White Paper

An Executive’s Guide To Maximizing Production Assets

ContentsExecutive Summary ..........................................................1

The Business Impact of Unplanned Downtime ..........1

Traditional Approaches to Maintenance Aren’t Enough ....................................................................3

Maintenance ‘By the Book’ ..................................................3

A ‘Shotgun’ Approach to Maintenance ...........................3

Optimizing Outcomes with Predictive Maintenance ...................................................4

Key Benefits of Predictive Maintenance............................4

How Predictive Maintenance Works ..................................5

Maintaining Models Over Time ..........................................6

Getting Started with Predictive Maintenance ............6

Predictive Maintenance: Helping Companies Become Top Performers ..................................................6

About SAS .........................................................................7

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Executive SummaryIt’s common for large businesses to have hundreds of millions of dollars invested in production assets. And today, these assets – which range from factory machines to combustion turbines – are increasingly being viewed as strategic contributors to a company’s P&L.

But traditional meter- and calendar-based preventive mainte-nance approaches make it difficult to maximize key metrics such as return on capital employed (ROCE) and return on capital invested (ROCI). Most organizations use basic monitoring consoles provided by their asset manufacturers to monitor equipment performance. These consoles are limited in scope with little emphasis on analytics, resulting in isolated views into separate tags of assets. This process is resource intensive, time consuming, subject to false alerts and driven primarily by domain knowledge and biased judgment. Because it’s focused on reactive rather than proactive maintenance, it can’t help companies prevent equipment failures and performance degra-dations to avoid the high cost of unplanned downtime.

That’s why many forward-looking executives are turning to predictive maintenance (PdM) solutions that support an analytics-based approach to improving asset performance and uptime by predicting unplanned downtime and optimizing maintenance cycles. Leveraging real-time, critical asset oper-ating information and risk and profitability analysis, companies can develop tailored asset management strategies that are tied to business performance.

This paper explores the business case for investing in predictive maintenance solutions and examines how they work to lower maintenance costs and minimize asset performance-related disruptions across operations. It also describes what’s required to get started with predictive maintenance today.

The Business Impact of Unplanned DowntimeFor years, maintenance departments received little executive attention. Siloed from the rest of the business, maintenance professionals simply followed manufacturer and engineering recommendations on how to care for production assets, sched-uling downtime and striving to replace or repair parts before machines actually failed. These preventive maintenance activi-ties – which are designed to minimize unplanned downtime – are still the primary way that most companies support their massive investments in equipment. And as long as things run smoothly, maintenance departments seem to have little need to identify potential problems and head them off early.

But things don’t always go smoothly – one reason why asset maintenance is increasingly top of mind for senior executives. Upper management is typically accountable for maximizing asset utilization – and executives are often compensated on asset utilization levels tracked by metrics such as ROCE and ROCI.

If you are an executive responsible for an enterprise with large investments in assets and equipment, you know first-hand that unplanned downtime is one of the fastest ways to hurt ROCE and ROCI. Consider the following:

• For businesses managing multimillion dollar lines with continuous processes, the failure of one part in one machine can shut down production and cost millions of dollars a day.

• Unplanned downtime can cause companies to miss service level agreement (SLA) targets and pay high fines – as well as lose valuable customers because their company is perceived as unreliable.

• Gaining competitive advantage and maximizing share-holder value requires the continuous improvement of asset utilization.

As shown in Figure 1, the true and total cost of equipment failures runs deeper than you may think. When an equipment failure occurs, there is a significant loss of profits and accumula-tion of costs. The cost of equipment failure includes lost profit, the cost of the equipment repair, the fixed and variable oper-ating costs wasted during the equipment downtime and myriad consequential costs that reverberate and surge through the business. These are all paid for by the organization and viewed as poor financial performance by business operations manage-ment. When examined across industries, the costs of equipment failure amount to billions of dollars of lost profit per year.1

1 Mike Sondalini, “Equipment Failure and the Cost of Failure,” Business Industrial Network, http://www.bin95.com/equipment_failure_cost.htm.

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Figure 1: Borrowed from Mike Sondalini, Defect and Failure True Costing, 2006

Impacts of unplanned downtime can also extend beyond the general ledger. Share values are directly affected by maintenance and reliability practices, as failures can damage brand reputation and lower investor confi-dence. For example, for an oil and gas company, maintenance failures on an oil rig can mean reduced output at best – and catastrophic environmental damage or loss of life at worst. Events like these cause significant and lasting collateral damage to brands and stock values.

