Applying Manufacturing Intelligence to OEE for Real-Time Decision SupportBest-in-class manufacturing requires complete, re...
Performance gains further benefit from applying standardindustry reference models like ISA 95 to the components andthe lin...
Any single value of a metric is only a snapshot with no sense ofpast or future behavior. MI and a process view integrate p...
The Value of OEE as a KPIThe drive to reduce waste and improve performance has led well run companies to aggressively use ...
Not surprisingly, the studies found the levels of OEE deployment highestamong the best business performers (figure 4).The ...
Applying SPC to OEE for Bottom-line ResultsA leading manufacturer ascribed to this approach and leveraged Northwest Analyt...
MI systems enable decision makers to drill down to theindividual filling line components such as the filler. One can useSP...
SummaryGood practice standardizes parameters and metrics across the entire operation to enable meaningful manufacturingdec...
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Applying MI to OEE for Real-Time Decision Support

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Good practice standardizes parameters and metrics across the entire operation to enable meaningful manufacturing decision support and continuous improvement. Frequently manufacturing and business parameters are combined into Key Performance Indicators (KPI) to simplify monitoring more complex functions. One commonly deployed KPI is Overall Equipment Effectiveness (OEE) which combines measures of availability, throughput and quality.
There exists tremendous value potential for companies coupling OEE with SPC, and making it part of manufacturing-decision support. It sets the company on the path to state-of-the-art manufacturing process management by enabling them to:
Apply SPC to automated OEE solutions – looking at single values of a KPI adds little to one’s process management capability, but using control charts and process capability analysis will enable developing world-class manufacturing;
Rapidly determine where improvement opportunities exist;
Focus on information, not data – data is the raw material; information provides the decision support that will improve performance levels.

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Applying MI to OEE for Real-Time Decision Support

