Asset management in the Industry 4.0 era; the merge of the physical and digital world
In our previous blogs, we have outlined the important role digital asset engineering can play in getting the best out of physical assets and how digital supply chain engineering can be used by the industry to solve the problems associated with implementing a strategy for decommoditisation. In this blog, we will describe how digital technologies will increasingly help asset management practitioners determine the best ways in which industrial assets can be optimised based on the value adds from production, operation and supply chain perspectives.
The concept of asset management is far from new. Standards for its practice - from the British Standards Institution’s PAS-55 (published in 2004) to ISO’s 55000 series (launched in 2014) - have been available for some time. In 2017, ISO/TS 55010 was launched to provide guidelines for the alignment of financial and non-financial asset management functions. This standard was designed to improve the internal control that an organisation has of its management systems, the rationale being that the alignment of these functions would enable greater value to be derived from the implementation of asset management.
Asset management enabled by digital technologies
Digital asset management is also not new. Companies have developed information management systems to track the status of their assets according to the aforementioned standards. Tools of different scales have been developed based on various needs. For example, for civil infrastructure asset management, Geographic Information System (GIS) based tools are widely used. The functioning of these depends on proprietary software that tracks the physical status of assets, and their component parts, based on their location.
However, the practice of asset management encompasses much more than the tracking of 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 the condition of an asset to a level it supports its business objective. 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 iscollected in the information system. For supply chain and process modelling, however, less granular data is required. The data gathered for these applications should focus mainly on the most crucial components.
With the development of digital technologies such as big data, artificial intelligence (AI) and digital twins, digital asset management can step-up a level - combining these technologies to carry-out, for example, predictive or even prescriptive maintenance and linking asset management systems with process supervisory management systems.
Asset management challenges
Through our years of experience in asset management in different sectors, we have observed that many companies lack a mature asset management strategy. On the one hand, where a company’s strategy and other disciplines might not be linked properly to physical asset management practice, there is a strong need for cultural and organisational change. On the other hand, much of the data required for this change is not available.
A further issue is the lack of a single tool, or synchronised tools, that is/are able to manage different types of assets. As a result, asset managers need to switch from one tool to another for the various types of assets they manage, and the data are not stored in a central place - making company-wide decisions difficult to make. While data is crucial in defining how to extend the lifetime or performance of an asset.
What is more, many asset management tools were developed years ago, so they are not compatible with recently emerging technologies such as big data and machine learning techniques. As such, the gathering of big data and the use of AI to implement predictive maintenance and real-time digital twin simulations are challenging.
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. Therefore, each company has its own ways and standards for classifying assets, and – owing to a lack of data governance – the information that can be gleaned from the same types of assets at different sites is limited.
A successful digital asset management approach
To offer our clients a future-proof digital asset management service in a fast and effective way, Royal HaskoningDHV has accumulated decades worth of expertise in physical assets, asset management practice and is combining this now with digital technologies like AR/VR, data science and predictive simulation.
To implement the strategy of a company across all of its departments, we take a strategic asset management planning approach. Asset managers should work together with the owners of a business from the beginning to identify strategies together (top-down approach). Through this process information such as the organisational mission, objectives, policies, priorities, risks and decision-making criteria should be gathered and evaluated. Detailed information concerning assets should also be collected and examined (bottom-up approach). Here, asset portfolio, capabilities, risk opportunities, existing tools, asset management maturity and production capacities should be identified. With this knowledge, a customised long-term strategic asset management plan can be created in partnership with the client. Part of such a plan should be a robust data strategy and governance, for example based on a framework like Data Management Body of Knowledge.
Our quick scan asset-maturity tools can determine ways to save you time and money across the lifetime of your assets. With our detailed technical and Health Safety Environment (HSE) due-diligence tools, we can evaluate assets rapidly. We can model assets in a common data environment in a standardised way. When there are no international standards available, we can apply our experience of data engineering using semantic web technologies such as linked data. As a result, we can leverage our experience of working with different clients with the same types of assets.
With our developed cloud solution and digital twin capacities, we help centralise data and enable machine learning based on big data collected by sensors or regrouped from ERP-systems, in a secure and reliable way, counting on the common data schema we use for the same types of assets. In this way, we ensure that the needs of our clients are always met.
The key takeaways:
- Asset management and digital asset management are not new concepts, but in many organisations their implementation is far from ideal.
- Legacy digital asset management tools are not standardised, compatible with Industry 4.0 technologies or specialised enough to enable their organisation-wide use.
- We take a holistic approach to developing digital asset management programmes with our clients. With decades of experience in the field, coupled with expertise in emerging digital technologies, we help clients to run their organisations more efficiently based on their upfront identified value adds.
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