Despite the hype surrounding data science and analytics, there is a disconnect between the positive perception many seem to have of analytics and the number of companies actually using them. This is especially true in supply chain, where analytics, Internet of Things (IOT) and Industry 4.0 has the potential to completely revamp the way we approach supply chain management.
While the majority of Chief Procurement Officers (CPOs) agree in the transformative potential of data and analytics, a 2018 Deloitte CPO survey found that only 1/3 of CPOs are implementing technologies such as predictive analytics and collaborative networks.
Without the proper data foundation, organizations find it difficult to unlock the full potential of analytics. Process automation is one way to create that foundation, reducing human errors and improving data quality.
What Causes Poor Data Quality
The axiom “garbage in, garbage out” is truer in today’s fast-paced world than ever before. Rather than working with complete, clean and high quality data sets, supply chain analysts are often consolidating reports from various systems and cleaning the data before they can even begin to analyze it.
It comes as no surprise that the top two barriers to digital transformation, as reported by CPOs in the Deloitte survey, are the lack of data integration and the quality of data.
These two barriers often stem from disparate and legacy systems. While the systems have worked up until now, they often provide poor data flow visibility and include various work-arounds and “one-off” solutions. This patchwork of legacy systems often creates data siloes and “swivel chair” activities in which a data worker must interface between multiple systems to complete a single process.
Swivel Chair activities often include manually copying data from one system into another system. These sort of disjointed and highly manual processes often lead to increased errors and low data quality.
New systems implementation often takes months, if not years, however. As a result, organizations must also include short-term and medium-term strategies to improve their data quality.
How Robotic Process Automation Can Automate Processes (and Help Enable Analytics) in Relatively Quickly
One strategy for organizations is to reduce the amount of swivel chair activities being tasked to humans. Integrating the organization’s IT systems and automating the transmission of data reduces the risks of human error and increases the quality of the organization’s data.
However, the methods of integration — such as application programming interface (API) based integrations — are costly and time-consuming. Because API integrations are system specific, each system to system connection requires a new integration to be developed.
Enter emerging technologies such as Robotic Process Automation (RPA), which has shown the potential of system-agnostic tools. Unlike API based integrations, RPA can interact with any system the same way a human can, allowing it to integrate with any number of systems. Even CPOs are coming on board, with the number of CPOs with a positive perception of RPA doubling from 13% in 2017 to 24% in 2018, as reported in the Deloitte survey.
In addition to automating highly manual repetitive tasks, RPA also increases process quality/accuracy and compliance. RPA sits on top of a user’s machine and interacts with various systems through the user interface the same way a person would. The benefits of RPA include:
- Integration with legacy systems without disruption
- High data flow visibility
- Acceleration of data flow integration
- Automation of data transaction processes
The speed and ease of automation with RPA also offers an advantage — deployment is often in a matter of weeks and the ROI is typically within 12 months.
Given its short development and deployment timeline, RPA can be used as a “bridge” solution. RPA allows CPOs better data visibility and integration with their current systems, rather than having to wait until the new system is fully implemented and integrated before having better data.
RPA can significantly decrease the number of systems a data worker interacts with. By using a bot to process the swivel chair activities, organizations can reduce error rates, improve productivity and create higher quality data.
In addition to a long-term systems upgrade, organizations must assess the current state of their systems, processes and data. Solutions such as RPA can supplement an organization’s digital transformation strategies, enabling better data visibility and data quality within a matter of weeks rather than months.
Contact John Cavalier at jcavalier@cohenconsulting.com or a member of your service team to discuss this topic further.
Cohen & Company is not rendering legal, accounting or other professional advice. Information contained in this post is considered accurate as of the date of publishing. Any action taken based on information in this blog should be taken only after a detailed review of the specific facts, circumstances and current law.