In today's tough business environment, companies are looking intensively at innovations that will drive efficiency and reduce operational costs, but still effectively manage operational risk and maintain safety. A key area of focus is on the use of big data technology for information management and predictive analytics that can help companies become 'data smart' in their operations.
Data smart operations
The insights gained from the vast amounts of available information will have a profound impact on how we do business in all of the asset-intensive industries that we serve, such as shipping, oil and gas production, oil and gas processing and delivery or the energy sector with the generation and delivery of electricity.
It has long been the case that asset operators have not made, or in many instances have not been able to make, full use of available information.
A single unscheduled downtime can cost in the range of $2-5 million per day. Approximately 50% of such incidents are due to various types of mechanical failures, which our products are ideally suited to help customers prevent. Poor information management is often a hidden cost, accounting for up to 20% of operational budgets. At a time when operational margins are low, this can make or break the operational viability of an asset.
Information management during asset lifecycle
An example is lost data, which is costly and sometimes impossible to track down or reproduce. Add to that the cost of replicated data, which over time becomes something of a parallel reality used in many business processes, and often fails to reflect the current state of the asset. Information management is important during changes in major lifecycle stages, for example from design and manufacture into the construction phase, and even more critical when the asset is moved into the operational phase. On top of this are the ever-increasing trends in mergers or assets changing ownership, particularly toward the end of design life when asset history information handover is seldom a priority. At this point the need for accurate historical detail is at its most critical in the assessment of fitness for purpose and extension beyond design life.
With the rapidly increasing availability of economically viable real or near real-time information via sensors and inbuilt health diagnostics encompassed in the industrial IoT (Internet of Things), the challenges of managing and using information smartly are rapidly increasing. Overcoming these challenges comes with outstanding potential benefits for our customers, when they are able to reduce unscheduled asset downtimes, improve asset efficiency, reduce environmental impact and eliminate regulatory compliance infractions. Regulatory requirements are increasingly stringent in all regions. In the US alone the penalties levied are around $33 million per year, and are a key concern, both due to the financial impact and the impact on business reputation.
Harnessing big data technology
To help you overcome these costly operational issues and take full advantage of technological advances, we are harnessing big data technology and advanced analytics for use in industrial applications – in particular for operational risk management of assets. This complements and helps advance the information management and advanced engineering innovations which DNV GL – Digital Solutions has delivered for over four decades.
Central to our next-generation offering, and supporting an ecosystem of asset-centric engineering applications, is the concept of a cloud-based digital twin. The digital twin is a virtual image of your asset, maintained throughout the lifecycle and easily accessible at any time. One platform brings all the experts together, providing powerful analysis, insight and diagnostics.
The digital twin addresses both the historical industry weakness of poor information management and simultaneously provides the platform to fully harness the vast increase in real and near real-time data that is now economically and technically viable to capture. The digital twin will be a single source for all asset information, including physical properties, mill certificates from steel production, construction inspections and acceptance tests, the operational business process state, production demand history and projections, risk levels, remaining life estimate and structural reliability. Via IoT technologies and data historians, the digital twin will also provide dynamic updates on condition and operational parameter states. The digital twin leverages your existing investment in enterprise asset management and design software by linking directly to this information and delivering it to the ecosystem of DNV GL – Software analytics applications in a consistent manner. The configuration of this ensures the most up-to-date information, and is a core part of the service.
With the digital twin in place, we will enable a seamless platform for collaboration across DNV GL, our customers and their customers and other stakeholders. Services and their results will be delivered digitally, accessible to the customer via the digital twin. The digital twin, combined with advanced analytical tools and machine learning, will provide a platform that changes the traditional way of how we look at the analysis of asset condition and performance. It will enable a new generation of advanced predictive analytics.