Case Study Predictive Maintenance | Water & Energy

1. Predictive Asset Management
Asset rich organisations such as
utilities and transport companies
all over the world are challenged
by ageing assets, risk of service
deterioration and preventing wider
consequence costs such as blackout
and pollution.At the same time,
financial constraints demand an
increased return on investment with
reduced maintenance budgets and
spending, whilst facing rising water
and energy demand, and increasing
population.
These contradictory demands can
be met through optimised asset
management and lifetime costing.
This requires accurate and reliable
models that consider both technical
and economic criteria.With the
right Predictive Asset Management
(PAM) solution you will find your
organisation’s assets and data hold
the answers to many of the business
and regulatory challenges your
organisation faces right now.
Without changing any of your systems, we can reduce your
OPEX and CAPEX spend significantly by improving the
performance of your active asset base.
Predictive
maintenance
triggers
Monitoring
and alert
triggers
Economic assessment and next best interventions
shifting from fail-and-fix to predict-and-prevent
PAM
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Grid
Energy
Water
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Case Study
Predictive Maintenance | Water & Energy
Challenge
The client challenged Overbeck Analitica to develop a system underpinned by a common modelling framework which would
determine Asset Deterioration Curves that are derived empirically from Maintenance Event Data for both their Clean and Waste
Water Management services.
Discovery
Working with the client Overbeck Analitica determined the factors which would lead to quality data for the model from their
Maintenance Event Data to create optimum accuracy in predicting asset deterioration.
Our simulation model showed that increasing the ratio of proactive to reactive interventions reduces the hazard rate significantly.
This would allow the client to amend issues before they become critical, helping them shift towards proactive asset maintenance
intervention rather than reactive intervention once the asset has already failed.
Using live Maintenance Event Data the model supports both a strategic and tactical version of deterioration curves, enabling
economic maintenance planning and giving the client the ability to make preventative maintenance decisions.
Impact
Increasing preventative maintenance cost of 5% reduced hazard risk significantly with combined maintenance cost savings of over
£500,000 in the first year, this figure is due to increase as the preventative maintenance percentage increases over time, shifting from
reactive fail-and-fix to predict-and-prevent maintenance.

2. Testimonials‎...
“ I have found the implementation of the highest
standard, as well as adding considerable value to
our capital investment decision making. ”
Dr. Stephen Bird
COO, South West Water, UK
“ We are experiencing improved business process
and operational efficiency each month from 2 to 3
weeks labour intensive process to 2 to 3 days with
benefit of an automatic audit trail. ”
Clean Water, Ofwat audit
“ As a result of the model, increasing predictive
maintenance cost of 5% reduced hazard risk
significantly with combined operational cost
savings of over EUR 0.5M in the first year. ”
Capital Review & Asset Performance
“ Using operational data the model supports both
strategic and tactical predictions, enabling economic
maintenance planning and giving us the ability to make
preventative maintenance decisions. ”
Waste Water Infrastructure, UK
“ The model successfully selects meters for economic
replacement, this increased revenue protection
tenfold to over £500k in the first year. ”
Clean Water, Ofwat audit
“ During the working life of the asset, precision in
operations and maintenance is crucial to making
assets perform to the ongoing expectations of the
business, in the presence of variation and risk. ”
Corporate Manager
Major Energy Provider, Middle East
What
our
Customers say
“ We use a range of tools to tackle the 3,000 blockages in our network each year, including a predictive model that
identifies ‘hot-spots’ in the system most at risk of flooding.This helps us make repairs before problems occur,
such as collapsed sewers and blockages, which can lead to flooding. ”
Richard Gilpin, (Head of Waste Water Management, South West Water, UK)
Contact us
Via San Paolo Apostolo,
00044 Frascati,
Rome, Italy
Total Enterprise Modelling: Connecting people, systems and data to enable the predictive enterprise.
Voice: +39 339 1999351
Fax: +39 0694 64520
Email: info@overbeckanalitica.com
Dr. AtaiWinkler
Mobile: +44 (0)7817 263016
Email: atai.winkler@
overbeckanalitica.com
Ralph Overbeck
Mobile: +49 (0)1759 484098
Email: ralph.overbeck@
overbeckanalitica.com
United Kingdom: Germany: