Water & Wastewater Treatment

WWT March 15

Water & Wastewater Treatment Magazine

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www.wwtonline.co.uk | WWT | march 2015 | 39 Analytics Sophistication mediate scope for efficiency improve- ment by analysing asset status data and rainfall forecasts. Models can be developed to predict water demand or the likely location of sewer network escape events. These impacts are directly related to AMP6 regulatory performance commitments so any improvement to existing operational practices has a direct financial benefit. In business planning, advanced analytical techniques can be used to monitor the economic effectiveness and efficiency of planned maintenance projects. The models used to develop the business plan can be reconciled with observed asset performance to undertake thorough post-project appraisal and close the gap between asset planning and project delivery. In general the relationship between asset interventions and customer sat- isfaction is poorly understood in the water industry. Advanced analytical models can be developed to describe this complex relationship. These con- cepts can be extended to demographic analysis to fully understand the vari- ation between individual operational areas. These models can be used to tactically manage the SIM score, but in the longer term the modelled relation- ships can also inform operational strategy from a serviceability perspec- tive. These are examples of areas where recent project experience has shown that advanced analytics can be of significant benefit. There will be many other water company functions where advanced analytics can be of benefit. In the water industry advanced analyt- ics is in its infancy, but there is consid- erable maturity in other sectors. In his 2007 book (ISBN 1422103323), Thomas Davenport, a leading exponent of analytical approaches, described four characteristics of an organisation that enable successful analytics: a) the or- ganisation needs to have senior man- agement commitment to the approach; b) analytics need to be used across multiple different business functions; c) there needs to be an enterprise-level analytical platform to enable this; and d) the organisation needs to have sufficient expertise in both statistical/ mathematical and business terms. The successful implementation of advanced analytical projects in many different sectors means that there are several so'ware vendors with enter- prise level so'ware solutions. The discipline is at a state of maturity such that the technology research company Gartner have recently produced a magic quadrant report analysing the comparative capabilities of different advanced analytical platforms. As stated earlier, water companies have recently developed large corpo- rate datasets suitable for the develop- ment of advanced analytical solutions. Experience from other industries has noted several barriers to the effective deployment of advanced analytics. There is a major difference between identifying a relationship and realis- ing efficiency. The gap is frequently due to difficulties in changing existing business processes to take advantage of advanced analytical techniques. Effect- ing change can be a major undertaking requiring a carefully designed change programme. There is a short term risk that the change will not be fully em- braced and a tendency to revert to the previous way of doing things. Senior level commitment is required to ensure change. One evident danger in using advanced analytical techniques is in spurious correlation: since there is always a chance that any two datasets may correlate without there being any genuine underlying reason, managing a business on such correlations is clearly a dangerous thing to do. Another problem is where correlation arises as an artifice of the data collection process. This has been seen in a project that modelled the relationship between customer contacts and network inter- ventions. The design of the incident forms required the user to assign one customer contact to each network inter- vention. Naturally, the analysis revealed that every intervention was associated with one customer contact but the true relationship could have been have been stronger or weaker. When any relationship is modelled the appropriateness of the statistical technique, the data collection process and the underlying engineering knowl- edge should all be reviewed to sense check the results and ensure that the results describe genuine processes. The development and application of analytical models is an evolving process. The analytical methods, un- derlying data quality and interpretation of the output must be understood and reviewed continually by an expert team so that continual improvements can be made. Failure to fully understand the models means there is a real danger that the business is being run on inap- propriate assumptions. In summary, advanced analytics have the potential to realise significant efficiency savings both during AMP6 and in the longer term. Water compa- nies have invested in the systems to provide data for advanced analytics. However in the water industry this type of technique is in its infancy and to re- alise value requires significant change to well established business processes. To make the step change requires ambi- tion and senior level commitment. Adapted from IBM with permission

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