Robotic process automation is based on the use of software robots, or bots, aided by artificial intelligence. RPA is often used to automate repetitive manual processes.
In traditional workflow automation, a software developer codes a list of actions to automate and interface with the back-end system through application programming interfaces (APIs) or dedicated scripting language. RPA systems, by contrast, create the action list by watching the user perform tasks in the application's graphical user interface (GUI), and automate by repeating those tasks directly in the GUI.
An enterprise business process can be operated by numerous bots. A simple order fulfillment process, for example, might use multiple bots to automate various order sub-processes. However, as the number of deployed bots increases, it becomes a major challenge to manage and synchronize them with the central controller.
Robotic process automation management (RPAM) can play an especially valuable role in the third-party logistics (3PL) industry. It can lower providers’ total cost of ownership and elevate operating margins by reducing operating and maintenance costs.
Every 3PL service entails a series of discrete processes to accomplish fulfillment. Responding to a new loading and pricing request for shipment, for example, involves opening an e-mail attachment containing details of the load and shipment date requirements, among other information. The process also requires identifying available carriers in the area, carrier load pricing, shipment schedule and total cost. All of this takes a minimum of about five minutes for a human to complete. A robot can do it in less than a minute.
Another example is delivery of product ordered from an e-commerce site. The manual process would be to retrieve the order information; locate the product in the warehouse; pack, label and move it to the shipping area; create a shipping manifest, and place the package in an outbound area for shipment. This entire process can be automated with RPA.
In transportation and logistics, RPA can aid in the tracking of goods, reducing the number of customer calls seeking delivery status. RPA enables the re-routing of freight to the best fulfillment center, with automated reporting and feedback to drivers, and efficient inventory management across the supply chain. Use cases of RPA by 3PLs include shipment scheduling and tracking, invoice processing, inventory processing, and effective communication.
Less known is the ability of RPAM to serve as a means of lowering 3PLs’ total cost of ownership. The industry has yet to embrace the technology as an intermediary between bot orchestration and deployment environments, to manage bot operations and prevent failures. An effective RPAM strategy is a crucial lever for bot lifecycle management and cost improvement. On average, maintaining and servicing eight to 16 bots requires one full-time equivalent (FTE) employee annually, according to Reza Asgari, chief executive officer of ChoiceWorx. This equates to between 130 and 260 hours of annual manual support per bot.
Proper management of bots is critical to avoiding operational failures caused by any number of factors, including changes in software, infrastructure and cloud or virtual environments. The greater the number of operational bots, the larger the risk of failure, leading to service issues such as increased operational costs and poor customer satisfaction. RPAM ensures that bots function as expected, while continuously monitoring the environment.
Because bots operate with multiple dependencies (applications, devices, networks, infrastructure and the like), repairing a bot requires skills in all these domains. Companies often use multiple bot vendors, which further increases support costs due to bot diversity.
Intelligent and automated bot support is the only solution to reducing the total lifecycle cost of RPA ownership. An RPAM environment effectively manages the complexity of large-scale bot deployments by monitoring, measuring and maintaining bots, while proactively remediating failures. RPAM environments should also be designed with a controller to act as an intermediary between the RPA software orchestrator in the cloud and the operational bots. In this capacity, RPAM can observe the interdependencies between the orchestrator and the bots, and head off any impending failures. Equipped with proactive reporting and appropriate alerts, the system engages high-value human resources only when needed.
Intelligent and automated RPAM is a win for all: the customer, employees, and the entire organization.
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