Solution Brief
Maximize equipment performance
Quickly increase overall equipment effectiveness (OEE) and meet or exceed production goals.
The issue
Improving asset or equipment uptime and availability is challenging. What can you do to predict unscheduled downtime on critical equipment? How – and where – can you improve asset inefficiencies for greater throughput? How can you shift from reactive and preventive maintenance to a predictive maintenance program for more uptime?
Suboptimal asset maintenance and the inability to resolve production issues result in lower availability, performance and quality while increasing production costs. Manufacturers need predictive maintenance solutions to meet their production goals.
Moving from reactive maintenance to functional predictive maintenance results in industrial average savings of:
- 10x return on investment.
- 25%-30% reduction in maintenance costs.
- 70%-75% elimination of breakdowns.
- 35%-45% reduction in downtime.
- 20%-25% increase in production.
– US Department of Energy, O&M Best Practices Guide
And when you add analytics that drives decision making to predictive maintenance (PdM 4.0) you can realize additional savings:
- 12% cost reduction.
- 9% improvement in uptime.
- 14% reduction in safety, health, environment and quality risks.
- 20% extension of the lifetime of an aging asset.
– PricewaterhouseCoopers, Predictive Maintenance 4.0 Beyond the Hype: PdM 4.0 Delivers Results
The challenge
Asset inefficiencies
Inefficiencies from aging and underperforming assets lower productivity, increase energy and maintenance costs, and can lead to quality issues. Root causes can be difficult to diagnose quickly.
Unplanned, unnecessary downtime
When maintenance is reactive, machines don’t run as often as they could, take longer to repair, interfere with other maintenance and can create safety issues.
Quality control
Poor quality keeps you from meeting production goals and eats away at time, resources, yield and revenue.
Sustainability
Manufacturers must meet the carbon reduction demands of external stakeholders, including investors, customers and governments – in addition to navigating the instability of cost and supply.
SAS enables you to:
(Adapt to emerging AI technologies to improve OEE.;Meet production goals while reducing costs so you stay competitive.;Predict and optimize maintenance.;Increase efficiency and uptime.;Lower energy costs and reduce waste.)
Our approach
Manufacturers struggle with reaching productivity targets for various reasons, such as aging equipment that’s no longer reliable or bottlenecks that lower throughput. Inability to optimize asset availability, equipment performance and product quality keeps manufacturers from realizing their true potential. SAS advanced analytics, powered by data and AI, helps you discover how to improve manufacturing OEE so you can optimize throughput, increase customer satisfaction, lower costs and make progress toward your sustainability goals.
We approach the problem by providing OEE software and services to help you:
Maximize equipment performance for greater efficiency and uptime
Increase throughput with trustworthy AI that helps you quickly see and address inefficiencies across all assets.
Optimize throughput and reduce costs
Meet production goals and lower unnecessary expenses with higher quality products.
Increase availability by minimizing planned and unplanned downtime
Achieve predictive maintenance to proactively lower the impact on operations, repair time and output with predictive maintenance solutions from SAS.
Improve sustainability efforts
Optimize the use of resources to reduce waste and energy output while reducing costs and protecting ESG scores.
Georgia-Pacific relies on SAS® Viya® on Amazon Web Services to improve equipment efficiency, reduce downtime, optimize shipping logistics and predict customer churn.
“We used advanced analytics to help increase the productivity of our facilities,” says Steven Bakalar, Vice President of IT Digital Transformation at Georgia-Pacific. “During the pandemic, we used analytics to improve our overall equipment efficiency by 10%, which helped us get more products in stores.”
Automation capabilities of the SAS platform also help reduce downtime. The efficiency gains the manufacturer has seen are impressive. "Our facilities that use these tools have experienced a 30% reduction in unplanned downtime,” Bakalar says.
SAS difference
OEE software from SAS enables you to discover unrealized opportunities from your existing equipment while reducing interruptions to production and monitoring and repairing quality issues. SAS anomaly detection algorithms look at the entire asset, not just one specific sensor. The results pinpoint the main issue so you can quickly discover what’s wrong via root-cause analysis.
SAS helps manufacturers:
- Improve performance
Automated monitoring and predictive alerts help you avoid major defects, prevent long downtimes and address potential performance issues before they escalate. SAS lets you perform the right maintenance at the right time for maximum uptime. SAS helps manufacturers balance speed and quality to maximize efficiency and profitability. - Shift to predictive maintenance
Examine the relationships and quickly predict the impact of key data that comes into play with your equipment so you can shift your maintenance strategy from reactive to predictive. SAS has helped customers prevent hours of downtime and reduce unplanned/unwanted events. - Improve problem-solving efficiency
Solve root cause issues faster and analyze in seconds what could take a half an hour with other software – anomaly detection algorithms look at the entire asset, not just one specific sensor. SAS helps manufacturers shrink diagnostic and repair times. - Empower users across the business
Provide users across the business with insights in a single place that includes self‐service data visualization, KPI dashboards and asset replacement decision support. - Save on unnecessary expenses
Save money on rush part deliveries, overtime repair costs, unnecessary part replacements and expensive safety stocks with best-in-class AI models all skill sets can create to maximize throughput. - Reduce energy without impact
Use advanced analytics models at the click of a button to dynamically compare current and historical production runs and show the drivers of energy consumption. By predicting the outcome of quality tests, you can amend the production process to lower energy while still maintaining a specific level of quality. SAS helps manufacturers save on energy usage and costs.
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Improve performance
Automated monitoring and predictive alerts help you avoid major defects, prevent long downtimes and address potential performance issues before they escalate.
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Build confidence in your assets
Predictive modeling identifies potential issues from aging and underperforming high-capital assets so you can trust them and keep them running at maximum efficiency.
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Reduce unnecessary expenses
Save money on rush part deliveries, overtime repair costs, unnecessary part replacements and expensive safety stocks with best-in-class AI models all skill sets can create to maximize throughput.
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Achieve predictive maintenance
Examine the relationships and quickly predict the impact of key data that comes into play with your equipment so you can shift your maintenance strategy from reactive to predictive.
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Increase productivity and save energy with greater efficiency
Provide engineers with the missing insights needed to quickly tune process setpoints with prebuilt ML models, visualizations and dashboards to run assets at maximum efficiency for higher productivity and energy savings.