Void Properties
Void (vacant) properties have a huge impact on the revenue for utilities. They end up spending a lot of money to identify customer details and collect dues from them. However, false void scenarios that are causing revenue leakage, are the biggest challenge for E&U companies.
A false void is a property listed as void in the company system, but is in fact occupied and using water. In such circumstances, the customer in the property is gaining free water and the rest of the customer base is effectively subsidizing them (through revenue control). Locating false void cases increases the company expenditure many times over. At the same time, companies can face penalty charges from regulatory bodies, if they are unsuccessful to meet their targets. Gathering and managing void property data is quite a complex task for utilities – this is crucial to analyze the customers’ current situation and underlying data patterns.
WNS can help utilities to accurately locate actual false void cases quickly by providing void property data in partnership with third-party data providers. This will not only help them to manage their real-time data, but calculate their revenue leakage and actual cost to locate the false void cases.
Carbon Impact Insights
The benefits of harnessing the enormous volume of data captured by transportation and logistics industries cannot be overstated.
One very important and frequently overlooked benefit for this carbon-intensive industry is the ability to use analytics for carbon footprint reduction, which has both financial and reputational ramifications. Combining data analytics with process automation helps drive significant efficiencies, reduce costs, streamline operational processes, and improve communication between shippers, carriers and brokers.
Applying AI and machine learning to data analytics helps to streamline operations and reduce emissions in several ways.
AI-powered systems monitor data generated by day-to-day logistics activities. This includes analyzing volumes, distances and mode selections, and documenting inefficient modes, routing and empty miles that flow from poor utilization. They also take into account the impact of fleet planning and routing, dwell time and detention tracking (during which trucks sit idle while waiting for scheduled pick-ups and drop-offs), and a myriad of other factors impacting carbon fuel utilization.