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Prevent asset failure, detect quality issues and improve operational processes
Predictive Maintenance & Quality
Hendrik Leddin
Client Technical Specialist
Predictive Maintenance & Quality
IBM Deutschland GmbH
© 2015 IBM Corporation 17 February 20162
Predictive Maintenance & Quality
Predictive maintenance is what its name implies: Maintaining resources and machines, whether large or small, according
to fact-based expectations for when they will fail or require service. A predictive maintenance and quality solution can also
detect anomalies within processes, compare parts against their master, and conduct in-depth root cause analysis.
Predict part quality
- Improve quality and reduce recalls -
- Reduce time to identify issues -
- Improve customer satisfaction -
Predict asset failure
- Estimate and extend component life -
- Increase return on assets -
- Optimize maintenance schedule -
© 2015 IBM Corporation 17 February 20163
Leverage the full potential of your processes
Variation in production or maintenance processes is unavoidable. Even state-of-the-art machines cannot hold perfectly
the target setting. Achieving consistent quality requires understanding, monitoring, and analyzing variation of parameters.
Using mathematical techniques to determine the stability and predictability of a production or maintenance process.
Statistical Process Control
as a method of quality control to monitor
and analyze machine data
Quality Early Warning System
detection of developing quality
problems at a low rate of false alarms
© 2015 IBM Corporation 17 February 20164
Find deeper insights and drive real-time actions
Turn data into deeper insight, confident decisions and faster, in-time actions, with extensive analytics capabilities from
IBM. With extensive analytics capabilities, you can understand what happened, what will happen and what should
happen. Easily engage both business and technical users to uncover opportunities and address big issues.
Predictive Analytics
empowers you to analyze trends, patterns
and relationships in your
structured and unstructured data
Optimization
enables for improving efficiency,
reducing costs, and increasing profitability
© 2015 IBM Corporation 17 February 20165
Make the right production adjustments and meet tight tolerances
Each day, production-quality analysts run the more than 500 production-line variables they track through predictive
models that tell them which specific parts of the line need to be adjusted to ensure that products remain within their tight
tolerances. The solution also provides predictive insights on which production assets should be preventively maintained to
avoid future problems.
25% increase
in the overall productivity of the
customers production line
>50% reduction
in the time required to ramp up the
process to target levels
© 2015 IBM Corporation 17 February 20166
Data analytics solution optimizes and reduce energy costs
A supermarket group conducted a feasibility study that found there was a huge variation across stores in the way
refrigerator temperatures were managed. By acquiring and harvesting the readily available data from one store and
applying big data statistical approaches such as predictive analytics to a large data set, the supermarket group was able
to validate refrigeration performance and identity case anomalies against standard policies
20% reduction
in energy costs by helping ensure that
refrigerators operate at optimal temperature
USD25 million
annual savings that could be realized
through energy analytics applied to
wider store chain
© 2015 IBM Corporation 17 February 20167
Predict equipment malfunctions and avoid product defects
The optimisation of plant production is the key to profitability. Some of the main parameters include throughput, product
quality, machine availability and efficiency. Small flaws, such as temperature imbalances in a furnace or the wrong tension
setting on a steel roller, can lead to defective products, customer dissatisfaction and costly delays. The IBM solution
analyzes large volumes of production data to seek patterns in equipment operations, product quality, failure patterns.
USD2 Million
reduction of costs for every 0.1% improvement
in production efficiency
Boost skills transfer
by embedding process knowledge into
equipment and process optimization
algorithms
© 2015 IBM Corporation 17 February 20168
Demonstration: IBM Predictive Maintenance & Quality
The IBM Predictive Maintenance and Quality solution helps you monitor, analyze, and report on information gathered from
devices and recommend maintenance and quality activities for those devices. With this integrated solution, you can
accomplish various tasks to gain efficiency and save costs.
© 2015 IBM Corporation 17 February 20169
Next Steps – Become a data-driven organization now!
A five-segment methodology spans a predictive analytics discovery workshop with desired outcomes. Together we will
narrow down different use cases in order to find and discuss the right advanced analytics approaches in the right areas
and outline the business impact and benefits when applying advanced analytics to your organization.
