Digital asset management

Monitoring and managing your assets is an integral part of ensuring your organisation can perform. But often, asset management strategies and solutions fail to meet the required standards.

In this blog, we’ll discuss how digital technologies can help asset management practitioners determine the best ways to optimise industrial assets from a production, operation and supply chain perspective.

Digital asset management

The concept of asset management is far from new. Standards for its practice have been available for some time, from the British Standards Institution’s PAS-55 published in 2004, to the ISO 55000 series that launched in 2014.

 

In 2017, ISO/TS 55010 was introduced to provide guidelines for the alignment of financial and non-financial asset management functions. This standard was designed to improve the internal control organisations have of their management systems, allowing greater value to be derived from asset management.

 

Digital asset management is also not new. Companies have developed information management systems to track the status of their assets based on various needs for some time now.

 

Notable examples can be seen in civil infrastructure asset management, where Geographic Information System (GIS) tools are widely used. The function of these tools depends on proprietary software that tracks the physical status of assets, and their component parts, based on their location.

 

However, true asset management involves much more than simply tracking an asset’s physical condition. Asset management should be linked to enterprise strategy.

 

For example, if a company wants to extend the lifetime of an asset, then the tools used should be able to stipulate the minimum expenditure required to maintain its condition. However, if a company wants to improve the use of an asset, then the parameters will be very different.

 

The scale of asset management should also be tailored for different purposes. In machinery asset management, for example, detailed data from the most critical parts of each machine is collected in the information system. For supply chain and process modelling, however, less granular data is required.

 

Today, with the development of digital technologies like big data, artificial intelligence (AI) and digital twins, we can take digital asset management to the next level – carrying out predictive or even prescriptive maintenance and linking asset management systems with process supervisory management systems.

The challenges of asset management

Through our years of experience overseeing asset management in different sectors, we’ve observed that many companies lack a mature asset management strategy.

In some instances, a company’s digital strategy might not be linked properly to its physical asset management practice. In this case there is a strong need for cultural and organisational change. In other cases we find much of the data required for this change is not available.

A further issue is the lack of a single, unified tool – or synchronised set of tools – capable of managing different types of assets. Asset managers often need to switch from one tool to another for the various types of assets they manage, and data is rarely stored centrally.

This makes it incredibly difficult to make informed company-wide decisions. And data is crucial in deciding how to extend the lifetime or performance of an asset.

What’s more, many asset management tools were developed years ago, and aren’t compatible with recently emerging technologies like big data and machine learning. As a result, opportunities to gather big data and use AI to drive simulations and implement predictive maintenance are missed.

Last but not least, these tools have been developed by engineers whose expertise often lies in IT, not in the design and management of physical assets.

As a consequence, each company has its own standards for classifying assets, and – owing to a lack of data governance – the insights that can be gleaned from monitoring the same types of assets at different sites is limited.

The Royal HaskoningDHV Digital approach

At Royal HaskoningDHV Digital we’ve accumulated decades of expertise in physical assets and asset management. Today, we combine that expertise with the latest digital capabilities like digital transformation consulting, data engineering, data science, data consulting, and predictive simulation.

In doing so, we can provide our clients with a fast, effective and future-proof asset management strategy. And a strategic approach to asset management that ensures consistency across all departments.

For this to work, a combination of a top-down and bottom-up approach is required.

To begin with, asset managers need to work alongside business owners to identify strategies from the outset. This involves evaluating and deciding on key factors, like overall organisational mission objectives, policies, priorities, risks, and decision-making criteria.

Detailed information concerning assets should also be collected and examined. Here, asset portfolio, capabilities, risk opportunities, existing tools, asset management maturity and production capacities should all be identified.

With these elements in place, we can help our clients create a customised, long-term strategic asset management plan. Part of such a plan should be robust data strategy and governance practices, for example based on a framework like the Data Management Body of Knowledge.

With our quick scan asset-maturity tools, you can quickly determine ways to save time and money across the lifetime of your assets. And our detailed technical and Health Safety Environment (HSE) due-diligence tools, can help you rapidly evaluate the condition of your assets.

We can also model assets in a common data environment. And when there are no international standards available, we can apply our experience of data engineering and semantic web technologies like linked data. And leverage our experience of working with other clients with the same types of assets.

With our cloud solution and digital twin expertise, we can also help you centralise data, and enable machine learning based on data collected by sensors or regrouped from ERP-systems, in a secure and reliable way. In this way, we ensure that the needs of our clients are always met.