IBM > Case Studies > Predictive modeling used to help protect the environment and save costs

Predictive modeling used to help protect the environment and save costs

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Customer Company Size
Large Corporate
Region
  • Europe
Country
  • United Kingdom
Product
  • IBM SPSS Modeler
  • IBM Global Business Services — Business Consulting Services
Tech Stack
  • Predictive Modeling
Implementation Scale
  • Enterprise-wide Deployment
Impact Metrics
  • Cost Savings
  • Environmental Impact Reduction
  • Customer Satisfaction
Technology Category
  • Analytics & Modeling - Predictive Analytics
Applicable Industries
  • Utilities
Applicable Functions
  • Maintenance
Use Cases
  • Predictive Maintenance
  • Leakage & Flood Monitoring
Services
  • Data Science Services
About The Customer
Reading-based Thames Water Utilities Ltd. (TWUL) is a private utility company responsible for public water supply and wastewater treatment in large parts of Greater London and other areas of the United Kingdom. The company is committed to providing clean and safe water to its customers and maintaining the integrity of its wastewater network. It is constantly looking for innovative solutions to improve its services and reduce environmental impact.
The Challenge
Thames Water Utilities Ltd. in the United Kingdom needed to understand the relationship between flooding and pollution incidents in the utility holes in its wastewater network. The company aimed to create a cleaner environment and avoid costly leakages, unsavory publicity, and dissatisfied customers. The challenge was to identify the utility holes that are most likely to flood and cause pollution problems, especially holes with a history of flooding or located near watercourses.
The Solution
The company established a predictive model using IBM SPSS Modeler to analyze incident data and identify the utility holes that are most likely to flood and cause pollution problems. This predictive model allows the company to prioritize these utility holes for preventive maintenance. The maintenance activities range from cleaning pipes to replacing water mainlines. The solution was implemented with the help of IBM Global Business Services — Business Consulting Services.
Operational Impact
  • The predictive modeling has resulted in a tenfold increase in the precision of identifying maintenance holes most at risk of flooding and causing a pollution incident.
  • The company is now able to prioritize these utility holes for preemptive maintenance.
  • The maintenance of 2,000 utility holes is expected to prevent about 120 pollution incidents over a two-year period.
Quantitative Benefit
  • Financial benefits, realized through a reduction in regulatory fines, are estimated to be USD4.7 million per year.
  • The solution has resulted in a one-third reduction in regulatory fines.

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