 100% accuracy and compliance.
Safeguards the quality of sensitive PII data
When you use RPA to process and manage sensitive personally identifiable information (PII), you protect the information’s integrity and ensure its quality. Because PII is not exposed to humans, there is little chance it will be changed, deleted, or corrupted by human error—or intention. Its quality and integrity remain intact.
Complies with governance rules and regulations that ensure high quality
Typically, RPA bots are subject to governance controls as they perform their tasks. This audit capability increases quality, as any issues can be easily caught and fixed.
But often deploying RPA includes rethinking and designing processes to optimize efficiencies. In such cases, it’s important to ensure that the appropriate governance controls are put into place for the bots. Also, businesses must make sure they have a controlled way to deal with exceptions that arise. For example, an accounts payable bot should flag any invoices over a certain amount for a human employee to check. Thus, the quality of the business’s financial operations is assured.
Can be easily audited
Because bots perform in continuously predictable and reliable ways, and because their activities are recorded, it’s easy to audit them. This is important because as bots begin to penetrate business functions throughout the enterprise, auditors will need to audit them to ensure they comply with established norms and practices. The audit capability makes sure that the results of the processes are of sufficiently high quality.
Protects data quality
This is probably the most important quality measure: how trustworthy is your data? Data is the heart of today’s modern business. Strategy is built on it, decisions are made with it, customers are served well or poorly depending on data quality.
How can RPA help with data quality? To best understand this, it’s important to review what some of the key causes of poor-quality data are. These include poor data entering the workstream when data-related tasks are done manually. Also, the lack of a central, master database that contains a single source of truth is a common problem. Data from different systems can contain different versions of the supposedly same data. For example, a customer name can be spelled two different ways, or an address may contain conflicting zip codes.
RPA can be an enormous help in these scenarios. It can identify, flag, and even fix poor data before it enters a system. The process of validation includes the following things that RPA can do:
- Verify whether data is missing.
- Apply rules when validating data such as checking the formatting (dates, monetary figures), and making sure the correct data types are included in the proper fields.
- Transform data into the correct format.
- Do complex pattern matching using regular expressions and wildcards.
The new quality economics of a robotic workforce
More good news is that businesses get significant cost advantages to using bots to improve quality. Quality tasks that would previously have been unthinkable—say, cleansing immense amounts of data before moving it to cloud storage—because of high cost are now possible.
With better quality/fewer errors in production, order-taking, shipping, and customer service—throughout the entire customer buying cycle—RPA can make a significant difference in improving customer service and a company’s bottom line.