How RPA Robots can help Automotive Industry to Continue the Process Started by Industrial Robots
The automotive industry has been pioneers in employing industrial robots and other productivity enhancing options. Without assembly line production that they adopted back in the 19th century, cars would have remained an unaffordable luxury to most. Next were industrial robots, “arms” moving horizontally, cylindrically, spherically or in flexible articulated ways or along gantries enabling numerous repetitive tasks to be performed precisely, quickly and without getting tired.
It is these industrial robots that helped the industry to reduce costs and deliver cars at affordable prices to customers.
Increasing competition is again putting pressure on the industry to explore ways to reduce costs further. It is in this context that RPA, which employs software robots, come into the picture.
RPA’s Software Robots
Unlike industrial robots, software robots lack visibility. They consist of code inside a computer or network and do back-office tasks behind a cloak of invisibility. They can do the tasks human workers do – like reading data from different sources and entering these into structured databases, checking inventory levels and initiating re-orders for parts getting exhausted and checking due dates for paying suppliers and preparing checks ready for signature and mailing.
What distinguishes RPA from traditional coding is that it does not involve disruptions to current operations. Developing IT solutions to meet new needs, on the other hand, can be a big task that causes significant disruption. RPA robots can be introduced at individual operator level, training them to do tasks that the operator is doing, and to do these in the same way as before (with the only difference that instead of a human it is machine code that will do the tasks).
RPA is a great solution for high volume, repetitive tasks that are executed following specific rules. Tell it what to do, and the rules for doing it, and it will do it again and again dependably, without getting tired and in precisely the same way. Just check initially that it is doing the work correctly.
Combine RPA with emerging technologies like AI, and the robots can even learn things on the job without human intervention.
RPA can deliver higher productivity, better quality and lower costs just as industrial robots did. It does these in areas neglected by the industrial robots, in back office tasks and in managing operations. For example, it can monitor demand supply position in real time and decide which parts have to be stocked to meet emerging demand conditions.
Let us look at the role RPA can play in a task critical in the automotive industry, managing inventory levels of manufacturing parts.
RPA and Inventory Management
The goal of inventory management is to ensure availability of needed materials of required quality at the right time, and achieve this goal at economic costs. It seeks to do it by:
- Developing specifications for the materials so that quality of items can be checked
- Locating dependable suppliers of materials of required quality at best prices
- Fixing minimum and re-order levels for each material so that these can be stocked in adequate quantities at all times
- Fixing economic order quantities for the materials so that procurement costs can be minimized
- Monitoring stock levels of each material constantly and initiating replenishment action in time to ensure that needed materials are always available
- Initiating procurement actions for materials that need to be replenished and monitoring receipt of materials in time to meet production needs
- Monitoring movement of materials in inventory to ensure that non-moving materials are not pushing up inventory costs
- Monitoring the performance of suppliers to identify those who deliver quality materials by agreed times
Where the number of items in inventory are numerous, the work involved in attending to all the above (and other incidental) tasks can become unmanageable if done manually. Traditional IT systems came to the rescue of inventory managers by providing them information that helped them keep an eye on things, such as items reaching re-order levels.
However, the traditional systems still required considerable amount of human work. Details of inventory transactions had to be entered into the system for it to generate reliable information. Much of the details could be in unstructured formats that need to be converted into formats the system can work with.
RPA robots can work with structured and unstructured data, and enter it properly into the system. It can interface with multiple systems as humans do, and say, initiate procurement action after noticing falling inventory levels.
It can even check that the emerging demand pattern justifies ordering a particular material in the specified volumes.
The big advantage of RPA is that it can be introduced at the micro level. You don’t need to patch up existing code and run the risk of unexpected consequences elsewhere. Instead, you train the robot to do what you are doing at your workstation and the work continues without interruption (or disruption elsewhere).
Over a period of time, the entire department or even the entire enterprise can get RPA robots to do much of the work being done by humans. And the humans can be re-deployed in more meaningful work, work requiring personal interactions and judgment that machines are not yet capable of.
August 14, 2018
March 17, 2018