Powerful workflows – Integrating Insights
Increasing workflow efficiency is a key area of Asset Performance Management (APM). Poor information management is a hidden cost that can account for up to a fifth of operational budgets. New technology will enhance the existing workflows and create new ones!
In DNV GL, we are investigating workflows that will increase work efficiency and support the decision-making process during the operational phase.
We have been sharing these ideas at a few events around the globe – engineers have been welcoming these new ideas and sharing their concerns!
So, let’s start by considering what’s possible.
What workflows are possible?
The digital twin will be the hub where all information suitable for different analyses is made available. This includes design information, operational conditions, production targets and so on.
To this hub, we can connect all the different analytical solutions. These tools have been extensively implemented in different industries – there are several standards and recommended practices available which offer guidelines on how to implement and create value from some of these analyses.
One of the key areas of interest is the ability to easily update analytical models. Different services can be connected and trigger updates or simulation runs. This will produce new information based upon recently acquired data that might change the decision around a specific strategy.
So, the following is what we believe is possible given our existing tools and experience:
Apart from the connection to updated/real-time data, we can also start considering how the different analytical methods should work together. New workflows will reduce the barriers on information exchange between the department silos and increase the efficiency of information flow. This approach will ensure that information can be easily passed into the different methods, as soon as it becomes available.
Let’s consider one of the popular workflows we have been working on.
Workflow: Reliability Centered Maintenance plus Performance Forecasting
To address these two challenges, we could potentially make the screening process of RCM based on criticality analysis from Performance Forecasting. This will allow for a dynamic RCM process to take place, prioritising the production critical components and equipment items in the asset.
This also fits well with the different levels of analyses. Reliability data during the operational-phase could be fed into a Performance Forecasting model – this data does not need to be broken down into multiple failure modes. This will offer a top-down approach for the calculation of risk to production. RCM is a bottom-up and thorough process. Combining the quantitative approach of RAM analysis to the production risk measure to the detailed maintenance strategy analysis of RCM (via FMEA or FMECA) is extremely powerful.
Another benefit of this new workflow is the ability to validate the impact of the new maintenance strategy by simulating its potential behaviour using Performance Forecasting. Further KPIs can be incorporated to the workflow such as Operational Expenditure, profit loss based on unplanned shutdowns and long-term Net Present Value.
One limitation of this workflow that needs evaluation is restricting the risk assessment the production loss. However, this could be easily overcome with the association of other methods (SIL, RBI…) or even the consideration of different risks to the plant in the matrix.
All very exciting! And, as usual, any feedback is highly appreciated!