Automation has become synonymous with the automotive industry. With industrial robotics in place, welding, wheel mounting, windshield installation, painting and other assembly-line tasks can be entirely automated. With the mass adoption of these automation tools, vehicle manufacturers are now able to produce thousands of cars per day while cutting operational costs, increasing reliability, and freeing up workers from performing labor-intensive tasks.
However, the scope of operations that automotive companies need to perform goes way beyond the assembly line. Nowadays, organizations need to manage a myriad of other operations including dealer network management, payment processing, inventory management, insurance claims processing, and more. This is where robotic process automation (RPA) comes into play.
Briefly on RPA
RPA is a way of automating business processes through bots performing tasks based on preprogrammed sets of rules. With optical character recognition, keystrokes, and application integration, RPA can perform a wide range of previously manual tasks.
RPA continues to prove its worth in many areas of the automotive industry. Many time-consuming and repetitive tasks that are vulnerable to human errors can be performed by RPA tools. Their most praised benefits include the ease of implementation and quick ROI.
Even though the capabilities of rule-based RPA are not endless, companies can significantly extend their scope by augmenting RPA with machine learning to tap into data-driven decision-making. Intelligent process automation (IPA), already implemented by leading RPA platforms such as UiPath and Automation Anywhere, is the next logical step for companies looking to transform into a digital enterprise:
With economic pressures caused by the COVID-19 pandemic, the opportunity to enhance process efficiency and reduce costs has made RPA consulting services even more demanded. Let’s explore the most potent use cases of RPA in the automotive industry and discuss potential adoption strategies.
Streamlined inventory control is at the core of efficient supply chain management. Traditionally, automotive manufacturers employ supervisors who ensure that their inventory levels match the demand. By its very nature, this is a manual, low-value-adding task, which is also highly susceptible to human errors. With the increasing adoption of industrial IoT and abundant data on partners and customers at hand, automotive manufacturers can maintain their sufficient stock balance by integrating RPA.
For example, one Fortune 500 automotive company required demand planners to manually enter their estimates in the ERP system. The organization decided to deploy a bot, developed by Birlasoft, that now mines for important data points in demand planners’ emails and updates the safety stock levels without any human intervention.
To go even further, such demand planners’ other tasks could also be automated. While this wouldn’t be possible with the rule-based approach, a simple ML algorithm could accurately calculate safety stock levels.
In the auto insurance industry, the claim processing speed and accuracy are the core factors affecting customer satisfaction. With ever-rising customer expectations, streamlined claims processing is paramount to the overall business success.
However, processing claims manually is a very labor-intensive and error-prone task. Traditionally, claims adjusters need to gather relevant data from disparate sources, analyze it, and transfer it into the policy holder’s digital record. In this case, RPA can additionally bring document management benefits, as reformatting and transferring data require repetitive actions.
Nowadays, thanks to the all-permeating digitization, filing an insurance claim immediately after an incident has become a standard practice. In many such cases, car towing is required. Given that the majority of auto insurance companies pay for roadside assistance, the processing speed becomes critical. However, by the time a human worker manually processes data, transfers this data to a contracting auto mechanic company, and the tow truck reaches the accident point, the policy holder’s satisfaction will inevitably drop below any acceptable level. Fortunately, RPA in insurance can cover the entirety of this process while enhancing the customer’s experience, saving working hours, and ensuring correct data entry.
Similarly, RPA can speed up underwriting. For example, auto insurers often need to browse public databases to check claimants’ criminal records. RPA can autonomously access these records and transfer data to the company’s internal system.
However, despite the auto insurance industry is seemingly perfect for RPA, it’s critical to note that a bot will be stuck if there are even minor deviations from the preprogrammed process. For example, if some data from public records is moved to a different page, RPA wouldn’t be able to complete the task. This is why establishing solid governance practices is critical to a long-term RPA success. On the other hand, if an RPA solution needs to be repeatedly reconfigured due to changes in the external system layout, augmenting RPA with machine learning might be a better solution.
As with robotic process automation for the banking industry, automation can be beneficial for car financing as well. Currently, the majority of auto lenders use complex legacy systems that often require cumbersome manual operations. Just like automobile manufacturing is now unimaginable without assembly-line automation, we can expect a similar shift in auto lending industry as well.
As auto lending offerings are often very similar to each other, it’s a company’s quality of service and speed of operations that help gain a competitive advantage. While RPA is often viewed as a tool that automates repetitive back-office processes, it can also by extension enhance customer and dealer experiences.