Catastrophic Failures Aren’t UncommonCatastrophic failures happen more often than you may think – even for large enterprises with the resources and commitment to optimize asset maintenance. Here are a couple of recent examples involving BP, the largest producer of oil and natural gas in the United States:

•ABPoilrefineryinTexassufferedanexplosionwhenan octane-boosting unit overflowed after maintenance crews restarted it. Gasoline vapors built up in an inadequate vent system, which ignited a blast that destroyed windows up to five miles away. BP assumed responsibility for the accident and faced a record $21 million fine from the US Occupational Safety and Health Administration. The company used a $1.6 billion fund to settle nearly a quarter of the 4,000 blast-related claims for injuries and property damage and now faces up to $2 billion in claims from another group of victims.2

•Afterdiscovering“unexpectedlyseverecorrosion”inits pipelines in Alaska, BP began a shutdown in the largest oilfield in the United States – Alaska’s Prudhoe Bay. According to CNN Money:

“ [Fadel] Gheit, [an oil analyst for Oppenheimer & Co.], said the problems with the pipeline should not be a surprise, adding it’s been well known that oil compa-nies are not doing enough regular maintenance on their infrastructure. When oil prices were low, they were reluctant to spend on that kind of maintenance, he said. But when prices soared in recent years, the cost of shutting down a pipeline or other facilities for mainte-nance would have meant too much lost production. ‘This thing has been in operation for more than 30 years. Corrosion has to happen. Something has to give,’ Gheit said. ‘This is going to be a warning to other companies’.”3

Although these examples involve a single company, the fact is no company is immune to the devastating conse-quences of major equipment failures. This means asset maintenance is not only an executive-level issue, but also a board-level issue.

2 Laurel Brubaker Calkins and Margaret Cronin Fisk, “BP Victims Call Deal `Lenient;’ Seek $2 Billion Fine,” Bloomberg News, http://www.bloomberg.com/apps/news?pid=20601087&sid=aZU5FlbVxjUI&refer=home.

3 Chris Isidore, “New worry for drivers: BP shuts oilfield: Damaged pipeline in Alaska affects 8% of U.S. oil production; crude surges; record gas prices seen,” CNNMoney.com. http://money.cnn.com/2006/08/07/news/international/oil_alaska/index.htm.

Defect and Failure

Total Costs

Product InventoriesInsurance SparesEmergency SparesReplacement Equipment

Additional CapitalDirectOverheadsOvertimeOEM

LaborIndexSubcontractCall-outs

Emergency HireContractorsTravel CostInvestigative Bodies

ServicesConsultationsInsuranceTime Loss

Emergency RentalE�ciency LossesEnergy WasteIsolation & Handover

EquipmentProcess CooldownStand-by PlantProcess Restart

Alternative SupplyAlternative ProcessingQuality DefectsLost Production

ProductWaste ProductClean-upReprocessingLost Sales

DirectDisposalQuality TestingShippingAdapted Design

MaterialsInventory/StoragePremium PriceInprocess WasteTime LossReplenish Raw Materials

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Traditional Approaches to Maintenance Aren’t EnoughRelying on traditional preventive-reactive maintenance strate-gies gives you little real-time insight into the actual condition of your plant assets. And this “blind spot” is the root cause of unplanned downtime, high maintenance costs and cata-strophic failures such as those just described. Let’s take a closer look at why.

Maintenance ‘By the Book’When you primarily rely on vendor manuals to develop main-tenance plans, you schedule maintenance according to a predetermined schedule, such as calendar time or equipment run time. This approach tends to drive up maintenance costs and increase planned downtime, as maintenance teams may replace parts or even entire machines before they actually have to. These costs can add up quickly. Research indicates that a third of every dollar of all maintenance costs is wasted as a result of unnecessary or improperly carried out main-tenance. Across the United States, the results of ineffective maintenance management represent a loss of more than $60 billion each year.4

In addition, how you set up, run and maintain machinery may not be as “optimal” as assumed by those developing standard guidelines. Your particular installation, for instance, may be creating excessive vibration, which will eventually lead to premature cracks or bearing failures. Unaware of these underlying problems, you may maintain machines “by the book” – but still experience frequent, unexpected failures and unplanned downtime.

A ‘Shotgun’ Approach to Maintenance If you combine vendor recommendations with asset-specific monitoring, you can improve outcomes – but not as much as you may think. Your maintenance department probably uses basic monitoring consoles provided by asset manufacturers to track equipment performance. These stand-alone consoles provide little analytical functionality, resulting in isolated views into separate tags of assets. Data (e.g., temperature, vibration

and pressure levels) from smart, microprocessor-based field instruments and sensors scattered across the production environment are uploaded to these consoles. In the end, main-tenance departments end up with massive amounts of frag-mented data about individual assets – but gain no insight into what conditions, change, or events typically precede failures that ultimately result in unplanned downtime.

