The new frontier in asset management is prescriptive in nature, and fresh tools are emerging to pave the way. Prescriptive analytics and prescriptive maintenance have a solutions focus. They integrate data and knowledge from multiple sources to identify the best course of action going forward. While prescriptive analytics can be used to drive broader business objectives, prescriptive maintenance uses targeted analytics to produce actionable asset management recommendations.
Combining data and knowledge to prescribe actions
The combination of asset data, first principles and empirical models, process knowledge, advanced analytics, and machine learning enables AspenTech Asset Performance Management (APM) solutions “to create a world that doesn’t break down,” says Robert Golightly, senior manager of asset performance management at AspenTech.
As a result, Golightly explains, organizations gain the process insights needed to accurately predict failures and obtain the prescriptive guidance that will help prevent and mitigate problems, building a sustainable competitive advantage throughout the asset’s entire life cycle.
The AIMMS Prescriptive Analytics solution uses optimization modeling to produce recommended actions. Gertjan de Lange, senior VP of connecting business and optimization at AIMMS, offers maintenance planning as an example. Assessing resource availability in the context of specific service maintenance requirements and staying operational at critical moments is a large puzzle with many variables and constraints that needs a lot of computational horsepower, he says.
“Applying prescriptive analytics increases operational uptime and allows you to schedule maintenance in such a way that it does not affect throughput and takes into account the scarcity of resources,” says de Lange. “A great example is our client Sasol, an energy and chemical company, who uses AIMMS to create optimal service schedules for its gas engines, which increased production by 4.6%.”
IBM Prescriptive Maintenance takes a similar approach. “By understanding operational data, it classifies assets as over-, under- or well-maintained, providing insight into factors that contribute to or detract from asset reliability, so reliability engineers can continuously improve their maintenance practices and resources,” explains Jiani Zhang, program director of offering management for IBM Watson Internet of Things.
She says that IBM Prescriptive Maintenance on Cloud can offer recommendations to improve maintenance strategy and optimize maintenance schedules, prescribe proactive actions to take based on predictive scoring, and provide a detailed comparison of historical factors that affect asset performance.
The Enterprise Operations Intelligence (EOI) product from IFS now leverages the IFS Dynamic Scheduling Engine (DSE), enabling prescriptive analytics to be performed “in any business scenario that involves scheduling.”
EOI gives a company the ability to map an organization from its strategy to operations down to the work execution steps, including inputs and suppliers, says Chuck Brans, VP of enterprise operational intelligence at IFS North America. “Because EOI knows the variables and drivers of the key metrics, EOI provides ‘what-if’ scenarios so managers can determine the base choice to optimize their performance, or EOI can even determine the best option.”
Opportunities for manufacturing and utilities
Analytics from the plant floor can solve common problems that manufacturers face. Rockwell Automation’s new FactoryTalk Analytics for Devices appliance targets lost productivity from unscheduled downtime. It transforms data from smart automation assets into health and diagnostics analytics dashboards, right on a mobile device.
“That same information will also help the application make prescriptive recommendations,” says Mike Pantaleano, global business manager of device/edge analytics at Rockwell Automation. “This way, manufacturers can improve equipment uptime and lower maintenance costs.”
ABB applies the prescriptive approach in its grid automation business to enable a “stronger, smarter, and greener grid.” Its Asset Health Center software, an APM solution, uses predictive and prescriptive analytics as well as customized models to help companies evolve from simple descriptive analytics to prescriptive analytical recommendations. As a result, utilities can improve asset performance and reliability as well as their processes for risk-based investment optimization.
The latest-generation Asset Health Center “combines the domain expertise embedded in ABB’s software-based technologies with the global scale of Microsoft’s Azure cloud platform,” says Rick Nicholson, manager of the global product management team within ABB’s Enterprise Software product group.
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