Auto lenders often need to simultaneously access multiple siloed systems and databases to complete certain operations. While the most logical workaround is to merge these core systems into one, it rarely provides an acceptable ROI as the integration is lengthy and typically requires substantial investments.
RPA can be used to automatically consolidate information stored in disparate systems into one interface, which can speed up analysts’ decision-making and, consequentially, improve customer satisfaction. Most importantly, RPA is comparatively fast and easy to implement as modern solutions require far less coding than traditional automation initiatives. As a payoff, auto lenders can mitigate the risks of human errors and leave employees with more time to engage in value-adding and mentally-demanding activities.
Here are some examples of RPA in the auto lending industry:
- Data verification and validation. Auto lenders can automatically verify the accuracy and relevancy of customer information in borrower forms, service contracts, and warranties.
- Loan and default servicing. Lenders can automate various loan administration operations, including vehicle title maintenance, customer letter processing, and complaint analysis, as well as invoice and repossession processing.
- Financial analysis. Financial data collection and formatting from different systems can be automated for further analysis by a human.
The majority of automotive companies have to communicate with dozens of suppliers to operate. With seamless data sharing being a pinnacle of efficiency, automotive companies now explore RPA capabilities to securely transfer their corporate information.
For example, a leading automotive component maker, Yazaki, turned to RPA to streamline digital collaboration with its customers and suppliers in the Americas and Europe. In their case, RPA helps to streamline supplier onboarding through automated provisioning, which significantly reduces the time it takes to onboard new partners. Essentially, RPA helps solve the challenge of data being scattered across different systems, ensuring uninterrupted data flow between the business ecosystem participants.
Along AI tools for transportation, RPA can significantly enhance freight management, conventionally plagued with manual deficiencies. Traditionally, employees would need to enter customer data into the TMS, identify the best possible freight options and transport routes, send this information to the customer and wait for confirmation. While it seems like carrier choice requires human decision-making, it is, in fact, an entirely rule-based process. By integrating RPA into the TMS, the system can autonomously assess the information, generate the quote, and book shipment, which significantly speeds up the operation.
For example, one global auto leader has implemented an RPA solution to deal with demand spikes and facilitate the highly complex and cumbersome process of preparing Shipper’s Letter of Instruction (SLI) for overseas shipments. With their RPA solution, the company managed to automate this process in its entirety, save 2-4 person-hours per day, and significantly decrease SLI preparation cycle times.
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RPA implementation: things to consider
Given the industry’s heavy reliance on manual processes, there is no shortage of potential use cases. However, not everything that can be automated should be. For automotive companies to squeeze the maximum value from RPA, it’s critical to establish a solid framework for identifying opportunities with the highest ROIs.
Here are the main attributes of an RPA-ready business process:
- It’s repetitive. First, look for repetitive processes that don’t require any cognitive input. Highly mundane and repeatable tasks should be the top priority for RPA integration.
- It’s stable. Even tiny changes in the system’s structure will inevitably require RPA reconfiguration. Ask these important questions: “Is the process environment susceptible to unexpected failures?” and “Is the system expected to change in the future?”
- It’s rule-based. Unlike AI, RPA can only make decisions based on pre-defined rules. The technology will always require a human assistant if decision-making flexibility is necessary and the probability of exceptions is high.
Contrary to the implementation frameworks of automotive AI, aiming for quick wins tends to work best in the RPA context. It’s recommended to methodically assess the level of effort, execution complexity and ROI of identified RPA opportunities. Ideally, investments into a large-scale RPA program should be compensated by early wins as a result of the implementation.
Again, choosing the right use case is paramount to successful RPA implementation. Besides ROI calculation and KPI estimates, it’s critical to consider the benefits of robotic process automation that can't be measured. Employee morale boosted by manual task automation can be a priority just like higher productivity, for example.
Volkswagen, in fact, has started their RPA program with internal motivation being one of the main reasons for implementation:
Morale boosting was one key thing for RPA. We always promoted the digital workforce as an assistant to employees, motivating them to give up monotonous work.
Next, despite the comparatively straightforward development and deployment, RPA solutions require comprehensive governance and regular monitoring. To realize the full potential of this technology and avoid risks, cybersecurity, regulatory compliance, and privacy protection should be considered. This is why it’s suggested to involve compliance professionals from the very inception of the program.
In a nutshell, solid internal standards should be established prior to RPA adoption. Considering past experiences of companies tapping into RPA implementation, establishing dedicated in-house centers of excellence has proven to be a reliable way of ensuring long-term project success.