Key new trends in Transportation Automation with RPA and AI

Many manual processes are involved in the transportation business, including order entry, track and trace, appointment monitoring and generating proof of delivery for customers.

DHL has created eight RPA software robots running out of a centralized control tower in Detroit to support customers. Kreider reported that the company is saving hundreds of man-hours per day on repetitive tasks, such as looking up transportation delivery status in carrier websites, generating email updates on delivery status, creating transportation orders from a list of ready-to-ship items and capturing proof-of-delivery documents from websites to update transportation management systems. “RPA is a mission-critical part of our service offering,” Kreider said, “and has also helped us to eliminate paperwork, which has operational and environmental benefits.”

Multi-problem solver

Breaking complex processes into subprocesses built from simple tasks can help eliminate bottlenecks. RPA is best at automating the movement of structured data used in standardized tasks across multiple, stable systems, especially highly repetitive tasks — not processes — that require high volumes of rekeying with greater potential for errors.

Overcoming disruptions with RPA and AI

RPA can help to fill visibility gaps across systems, particularly for businesses that deal with several smaller business partners. A major company, for example, can have a number of large retail clients with thousands of small suppliers that rely on different invoicing systems. RPA, in conjunction with other technologies, could determine why an invoice looks different than expected and help reduce the time humans spend disputing it and ensure that suppliers get paid on time.

Many supply chain decisions involve sourcing origin and cost. In the wake of COVID-19, massive amounts of supplier data that can affect risk assessment are continuously changing. “RPA plays an important role in acquiring and inputting this data, while AI and machine learning can quickly assess the associated risk factors involved,” said Noel Calhoun, CTO at third-party risk management software maker Interos.

Procreating RPA bots

One emerging strategy is to use process mining and intelligence tools to identify all the tasks associated with key business processes to help prioritize automation efforts. Using AI for process mining and discovery ahead of deploying RPA is one of the easiest ways to make smart automation decisions, suggested Reginald Twigg, director of product marketing at digital intelligence tools provider Abbyy. “Process intelligence,” he said, “can tell you where in the supply chain there are bottlenecks, reoccurrences, variations and where it is functioning properly.”

Read more here: RPA and AI strengthen weak links in supply chain workflows

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