Ensuring You Have Good Data to Support Decisions

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Ensuring You Have Good Data to Support Decisions

• Dr Peter Skipworth
• Managing Director, SEAMS Ltd

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SEAMS Some Background

• Provide Asset Investment Planning (AIP) software
  and services
• ..to help infrastructure rich organisations
• ..achieve long-term, sustainable business
  performance

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Asset Investment Planning (AIP)

• ..an emerging discipline on companys
  operational landscape

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SEAMS Some Background

• Provide Asset Investment Planning (AIP) software
  and services
• ..to help infrastructure rich organisations
• ..achieve long-term, sustainable business
  performance
• Products and Services
• Software _ Software hosting _ Modelling _
  Optimisation _ Training _ Consultancy
• Sectors
• Water _ Highways _ Rail _ Gas

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Clients, Business Audit Partners

• Clients
• Partners
• Technology Quality Assurance
• Consultancy Auditors

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Questions

• How can extra data quality be justified and
  factored into AMPs?
• Automating data collection
• how investment now can save time and money in the
  future
• Building models for future planning
• Should companies be sharing historical data?
• Managing inflows of data from different
  contractors
• WHY do we collect data?
• how can we VALUE this data build a Business Case?

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WHY do we collect (asset) data?

• Operations
• Customer service and regulation
• Legal and accounting
• Collecting data for Asset Investment Planning
• Inspection data
• But.can be an after-thought or a dual use of the
  data

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How can we VALUE data?

• Asset Investment Planning (AIP)
• redressing the reactive/pro-active balance
• opex/capex trade-off
• better decision-making ? efficiencies
• We can make the best use of any data but..
• improved quality and coverage of data
• improved certainty
• improved decisions

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improved quality and coverage of data

• Does this actually allow us to put a VALUE on
  data?
• All weve done is reduce uncertainty (the risk
  carried by the company)
• .Case Studies from SEAMS

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Case Studies from SEAMS

• Two large Water and Sewerage Companies (WaSCs)
• planning investment on wastewater network
• COMPANY 1
• A large area (1 million pop) had no data
• STAGE 1
• Data extrapolation from another area investment
  planning carried out
• (with uncertainty investigation)
• STAGE 2
• Several years later analysis repeated with
  indigenous data
• projected investment levels the same
• POSSIBLE CONCLUSION
• Value of data only in uncertainty (in the risk
  carried by the company)

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Case Studies from SEAMS

• Two large Water and Sewerage Companies (WaSCs)
• planning investment on wastewater network
• COMPANY 2
• 3 million pop
• STAGE 1
• investment planned using indigenous data
• STAGE 2
• investment planned using much improved indigenous
  data
• saw a reduction by 1/3 of investment requirements
• POSSIBLE CONCLUSION(S)
• Efficiencies through investment planning
  techniques
• No, this was consistent
• Data reducing uncertainty
• Yes, both scenarios within an uncertainty
  distribution
• The company was carrying too little risk due to
  inadequate data

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Questions

• How can extra data quality be justified and
  factored into AMPs?
• Automating data collection
• how investment now can save time and money in the
  future
• Building models for future planning
• Should companies be sharing historical data?
• Managing inflows of data from different
  contractors
• When modelling comes alive substantial savings
  can be made
• This is where the justification for data spend
  can be made
• e.g. through strategic optimisation
• ensure the right decisions on (inter alia)
  maintenance and replacement, at the right times,
  to get the required outputs over a time period

BUSINESS CASE
good data reduces risk
is this a powerful argument?
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• Financial Savings without Sacrificing Service

Problem Planning investment to meet contractual
obligations to maintain condition of assets
delivering cost efficiencies to the
business Solution Condition deterioration models
based on inspection reports. Optimise to get
least whole life cost, balancing capital projects
with maintenance. Result Investment plan to
deliver condition profiles at least cost over
contract period (30 years)
A saving of 12 on a capital and operational
budget of 450m pa
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improved quality and coverage of data
standard deviation
?
Effective AIP e.g. strategic optimisation
ensure the right decisions on (inter alia)
maintenance and replacement..
..at the right times, to get the required
outputs over a time period
mean
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Conclusion

• Improved Data and Improved Modelling leads to
• Reduction in the Risk companies carry (management
  of uncertainty)
• Financial Efficiencies

Modelling
(could be other processes affecting AM)
Effective Asset Management
Data
(data is the magic ingredient)
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Conclusion

• Improved Data and Improved Modelling leads to
• Reduction in the Risk companies carry (management
  of uncertainty)
• Financial Efficiencies
• Choose Modelling (I would say that!) the
  Chicken
• Data is inert the Egg
• Advanced modelling uncovers
• The value of data
• The risk that is being carried due to data
  deficiencies (and other things)
• The best-value path to improvement

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Questions

• How can extra data quality be justified and
  factored into AMPs?
• Automating data collection
• how investment now can save time and money in the
  future
• Building models for future planning
• Should companies be sharing historical data?
• Managing inflows of data from different
  contractors

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Should Companies Be Sharing Historical Data?

• For
• Data aggregation can be helpful in analysis
  techniques
• Substitution of relationships derived by others
  can be useful
• COMPANY 1 showed data transfer within company
  worked
• Against
• However, differences between companies
• in infrastructure
• should be compensated for if models are good
• definitions
• e.g.1 when is a blockage a blockage?
• e.g.2 condition assessment systems differ
• data collection protocols
• Alternative
• Or should you just improve your own data?

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Should Companies Be Sharing Historical Data?
KPI

• divergent definitions

• wrongly assumed definition

• correctly assumed definition

ability to benchmark
time
Conclusion

• Sharing data is possible and can bring
  advantages, but may not be necessary
• There are pitfalls which can be overcome
• Skill, awareness and experience are required in
  overcoming them

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Questions

• How can extra data quality be justified and
  factored into AMPs?
• Automating data collection
• how investment now can save time and money in the
  future
• Building models for future planning
• Should companies be sharing historical data?
• Managing inflows of data from different
  contractors

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The Pace of Innovation the Opportunities it
Brings

• Mobile Telephone 1980s
• Mobile Telephone 2007

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The Pace of Innovation the Opportunities it
Brings
Capacity
Transfer
Patterns
Compression
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Comes Down To Managing Your Data Process

• After all this technological advance
• .the human factors and contractual factors
  can dominate
• what incentives do contractors have to collect
  good data?
• is it down to the contract letter to provide
• equipment and systems?
• contract terms around data?
• to incentivise collection of quality data
• Data-use specialists advising clients on
• contract terms
• supply chain, PFI, workflow, incentives
• to get the right Asset Management Culture

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Overall Conclusions

• Modelling and Data Improvement
• Modelling
• uncovers the value arguments
• uncovers the risk that is being carried due to
  data deficiencies
• releases the value from data
• identifies the best-value path to improvement
• Sharing data
• can bring advantages, may not be necessary
• pitfalls can be overcome with skill, awareness
  and experience
• Technology offers possibilities in managing data
  inflows
• but poor contracts and behaviour can nullify the
  benefits of technology

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And Finally.A Warning About Data

• The past (historical data) isnt necessarily a
  good indicator of the future!
• But in all my experience, I have never been in an
  accident.of any sort worth speaking about. I
  have seen but one vessel in distress in all my
  years at sea. I never saw a wreck and never have
  been wrecked nor was I ever in any predicament
  that threatened to end in disaster of any sort.
• Captain E.J Smith, 1907, Captain, RMS Titanic

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Ensuring You Have Good Data to Support Decisions

• Dr Peter Skipworth
• Managing Director, SEAMS Ltd

Questions?