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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
Renewables
Transport
Grid
Energy
Water
Total Enterprise Modelling
swiss cloud solutions
powered by
Case Study
Sewer Flooding Prevention | Water & Energy
Challenge
The client looked at Overbeck Analitica to identify geographical areas that are most at risk of sewer flooding incidents.The client was
reporting below average results in OfWat’s levels of service periodic review in relation to DG5 sewer flooding compliance
and needed improving.
Discovery
Overbeck Analitica started by assessing fixed and variable risk factors in relation to controllability of sewer flooding risk mitigation.
Identifying and mitigating risk proactively rather than reactively saw the model report decreased risk over time and significantly less
sewer flooding incidents reported to the environmental agency and industry regulator.
To enhance the maintenance process Overbeck Analitica deployed a heat-map reporting system, which highlights variable risk
increasing over time (i.e. shows when risk is growing where problems remain unattended or are not resolved) and it also highlights
variable risk decreasing over time (i.e. shows when risk is reducing where problems are resolved by maintenance teams).
Impact
The client now scores above average with >25% better than industry mean under OfWat’s performance audit of flooding from sewers.
Our Sewer Flooding Risk Model identifies ‘hot-spots’ in the system most at risk of flooding.This helps the client to target problems
before they occur, such as collapsed sewers and blockages, which can lead to flooding.
Targeting areas most at risk of sewer flooding, the underlying factors affecting risk and changing risk over time has enabled the
client to better prioritise investigations, sewer cleansing and repairs and increase confidence in the level of capital maintenance
expenditure required.
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:

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PAM Case Study 3 - Flooding Risk V1

  • 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 Renewables Transport Grid Energy Water Total Enterprise Modelling swiss cloud solutions powered by Case Study Sewer Flooding Prevention | Water & Energy Challenge The client looked at Overbeck Analitica to identify geographical areas that are most at risk of sewer flooding incidents.The client was reporting below average results in OfWat’s levels of service periodic review in relation to DG5 sewer flooding compliance and needed improving. Discovery Overbeck Analitica started by assessing fixed and variable risk factors in relation to controllability of sewer flooding risk mitigation. Identifying and mitigating risk proactively rather than reactively saw the model report decreased risk over time and significantly less sewer flooding incidents reported to the environmental agency and industry regulator. To enhance the maintenance process Overbeck Analitica deployed a heat-map reporting system, which highlights variable risk increasing over time (i.e. shows when risk is growing where problems remain unattended or are not resolved) and it also highlights variable risk decreasing over time (i.e. shows when risk is reducing where problems are resolved by maintenance teams). Impact The client now scores above average with >25% better than industry mean under OfWat’s performance audit of flooding from sewers. Our Sewer Flooding Risk Model identifies ‘hot-spots’ in the system most at risk of flooding.This helps the client to target problems before they occur, such as collapsed sewers and blockages, which can lead to flooding. Targeting areas most at risk of sewer flooding, the underlying factors affecting risk and changing risk over time has enabled the client to better prioritise investigations, sewer cleansing and repairs and increase confidence in the level of capital maintenance expenditure required.
  • 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: