According to a research by Aberdeen Group, it takes between 4.1 and 16.3 days approximately for companies to process an invoice from receipt to payment approval. This is not surprising as there are quite a few brands that are still stuck up with outdated legacy systems. The problem is, legacy systems are the foundation for a lot of business processes. Hence, legacy modernization becomes a challenge pertaining to data migration and data accessibility.
A report by KPMG says that the intelligent automation market is here to stay and rule. Enterprise investment will shoot up to $231.9 billion dollar by 2025 from $12.4 billion and Robotic Process Automation (RPA) will be among the key players in the automation market. RPA services are a promising solution for businesses because it enables them with a seamless transition from their legacy systems.
Industries like retail, healthcare, financial services, and manufacturing has infused RPA into their legacy systems at a large scale. So let us examine how linking robotic process automation solutions with legacy structures can enhance system integration for an industry.
Infusing RPA into Business Processes
A study by ISG says that RPA implementation results in 37% cost savings. Retail is among the growth oriented sectors greatly favors RPA implementation. The reason is, it helps them establish omni channel retail integration. Invoicing is a strong use case for RPA to show its power. A company receives invoices in multiple formats like PDF email attachment, paper copy, word document or even a fax. The finance team of the organization has the ownership to transfer all the data from the various invoices to the database of the company. The challenge here is to handle all the invoices in a similar way each time as it involves excessive manual labor. Similarly, the unstructured data in the invoices also leads to redundant errors.
RPA solutions can automate the entire data input processes, error reconciliation, and also decision making. The result is a faster and error free invoicing process.
Similarly, robotic process automation implementation plays a major role in supply-chain management (SCM). Some of these include supply and demand planning, supply relationship management and procurement, and transportation and logistics. The supply and demand phase in a SCM requires collation of numerous data from various sources. For instance, when a supplier wants to materialize the purchase order, the purchase plan is created which in turn incorporates factors like sales history, price trends, sales target, inventory levels, seasonal fluctuations, and much more. The problem lies in collating and correlating the humongous volume of data. But RPA reduces the time taken and the number of errors.
Symbiosis of RPA and Machine Learning
It is true that RPA bots accomplish routine and repetitive tasks more effectively; but what happens to tasks that requires insights. This is where the convergence of RPA and Machine Learning (ML) brings in a multitude of opportunities. Adoption of RPA is helping enterprises switch from legacy systems and become more flexible and competitive. Nonetheless, the problem of these bots is that they cannot adapt to the changing conditions or learn from experience. ML on the other hand uses Artificial Intelligence to associate cognizance and context to tasks performed by the RPA bots, thus, accelerating the overall business productivity. A typical example, of this would be when extracting field values from unstructured data, RPA bots fetches data based on a set of rules while ML learns from the most common labels for the fields and uses the learning obtained in the future scenarios. This makes the automation processes way faster. ML helps the bot build knowledge based on historical data which is used for decision making and predictions. To put it simply, a synergy between ML and RPA is like introducing a smart agent into the otherwise routine-based and repetitive tasks.
Last but not the least, combining RPA and ML can create powerful automation solutions but it is imperative to re-train the ML models periodically so that it is up-to-date.