The Use of Clementine at South West Water to reduce sewer flooding incidents
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1 The Use of Clementine at South West Water to reduce sewer flooding incidents Tony Hoar - Business Performance Manager Ben Plastow - Data Quality Analyst
2 Presentation Topics (Tony) Overview of South West Water (SWW) The Business Performance role Capital asset statistics The analysis weakness we faced 2 Why we selected SPSS Clementine Why we selected DG5 as our first project
3 Presentation Topics (Ben) The solution applied The implementation process: Defining risk Verifying model accuracy Excel reports SWWIM (digital mapping) integration 3 The results achieved Future developments Current and Future projects
4 4 Who are South West Water? The 10 UK Water & Sewerage companies
5 SWW Key Facts (1) Formed in ,330 employees Turnover of 351m in Our customer base comprises: 1.6 million residents 750,000 billed customers 8 million visitors 5
6 SWW Key Facts (2) We provide: Clean water to 97% of homes Sewerage collection and treatment to 88% of homes The regulators: The Water Services Regulatory Authority (OFWAT) Environment Agency Drinking Water Inspectorate (DWI) 6
7 The Business Performance Role Business Performance (BP) is part of the Asset Management & Development department in SWW BP undertake the following capital asset related activities: Business efficiency investigations Key Performance Indicator (KPI) reporting Information provision Data analysis Dataset and data quality management New business processes 7
8 Our Asset Base (1) Clean Water 22 impounding reservoirs 32 treatment works 320 service reservoirs 15,000 km of water pipes Waste Water 600+ treatment works 8,700 km of public sewers 800+ pumping stations 2,000 storm overflows pumping stations 300,000 manholes
9 Our Asset Base (2) Gross replacement cost of capital assets is 8.4bn Over 2.5bn invested in our capital assets since privatisation Capital expenditure circa 200m per annum 9
10 Changing Information Requirements 100% The Rise in Maintenance Spend (%) 80% 60% 40% % 0% Pre- Privatisation Maintenance Quality Supply & Demand K1 K2 K3 K4 (Plan) K5 (Estimate) Period
11 Why SPSS Clementine? The initial presentations by SPSS Subsequent contact and presentations A review of similar software Requirement for a non-specific predictive analysis tool The SPSS people we met Use of Clementine within the water industry 1 1
12 Why DG5 Sewer Flooding? 6,000 blockages per year 75 customer properties and 300 gardens flooded with sewage per year Lost Operational Performance Assessment (OPA) points Pollution of rivers and other water courses Risk of prosecution SPSS experience in this area 1 2
13 The Project Objectives The prime objectives are to: Identify areas most at risk of sewer flooding, the underlying factors, and changing risk over time. To better prioritise investigations, sewer cleansing, and repairs. Reduce the number of sewer flooding incidents in the most cost effective way. Increase confidence in the level of capital maintenance expenditure required. 1 3
14 Defining Risk: Fixed Factors Risk is based on the following fixed factors which do not change over time: Sewer assets (depth, criticality, capacity, length, quality) Sewer cleansing (historic build up rates of fat, silt, sand, & roots) Commercial food properties (restaurants, takeaways) Older properties (aged pre-1896) Socio-economic factors (rateable value of properties) 1 4
15 Defining Risk: Variable Factors And the following variable factors: Previous internal flooding incidents Previous external flooding incidents Previous non-flooding blockages 1 5
16 Verifying Model Accuracy One Year Variable Risk Model Black diagonal line = base rate (model predicting outcome at chance level) Red line (Best) = results if perfect model applied to data $L-Risk_Num = model results 1 6
17 One Year Model By Type Of Risk Predictive factors (by weight) Internal flooding (23.1%) External flooding (20.3%) Fat build up rate (14.5%) Non-flooding blockages (10.5%) Sewer length and quality grade (6.4%) Socio-economic factors (5.5%) Sewer Criticality Group A (4.8%) Sewer capacity (length/depth) (3.9%) Commercial food properties (3.7%) Older properties (pre-1896) (3.6%) Sewer depth (3.5%) 1 7
18 One Year Model: Leading Factors Number of Internal Blockages Weighted Sewer Length Grade Number of Critical A Sewers Weighted Socio-economic Group 1 8
19 Excel: Executive Summary Sheet Shows the main league positions including counts of number of internal, external and non-flooding blockages and recency 1 9
20 New Business Process MIMS-Ellipse Work Orders Feedback SynchroWeb Work Orders Feedback TaskMaster Excel Interface Clementine 2 0
21 2 1 SWWIM Quadrant Risk Analysis
22 SWWIM Legend (user guide) Fixed Risk Quadrants Risk Bands Band 10 High Risk Band 9 Band 8 Band 7 Band 6 Band 5 Band 4 Band 3 Band 2 Band 1 Low Risk Variable Risk Quadrants One Year Model Three Month Model One Month Model 2 2
23 Measurable Business Success Criteria The following business results are expected: A significant increase in the blockage detection rate (month on month) recorded through Work Orders. A reduction in the level of quadrant risk scoring. Evidence of a more proactive (and less reactive) approach to sewer maintenance investigations. A reduction in the number of sewer flooding incidents in targeted quadrants. 2 3
24 SWWIM Variable Risk Analysis Variable risk increasing over time = risk is becoming more recent Variable risk increasing over time = risk is greater as problems remain or are not fixed Variable risk decreasing over time = risk reduces as problems are fixed by the maintenance teams 2 4
25 Business Results Achieved More comprehensive and proactive approach. Sewage Maintenance Team Leader is co-ordinating the sewer maintenance plans using the output of Clementine Reports. Initial increase in sewer cleansing budget for by 34%. This is an increase in proactive (rather than reactive) expenditure. 2 5 Planned allocation of 2 inspection crews to carry out minor quick repairs to the sewer pipe system.
26 Future Developments Integration of rainfall data into the 3 variable risk models. Integration of customer address details to quadrants risk ranked in SWWIM. Ongoing monitoring of the quadrant league table reports. Close liaison with the Operations Department and the Contractors. 2 6
27 Clean Water Mains Bursts Project objective: Predict the likelihood of a clean water main to burst and any potential consequence (including financial costs). Key outputs: Likelihood of a pipe to burst (logistic regression). Predicted mode of failure (decision tree). Costs: (1) Cost of repair to fix the burst; (2) Estimated customer cost to the company (loss of supply, poor publicity, compensation). 2 7
28 Current and Future projects Current projects (All assets & Clean Water): Mean Time Between Asset Failure Clean Water Network Mains Bursts Future projects (Clean & Waste water): Clean Water Mains Deterioration Sewer Pumping Station Failure Sewage Treatment Works Failure Sewer Network Asset Failure 2 8
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