Build a comprehensive RPA Bot governance model to reduce operation hassles, improve bot performance and scale automation programs

Service Providers are now riding the automation wave. Painful manual tasks, which burdened staff for ages, can be easily handled by the software bots. However, in the process of onboarding the digital workforce, most service providers have missed establishing robust and unified governance. In a survey done by Forrester Consulting, 69% of the respondents said they face difficulty in managing rules that guide bot behavior and 61% responded that control & operations of RPA bots are immature.

The lack of unified governance of the digital workforce significantly impacts different users such as the RPA Center of Excellence (COE), Business Unit Owners, Production Support, and Operations Team. These users face challenges such as managing bot license and application credentials, orchestrating bots across platforms and analyzing real-time bot performance and its utilization. They also lack real-time alerts on process failures & forecasts, which often lead to missing the SLA for critical deliveries.

Service providers must establish an effective RPA bot governance model by focusing on key areas. A few of them are listed below:

• Integrated Visual Control Room- Provides a high level of collaboration & transparency while managing bots across processes and platforms. This helps to find the root cause of non-functioning bots
• Delivery Forecast & Inflow Alert Mechanism: Helps to visualize key metrics in real-time to meet the SLAs
• Automated Application Credential Management & Bot License Tracker: Prevents production outage by avoiding account lock and license expiry issues

Leverage automation strategies to streamline the Trouble to Resolve (T2R) process, providing customers with quick resolution and greater satisfaction

TM Forum, a global industry association for service providers and their suppliers in the telecommunications industry, has a business process framework -eTOM’s (Enhanced Telecom Operations Map) Trouble to Resolve (T2R) process. It reveals how to deal with a trouble (problem) reported by the customer, analyze it to identify the root cause of the problem, initiate resolution to meet customer satisfaction, monitor progress and close the trouble ticket.

Most Service Providers follow the eTOM T2R process, however, they encounter key challenges that affect the overall T2R operational efficiency and increase the OPEX.

• Multiple siloed systems to complete a network event’s lifecycle leads to high manual effort and increased OPEX
• Difficulty in identifying the right impact of a network event-
  • No proper tools for auto-identification & prioritization of critical events that would cause major business impact
  • Resource wastage: Network Operation Centre (NOC) tends to spend a significant amount of time handling huge volumes of
    alerts
• Difficulty in meeting business KPIs due to unavailability of fully integrated systems and automated processes

Service Providers in the connectedness industry must develop an effective strategy for integrating the systems and bringing end-to-end automation to the T2R process flow. The majority of service providers have a basic level of automation, however, there is a huge scope for complete lifecycle automation. This Insight showcases an effective approach for implementing end-to-end automation of network event lifecycle from event creation to resolution. The approach is based on the implementation experience of leading service providers at multi-geographic locations.

Leverage RPA based Automation Framework to accelerate data migration and improve accuracy

Data migration involves moving data between locations, formats, and applications. This need is on the rise due to ongoing trends such as mergers and acquisitions (M&As), migration of applications to the cloud, and modernization of legacy applications. However, the execution of data migration using traditional methods is not at par with the increasing frequency!

According to Gartner, 50% of the data migration initiatives will exceed their budget & timeline by 2022 because of flawed strategy & execution. Most of the service providers in the connectedness industry adopt the traditional approach for data migration that involves three broad steps: migration planning & preparation, establishing governance, and execution.

Service providers follow the fundamental extract, transform, load (ETL) data migration execution methodology, which is full of challenges. It entails high cost and time due to mock runs and testing for each module. Moreover, it involves manual efforts, which leads to a lot of re-work due to errors and causes fallouts due to data integrity issues. Also, ramping up and down the teams is difficult.

To overcome these challenges, an RPA based automation framework for data migration execution could be an effective approach. The framework encompasses components such as:

• Smart processor: Identifies data quality & integrity issues in the source data at a very early stage
• Automation bot: Performs migration/upgrade by extracting & updating data at various layers of the application
• Fallout management mechanism: Automates the fallout handling, i.e., Fix data quality & integrity issues in CRM, inventory systems, etc.

Proven methodologies to create a steady pipeline of processes to be automated

Service providers across the globe are at various stages in their journey to embrace robotic process automation (RPA) to increase their operational efficiency. But scaling RPA and making it an organization-wide success is a big challenge. As per Deloitte’s Global Robotics Report 2018, over 80% of organizations implementing RPA were happy with the results, but only 1% of them could scale considerably in the past 1 year (50+ bots in a year).

The inability to identify appropriate use cases after initial implementations are the major bottleneck for service providers. The lack of end-to-end visibility of the process by the siloed business units further adds to this plight.

Service providers must explore various methodologies to create a steady process pipeline that can be automated. These techniques are – comprehensive analysis, design thinking workshop, and process mining.

Figure 1: RPA Demand Generation Methodologies

Download the Insight to learn about the techniques in detail and learn how to choose the correct method as per your organization’s maturity stage.