Category: Operational Excellence

Categories
Operational Excellence

Recipe for managing the digital workforce effectively

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

Governance of the Digital Workforce is becoming a consistent challenge while adopting Robotics and Cognitive Automation. A Forrester Consulting report shows that 70% of service providers struggle with BOT performance and scalability issues.

Categories
Operational Excellence

Creating a smart field workforce with an AI-powered video guide

Leverage video AI to improve field engineers’ efficiency, reduce site visits, and accelerate install to commission cycle time by 3X

Inefficiencies in field services contribute the most to the capital expenditure of service providers. One of the major reasons for field service inefficiency is repeat site visits or rework, leading to a 5X increase in repair cost and delay in order delivery time.

In the case of field surveys, data shows that 40-60% of installation orders require a site survey, out of which 18% require repeat surveys. The sites survey is done manually, requiring manual data capture and physical audits leading to errors and incomplete data. Hence, the process becomes extremely time-consuming.

To overcome these challenges, service providers must leverage the power of video intelligence. Prodapt’s AI-driven video intelligence framework powered by Vyntelligence can create a smart field work force. Surveyor captures a video and voices it over, using a guided storyboard. The framework auto-captures the details and sends alerts for missing details. A survey is submitted with 100% details and can be a point of reference for specific details or future changes. This leads to 3X acceleration in installation time and improved customer experience.


Enable field engineers with AI-powered devices to improve ‘right-first-time’ field work and enhance customer experience through reduced
‘time-to-resolve’

The three main components of this framework are –

  • AI-assisted video guide – Provides a structured guided storyboard for field engineers to effortlessly capture the data
  • Recommendation engine – Enables guided actions to various business stakeholders. Gives AI-powered recommendations and real-time visibility into the jobs to supervisors, auditors, and field engineers
  • Smart dashboards – Provides end-to-end visibility into jobs driving smarter actions for management and business as a whole
Categories
Operational Excellence

Combining the power of RPA and AI to keep customer experience unharmed during network outages

Leverage RPA and AI to build and implement a proactive two-way Conversational Framework to reduce OpEx, boost agent productivity and improve NPS

According to recent statistics, 30% of the service providers’ contact center calls are network outage related. Their inability to predict these outages on time and provide prior information to the customers results in contact center call spikes, customer dissatisfaction and a low NPS score. This also increases the OpEx for contact centers and may lead to a reputational loss for service providers.

To overcome these challenges and improve NPS, service providers must create a central Intelligent platform capable of orchestrating seamless conversation between the contact centers and customers. This is established by implementing a “Two-way conversational Framework”. The steps involved are:

  • Step 1: Auto-identification of outage information
    Build a standardized process to identify relevant outages in the network monitoring systems. Integrate them with an outage monitoring dashboard for BOT to auto-extract outages and store them in a central database.
  • Step 2: Schedule notification
    Perform automated validation and intelligent scheduling to send proactive notifications to the impacted customers in a well-organized structure.
  • Step 3: Notify and engage with customers using a Conversational AI BOT
    Send proactive notifications, and if the customer has additional queries, the bot can engage in a conversation using the conversational AI


Conversational AI Bot orchestrates bi-directional communication and provides seamless customer experience during common network outages.

Categories
Operational Excellence

Improving the efficiency of your Field Service Workforce

Leverage machine learning to eliminate blind dispatches and improve the first-time fix rate (FTFR)

Field Technicians are the face of your service organization, and it is imperative to equip them with the right tools and knowledge to handle any field challenges. With efficient management and empowerment of technicians, your organization can deliver fast, effective, and efficient services to customers.

A business should strike a balance between the speed and accuracy of on-site customer requests to increase the productivity of technicians and improve customer satisfaction. But, in reality, technicians are frequently not able to deal with customer problems on time and are forced to make multiple trips to the client location due to process inefficiencies. Thus, instead of servicing new customers or optimizing current customer relationships, technicians invest valuable time and resources in non-revenue-generating activities.

Today, 70% of field technicians visit sites without prior information about the nature of the problem, issue location and solution recommendation. It leads to repeated dispatches, longer resolution time and high customer churn.

Going digital is the cornerstone of success for a modern services organization. Adopt the ‘AI-Powered Field Service Framework’ to optimize field services and increase technician productivity. The framework encompasses three vital components to achieve a higher First Time Fix Rate (FTFR) and reduce Mean Time to Resolve (MTTR):

  • Fault Location Classifier– Predicts the fault location and sends email/SMS notification via mobile app to technicians
  • Recommendation Engine– Suggests guided actions and next best resolution steps to improve technicians’ efficiency
  • Technician Dashboard– Provides a one-stop view of all dispatches and actionable insights to technicians


70% of field technicians visit the sites without prior information about the problem leading to repeated dispatches, longer resolution time and high customer churn.

Categories
Operational Excellence

Turn your network issues into customer delight

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.

