Once the asset is understood and modelled, different types of asset analytics can be performed to answer various business questions. The goal of each of the analysis elements is unique, however, they draw from the same common asset model framework. Some examples of analyses are to:
Maintenance strategy optimisation works by varying the applicable tasks and timings to determine the combination that delivers the most value, for the lowest (monetised risk) cost. Some subsets of maintenance strategy optimisation include Risk Based Inspections (RBI) as well as adaptive and online strategies. The optimised maintenance strategy for an asset can include elements of predictive, run-to-failure, time based, condition-based, or other approaches at a failure mode level. The strategy can also and is often optimal to vary over time.
Operational strategy optimisation is less common than maintenance strategy optimisation, however it still has it's place in increasing value. It works by altering the operational requirements of the asset, and measuring the trade-off between output and reduction of asset life. Overloading or under-utilisation can have a dramatic impact on asset performance as well as life. This optimisation approach aims to determine the operating parameters that will deliver the most value over the period.
Condition Assessments update the current status of the asset. From there, and according to whatever logic you require, Preventative maintenance (PM) plans can be generated.
Knowledge Management is about capturing the current understanding of your assets and business processes in a dynamic way, such that documentation and required business outputs can be generated instantly using the latest information at any time.
Asset Performance Management focuses on the performance metrics of an asset, and the factors or causes that lead to changes in performance. Understanding these relationships using asset models, allow organisations to reflect their current understanding throughout their analytics processes and carry over into the decision making.
Modla enables asset performance based digital twins by combining asset models with analytics. The models themselves output the digital twin representation of each asset, and the asset analytics creates the desired scenarios for comparison and reporting.
Spares analysis can be used to project failures and component usage rates based on the contents of each asset model. These can then implement minimum, maximum and restocking levels for depots or maintenance locations.
OEM recommendations are designed to minimise warranty returns. However there may be better approaches to maintenance that maximise the cost-benefit for your business. If context, condition, goals and objectives are also considered, then improvements can be made to the future plans and interventions for each asset.
Failure modes and effects are foundational to any great asset analytics journey. FMEA and FMECA are no longer a process that needs to be performed, but rather an output of the larger asset modelling approach.
Predictive analytics has two components. The first is understanding the current state of the asset, which includes condition assessment using asset information and predictive technologies and sensors. The second is to determine the likely (or predicted) degradation of the asset components and modes. Understanding the likely future projections enables better decision-making around interventions and plans.
Prescriptive analytics takes asset modelling to the next level, and makes recommendations around (or prescribes) the best way to proceed. This typically means recommending interventions and secondary actions at various stages of an asset's life.
Auditing plans and policies is easy when comparing the developed plan with a set of asset rules (from asset models) and a set of business rules (from asset analytics). This allows organisations to audit a plan or future pathway, against the most up-to-date thinking of the asset subject matter experts.
Determining the appropriate intervention decision around replacements and refurbishments is another area that can be improved. Each option may be beneficial at various stages throughout an asset's life, and modelling this can help determine the best way forward given the asset's current condition and context.
When modelling the complete value chain from condition to tasks, to costs and risks, an asset model can be used to generate profiles for budget setting and reporting. The trade-off between cost and risk is often one that is overlooked and/or poorly understood. Incremental additions to the maintenance strategy often result in over maintained assets. Understanding these relationships allows for traceable, and transparent decisions to be made, enabling effective management of risk and cost.
Equipment selection is an important decision making point for replacement equipment as well as new or greenfield projects. Optimum equipment selection is determined by varying the asset characteristics to select the best combination for the environment, operational requirements, and risks.
Equipment selection is an important decision making point for replacement equipment as well as new or greenfield projects. Optimum equipment selection is determined by varying the asset characteristics to select the best combination for the environment, operational requirements, and risks.
If you can articulate a method or process for answering a question from asset model outputs, we can build it into an automated block for connection in a Knowledge First Architecture (KFA).
