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The Art of the Possible: Towards Data-Driven Outperformance

Data: The Art of the Possible

By Christopher Steele, Head of Information Management & Analytics, Black & Veatch Water Europe

Water companies anticipate great things from digitisation, data analytics and smart tools. "We believe that digital technology is a key enabler to bring our vision to life," Liv Garfield, Severn Trent's Chief Executive has told investors; while appointing the company's first digital chief officer will, “transform the way we work,” according to Thames Water Chief Executive Steve Robertson. Yorkshire Water has pledged to become the first 'open data' water company as part of a bid for greater transparency and enhanced performance.

With the supply chain central to realising these ambitions, the desire to become an intelligent client is coming to the fore in discussions about how best to achieve data and insight driven outperformance of financial and operational targets. Here is an outline structure to reaching this point: practical steps – the art of the possible – to becoming an intelligent client.

The volume of data accessible to a water utility is potentially overwhelming. In addition there is a cost to capturing, storing and accessing each item of data. So it is vital to define the data that best supports water companies' business goals – and focus on that data only. Failure to achieve this has resulted in data gathering initiatives that cost more than the savings they were expected to yield. This is because of the costs associated with capturing and storing data and – most importantly – maintaining accurate, up-to-date information.

Like physical assets, asset data has a lifecycle. Around 20 percent of the cost of gathering asset data comes in the capital phase of the asset's lifecycle. The remaining 80 percent of data costs are generated during the operation and maintenance (O&M) phase of the asset's lifecycle. This is due in part to the length of the capital phase compared to the O&M phase, but mostly because the O&M data is live, evolving, and in need of ongoing monitoring, storage and updating. Understanding the costs associated with the different phases of the asset data lifecycle – and planning data acquisition accordingly - is the cornerstone of becoming an intelligent client. Harvesting data you do not need combined with the risk of using bad data comes, literally, at a price.

After the assets and associated data have been identified their criticality can be understood. This step focusses on what an asset or process is intended to do and identifying factors that stop it from performing as required. This information is used to inform measures to mitigate the factors degrading asset performance; creating a condition or output -based maintenance regime at the optimum balance between cost and risk.

This root cause analysis and failure mitigation phase will allow water companies to better understand planned and unplanned costs across comparable processes and, if they differ, understand why. This will give vital insights into the true cost-to-treat. Because this reporting is dynamic it reveals how efficiency degrades over time, shedding light on the causes and consequences of degradation. Full, real-world understanding of the performance of critical assets is revealed through a dynamic profile of risk.

Once asset performance is understood in this way the data can be used to inform responses that enhance performance, and ensure assets are optimised to a cost and risk profile that meets a water company's level of service encompassing financial and business goals. When an asset needs replacing the data life-cycle completes facilitating an informed decision about whether to do what has been done before  – like-for-like replacement – or, equipped with a full understanding of how the asset has functioned throughout its life, whether an alternative will better fulfil the utility's goals. With total cost/risk understanding a water company is able to prove – with its own data – what approach best meets its needs.

To take an asset life-cycle example, acceptance testing is generally a uniquely intense period of performance analysis, at the end of a capital project, to evaluate whether the new asset performs as required. Once a Failure Modes Effects and Criticality Analysis (FMECA) is expanded to produce a condition-based maintenance programme - the process outlined above - a water company has an embedded process that can sustain, for the duration of their lifecycle, that same acceptance criteria. With this knowledge they are empowered to work, with the supply chain, towards optimised performance-levels.

Looking at some of the emerging enablers to this process, the internet of things (IoT) offers a way to gather asset data to support prescriptive analytics. Based upon failure mode analysis, prescriptive analytics propose a remedial action when data indicates a failure is likely.

In the broadest sense, the IoT encompasses everything connected to the internet. Increasingly, however, IoT is used to define objects that "talk" to each other. The IoT is made up of connected devices – from simple sensors to smartphones. The internet's ubiquity and the availability of cheap sensors make possible ever increasing cost-efficient gathering of condition data which, in an ‘Intelligent Utility,’ is validated against the value it will add.

This differs from using more complex, and more costly, instrumentation that’s primary function is to protect or control, and is l typically incorporated into a Supervisory Control and Data Acquisition (SCADA) or Data Collection System.

Condition sensors connected via the internet for the purposes of analytics make the visibility of performance cheaper. This gives the flexibility to extend lower cost performance monitoring into areas where the level of criticality does not justify more expensive control and protection systems; but were the asset failure would not be without cost implications.

The steps outlined here are the art of the possible, proven and scalable. Single or multiple site trials are possible, but the benefits are most fully realised by enterprise level implementation. The result for one utility included a 30 percent reduction in reactive maintenance within 6 months of implementation.

Although these steps individually are not new, looking across the asset and data life-cycle holistically provides considerably more potential to meet and sustain a utilities level of service at optimum cost risk.

This article first appeared in The Water Report, June 2018.

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