Leverage an RPA-based solution strategy to address inventory data inaccuracies and have a better insight into your network
Network inventory is the cornerstone for any service provider. To deliver seamless network services, assets and infrastructure (computers, routers, servers etc.) are a must. And the inventory available with an operator determines the kind, quality, and capacity of any services they offer. Therefore, these assets are crucial as service providers in the connectedness industry cannot operate without them.
However, service providers face several challenges in managing the completeness and accuracy of inventory data. Inventory data issues majorly arise due to human errors by field engineers, multiple sources of truth as a result of mergers and acquisitions and non-digital data storage formats. These issues eventually increase the lead time for new installs/repairs, high volumes of calls by field technicians to the customer care, frequent provisioning fallouts, etc.
Service providers must 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 they are labor-intensive, time-consuming and cannot handle network environments that are rapidly changing. Adopt an RPA-based automated inventory reconciliation framework to accelerate data integrity programs and improve service.
Figure 1: RPA-based Automated Inventory Reconciliation Framework
Service providers’ network inventory systems are often 20-30% out of sync with the physical and logical state of the network. An RPA-based automated inventory reconciliation framework can accelerate data integrity programs.