This “shotgun” approach to scheduling maintenance, while more effective than relying on machine manuals alone, is prob-lematic because it attempts to measure everything – rather than what’s most important – and does nothing to help maintenance teams prevent failures. In addition, it is:

• Time- and resource-intensive – Because this approach involves having a monitoring system for every asset, you have to train employees to use many systems and generate reports using different methods, which increases inefficiency and drives up costs.

• Subject to false alerts – As soon as a sensor detects condi-tions that are out of range, it generates an alert. Given that large machinery can have many sensors (for example, a single gas turbine can have 700 sensors), maintenance teams can get overloaded with several thousand alerts per day – many of which are meaningless. They need a way to combine and analyze alerts and focus on what’s meaningful from a maintenance perspective.

• Driven primarily by domain knowledge and biased judgment rather than actual data – Because maintenance teams find themselves drowning in sensor data, they end up relying on the intuition and gut instinct of experienced employees to develop maintenance plans. These experts are sometimes very good at diagnosing problems after the fact – but they can’t identify root causes or patterns of conditions that lead to failures. And when these employees retire, you lose their expertise.

In either case, if you rely exclusively on a preventive-reactive approach, your maintenance activities will not only cost too much – but also fail to help you achieve the highest levels of plant and machine reliability.

4 R. Keith Mobley, An Introduction to Predictive Maintenance, 2nd Edition. Butterworth-Heinemann, 2002.

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Optimizing Outcomes with Predictive MaintenanceAs an executive, what can you do to maximize reliability and optimize asset utilization? To prevent failures of complex machinery and equipment, you need powerful analytics to detect the root causes of failures and their indicators – not just their symptoms. Armed with this insight, you can proactively address root causes and actually prevent failures.

New PdM – or asset performance – technologies provide this level of analysis so your maintenance department can take timely, corrective action. Predictive maintenance solutions help you determine the condition of in-service equipment in real time – and then use this information to predict when maintenance should be performed. You can identify patterns of leading indicators that typically occur before you have a problem, use this insight to predict when an asset will fail and then adapt maintenance plans to prevent costly or catastrophic incidents from ever occurring.

This approach is different from preventive maintenance, which occurs on a predetermined schedule. Predictive maintenance is based on the actual condition of the equipment. Because main-tenance tasks and part replacements are performed only when they are actually required, you can significantly reduce planned and unplanned downtime, as well as lower overall maintenance costs. You can also better plan for maintenance downtime and therefore minimize business impact; for example, activities can be performed during scheduled outages so you don’t have to interrupt operations twice, or when machines are not running at full capacity. In other words, you can achieve optimized, sustain-able maintenance strategies that improve the performance, availability and reliability of production equipment.

Key Benefits of Predictive MaintenanceWith a carefully deployed predictive maintenance solution, you can increase the longevity and reliability of plant and other assets – and ultimately increase the profitability of operations. You can also differentiate from the competition by consistently satisfying customer needs and meeting SLAs. These benefits are possible because you can:

• Reduce downtime and cost: Near real-time monitoring and predictive alerts help you detect root causes and correct issues earlier to mitigate the risk of failures and outages. You can improve equipment reliability and avoid major defects that can cause long downtimes.

• Lower maintenance costs: Early warnings also enable you to achieve more cost-effective maintenance. For example, you can shift assets from the conservatively calculated preventive maintenance cycles to predictive maintenance, so you only service the assets when needed. You can also reduce man-hours and maintenance costs by pinpointing problems and properly aligning resources.

• Reduce unscheduled maintenance: Predictive and near real-time performance alerts enable your maintenance teams to fix issues during previously scheduled maintenance outages, which reduce the need for more costly unscheduled maintenance.

• Improve root cause analysis: The analytics and predictive data mining capabilities of PdM solutions support contin-uous improvement in equipment reliability, efficiency and quality. They also help you identify the real drivers of perfor-mance issues from among hundreds and even thousands of measurements and changing conditions – insight that engi-neers can use to determine and initiate the best mainte-nance plans and corrective actions.

• Improve visibility: Enterprise maintenance-centric data models enable you to capture large volumes of data, regard-less of format or source and then transform, standardize and clean this data for analysis. You gain unprecedented visibility into the current and future state of your assets.

• Increase the longevity of assets: By creating maintenance schedules that are based on historical data, you can create tailored plans that optimize the longevity of assets, which drives up asset utilization and lowers asset investment requirements.

