Preliminaries
The current wave of the industry 4.0 revolution, along with advanced technologies like as artificial intelligence, cloud computing, and machine learning, is increasing demand in process automation industry, which produced $984.4 million in 2020. The global Robotic Process Automation (RAP) market is expected to develop at an outstanding CAGR of 31.1% from 2021 to 2026, owing to the prevalent digital migration and automation trend in every end-user industrial vertical. The obvious impacts of RPA on the automotive industry can be seen through the complete elimination of manual errors and the time spent on reworking. 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. 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. Companies can significantly extend their scope by augmenting RPA with machine learning to tap into data-driven decision-making. Intelligent process automation (IPA) is the next logical step for companies looking to transform into a digital enterprise:
How RPA Works
Contextual to UIPath RPA, it deploys “robots” or software agents that mimic how users interact with computers. These robots are programmed or configured to manipulate a user interface (UI) instead of humans. New generation RPA platforms include artificial intelligence (AI) and machine learning (ML) capabilities to handle high-volume, repeatable tasks and transactions. An RPA robot can log into an application, enter data, perform calculations, complete tasks and then log out.
UiPath has of 3 main components:
- UiPath Studio: UI tool to visually design the process to automate.
- UiPath Orchestrator: Web application that manages the creation, monitoring, and deployment of all robots and processes.
- UiPath Robot: Runs processes that were built in UiPath Studio. Execution agent that is installed and executed in the actual machine.
In short, this is how it works:
- Visually design a process using UiPath Studio in a developer PC,
- Publish/deploy the process to UiPath Orchestrator to be managed, scheduled, and monitored.
- The actual process itself is executed by the UiPath Robots installed in the respective machines.
Robotic process automation tools offer two distinct modes of deployment: assisted/ attended automation and unassisted/ unattended automation. Hybrid RPA combines the two models.
- Assisted / Attended RPA. In assisted automation, the RPA automates applications running on a user's desktop typically for the purpose of helping the user complete an involved process in less time. This usually generates cost savings and helps deliver a better user and customer experience.
- Unassisted / Unattended RPA. Unassisted automation requires no human agent. The RPA software performs on its own, notifying the user only when something goes wrong. Unassisted automation can work 24/7 -- "an ideal scenario for optimizing a process,"
- Hybrid RPA. In hybrid RPA, the employee and bot essentially work as a team, passing tasks back and forth. Hybrid RPA automates the work that can be completed solely by the bot (unassisted) as well as work that that involves unstructured data or requires decisions by an employee (assisted). In hybrid RPA, the software bots and employee can work on different tasks at the same time for optimal efficiency.
RPA bots can use the operating system applications like a human user. Bots are capable of these but please note that this is not a comprehensive list. RPA is too flexible for us to provide a full list of bot actions:
- Launching and using various applications including
- Opening emails and attachments
- Logging into applications
- Moving files and folders
- Integrating with enterprise tools by
- Connecting to system APIs
- Reading and writing to databases
- Augmenting your data by
- Scraping data from the web including social media
- Data processing
- Following logical rules such as “if/then” rules
- Making calculations
- Extracting data from documents
- Inputting data to forms
- Extracting and reformatting data into reports or dashboards
- Merging data from multiple sources
- Copying and pasting data
Given below is comparison of top 4 RPA Tools:
Automotive Industry Use Cases
Inventory Management
In the Industry 4.0 era, manufacturers should be able to anticipate demand flux and scale up/down their inventory accordingly. Such predictive capabilities will make supply chains more agile and ready to monetize business opportunities with jeopardizing the margin. Automotive manufacturers and suppliers supervise their inventory levels to ensure they have enough and right products in stock to meet demands. Currently, this is a manual, low-value-adding task and highly susceptible to human errors. With the increasing adoption of industrial IoT/ ML and abundant data on partners and customers, these manufacturers can maintain their sufficient stock balance by integrating RPA. RPA is valuable in this case because the software bots can monitor inventory, generate notifications when levels are low, and reorder products when levels go low below a threshold. Real-time reporting provided by RPA determines optimal inventory levels based on previous needs and modify levels based on patterns in demand. Intelligent RPA can even go as far as to recommend the right decisions for supply chain managers, given the data set at hand.
