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

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

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

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?

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)

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

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:

New Work Order mobile app

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

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

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)

‘Best Practice’ Business process maps Files from IBM Bluemix, see http://www-01.ibm.com/support/docview.wss?uid=swg21994865 HSE Process Maps use APQC process classification framework

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

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.

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