1. Asset condition and priority in Maximo
Moving from works management to asset management
Vic/Tas Maximo User Group, Nov 2018, Zoltán Kelly

2. Traditional approaches at Melbourne Water
Water supply is critical infrastructure: approaches to asset management are highly risk averse, leading to over-servicing of assets
Set and forget: Maintenance and asset replacement regimes defined by standards without ongoing consideration of operating context
Reviews and optimisation require effort - manual aggregation of data
Strategy
Planning
Maintenance
Operations

3. Approach so far
Maintenance – KPIs for work effectiveness (on time/on cost). Assumptions that the correct work is being requested
- High volume of manual inspections
Operations – Driven by product quality as top priority. Decision making through based on plant performance within SCADA
Strategy and planning – renewal planning and risk assessments managed outside of the asset management system at a high level

4. Revised approach
Where and how is it cost effective to introduce condition assessments?
How can we convert asset condition to asset and work priority?
How can we ensure the insights are consistently interpreted throughout the asset lifecycle?

5. What does this mean in reality?
AUTOMATED (real time)
Integrations from SCADA+IoT in to Maximo to trigger work (meter based PMs)
FORM-BASED (daily/weekly)
Present data in mobile apps (service requests, work orders). Fit for purpose electronic forms (PEGA, ESRI Survey123) synced back to Maximo to capture in-field condition assessments
EXTERNALLY CAPTURED (monthly-annually)
Where real-time/sensor data is not available, capture SME knowledge as quantified measure (e.g. meter reading or static attribute on assets). Follow consistent business process to maintain this (annual reviews)
Make asset condition data available wherever the decision is being made
2-way integration. Show live data or near real-time data in desktop tools (SCADA); mobile systems, dashboards, and analytics platforms (BI)

6. Maintenance
What is this informing?
Condition assessments > work order prioritisation
Incident response
Secondary: inventory management; workforce skill prioritisation
How can we use Maximo to inform these decisions?
Use Condition for Work (HSE) and Reason for Work (HSE) to calculate Work Priority and remove subjectivity
Make visible in Scheduling applications (manual)
Future state:
Drive schedule through optimisation engine

7. Prioritisation Matrix (HSE)
Asset Criticality Consequence x Likelihood >> Location Priority
Consequence
Low
High
Likelihood E D C B A 1 2 3 4 5
Reason for Work
INVESTIMPROVE
HEALTHSAFETY .
Loc Priority 1 2 3 4 5
Low 7 11 8 12 15 6 9 13 16 18
High 10 14 17 19 20
Location Priority x Reason for Work >> Work Priority:

8. New Work Order mobile app

9. Operations
What is this informing?
Priorities for plant availability
Permit and isolation management
How can we use Maximo to inform these decisions?
Capture operating context and operating procedures
Consideration of priorities when raising defects / breakdowns
Future state
Capture benefits and losses
Incident response

10. Real time data, analytics, and Maximo
Receive:
Meter readings
Send:
Asset history
Work history
Federated MBOs

11. Planning and asset strategies
What is this informing?
Replace or extend asset life
Maintenance regime cost optimisation (e.g. iterative RCM)
How can we use Maximo to inform these decisions?
Analyse performance vs maintenance
Age-based degradation
Predictive (meter-based) PMs
Future state
- All of the above (not yet started)

12. ‘Best Practice’ Business process maps
Files from IBM Bluemix, see HSE Process Maps use APQC process classification framework

13. Using ML to bridge gaps in data
PCR codes
- Initial proposal - use natural language processing (e.g. IBM Watson Natural Language processing) to recommend a PCR codes to be assigned to a work order based on input from structured (asset class) and unstructured data (descriptions)
- Use this to backpopulate PCR codes for historic work orders
Environmental health of waterways
Waterways condition assessed at spot points along waterways (water quality and bug monitoring)
Boosted regression tree model to interpolate data to give all waterways assets a priority ranking based on habitat suitability
5 year research program with Melbourne University

14. Combined result – observed vs expected sewer flows
Observed (SCADA) over expected (modelled) flows Operations can pre-empt network issues and changes. Informs maintenance coordination. Feedback loop to planners regarding model accuracy.

15. Move from a top-down approach to an iterative lifecycle
Result
Design
Acquire
Install
Operate
Maintain
Refurbish
Move from a top-down approach to an iterative lifecycle
Consistent understanding across the business
Secondary benefits: allows for staff rotation; comparison across different sites