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Auto Tech Outlook | Thursday, May 20, 2021
Automotive manufacturers can maintain an adequate stock balance by incorporating RPA, attributed to the increasing adoption of industrial IoT and the abundance of data on partners and customers.
FREMONT, CA: In several areas of the automotive industry, Robotic Process Automation (RPA) continues to prove its value. RPA tools can automate several time-consuming and repetitive activities that are prone to human error. The ease of deployment and fast return on investment are two of their most praised features.
Even though rule-based RPA's capabilities are restricted, organizations can greatly expand their reach by combining RPA with machine learning to take advantage of data-driven decision-making. Intelligent Process Automation (IPA) is the next logical step in transforming a business into a digital one. Explore the most compelling use cases of RPA in the automotive industry below.
Inventory management that is streamlined is at the heart of effective supply chain management. Automobile producers have traditionally employed managers to ensure that their inventory levels correspond to demand. This is a manual, low-value-adding task that is prone to human error by its very design. Automotive manufacturers can maintain an adequate stock balance by incorporating RPA, attributed to the increasing adoption of industrial IoT and the abundance of data on partners and customers.
To work, the majority of automotive companies must connect with hundreds of suppliers. Automotive companies are now looking into RPA capabilities to securely move their corporate information, with seamless data sharing being the pinnacle of performance.
For example, a leading automotive component manufacturer used RPA to streamline digital communication with its customers and suppliers in America and Europe. In their case, RPA aids supplier onboarding by automating provisioning, reducing the time it takes to onboard new partners substantially. RPA, in essence, assists in resolving the problem of data being dispersed through multiple systems by ensuring continuous data flow between business ecosystem participants.
Freight management, which manual flaws have traditionally plagued, will greatly benefit from RPA. Employees will traditionally insert customer information into the TMS, determine the best freight options and transport paths, submit this information to the customer, and wait for confirmation. Although it should seem that choosing a carrier necessitates human judgment, it is actually a rule-based procedure. By incorporating RPA into the TMS, the system can analyze data independently, produce quotes, and schedule shipments, greatly speeding up the process.