InfoServe365 - Sample analysis
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Full webcast available here: http://www.anymeeting.com/WebConference/RecordingDefault.aspx?c_psrid=EB51D889864C We have here a short series of slides that contain graphical, pareto type charts taken from a standard AssetManager report for a coating line application. For this exercise we have chosen a web coating example but the system connects to any manufacturing machine and these methods apply to all. Its important to point out that the data we'll use has been modified somewhat to preserve client confidentiality but the number of reasons, their nature and actual down times are real and accurate
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InfoServe365 - Sample analysis
- 1. AssetManager Production Data Analysis Example
- 4. Categories of Unplanned Stop Reasons 2. Logistics – supply management 3. Operator Issues 4. Plant & Process Issues 5. Plant Failure
- 9. Final Analysis Investigation Areas Logistics 11.26 Material supply management Non-Breakdow n 22.37 Process components review -operator training-maintenance schedule review Breakdow n 4.54 Maintenance Operation 16.31 Operator training Total Lost Tim e - Unplanned 54.48
- 10. An average OEE attainment of 25.1% for the coating line OEE metrics for individual shifts indicates higher availability with lower performance attained by Shift B Limited analysis indicates significant improvements could be made in each of 4x group categories. No “Silver Bullet” but many small steps could make an impressive improvement
Editor's Notes
- For this exercise we have chosen a web coating example but the system connects to any manufacturing machine and these methods apply to all. Its important to point out that the data we'll use has been modified somewhat to preserve client confidentiality but the number of reasons, their nature and actual down times are real and accurate. To put some context to the AssetManager system, it gathers performance, quality and availability data directly from the client machine so the event data and times recorded are accurate. Operators are required only to enter the downtime reasons from a simple touch screen HMI which we find has a high accuracy.
- On the first slide we see a waterfall chart showing the planned downtime events for our web coating line. The chart has downtime reasons on the vertical axis of the chart and the corresponding time scale along the horizontal axis. Our auto analysis engine makes it easy to see the most significant reason at the top of the chart, this being shift changeover times. The red section of that bar “log out” is associated with a 3rd shift that doesn't currently operate. We can immediately read that there is spare production capacity on the line. The yellow and blue portions of the bar show a notable difference in the changeover times between two shifts, A & B. The 3 rd shift that doesn’t run) skews the chart data and puts the shift change as the most significant influence but in actual fact it is the coat change that is the main cause of production loss. The chart shows the results of the data analysis in a graphical form and is arranged to quickly identify the most significant causes of production loss. Each chart is supported by tabular data – it isn’t shown in this slide – so numerical quantities are readily available, these are shown on the next slide.
- The table presents the associated data for the graph shown on the previous slide. We can see that a total of nearly 20 hours were lost in this month due to shift changeover and over 30 hours effecting a coating fluid changeover. Breaks (that’s tea breaks) show over 22 hours, but analysis of the average tea break time shows that shift A takes an average of 28.1 minutes with shift b taking 18.4 minutes. The planned tea break time is 10 minutes. So 12 ¾ hours per month is lost on extended tea breaks. Clearly a costly overrun that compliance and perhaps facilities location planning could make a serious impact on. Further control measures such as staggered tea breaks etc. could make an even greater impact on productivity. The clean down activity again shows that, although shift A incurs more instances, their average time is over 30% longer than shift B so perhaps some operator training is warranted?
- I we move on to slide 3 now, showing unplanned downtime. A similar chart format, but with more reasons listed, again shows us the most significant reasons of production losses. With this data we allocated various downtime occurrences to one of 4 categories. You can see them listed on the right hand side of the slide.
- Slide 4 shows us the causes of lost production due to the supply – or lack of supply – of essential goods to allow continued production. My first observation is that the majority of the reasons have both yellow and blue portions of the histogram bars, which indicates that both shifts suffer from these occurrences. When one considers that this machine is a coating machine and almost 8 hours are lost due to a lack of coating mix, it is a costly planning shortfall with almost a full shift being lost to a fundamental component. Similarly with the “barcode labels” and “waiting for cores” which when aggregated show a loss of over 10hurs production in the month of January. “ Damaged cores” is the least significant reason allocated to Logistics/supply chain at only ½ an hour.
- The next slide describes a similar analysis of lost time due to standard, though extended, machine operations, again across both shifts. Worth noting is the most significant reason at the top of the chart, “awaiting downtime entry”. This is due to short duration stoppages for which the operator is not required to provide a reason. The assetmanager system has a user settable time filter function for this. It is clear that whilst individually these instances are small, when aggregated they contribute a significant proportion of the monthly downtime at 15 hours total losses for shifts A & B. Ignoring these short duration stoppages, there was still a loss of 16.31 hours of production due to unplanned machine operation issues.
- The Plant and process issues analysis on slide 5 show another 22.37 hours. These causes are not plant failures where a machine repair is required. These causes are due to machine component setup and irregular housekeeping problems.
- On slide 6 we see that outages caused by machine failure aggregate at 4.54 hours in the month.
- All reason aggregation over the month give a value of 54.48 hours lost production due to planned and unplanned interruptions. A significant number with a correspondingly high cost. Some proposed areas of investigation for resolution of the lost time are presented on this tabular slide.
- The final slide in the series shows the OEE productivity achievements for the two operating shifts. All three component metrics are indicated in the line type series. For this example the quality component is fixed at 100%, performance and availability are measured actuals and give a resultant overall figure for each shift at 23.5% for shift A and 26.7 for shift B. It is easy to see from the analysis charts that the resolution of these significant losses and improvement of the OEE figure is relatively straightforward. A number of small steps will make a serious impact in the goal to improve productivity and the client is not searching for a silver bullet solution.