  1. 1. Applying Manufacturing Intelligence to OEE for Real-Time Decision SupportBest-in-class manufacturing requires complete, real-timeprocess performance monitoring and analytical decision supportfor process management and improvement. Two recent MESAstudies (MESA 2012a, MESA 2012b) demonstrate the profoundimpact of such an approach.Key Performance Indicators (KPI) are the business performancemetrics created to evaluate and visualize specific operationswithin the organization. Their purpose is to focus on the mostimportant measures and give a more complete understanding ofprocess performance.To do so, a KPI integrates one or more pieces of data into a valuethat enables better understanding of how performance goals arebeing met. Properly designed, they simplify and enable a quickerunderstanding of process status.Best-in-class companies take it further by standardizing KPIs. Indoing so, those manufacturers boast a 40% higher rate ofstandardized metrics than laggard manufacturers (figure 1).
  2. 2. Performance gains further benefit from applying standardindustry reference models like ISA 95 to the components andthe linkages needed to make a suitable manufacturingenterprise data system (figure 2).When data is combined from multiple sources, as withmanufacturing intelligence (MI) solutions, it can be given a newstructure or context that helps users find what they needregardless of where the data resides.The primary goal is to turn large amounts of manufacturing datainto real knowledge and drive business results. As the MESAstudies show: • Use of MI yields larger increases in profit and quality; • Aligning metrics across the organization develops more productive management at all levels; • Clear definition of metrics and better understanding of source data ensures accuracy and buy-in for the results from all departments.A major factor in the demonstrated success of MI is that itenables the process-based view of manufacturing. Allmanufacturing occurs within a process, and statistical methodsare the tool-of-choice to understand and manage the process.
  3. 3. Any single value of a metric is only a snapshot with no sense ofpast or future behavior. MI and a process view integrate pastperformance with the current value and deliver a defensibleprediction of future behavior. This forms the basis of truly usefulmanufacturing decision support.MI incorporates the methods of statistical process control (SPC)and process capability analysis which are recognized as the mosteffective way to understand and deal with process behavior. SPC isone of the foundational techniques for TQM and Six Sigmaprograms and a core competence for process management andimprovement.The isolated metric provides only a small amount of decisionsupport. The single value can be compared to a specification, butit does not provide any indication of previous process behavior orwhat are reasonable expectations for future performance. Whenput into context with SPC, that same metric value deliverssubstantially greater process management information.For example, if a batch viscosity is measured at 1509 and thespecification is 1600, engineering could assume all is well and feedthe product into the filler with no concern. However, when thatsame value is plotted on a control chart (figure 3) it is immediatelyapparent that value is out of control and alerts staff to examine theprocess stability.MI impacts process management by increasing the operationalvalue of metrics with analytics. The MI synthesis of quality datacollection, process based analytics and timely delivery of rolespecific information has a demonstrated positive impact oncorporate performance.This was confirmed in the MESA studies, “Throughout the MESAMetrics research series, speed of the process to collect, analyze,and display the data has been a clear differentiator for those whoimprove their business results (MESA, 2012a)”.
  4. 4. The Value of OEE as a KPIThe drive to reduce waste and improve performance has led well run companies to aggressively use Overall EquipmentEffectiveness (OEE) to monitor performance. OEE is a KPI that combines measures of availability, throughput and quality to providea more complete evaluation of equipment or production line performance.Although used and understood in many industries, OEE, like many metrics, is often treated as an isolated value and not as a processparameter with variation and trends. By shifting to process based thinking, management and staff can develop a moresophisticated understanding and manage the process at a higher performance level. By applying process analytics such as SPC thecompany derives the maximum decision support and business return from using the OEE KPI.OEE is defined as OEE = Availability x Throughput x Quality.Each individual OEE component is defined as: Availability = Running Time/Available Time Throughput = Total Units x Ideal Cycle Time/Running Time Quality = Good Units/Total UnitsOEE provides rich insights into manufacturing performance and when coupled with analytics such as SPC enables one to reducemanufacturing costs by getting more production from existing facilities. Management can identify problems quickly and, mostimportantly, make decisions based on facts not assumptions.Not only does this improve the return on assets, it functionally increases capacity without requiring new assets. Even smallimprovements in OEE scores can produce substantial improvements in efficiency and profitability and deliver good ROI for theprocess monitoring and improvement effort.When properly deployed with SPC, OEE will: • Identify Problems Quickly • Reduce Manufacturing Costs • Improve Return On Assets • Increase Capacity without New Assets • Focus Capital spending for maximum return
  5. 5. Not surprisingly, the studies found the levels of OEE deployment highestamong the best business performers (figure 4).The MESA studies also identified that the most profitable companies asthose who were most successful in improving OEE values in their plants(figure 5).Increasing the Value of OEE with SPCBy using SPC and treating OEE like any other process parameter, morevalue is extracted from the monitoring process and provides moreeffective operational decision support. With statistically based trendanalysis one can quickly identify areas having problems in quality,throughput or availability. SPC-based alerts enable personnel toproactively use predictive trends in operational data to maintainprocess stability and increase the capability to meet specifications.Using a content-rich metric such as OEE indicates more maturemanufacturing operations management. Using SPC and treating the OEEmetric as a continuous variable embedded within a system represents afurther advanced understanding of manufacturing processmanagement.More value can be extracted from the monitoring process by using SPCand treating the OEE KPI as a process parameter. The most commonbenefits include the ability to: • Quickly identify areas having difficulties in either quality, throughput or availability • Perform detailed statistical analysis on automated data collection systems • Alert personnel to predictive trends in operational dataThe staff can do this effectively because the systems support integratedquality/operations management. Further by using SPC to monitor theOEE KPI they can also drill down to the behavior of the individual KPIconstituents, availability, throughput and quality.
  6. 6. Applying SPC to OEE for Bottom-line ResultsA leading manufacturer ascribed to this approach and leveraged Northwest Analytics solutions to generatebottom-line savings, using OEE to monitor and improve performance of a filling line. It illustrates how SPC can beused to interpret the meaning of OEE values and direct process management and improvement.The filler line has six components:Each component is monitored by automated data collection systems.The data is stored in centrally accessible databases with known andcompatible data structures including consistent variable naming.This manufacturing-intelligence strategy enables straightforwardprocess monitoring and alerts. For example, in figure 6, box plotsdisplay overall OEE for all the filling line components. This level oftransparency supports complete process awareness and the ability tolook for any suspicious process variation that requires attention.
  7. 7. MI systems enable decision makers to drill down to theindividual filling line components such as the filler. One can useSPC control charts to separate process signals from the noise toguide process management. In figure 7 the two points where theoverall OEE is out of control are indicated by the red symbols.The drill down displays all the descriptive informationconcerning one of the points.Note the values for the three components of OEE: Availability 24.63% Throughput 58.88% Quality 98.72%It is clear that availability is a major contributor to the poor OEEvalue followed by throughput. One then plots the control chartfor Availability (figure 8).By using SPC methods one can dissect the filler performance andwork on determining the special causes of the poor availabilityvalues, begin to bring availability under control, and work toincrease the process capability of the system.The complete discussion of this case study can be found inArmel, 2011, “Improving Packaging Line Performance with OEEand SPC
  8. 8. SummaryGood practice standardizes parameters and metrics across the entire operation to enable meaningful manufacturingdecision support and continuous improvement. Frequently manufacturing and business parameters are combinedinto Key Performance Indicators (KPI) to simplify monitoring more complex functions. One commonly deployed KPIis Overall Equipment Effectiveness (OEE) which combines measures of availability, throughput and quality.There exists tremendous value potential for companies coupling OEE with SPC, and making it part of manufacturing-decision support. It sets the company on the path to state-of-the-art manufacturing process management byenabling them to: • Apply SPC to automated OEE solutions – looking at single values of a KPI adds little to one’s process management capability, but using control charts and process capability analysis will enable developing world-class manufacturing; • Rapidly determine where improvement opportunities exist; • Focus on information, not data – data is the raw material; information provides the decision support that will improve performance levels.References:• Aberdeen Group, 2011, Operational Intelligence, Aligning Plant and Corporate IT• Armel, 2011, “Improving Packaging Line Performance with OEE and SPC” http://www.nwasoft.com/resources/webinars/improving-packaging-line-performance-oee-and-spc• ISA, 2005, Enterprise Control System Integration Part 3: Activity Models of Manufacturing Operations Management ANSI/ISA—95.00.03—2005• MESA, 2012a, Performance Improvement and Metrics Practices, White Paper #40, 2/12/12 https://services.mesa.org/resourcelibrary/showresource/ec69e8e2-12a6-451d-883f-4fb228f7875a• MESA, 2012b, Pursuit of Performance Excellence: Business Success through Effective Plant Operations Metrics https://services.mesa.org/News/View/4aac248a-940a-4899-b04d-57bbaf37e016

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