Department of Energy Confirms the Benefits of Predictive MaintenanceA recent cross-industry study published by the US Department of Energy confirms that companies using a predictive maintenance approach realize significant benefits, including:

•Ten-foldincreaseinreturnoninvestment.

•A25to30percentreductioninmaintenancecosts.

•Eliminationof70to75percentofmachinebreakdowns.

•A35to45percentreductionindowntime.

•A20to25percentincreaseinproduction.

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• Facilitate compliance: PdM makes it easier to meet chal-lenging regulatory compliance mandates to avoid penalties, liabilities and brand damage.

• Optimize model management: You can better comply with business rules and guidelines when taking models into production by using software that logs all activities during the life cycle of a model and makes it available for auditing.

How Predictive Maintenance WorksSo how does the crystal ball of predictive maintenance work? As shown in Figure 2, PdM involves a four-step process:

Analyze, Alert and Predict

The next step is to build predictive models that will score incoming sensor data against the model and generate accurate alerts before an event occurs. Models should combine historical events with related utilization and sensor data (such as tempera-ture, energy consumption and vibration levels) in order to identify root causes and make correlations between patterns and events. By analyzing the conditions that led to issues in the past – not only at the time of an event, but also before it occurred – you can identify real indicators of future events and trigger alerts as early as possible so that maintenance teams have time to react. Analysis can also incorporate other business information to broaden insight and support other types of decisions. For example, by analyzing financial data along with sensor data, you can determine the business impact of various potential failures and prioritize alerts accordingly.

After you create predictive models, you fine-tune them by collaborating with reliability or maintenance engineers and testing them against historical sensor data. The goal is to create models that are sensitive enough to detect all upcoming failures – but not so sensitive that they generate false alerts.

Understand, Optimize and Report

Using insights obtained from predictive models, the next step is to optimize maintenance schedules in ways that reduce downtime, lower maintenance costs and prevent catastrophic failures. Strategic maintenance schedules typically include a mix of reactive, preventive and predictive maintenance. Armed with analytical insight, you can determine just the right mix for your business by using:

• Predictive maintenance for business-critical machines.

• Preventive maintenance for less critical machines.

• Reactive maintenance for relatively low-cost, inconse-quential machines.

Optimization can also involve extending maintenance sched-ules, combining jobs to minimize downtime and coordinating the schedules of traveling maintenance professionals. For example, by identifying areas where you’ve never had a failure (and the impacts of a failure are low), you can choose to push out scheduled maintenance activities to a time when a main-tenance professional is already scheduled to visit a particular location. This enables one person to handle all maintenance tasks during a single visit, which increases efficiency, lowers costs and minimizes planned downtime.

Once you’ve developed a strategic, optimized maintenance plan, you can integrate it back into operational systems, such as ERP or CMM software, to drive execution across your organization.

Figure 2: PdM Drives Lifecycle Improvement Processes

Acquire and Manage Data

The first step is to collect and combine the data – most of which is collected by sensors attached to machinery. Vast amounts of data are analyzed in near-real time, or “right time,” so that maintenance teams can receive predictive alerts and fix issues before problems actually occur. But an end-to-end PdM solution requires more than just sensor data. You also need to incorporate and analyze data that comes from your ERP or CMMS (computerized machine maintenance) systems. For example, maintenance and inspection records allow you to perform root cause analysis and translate alerts into actionable maintenance orders.

Before the data can be loaded into a reliability-centric enter-prise data model, it has to be cleansed so that unstructured information is analyzable. You can also search maintenance or inspection records for specific data using text mining tools.

•Maintenance optimization

• Business process integration

•Data model•Data integration

• Condition prediction

• Optimization and alerts

•Rules management• Performance

management

Acquire andManage Data

Analyze,Alert and

Predict

Understand,Optimize and

Report

Act andAdapt

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• Automated monitoring and dashboards: Essential to any PdM solution is standardized, near real-time monitoring capabilities that consolidate views of sensor data. Ideally, you want to have one console for monitoring the real-time condi-tions of all machinery and assessing the likelihood of failures.

• Model management: Because equipment performance changes over time, you need a solution that monitors the accuracy of your predictive models across the life cycle of each asset. In addition, when you have a large number of models in production, you also need to document the history of the models along their life cycle.

• Advanced analysis workbench: Advanced analytics are critical to the success of any PdM solution, as these tools enable maintenance and reliability engineers to analyze performance issues in a highly interactive and visual envi-ronment using the most powerful techniques available. Analytical tools should serve a variety of users, ranging from the casual user to the high-end statistician. They should also provide a broad spectrum of tools to help you identify the root cause of performance issues, as well as metrics and conditions that indicate future problems.