“According to a report by McKinsey, many service providers have complex fundamental processes with multiple system integrations and are labor-intensive and costly. Leveraging digital technologies to simplify and automate operations makes them more productive and results in a significant cost reduction of up to 33%.”

Categories
Operational Excellence

Steering data migration, powered by RPA

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.

” According to Gartner, 50% of the data migration initiatives will exceed their budget & timeline by 2022 because of flawed strategy & execution.”

Categories
Operational Excellence

Go Beyond RPA to Speed Up Transaction Processing Time

Leverage effective continuous improvement techniques to achieve a high straight-through processing rate

Straight-through processing (STP) refers to the automated processing of transactions without manual intervention. Transaction processes are usually multi-staged, requiring multiple people across different departments and sometimes even involving paper checks. Companies often adopt RPA as a one-time solution to complete transactions and achieve a high STP rate. But is it really effective?

The estimated STP rate for any service provider in the connectedness industry is 75%-85%. However, the actual realization is only 30%-50%. One of the reasons that has contributed to the average rate is implementation of only RPA by service providers. Other widely used continuous improvement techniques like occasional continuous improvement and analytics-driven continuous improvement have proven to be less effective to achieve the targeted STP rate. Service providers must adopt effective continuous improvement methods to get more value from their existing RPA implementation.

Adopt the Automation Optimizer Framework, an efficient continuous improvement strategy to improve your STP rate. The framework identifies automation inefficiencies, root causes, and solutions for the identified gaps and continuously monitors the STP rates- all in an automated manner. Its key components are:

  • Intelligent RCA (Root-cause analysis) Engine: Drills down to transaction-level information to automatically identify the root-cause for fallout
  • Integrated Solutionizer: Constantly analyzes the output from an Intelligent RCA Engine and triggers respective action based on the identified root cause
  • Continuous Monitoring Tool: Tracks the STP rate progress over time for the defined objectives, KPIs and milestones


The estimated STP rate for any service provider is 75%-85%, however, the actual realization is only 30%-50%. Only RPA implementation will not suffice if the STP rate has to be improved.

Categories
Operational Excellence

Giving wings to your standard RPA bots

Combine the power of RPA with NLP to improve the automation potential of service provisioning

Most service providers in the Connectedness industry have started leveraging Robotic Process Automation (RPA) to automate various processes, especially in service provisioning. However, the standard RPA bot alone cannot automate the end-to-end provisioning process, as it involves a lot of unstructured data that requires manual intervention for processing. According to Gartner, “Today, 80% of enterprise data is unstructured”. Processing such a huge amount of unstructured data and performing end-to-end automation with a standard RPA is a major challenge for service providers.

To overcome this challenge, service providers can combine the power of RPA bot with a Natural Language Processing (NLP)-based engine capable of extracting information and processing the unstructured text. It further helps in deriving insights and providing the next best action, all in an automated way. This end-to-end automation helps the service providers to reduce the cycle time and provide efficient services to their customers.


According to Gartner, “Today, 80% of enterprise data is unstructured”. Standard RPA alone cannot process such a huge amount of unstructured data and perform end-to-end automation.

    Authors:
  • Madhusudhanan S
  • Velmurugan M
  • Gurunath L V
  • Mogan A.B.

Categories
Operational Excellence

Breaking down the barriers to scale RPA across the enterprise

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.


As per Deloitte, 80% of organizations implementing RPA were happy with the results, but only 1% of them were able to scale considerably in the next 1 year

Categories
Operational Excellence

Fiber is fast, but rollout needs to keep up

AI/ML can forecast delays before they occur, making the service delivery predictable and fast

The global pandemic has highlighted the fact that high-speed broadband is a necessity, not a luxury. And fiber is one of the ways to faster broadband. This appetite for fiber means that service providers need to roll out fiber-based connectivity services faster. However, with the rising complexities in the order management process, delivering the service within the specified timeline is becoming a nightmare. The main business issue is unpredictability, which may be as important as speed. Its absence means frustration for service providers and their customers.

The main cause of this lack of predictability stems from the structure of the process. In many cases, the enterprise service delivery process has evolved and grown organically. The most common causes of dysfunction are:

  • Multiple teams operating in silos prevent a clear view of the process and a single source of truth
  • Manual hand-offs leading to errors and delays
  • Dependency on external vendors, resulting in vendors operational issues being transferred to the service provider
  • Lack of strategies to forecast order delays
  • Lack of mechanisms for real-time tracking of service delivery flow

To overcome these challenges and tap into the next wave of opportunities, service delivery operations will require an advanced vision. AI/ML is at the heart of that vision. With AI/ML in service delivery, enterprises can predict and address delays before they impact the business. Enterprise AI can, over time, improve the prediction of potential delays and delivery dates at all points of the order journey. Over time, enterprises can achieve faster processing of orders with improved predictions.

null


The appetite for high-speed broadband demands a faster rollout of fiber-based connectivity services.