Maximizing fleet availability and utilization while making the best use of assets and controlling spend is a daily challenge. Maintenance and Operations teams need to understand the current state of the fleet, predict upcoming unscheduled maintenance, and plan for future operations, all based on an incomplete view of their fleet and using historical data. In reality, the challenge is not a lack of data, but how to effectively transform data into actionable forecasts.
The limitations of traditional forecasting
While all fleet operators have maintenance forecasting systems to try and anticipate parts and maintenance needs, developing an accurate forecast remains elusive for most. This is because traditional forecasting methods only take historical trends into account, overlooking other key variables that will affect asset lifecycle or performance. For a truly accurate and verifiable forecast, fleet operators need to adopt a Predictive Maintenance approach to forecasting.
The power of predictive maintenance
Clockwork’s Lifecycle Analytics Platform creates a Predictive Twin of your fleet as a bottom-up, reliability-based statistical model of each asset and all its relevant parts. In addition to providing a holistic view on the current health of the fleet, it is also the basis for high-fidelity parts and maintenance demands forecasting. Using an advanced analytics engine, Clockwork can simulate asset operations into the future while taking into consideration any changes in the asset’s operating environment. The result is a detailed, verifiable, accurate multi-year forecast that can dramatically reduce expenditures while maximizing fleet availability. The Predictive Twin model can also provide insights that help organizations identify and address problems before they occur by optimizing the maintenance and supply infrastructure.
Powerful “What-if” scenario editors allow for the simulation of alternative forecasts based on user-de ned changes to any aspect of the predictive model. By creating multiple alternate scenarios, asset managers can compare and contrast different strategic approaches that maximize availability while controlling spend and minimizing risks.