Asset Dynamics / Covaris Joint Paper at EEA Conference
Asset Dynamics will present a paper jointly developed with Covaris at this year’s EEA Conference in Christchurch on 9 September 2025. The paper shares case studies that involve using data that are typically available to organisations in new ways — to better understand risk, optimise maintenance, and align asset information strategy with desired asset management outcomes.
Sweating the Data: Three Ways Companies are Diving Deeper to Understand Asset Performance and Risk[1], explores how organisations can realise greater value from their asset management data assets. Through three in-depth case studies, the paper shows how organisations can:
1. Develop quick-fire asset risk profiles using available time-series data to complement and calibrate asset health indicators.
2. Strike an optimal balance of preventive maintenance and inspection tasking that manages down the risk of asset failures, while ensuring limited field resources are deployed to the highest value tasking.
3. Align asset data with strategic asset management goals, ensuring the collection and use of only the most critical, decision-driving data.
[1] Paper authors: Jules Congalton, Bob Platfoot, Andrew Gatland
Paper Abstract
Over the past decade, organisations managing electricity generation, distribution, and transmission assets in New Zealand have achieved significant progress in enhancing their Enterprise Asset Management Systems (EAMS) and other asset management information systems. These advancements have laid the foundation for more effective data-driven decision-making, enabling organisations to gain clearer insights into asset conditions, network performance, and fault data. The adoption of IoT sensors and real-time monitoring technologies has allowed organisations to take a more proactive approach to managing their assets, helping them identify risks earlier, improve maintenance scheduling, and extend asset lifespans. However, despite these advancements, many organisations are failing to fully capitalise on the potential value of their data.
This paper encourages organisations to push boundaries in their use of asset management data to unlock untapped opportunities. Three case studies are presented which provide examples of salient, high value insights that have be gained from asset management data held by organisations. One such opportunity lies with improving risk monitoring by developing stochastically determined alarm limits and risk profiles based on quantitative time-series condition measurements. These insights enable targeted interventions that minimise downtime, optimise resource allocation, and enhance overall asset reliability.
Another area of unrealised value is the optimisation of preventive maintenance (PM) practices. By analysing PM routines and inspection results more deeply, organisations can determine whether inspection frequencies are sufficient to detect asset deterioration without being excessive. Additionally, inspection data can highlight overlooked failure modes, enabling refined maintenance strategies that improve issue detection while reducing unnecessary inspections.
Work management effectiveness can be optimised by leveraging advanced, insightful analytics, enabling deeper understanding and continuous improvement beyond traditional metrics. Analysing maintenance task outputs against industry benchmarks can help identify inefficiencies and prioritise high-value improvements. Evaluating metrics more deeply such as planning lead times, backlog risk and schedule compliance can reveal areas where risk is increasing within the asset portfolio, enabling resource rebalancing and workflow streamlining.
Finally, aligning data management with asset management processes is crucial but often overlooked. By focusing on critical data points that support key processes and decisions, organisations can achieve and demonstrate progress towards achieving strategic goals. Through the case studies presented in this paper, organisations can explore practical ways to harness their data for improved asset management outcomes. Despite the advances in data now available, significant value remains unrealised. This paper demonstrates three ways in which electricity sector organisations can unlock greater value from their data.
Keywords: risk management, asset condition, maintenance planning, renewals planning, analytics