Use cases of RPA in the Automotive Industry

RPA allows manufacturers to simplify back-office operations and cut down on many managerial and administrative complexities. In doing so, RPA creates an overall agile work environment for automotive back offices. Let’s look at some features (and use cases) of RPA in the Automotive industry.

• Analyze real-time process monitoring and analytics Smart bots enable real-time reporting that allows Automotive Manufacturers to determine optimal inventory levels and calibrate current operations based on their customer demands. RPAs are not invasive and extract all the required information to help you with deeper analytics with specific data for each executed process.
  
  With RPA, you’ll be able to see the holistic picture of all your back-office operations and will therefore be able to make amends and modifications wherever required to streamline the processes!
• Supplier onboarding Automotive Manufacturers need to communicate with dozens of suppliers day in and day out. The majority of their operations will be handicapped without proper supplier onboarding. In today’s world, where seamless data sharing is the need of the hour, automotive companies are using RPA capabilities to transfer their information to suppliers and vice versa safely.
  
  For instance, a leading automotive component maker, switched to Robotic Process Automation to simplify their online collaboration with suppliers and customers across Europe. The company was able to simplify supplier onboarding using automation. It ended up saving them a lot of time that would otherwise be wasted in onboarding new partners. Essentially, RPA did this by making all the relevant data much less scattered, ensuring uninterrupted data flow, and providing an overall air of improved efficiency to the process.
• Freight management Freight management is one task that has always been riddled with manual deficiencies. As a result, this process can severely benefit from RPA solutions. Generally, employees would enter customer data into the Track Management Software, identify the best freight options and routes, send the info to the customer, and wait for confirmation before making any decision. On the surface, it might seem like carrier choice requires human decision-making. However, this is entirely a rule-based process that can be achieved by integrating RPAs into TMS to autonomously assess the information, generate the quote, and book shipment to speed up the operation.
  
  RPA helps in various other aspects of freight management as well, like recommending the best transport route and also managing freight-related payments or claims. The year 2020 saw significant progress on this front, with a major logistics provider announcing a 100% automated practice for matching manufacturing loads to freight trucks.
• Inventory management Streamlined inventory control is crucial for any supply chain management. Traditionally, Automotive Manufacturers would employ supervisors to manage inventories and ensure that the inventory levels match the demands. This is a manual, low-value-adding task, which can be riddled with human errors. With the ever-increasing data on customers and partners, Automotive Manufacturers can manage inventories much more seamlessly by integrating RPA.
  
  As an example of this, one of the leading Automotive Manufacturers required their demand planners to manually enter their inventory estimates in the ERP system. Then, the organization deployed a bot that would mine those entries for important data points and update the safety stock levels without needing any human intervention. To take this up further, other tasks of these demand planners can also be automated easily. While this won’t be possible with the simple rule-based approach, a simple Machine Learning model or algorithm could calculate safety stock levels with much higher accuracy and precision.