September 3, 2017

IoT in Utilities Industry

Rapidly expanding cities due to increasing population translates to tremendously growing customer base for utilities industry. Utilities being service intensive industry face many challenges when it comes to connecting with their customers, deepening their engagement and providing the highest level of service that means becoming a customer-centric, information-driven organization is no longer simply an option for most utility companies, it’s a business imperative now.

With rapidly growing consumption, more and more customers demanding the control and insight of the energy consumption, increasing customer expectations and concerns, enormous amount of smart meter data, excruciating cost of operations be it inventory, fuel or fleet, are some of the performance critical areas where utility companies can identify problems and predict the trends that may lead to customer churn.

Besides all of the above, utilities companies operating in specific geographical areas that are more prone to natural calamities e.g. tornado alley states have a crucial job of critical decision making and rapidly responding to catastrophic situations with full efficiency and IoT can play a vital role for such utility companies.

IoT can provide powerful, actionable insight and a great visibility to identify and look into the problem areas and enable the companies to develop an effective improvement strategy in order to improve service level and financial performance that leads to achieving operational excellence. Here are some of the areas where utility companies can use sensor derived real-time data analytics to reduce cost and achieve operational efficiency:

  • Supply demand balancing
  • Predict and prevent service outage
  • Reduce energy theft
  • Improve operation
  • Asset Maintenance
  • Operational scheduling of the asset utilization
  • Resource planning
  • Modernized and enhanced customer experience
  • Improved customer service
  • Field services and fleet performance
  • Capacity planning
  • Logistics and fuel consumption analysis
  • Servicing and Repair management
  • Inventory Management
  • Compliance and Regulatory reporting
  • Real time analysis

 

When paired with other predictive analytics solutions, data models can even identify at-risk customers and anticipate when they are likely to have difficulty paying their bills. Being able to predict payment behavior allows organizations to focus on accounts that are likely to fall into the collections process and stop customer churn before it actually happens.