Category: Operational Excellence

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Operational Excellence

Data-driven Process Optimization to Accelerate Digital Transformation

With most Digital Service Providers (DSPs) investing in digital initiatives today, transformation is no longer a choice but a must-have!

According to a Celonis study, “Most organizations are struggling with transformation initiatives because they are diving into executions without understanding what to change first. In a rush to innovate, 82% admit that they do not review their internal business processes while setting initial goals for a transformation program”.

Also, DSPs experience increasing process inefficiencies due to the ever-changing landscape. Traditional approaches to handling these processes are more focused on process discovery. They do not provide an accurate view of these processes and the real bottlenecks. Hence DSPs need to embrace a data-driven process optimization approach to look beyond discovery.

This whitepaper details on leveraging a process optimization framework for the DSPs to set business objectives across the transformation journey. It helps identify critical processes, accelerates cost savings by 60%, and improves customer satisfaction by 30%.

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Fig. Key steps of process optimization approach

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Operational Excellence

Robotic Process Automation: The key to accelerate digital transformation

To stay relevant in today’s digital world, the service providers in the connectedness industry should simplify their business and transform themselves into a digital organization.

The road towards digital transformation is a business-critical one, and the service providers embarking on this journey will need to consider how each aspect of their business can be optimized to fulfill the new digital objectives. To optimize the existing processes and keep pace with the competition, service providers should bring the power of RPA in digital transformation.

Automation is not new and Robotic Process Automation (RPA) with its highly evolved level of sophistication, has made it a lot easier to automate processes across a variety of systems and technologies and reap tangible ROI in a very short time. RPA is a delightful journey, and the end-to-end lifecycle needs to be planned across the below phases to accelerate digital transformation.


To optimize the existing processes and keep pace with the competition, service providers should bring the power of RPA in digital transformation.

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Operational Excellence

Increasing the pace of your process automation programs

Leverage bot development framework to empower RPA Centre of Excellence with bot velocity

Most service providers across the globe have been leveraging robotic process automation (RPA) to increase their operational efficiency and are at different stages in their journey. RPA helps an organization automate repetitive and mundane tasks using the development and deployment of bots or software robots.

But scaling RPA and increasing bot velocity to make it an organization-wide success is a big challenge. One definite solution to this challenge is setting up a robust RPA Centre of Excellence, which not only defines the best practices but also strives to achieve the organization’s goal.

An ideal RPA CoE has the following 7 components:

This insight focuses on the third component, i.e., bot development, specifically on the bot development framework. It throws light on best practices to create a robust development framework for driving bot velocity by standardizing development across the organization. By following these guidelines, organizations can reduce the bot development, testing, deployment, and review time.

A robust bot development framework helps in reducing development, testing, deployment, and review time by 30%

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Operational Excellence

Building a successful Technical Support Center

Use proven tools and techniques to improve the efficiency of Technical Assistance Center (TAC), reduce inbound repeat calls and customer trouble tickets

Today, telecommunication is no longer limited to voice. In the past few years, the industry has rapidly grown to accommodate multiple services. In this era of multi-play, service providers in the connectedness industry are adding various services to their catalog, such as voice, messaging, broadband, IPTV, DTH, VAS etc. And to support these services, service providers have technical assistance centers (TACs) to help customers resolve issues related to specific services.

However, service providers have been facing challenges in maintaining and improving the efficiency and productivity of TACs as much of their efforts go into non-value-adding (NVA) tasks causing resource wastage. NVA tasks can be classified into following categories:

Figure : NVA Waste Classification

Further analysis of NVAs (over-processing, rework, waiting, etc.) shows that managing a high volume of inbound repeat calls and tickets and operating with distributed tools are the major challenges of a TAC.

To mitigate these challenges, service providers must explore innovative and field-proven tools and techniques, including robot-assisted screening, Proactive Network Analyzer etc. By implementing these techniques, service providers can easily realize a 30- 40% reduction in inbound repeat calls and customer trouble tickets.

“In a typical service provider’s technical assistance center (TAC) landscape, many tasks are NVA (non-value adding), leading to resource wastage”

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Operational Excellence

Ride the fiberization wave with a lean and scalable operating model

How fiber operators could build a lean and scalable operating model to deliver with speed, keeping an eye on cost

Deep fiberization remains a strategic objective for global fiber operators to meet the data consumption demands. As per the EY report, global fiber deployment will double during CY2018-2026, majorly led by 5G. The global fiber optic cable market is expected to reach USD 20.8B by 2026 at a projected CAGR of 14.5% between 2020-2026. To ride on this fiberization wave, fiber operators must act quickly, keep an eye on cost and check data accuracy before making planning decisions.

Fiber operators need to rethink their business operations to overcome the challenges in their journey towards accelerating fiber rollouts. The three key domains of a fiber operator and the associated challenges are listed below:

  • Plan & Build Massive coverage targets, high cost to build, shortage of skilled labor
  • Service Delivery Longer cycle time, siloed and disconnected customer journeys, repeat visits and rework
  • Service Assurance Operational Efficiency due to lack of automation and standardization, reactive approach in network management

Fiber operators must build a lean and scalable operating model to overcome all the above challenges and achieve their fiberization goals efficiently. Here’s a proven 4-step approach to building a lean and scalable Target Operating Model by transforming your business capabilities:

  • Step 1: Perform due diligence and discovery of as-is fiber journeys
  • Step 2: Benchmark capabilities using Capability Maturity Assessment
  • Step 3: Collaborate and identify change initiatives
  • Step 4: Plan implementation and define a roadmap

Successful implementation of these steps can help fiber operators to reduce their operational expenses by 53% (in just 3 years) and create a lean and scalable organization.


A lean and scalable operating model will enable fiber operators to achieve fiberization goals efficiently by transforming their business capabilities.

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Operational Excellence

Bridging the gap between demand and capacity

Leverage AI-powered capacity planning to modernize field services

Most service providers face challenges in planning and allocating field technicians based on the demand vs capacity. According to Gartner, “Balancing available resources against the demand for those resources is essential to successful initiative completion“. Inefficient capacity planning often leads to over-staffing or under-staffing of field technicians. This further results in order fallouts and dissatisfied customers. The most common sources of dysfunction are:

  • Unavailability of tools to estimate capacity in real-time
  • Lack of strategy to identify the key influencing factors that impact the capacity planning process
  • Lack of mechanisms to assign the right technician for the right service
  • No end-to-end visibility into field service capacity


According to Gartner, “Balancing available resources against the demand for those resources is essential to successful initiative completion“.

To overcome these challenges and handle the diverse field data, service providers in the connectedness industry should move towards intelligent capacity planning, which helps in the real-time mapping of dispatches and the optimal usage of resources. Leveraging an AI-powered capacity planning framework helps the service providers to reduce resource wastage by 20% and improve the effectiveness of service response and customer satisfaction. Enterprise AI can, over time, improve the prediction of field technician work hours by considering the key factors such as weather, season and maintenance data.

Fig: Leverage AI-powered capacity planning framework for real-time field tech resource management

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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.

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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
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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.

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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.