Order fulfilment has always been a notorious pain point for the telecoms industry. A typical order, say for a mobile phone upgrade or broadband installation, sets off a chain of events that must be executed flawlessly to ensure customers receive their orders in time. However, too often it goes awry.
What’s more, in recent years, the order fulfilment process has become even more byzantine. Turbulent industry conditions and relentless change have led to mergers and acquisitions, investments in infrastructure and partnerships with smaller companies, further complicating what was already an intricate process, says Amit Kale of Wipro Ltd.
In theory, order processing is entirely rule-based and should therefore, be error-free. However, the reality is different. These errors can often come as a result of simple things like a missing post code from customer details, the fact that traffic management systems were not available when orders were being prepared for delivery or certain order restrictions were in force that weren’t made clear to shop assistants when orders were placed.
Not all agents maintain the same pace of process execution, they may not have timely access to information, or they make mistakes. There can also be delays in handoffs between teams, and decision-making is not always instant.
These mistakes all lead to late or unfilled orders, and lost customers and revenue as a result.
However, this need not be the case. Some Communications Service Providers (CSPs) have started turning to Robotic Process Automation (RPA), a technology that has wide applicability across the telecoms industry, to create a flawless order-to-activate process.
In a CSPs’ order management process, customer requirements are captured by dealers, such as retail stores or phone agents, and passed on to the provisioning teams. Typically, the time taken to deliver products to customers can vary from 5 to 100 days, depending on the type of product. However, most CSPs fail to meet delivery and standards, nor do they have a model which can predict failure in order to alert the end customer.
All rules-based work, such as gaps in data, cross-checking data, updating databases, sending mailers and alerts, which tend to be at the root cause of these delays, can be robotised using RPA technology.
Installed on servers, the robots perform all relevant tasks in the order fulfilment process such as validation of orders, order entry and order delivery instructions. Tasks that cannot be taken by the robots are instead pushed into a manual queue managed by a human consultant. Robots are monitored on a real time basis, with the CSP given key volumetric statistics such as AHT, orders completed and cycle time, in order to check on the performance of the robots.
A key advantage of this is that the robots are not only able to perform these tasks, but proactively and continuously improve the order fulfilment process. Every process metric can be automatically, immediately and accurately captured. This allows the data to be leveraged by analytical engines to turn anomalous processes from red to amber to green.
More importantly, the data can also be used to support predictive models. A predictive model helps identify failures in advance and means the CSP can take proactive measures to bring anomalous incidents back in line.
For example, in the order management cycle, there are multiple phases during the O2A (order to activate) process. The order activation date represents the customer required date (CRD). If the customer doesn’t receive their order by this date, the suppliers’ credibility is negatively affected. Based on data analytics around various product parameters (for example delivery bucket, STP orders, order type, location, clean and unclean order, order bundling etc.), the model predicts at the very start of the order entry stage whether it would be possible to meet the CRD, and if not, such orders are automatically routed for next best action (NBA) or jeopardy management status.
However, an RPA and associated analytical solution can’t work in isolation and are only cogs in a wider machine, and require support from an order orchestration platform. Think of an order orchestration platform as the last mile in a fulfilment system. The order orchestration platform sits on top of everything and includes dashboards on robot performance.
The benefits of this robotisation are numerous. Faster order entry times, error reduction and increased transaction can mean lower operational costs, early revenue realisation, reduced fines from regulatory bodies, better customer satisfaction and increased revenue.
For CSPs who are consistently adding to their portfolios and operating in a complex partner ecosystem, the challenge will always be to orchestrate and fulfil orders before customer satisfaction levels are negatively affected. The solution is to automate and robotise as much of the operations as possible with a layer of analytics and support from an orchestration platform so that fulfilment timelines can be met every time.