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

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

Predictive modeling used to help protect the environment and save costs

IBM

Technology Category	Analytics & Modeling - Predictive Analytics
Applicable Industries	Utilities
Applicable Functions	Maintenance
Use Cases	Leakage & Flood Monitoring Predictive Maintenance
Services	Data Science Services
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Applicable Industries

Utilities

Applicable Functions

Maintenance

Use Cases

Leakage & Flood Monitoring

Predictive Maintenance

Services

Data Science Services

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.

About 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.

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.

Impact #1	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.
Impact #2	The company is now able to prioritize these utility holes for preemptive maintenance.
Impact #3	The maintenance of 2,000 utility holes is expected to prevent about 120 pollution incidents over a two-year period.

Impact #1

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.

Impact #2

The company is now able to prioritize these utility holes for preemptive maintenance.

Impact #3

The maintenance of 2,000 utility holes is expected to prevent about 120 pollution incidents over a two-year period.

Benefit #1	Financial benefits, realized through a reduction in regulatory fines, are estimated to be USD4.7 million per year.
Benefit #2	The solution has resulted in a one-third reduction in regulatory fines.

Benefit #1

Financial benefits, realized through a reduction in regulatory fines, are estimated to be USD4.7 million per year.

Benefit #2

The solution has resulted in a one-third reduction in regulatory fines.

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Overview

Predictive modeling used to help protect the environment and save costs

Operational Impact

Quantitative Benefit