RPA based solution strategy to address inventory data inaccuracies

An inventory of assets and services is the cornerstone for any Digital Service Provider (DSP). Yet DSPs face several challenges in managing the completeness and accuracy of inventory data. Root causes that leads to inventory data issues include –

  • Manual errors in updates from field engineers
  • Mergers and acquisitions resulting in multiple sources of truth
  • Maintaining parts of inventory data in non-digital format

Inventory data issues eventually lead to increase in lead time for new installs/repairs, increased inflow of calls into customer care by field technicians, frequent provisioning fallouts, etc. 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. 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.
This Insight showcases an RPA-based automated inventory reconciliation framework to help DSPs accelerate their data integrity programs.

Figure1: RPA-based automated inventory reconciliation framework



  • Pradeep Balakrishnan
  • Mogan A B
  • Rajesh Khanna

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