Accelerating network inventory reconciliation process time by up to 60% using RPA

March 20th, 2020 by · 1 Comment

This Industry Viewpoint was authored by Prodapt

An accurate inventory of assets and services is the cornerstone for any digital service provider. Yet, Digital Service Providers (DSPs) face several challenges in managing the completeness and accuracy of inventory data. Most of the cases, a DSP’s physical and logical network inventory are often 20-30% out of sync. The manual data reconciliation projects are proving to be ineffective as these are labor-intensive, time-consuming, and cannot handle network environments that are rapidly changing. Root causes that lead to inventory data issues include –

  • Mergers and acquisitions (M&A) resulting in multiple sources of truth
  • Maintaining parts of inventory data in non-digital format
  • Manual and time-consuming procedures for updating and maintaining OSS Inventory
  • Manual updates from field engineers resulting in errors which lead to increased order fallouts
  • Provisioning and service delivery teams using quick-fix and workaround methods on the network side to satisfy customer, but never reconcile it on OSS Inventory

Business Impact on DSPs’ Service Assurance, Fulfilment & Billing areas

  • Increased inflow of calls by field technicians
  • Increased lead time for new installs/repairs
  • Increased service truck rolls/reduced field engineer efficiency
  • Frequent order fallouts causing provisioning delays
  • Revenue leakage problem due to billing mismatch issues

DSPs need to take a holistic solution approach to tackle the core challenges that are crippling them from effectively managing the network inventory data integrity issues. Robotic Process Automation (RPA) is a perfect choice for a fast-paced implementation of the inventory reconciliation process and automating the associated workflow.

Holistic Solution: RPA-based automated inventory reconciliation framework

An RPA-based automated inventory reconciliation framework can help DSPs accelerate their data integrity programs. Following are the key elements to consider, for building an effective automated inventory reconciliation framework

  • Bot-based data discrepancy identification, categorization & rectification process
  • Real-time inventory gap analysis bot in order-to-activate process
  • Automated bot-based inventory record clean-up for billing discrepancy issues
  • Integrating bot in field service workflow to capture frequent network changes

Bot-based data discrepancy identification, categorization & rectification process

RPA bot aggregates all the required details from the OSS inventory management system and network database to perform the reconciliation process. It does automatic discrepancy identification in a scheduled manner based on the standard business rule conditions.

Fig.1: RPA bot-based automated data discrepancy identification, categorization & rectification process

  • Inventory record issues that do not require SME validation (e.g., service delivery, service fulfillment, and assurance) can be categorized under the auto-fix and RPA bots can directly rectify such record issues. Typically, 30-40% record issues fall into the auto-fix category.
  • Inventory record issues that require validation from SMEs (e.g., field upgrade, faulty device replacement) must be categorized under the manual activity. Such record issues can be directly notified to support groups through auto tickets.

Real-time inventory gap analysis bot in order-to-activate process

Implementing real-time gap analysis bot in the order-to-activate process accelerates the overall order flow to a larger extent. In the absence of a bot, if there is an inventory record mismatch issue, it will only be found by field technician on the due date, leading to service activation delay and order fallouts. When the requested resource is available, the bot simply triggers automated provisioning flow. However, when the requested resource is unavailable due to inventory record mismatch, RPA bot does the following activities

  • Notifies the provisioning team for re-assignment of the provisioning order.
  • Initiates discrepancy rectification workflow on OSS inventory to fix the data integrity issue.

Fig:2 Real-time inventory gap analysis bot to reduce service order fallouts due to network inventory issues

Automated bot-based inventory record clean-up for billing discrepancy issues

Implementing a customized bot in the revenue assurance management process to handle the billing discrepancy (e.g.) DBB (disconnected but billing) workflow helps DSPs to reduce data integrity-related issues and improve cost savings. RPA automated process validates that there are no discrepancies such as billing for disconnected services, under or overcharging issues, etc., thereby reducing customer impacts.

Fig:3 Automated bot-based inventory record clean-up for billing discrepancy issues

Integrating bot in field service workflow to capture frequent network changes

Traditionally, field technicians work with customer service representatives (CSRs) to update the network side changes and in parallel work with the provisioning & activation team for order completion. These manual activities are time-consuming, error-prone, and it takes several hours/days to see the changes reflected in the OSS Inventory. Often, these network changes never get updated in inventory management systems causing data integrity issues.

Below field service update workflow shows that a customized bot integration to enable automated network inventory reconciliation procedure. Further, this bot allows for near real-time updates of network changes by field technicians onto the OSS Inventory. Up to 80% of record issues caused in manual field service updates can be prevented by implementing this bot-based approach.

Fig:4 Integrating bot in field service updates workflow to capture frequent network changes

Benefits of implementing RPA-based automated inventory reconciliation framework

While most DSPs are still using ad-hoc manual reconciliation procedures to address this crucial issue, many have recognized the need for an automated approach for data integrity management.

  • Faster order fulfillment and error-free provisioning
  • Major cost savings due to proactive identification billing discrepancy issues by leveraging inventory record clean-up bot
  • Recover stranded resources - reduce CAPEX
  • The improved customer experience (CX)
  • Better revenue assurance, faster service design and activation cycle time

Authors:

Pradeep Balakrishnan – Director, RPA Delivery

Rajesh Khanna – Director, RPA Practice        

Mogan A.B. – Manager- Strategic Insights, Prodapt Solutions

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Categories: Big Data · Industry Viewpoint · Software

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1 Comment, Add Yours!


  • Geetha says:

    I totally agree with the above article; every Telecom Service Provider has this challenge. It impacts customer experience, lost revenue, high cycle time. Technologies such as RPA, AI/ML, and robust service delivery tools should help minimizing the impact and improving the CX and business. Great and timely article!

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