It was Henry Ford who first introduced automation to the automotive industry in 1913 with his moving assembly line. He was quick to see the advantages and reduced the build time of a car from 12 hours to one and a half hours. However, it wasn’t until 1961 that General Motors started to use the first true robotic processes and sensor technology in the manufacture of cars.
These early introductions to the concepts of automation and robotics in the automotive industry paved the way for adopting further automation innovations. However, the extent of automation doesn’t stop at robotics, and there has been considerable take-up in automating back-office applications using RPA in the automotive industry.
In numerous departments, many of the time-consuming and mundane tasks that are required to make the business model function have been replaced with RPA in the automotive industry. Here it can speed up processes and complete them accurately and more cost-effectively as well.
1. Supplier onboarding
In terms of the supply chain, the automotive industry has always had its work cut out. With the proliferation of technology in cars it’s not unusual for a single model to require more than 30,000 parts. To complicate things further these parts are broken down into numerous different categories, such as mechanical, electrical, IT etc.
So, combined with the financial efficiencies and short ordering timeframe of ‘just-in-time’ (JIT) ordering, car manufacturers have their work cut out on supplies. Luckily, RPA has been found to be a particularly apposite solution in supply communications.
Any car manufacturer will need to have effective relationships with many suppliers to ensure the assembly lines keep rolling. The shortage of chips following Covid-19 demonstrated how the shortage of one element can quickly bring a manufacturer to its knees.
Setting up a new supplier on the manufacturing database is a time-consuming and complicated task when carried out manually. Numerous departments need to be involved and the exchange of information needs to be error-free.
RPA can assist particularly with the challenge where data is spread across different systems. RPA can be programmed to offer a reliable and cohesive framework, where data is quickly transferred between the participating parties.
2. Inventory management
The complexity of the products in car manufacturing means that inventory control is equally involved. A manual system is overseen by a supervisor who must repeatedly check and document stock levels, to ensure adequate stock is available to meet the demands of the production line.
Unfortunately, the manual requirement for frequent and tedious stock checks often leads to a high level of errors, which can impact production disastrously.
However, as the automotive industry increasingly adopts industrial IoT, the possibilities for automation of inventory control using RPA become increasingly advantageous. By integrating data about suppliers and customer demand, manufacturers are that much more informed about the present and future supply.
It’s not unusual for automotive companies to implement RPA for the task of demand planning. In a manual system, employees had to research and manually input their best estimates into the database. By programming RPA, the bots can now conduct their own research and enter the results automatically with no need for manual inputting.
This process is much faster, less error-prone, and much more cost-effective on the ROI of the RPA project.
3. Auto Insurance
The auto insurance market is fiercely competitive. The sales of auto insurance have been comprehensively overhauled in recent years so that now customers rarely have any human contact with the company.
One exception to this is the handling of claims processing following accidents. Historically, the complicated and subjective nature of most accidents meant that only human input could deal with the complex situations involved.
With the growth of automation in other business sectors, such as banking, customers increasingly require a better level of customer satisfaction, which is fast and accurate.
So, auto insurance companies have responded to this challenge by automating the claims handling process using RPA. With a manual system, a claims adjuster needs to coordinate data from numerous sources. These might include police reports, witness statements, insurance history, garage quotes, and many more. All of this relevant data needs to be transferred to the claim database to calculate the claim.
RPA can be programmed to collect data from each of these disparate sources and enter it into the claim database. With the time saved the claim adjuster has more time to liaise with the parties involved personally over information that can’t be gained automatically.
4. Vehicle financing
It’s clear from the well-lit and shiny interiors of car leasing companies and dealerships that they want to make a strong impression on the customer. Competition in vehicle financing is extremely competitive and any method of gaining a competitive advantage is valued.
As a result of the competition the physical offerings between different companies are very similar. This has led business owners to differentiate only with the factors that are left open to them to change, namely customer service and the efficiency of service.
Often RPA is viewed as a tool more associated with back-office tasks, but it can also be very useful when applied to customer-facing tasks such as vehicle financing. When sales consultants need to create quotes or complete a finance deal, they need to source data from various databases, which they collate manually.
RPA can automatically carry out the same tasks but in a fraction of the time and with far fewer errors. The process of customer verification and financial standing becomes more streamlined, while invoicing and warranties all take place much faster.
5. Payment processing
Keeping track of paying suppliers’ invoices is vital for any organization. The data involved becomes an important part of cash flow, which provides constant feedback on the state of the company. For most automotive companies there is still an element of manually input data, which is exposed to human error.
In payment processing, RPA is often coupled with AI. The AI capability will scan, digitize, and validate the key information from purchase orders so that it can generate an invoice. The process is much faster than any manual system and it helps to avoid paying financial penalties and the possibility of incomplete payments.
6. Customer Service
By their nature, customer service skills have a strong human element to them, however, that doesn’t necessarily mean they have to be exclusively dealt with personally.
In fact, using RPA to automate elements of the customer service function improves the overall level of service by removing routine and mundane tasks so that customer service staff have more time to devote to customers’ more complicated inquiries.
This freeing up of customer service staff can lead to a direct improvement of First Call Resolution (FCR) rates and Customer Satisfaction (CSAT) scores.
7. Order submission
The channels through which car dealerships sell cars grow bigger each year. Garage forecourts used to be the main destination for buyers however with the advent of online dealerships and third-party sellers through high street banks such as Santander and Cahoot, the marketplace is much more complicated.
With such a variety of sales channels, the complexity of keeping track of orders has similarly become much more involved.
RPA is ideally placed to assist with this complicated order management. Often RPA and OCR are combined to enable the reading and transfer of data from orders, written in a variety of formats, which is subsequently inputted accurately into the company’s order database.
Such an order management system is faster and more accurate than any manually inputted data. Sales staff have more time to engage with customers, who are much more likely to purchase a car with an attentive level of customer satisfaction.
8. Freight management
For the automotive industry, a good transport management system (TMS) is a vital part of the business process. It is a logistics platform that enables businesses to plan, carry out and optimize the movement of incoming goods and finished products. It’s a requirement that each shipment is compliant and has the associated documentation with it.
Traditionally, staff would first need to enter complex data into the TMS manually to establish the shipment. This would be followed by deciding the optimum transport choice, which is then forwarded to the customer for acknowledgment. These tasks are essentially rule-based and as such RPA can be programmed to work through the process autonomously.
RPA initially assesses the available information, researches the ideal freight route, and even books the entire shipment with the freight forwarder. The whole process is conducted faster and the accuracy without human error is much improved.
Daniel Cooper
