A recent survey by the Institute for Supply Management asked respondents a simple question: “Have you heard of RPA?” More than 60% of the 432 respondents said no.
Robotic process automation (RPA) is a technology that helps automate repetitive tasks on a computer. Think data entry or moving files from one place to another — processes that don’t change and have a clear path that can be programmed for a computer to follow.
But why should people in supply chain care about it? It’s about resources, argued James Fleming, the program manager of certification at the Institute for Supply Management at the organization’s annual conference in Houston Monday. Currently, across industries, about 1.8% of a company’s employees are dedicated to supply management and that percentage is still dropping, Fleming noted.
“If we thought we were going to get more people — we won’t,” he said. “If we would like to work more hours, they won’t let us. And I think a lot of people are really starting to grab onto this concept of ‘let’s find ways to really take the transactional work out of it, automate it, and take that resource base we have and really start to put it into a strategic focus.'”
While many respondents had not heard of the term RPA, a majority said they were looking to or were already in the process of deploying some sort of automation through AI/RPA.
The biggest challenges for companies that have deployed automation technology have been the number of IT resources required, as well as a lack of technical expertise and flexibility in the bots they have used. But the advantages are improved workflow, more productivity and time for other work.
A lot of early adopters are currently using the technology for things like automated data entry or classification.
The lowest hanging fruit is where we have data that we’re entering or we’re pushing and pulling data from one system to another. Those are the best initial use cases for a lot of this technology
The longer-term goals for companies implementing these technologies will likely shift from these low-hanging fruit, RPA projects, to more cognitive, AI-type projects. Cognitive implementations don’t follow a set of instructions and instead require an AI-system to make decisions based on historical data and what it sees in a given input — this can be a bigger lift for a company to put into practice
Despite the heavier lift, having more time for high-level thinking and tasks is often the goal. Fleming suggested that if a company doesn’t believe they have data that would allow them to take advantage of these technologies then they might want to think twice.
“If you don’t think you have data go and talk to your finance folks,” he said.