Agencies Lean Into Automation During the COVID-19 Pandemic

How New Jersey Uses Automation To Help With The COVID Pandemic

The state swiftly undertook a number of RPA-powered initiatives to shift 10,000 employees that had previously shown up to offices around the state to remote workers, rapidly built high speed secure platforms for virtual court sessions, and put solutions in place to empower the public to interact easily and efficiently with state Judiciary entities.

For many, it comes as little surprise that government agencies have disparate systems that do not easily connect and work with each other. When the pandemic hit, the need to rapidly connect disparate systems became of increasing importance driving many agencies to implement automation. For the State of NJ Court System this accelerated the automation project, creating a sense of urgency and bringing all stakeholders to the table to accomplish the mission in 3 weeks. Thus, Judiciary’s response to pandemic accelerated the implementation timeline to realize immediate value.

Examples of automation in practice

The first automation implementation, Complaints Payment for Municipal (CPM), stood out as the ideal candidate with the maximum value proposition. The business process being automated empowers the public to pay online various Municipal fees associated with complaints. However, to automate this process requires that these disparate Municipal systems interface and interact with one another. The State was able to achieve this integration in just days from concept to implementation leveraging RPA solutions. Building an Application Programming Interface (API) driven interface would have meant months of analysis, development, implementation effort, and then statewide rollout challenges and staff training. The State just didn’t have that kind of time to devote to a project that needed results within days.

A high value use case in OMAS Finance Unit glaringly stood out as a viable candidate for automation. OMAS Finance unit processes payments for Title IV-D service staff statewide. All 21 counties manually capture the details of payments & reimbursements for Title IV-D staff, in 2 excel files each. A total of 42 excel files are emailed every month to the OMAS Finance Unit to be validated and manually entered into NJCFS, a Non-Judiciary legacy payment processing system managed by NJ Treasury. An estimated 35 man hours is expended each month to complete this process manually.

Title IV-D automation was modeled after the first CPM automation. It was created to automate the end to end process. It consists of Dispatcher and Performer, executed by robots in attended mode on demand. Dispatcher reads and validates data from all excel files, loads them in the UiPath Orchestrator queue for further processing. Performer, the second part of the two-part process, is handled by the same attended robot. Performer logs into the legacy NFCFS system, picks one queue item at a time and enters 20+ data elements by navigating through various UI screens. On successful entry, the bot reads the unique record identifier (UID) and updates Judiciary’s Access system with appropriate details for audit and reconciliation purposes. This process runs in a loop through each record in the queue. If there is a system error, the bot will have the intelligence to retry or gracefully exit.

Challenges to RPA adoption

These automation efforts have saved countless man hours, reduced errors, and increased efficiency. However, there were some challenges to getting these RPA bots rolled out and adopted. One notable challenge was procurement. It took approximately 6 months, with multiple rounds of negotiation for an agreement to be finalized. Additionally, a technical challenge was automating flows within the mainframe emulator creating a couple of hurdles to overcome. Another notable roadblock was with development of mainframe automation. BlueZone, the State’s mainframe emulator, needed unique configuration at windows service level. Another challenge was optimizing data entry time in mainframe screens. Initial design used “Type Into” activity, with a normal human speed which took about two minutes to complete one record entry. This was soon re-designed to a copy paste activity at faster-than-human speed, which reduced the time to thirty seconds per record entry.

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