Auto Insurance
In the auto insurance industry, the claim processing speed and accuracy are the core factors affecting customer satisfaction. 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 brings document management benefits, as reformatting and transferring data require repetitive actions. In case of accident, auto insurance companies tow the cars, 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. RPA 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. 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.
Deploying RPA in its Highways Operations Centre speeds up the claims process where vehicle owners claim for damage caused by a highway defect. Hampshire County Council predicts it can save up to 200 workdays every year.
Vehicle Financing
Majority of auto lenders use complex legacy systems that often require cumbersome manual operations. As auto lending offerings are often similar to each other, it’s a company’s quality of service and speed of operations that gives a competitive advantage. 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. Some examples of RPA in the auto lending industry are:
- Data verification and validation - automatically verifying the accuracy and relevancy of customer information in borrower forms, service contracts, and warranties.
- Loan and default servicing - Automating loan administration operations, including vehicle title maintenance, customer letter processing, and complaint analysis, as well as invoice and repossession processing.
- Financial analysis. Automating financial data collection and formatting it from different systems for further analysis.
Supplier Onboarding
Most 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. Yazaki, a leading automotive component maker, turned to RPA to streamline digital collaboration with its customers and suppliers in the Americas and Europe. RPA helps to break down the silos that plague supplier onboarding processes. It powers automated Electronic Data Exchange (EDI) to share data seamlessly and securely across various stakeholders. Instead of processing complex paper documents, RPA quickly scans supplier data to populate a manufacturer’s ERP. Through automated provisioning, Yazaki has reduced the time required to add new trading partners, facilitating uninterrupted data flows across its 120 locations and 100s of customers/ suppliers.
Freight Management
Freight management, plagued with manual deficiencies, can immensely benefit from RPA. Traditionally, employees would need to enter customer data into transport management system (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 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. One global automotive leader has implemented RPA solution to deal with 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 decreased SLI preparation cycle times. Some examples of RPA in freight management are:
- Scheduling and tracking - automating a range of tasks from the initial pick-up request through to ongoing shipment tracking and a notice of final delivery. Robots can also automatically extract shipment information from incoming emails and improve on the accuracy of delivery estimates.
- Processing inventory, orders and loads - automatically picking up PRO numbers and tracking shipments and associated invoices. RPA can work across systems, easily integrating with third-party suppliers and carriers.
- Invoicing and accounting - Inbound invoices often require manual re-keying and processing. RPA integrates with bill payment systems, including the customer’s and can automate order-to-revenue processes.
Ryder, logistics service provider, uses RPA to optimize transport planning via their legacy transport planning system, also uses RPA to link up to third parties, with RPA looking up carrier websites to help schedule appointments. Toyota Motor North America uses RPA to simplify day to day operations including sourcing and compiling key information used by facilities and transport planners.
Supply Chain Communication
Effective communication between manufacturers, suppliers, transportation agencies and customers is crucial for an efficient supply chain process. Businesses uses RPA bots to automate the complete communication process. With RPA bots, email replies can be automated such as order has been requested, dispatched, struck midway, delayed, or received. These bots ensure all the parties concerned within a supply chain process get real-time notifications. Besides, Intelligent bots can read received emails, recognize the context of the content, and perform certain actions.
Conclusion
The prospect of AI was indeed attractive, and that it could eliminate several inefficiencies in the industry – starting from planning and design, right up to sales and even maintenance. It would certainly revolutionize everything, and it had the capacity to do that. All kinds of robots are now being used to perform repetitive tasks in automotive industries and its multiple departments for processes such as digitization, order processing, inventory management, regulatory compliance, process monitoring, and reporting are now executed much faster and more accurately. The consensus among auto makers seems to be that RPA technology is now proven and that it provides visible benefits to the companies that implement it. Software bots have brought a host of features for businesses of all sizes in the automotive industry. Processes that have typically been recognized as time-consuming, extensively laborious, and error-prone are adopted by RPA robots, helping decision-making teams pay more attention to the core business factors.