• Reporting and KPI dashboards with drillable alerts: At any time, PdM solutions should give you access to key mainte-nance and reliability performance information. Ideally, this information is available through a Web-based, point-and-click interface so that anyone – even someone without exper-tise in statistics – can generate charts, graphs and reports in minutes and easily share them across the organization.

Predictive Maintenance: Helping Companies Become Top PerformersPdM technologies can play a vital role in helping you achieve higher asset utilization and reliability – and ultimately greater competitiveness. But as noted by US Department of Energy, adoption of predictive maintenance approaches has been slow – primarily because the benefits of PdM have not been properly linked to operations as a whole or to P&L statements. This is where PdM can deliver significant, bottom-line business value. “A more compelling restatement of these benefits would report millions of dollars of production-enabled economic value of increased capacity and overall reduction in various production cycle times translated into inventory turns. It’s clearly time to close the gap between asset maintenance, management and operations performance reporting.”5

Act and Adapt

Once your predictive maintenance models are in produc-tion, incoming sensor data is automatically scored against the models. Whenever a combination of actual conditions matches one of the clusters of events linked to a potential failure, an alert is sent to the responsible technician or engineer. Predefined analysis should support routine analysis and root cause analysis. These analytical tools should be easy to use so that technicians without any statistical background can use them.

Maintaining Models Over TimeIt’s important to note that predictive models have a shelf life and need to be updated periodically. As asset performance and conditions change and utilization patterns shift, so will the indicators or precursors of events along the life cycle of an asset. When this occurs, the accuracy of models decreases over time. Ideally, you want to be able to automatically monitor and send out alerts when the accuracy of a model decreases beyond a certain value along the life cycle of an asset. When a model needs to be retired and replaced by a new, more accurate one, the entire predictive maintenance life cycle starts again.

Getting Started with Predictive Maintenance Implementing a successful PdM solution requires a multi-step process of gathering data; bringing it together in one compressed, cleansed data warehouse; and then using this data to perform predictive analytics. You also need to analyze all incidents that have occurred in the past and relate historical sensor data to develop a predictive maintenance model. All of these activities require a solid technical foundation that delivers the following:

• Integration and management of all relevant data: You need data management capabilities that allow you to consolidate data sources from multiple metering, monitoring and surveil-lance systems into a big-picture view of performance across all operations. Ideally, you want to be able to access informa-tion from work orders, maintenance schedules, regulatory requirements and resource availability – all from a single, reliable source. You also need embedded data quality to ensure the accuracy and integrity of all analyses.

• Predictive modeling and alerts: You also need sophisticated data mining functions that allow you to explore historical, real-time and right-time data and spot trends that could provide early warning of possible issues. Solutions should also support early warning alerts that you can send automati-cally to the appropriate person for intervention.

5 US Department of Energy cross-industry study on PdM, http://energy.gov/eere/femp/predictive-maintenance.

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Reliability expert Terrence O’Hanlon, CMRP and publisher of Reliabilityweb.com, further validates that PdM can play a critical role in driving corporate performance. He notes that according to recent research performed by Reliabilityweb.com, companies performing in the top 20 percent share three common traits:

• A senior level executive is directly responsible for plant asset maintenance and reliability.

• Proactive strategies such as predictive, condition-based maintenance and reliability-centered maintenance have largely replaced reactive and time-based maintenance strategies.

• Adoption of advanced technologies to monitor and report leading key performance indicators (KPIs) across the enter-prise with all stakeholders in real time.6

If you are interested in learning more about using PdM to lower risk, improve asset availability and ultimately improve overall business performance, please visit: sas.com/en_us/software/supply-chain/asset-performance-analytics.html.

About SAS SAS offers all of the capabilities described in this document to improve the quality of products, processes and services. SAS® Asset Performance Analytics helps organizations achieve optimized, sustainable maintenance strategies and improved performance and availability of equipment. Look to SAS for solutions that enable you to:

• Improve equipment reliability and reduce unplanned downtime.

• Provide early warnings for more cost-effective maintenance.

• Reduce man-hours and maintenance costs by pinpointing problems and aligning resources.

• Detect and correct issues earlier to mitigate the risk of failures and outages.

SAS is the leader in business analytics software and services and the largest independent vendor in the business intelligence market. With innovative business applications supported by an enterprise intelligence platform, SAS helps customers at 45,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world THE POWER TO KNOW.®

6 http://www.prweb.com/releases/2008/07/prweb1104684.htm

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