Predictive Asset Management

Find out how organizations can shift from corrective to predictive asset management and react prior to the failure of a component. In this demonstration the focus is on a recent rise in asset repair costs and the ability to easily visualize and model the fundamental variables affecting asset lifecycles

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    • Predictive asset management with SAP Predictive Analysis and SAP HANA.
    • In this demonstration, you’ll see how SAP Predictive Analysis software and the SAP HANA platform enable you to identify root causes of asset failures,
    • provide proactive maintenance plans, and increase asset utilization through predictive insight.
    • Consider BestRun company, which has found that it is incurring much higher than expected costs for the repair and replacement of its field assets.
    • Sam, the site manager, asks Maria, a maintenance analyst, to work with the engineering and scheduling teams to evaluate the situation,
    • determine the source of the problem, and propose a mitigation plan.
    • Maria uses SAP Predictive Analysis to connect to SAP HANA and gathers the maintenance data for the past two years.
    • Using the facets view, she can quickly look at the history of major repairs and sees 27 incidents in just the last two years.
    • To gauge the severity of the cost problem, she uses a smoothing algorithm to draw a repair cost forecast for the next year.
    • The software presents a grid of projected repair costs, but the significance of the forecast is more apparent when viewed as a chart.
    • At this rate, repair costs will quickly become astronomical – so what to do?
    • Maria goes back into the predictive analysis visualization tool to determine what is driving this recent uptick in repair costs.
    • In 2012, the culprit is clearly motorized vehicles.
    • When she separates repair costs by month, she can see that the motorized vehicle repair costs have been rising throughout the year.
    • To learn more, Maria adds the equipment category to the chart, which provides even greater insight: It’s clear that the “truck” category is driving nearly all of the cost increases for 2012.
    • Maria saves the information and sends her findings to Ernest, a BestRun maintenance engineer, who will perform a root cause analysis to determine what is driving this increase in truck maintenance.
    • Ernest uses SAP Predictive Analysis to create a “heat map,” which maps the frequency of repair causes against asset types,
    • and he can see that “Cooling System Failure” is driving the truck repair cost increases.
    • Ernest then saves his findings and shares them with Robert, a maintenance planner and scheduler at BestRun, who will then determine the corrective action to be taken.
    • Robert can run a decision tree algorithm against the same data that Maria and Ernest have used to gain a better understanding of the factors contributing to the increase in cooling system repairs.
    • When Robert views the output of this analysis as a graphical decision tree, he can see that unplanned cooling system repair costs
    • can be virtually eliminated by scheduling maintenance inspections every 16 days or less.
    • By scheduling additional preventative maintenance inspections and servicing the cooling systems more frequently, Robert can mitigate the risk of cooling system failures and bring costs under control.
    • Robert tells Maria about the changes in maintenance schedules and Maria revises her maintenance costs forecast.
    • With the changed maintenance schedules focused on truck cooling systems, Maria’s forecast shows that repair costs will decrease over the next year.
    • At the next weekly meeting, Maria can now present her findings to Sam and the other stakeholders to inform them about improvements to maintenance cycles.
    • As you have seen, SAP Predictive Analysis and SAP HANA can provide predictive insights that enable you to identify root causes of asset failures,
    • provide proactive maintenance plans, and increase asset utilization.
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