Hendrik Leddin
Client Technical Specialist
Predictive Maintenance
& Quality
IBM Deutschland GmbH
IBM-Allee 1
D-71139 Ehningen
Mail: D-71137 Ehningen
Mobile +49-173-25-35-37-9
hendrik.leddin@de.ibm.com

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  • 1. Prevent asset failure, detect quality issues and improve operational processes Predictive Maintenance & Quality Hendrik Leddin Client Technical Specialist Predictive Maintenance & Quality IBM Deutschland GmbH
  • 2. © 2015 IBM Corporation 17 February 20162 Predictive Maintenance & Quality Predictive maintenance is what its name implies: Maintaining resources and machines, whether large or small, according to fact-based expectations for when they will fail or require service. A predictive maintenance and quality solution can also detect anomalies within processes, compare parts against their master, and conduct in-depth root cause analysis. Predict part quality - Improve quality and reduce recalls - - Reduce time to identify issues - - Improve customer satisfaction - Predict asset failure - Estimate and extend component life - - Increase return on assets - - Optimize maintenance schedule -
  • 3. © 2015 IBM Corporation 17 February 20163 Leverage the full potential of your processes Variation in production or maintenance processes is unavoidable. Even state-of-the-art machines cannot hold perfectly the target setting. Achieving consistent quality requires understanding, monitoring, and analyzing variation of parameters. Using mathematical techniques to determine the stability and predictability of a production or maintenance process. Statistical Process Control as a method of quality control to monitor and analyze machine data Quality Early Warning System detection of developing quality problems at a low rate of false alarms
  • 4. © 2015 IBM Corporation 17 February 20164 Find deeper insights and drive real-time actions Turn data into deeper insight, confident decisions and faster, in-time actions, with extensive analytics capabilities from IBM. With extensive analytics capabilities, you can understand what happened, what will happen and what should happen. Easily engage both business and technical users to uncover opportunities and address big issues. Predictive Analytics empowers you to analyze trends, patterns and relationships in your structured and unstructured data Optimization enables for improving efficiency, reducing costs, and increasing profitability
  • 5. © 2015 IBM Corporation 17 February 20165 Make the right production adjustments and meet tight tolerances Each day, production-quality analysts run the more than 500 production-line variables they track through predictive models that tell them which specific parts of the line need to be adjusted to ensure that products remain within their tight tolerances. The solution also provides predictive insights on which production assets should be preventively maintained to avoid future problems. 25% increase in the overall productivity of the customers production line >50% reduction in the time required to ramp up the process to target levels
  • 6. © 2015 IBM Corporation 17 February 20166 Data analytics solution optimizes and reduce energy costs A supermarket group conducted a feasibility study that found there was a huge variation across stores in the way refrigerator temperatures were managed. By acquiring and harvesting the readily available data from one store and applying big data statistical approaches such as predictive analytics to a large data set, the supermarket group was able to validate refrigeration performance and identity case anomalies against standard policies 20% reduction in energy costs by helping ensure that refrigerators operate at optimal temperature USD25 million annual savings that could be realized through energy analytics applied to wider store chain
  • 7. © 2015 IBM Corporation 17 February 20167 Predict equipment malfunctions and avoid product defects The optimisation of plant production is the key to profitability. Some of the main parameters include throughput, product quality, machine availability and efficiency. Small flaws, such as temperature imbalances in a furnace or the wrong tension setting on a steel roller, can lead to defective products, customer dissatisfaction and costly delays. The IBM solution analyzes large volumes of production data to seek patterns in equipment operations, product quality, failure patterns. USD2 Million reduction of costs for every 0.1% improvement in production efficiency Boost skills transfer by embedding process knowledge into equipment and process optimization algorithms
  • 8. © 2015 IBM Corporation 17 February 20168 Demonstration: IBM Predictive Maintenance & Quality The IBM Predictive Maintenance and Quality solution helps you monitor, analyze, and report on information gathered from devices and recommend maintenance and quality activities for those devices. With this integrated solution, you can accomplish various tasks to gain efficiency and save costs.
  • 9. © 2015 IBM Corporation 17 February 20169 Next Steps – Become a data-driven organization now! A five-segment methodology spans a predictive analytics discovery workshop with desired outcomes. Together we will narrow down different use cases in order to find and discuss the right advanced analytics approaches in the right areas and outline the business impact and benefits when applying advanced analytics to your organization.
  • 10. Hendrik Leddin Client Technical Specialist Predictive Maintenance & Quality IBM Deutschland GmbH IBM-Allee 1 D-71139 Ehningen Mail: D-71137 Ehningen Mobile +49-173-25-35-37-9 hendrik.leddin@de.ibm.com