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Provideam V4.1 Update

October 11th, 2011 No comments

Provideam V4.1 is currently in Beta Testing and is due to be released in early November 2011. Below are some of the updates and enhancements we have packed in to this release. Please contact DTL if you would like to schedule an early update to V4.1.

Look and Feel

  • Standardised Look and Feel of User Interface
  • Added Ability to resize screen areas
  • Added more data to overall configuration tables. Administrator can now see more data without having to drill down.
  • Added sorting and filtering to configuration tables
  • Improved the CSV Import/Export facility for configuration data.

New Features

  • Added ability to link OEE Live view to external urls (eg. www.google.com)
  • Added ability to link OEE Live view to 3rd party html pages. These pages may be linked to Provideam or may be related to other issues such as personnel messages.
  • Added Contact Lists for Scheduled Reporting and Alarming. The Administrator can now create lists of users Email, Pager or Mobile(Cell) contacts. These contact lists can then be used for
  • alarm annunciation or for scheduled reporting.
  • Added a visual Alarm Status Indicated to the Active Alarms Page of the Event Monitoring Application
  • Added an automatic refresh option to the Current Status and Active Alarms Page of the Event Monitoring Application

Modifications

The User Security Model has been updated to facilitate more straightforward integration with Active Directory. Users are now created independently from User Groups. User Groups still control access rights but now a User can be a member of several User Groups. If the User is a member of more than one User Group then the User takes the combined rights of both User Groups.
In addition the User contact details have been upgraded. It is now possible to assign several  (or none) Email/Mobile(Cell)/Pager contacts to each User. If the User contact details are modified, the details in the Contact Lists will also be updated – thus simplifying greatly the management of scheduled emailing and alarm annunciation.
Modified the Installer so that the Provideam Database is installed without Demo Data. It is now an install option to install Demo Data.

Operating Environment and 3rd Party Applications

IE 9 and Firefox 6 Support
Windows 7 and Windows 2008 Support
MS SQL 2008 support
dotNet 4 Native Application (SQL 2005 requires dotNet 2 and SQL 2008 requires dotNet 3.5)

Calculating OEE – A Simple Example, Part 2

September 15th, 2009 No comments

In our previous post (Calculating OEE – A Simple Example, Part 1) we saw how to calculate OEE using only the Good Parts count and the Planned Operating Time. Now we are going to calculate OEE for the same example using the Availability, Performance and Quality Losses. This is a more complex way of calculating OEE but it provides us with the data to identify the main reasons for loss.

Remember our example:

Over a 12hour shift, our Filling machine fills 11,000 bottles. The manufacturer has specified that the Standard Time for this Filler to fill one bottle is .05mins/bottle. Over the course of the shift there are some Planned Downtimes: 2 x 15minute tea breaks and 1 x 30minute lunch break.

Planned Operating Time

 Planned Downtimes:

    15mins | Morning Tea Break
    30mins | Lunch Break
    15mins | Afternoon Tea Break

 Planned Downtime:  60mins

Total Time:   720mins

Therefore:
 Planned Operating Time = 720mins – (15mins + 30mins + 15mins)
     = 660mins

Availability

Now let's look at Availability. In our previous post we defined Availability as ((Planned Operating Time) – (All Availability Losses)) / (Planned Operating Time)

We know our Planned Operating Time is 11hrs. So if we know our Availability Losses we can calculate Availability. Availability Loss are all downtimes related to Breakdowns and ChangeOvers.

Over the course of our Shift we logged downtimes as follows:

 Availability Losses:

    25mins | ChangeOver
    10mins | No Caps in Hopper
    15mins | No Air

 Availability Loss: 50mins

Planned Operating Time: 660mins

Therefore:
 Actual Operating Time = 660mins – (25mins + 10mins + 15mins)
     = 610mins

and:
 Availability  = (Actual Operating Time) / (Planned Operating Time)
     = 92.4%

 

 Performance

The Performance Loss is a combination of Short Stops and Speed Loss. Short Stops are momentary downtimes which aren't Breakdowns or ChangeOvers which stop the machine and interrupt production but do not generally require technical support. In general for Manual Systems Short Stops are ignored as it can be onerous to record each stop. This results in the loss associated with Short Stops being built in to the Cycle Time Speed Loss. In Automatic Data Capture Systems it is more realistic to log Short Stops.

Rather than take a Cycle Time measurement and assume that this Cycle Time was constant over the whole shift it is often more realistic to calculate the Net Operating Time by working back from the more easily measured Throughput (ie sum of Good Parts and Defect Parts).

We know that the number of Bottles filled was 11,000

Defects:

250 | Underfilled

100 | No Cap

Defect Parts: 350

Good Parts: 11,000

Thus we can calculate our Good Time (ie Standard Time to produce Good Parts) and our Defect Time (ie. Standard Time to produce Defect Parts).

Good Time = 11,000 x 0.05

= 550mins

 

Defect Time = 350 x 0.05

= 17.5mins

 

Where 0.05mins/bottle is our Standard Time to produce one bottle.

 

Therefore:

Net Operating Time = (Good Time) + (Defect Time)

= 567.5mins

 

and working back we see that:

 

Perfomance Loss = (Actual Operating Time) – (Net Operating Time)

= 42.5mins

 

We now know the Net Operating Time therefore we can calculate the Performance.

 

Performance = (Net Operating Time) / (Actual Operating Time)

= 93.0%

 

Just for the sake of completeness let’s say that the machine stopped 10 times due to Falling Caps. Each stop was 6 seconds in duration and didn't require any technical intervention. We can say that the machine suffered 1min of downtime due to Short Stops.

Subtracting the Short Stops from the Performance Loss gives us the Speed Loss (Slow Running). ie the Loss due to the fact that the machine was running at a slower rate than the optimum rate specified by the manufacturer.

 

Speed Loss = (Performance Loss) – (Short Stops)

= 41.5mins

As a matter of interest our average Cycle Time over the Shift was ((Net Operating Time) + (Speed Loss)) / ((Good Parts) + (Defect Parts)) = (609) / (11,350) = 0.0537mins/bottle. Thus the Filler took on average 0.0037mins more then the Standard Time to fill each Bottle.


Quality

Finally we consider Quality Losses, ie the time taken to produce Defect Parts. In the above section we have already calculated the Good Time and the Defect Time. Defect Time is another name for Quality Loss and Good Time or OEE Time is the same as Fully Productive Time.

Thus we have every thing to calculate Quality.

 Quality  = (Fully Productive Time) / (Net Operating Time)
    = 96.9%

Note: Quality is only equal to (Good Parts count) / ((Good Parts count) +(Defect Parts count)) when the Standard Time is the same for all parts run on the machine over the shift.

Now the final calculation;

 OEE   = Availability x Performance x Quality
    = 92.4% x 93.0% x 96.9%
    = 83.3%

By taking the long way round we have generated 3 additional KPIs and we have a lot more data which we can use to focus in on the causes of loss.

Let's briefly take a look at the Level 1 Losses in a table ordered by size of loss

Availability Loss 50.0mins
Performance Loss 42.5mins
Quality Loss 17.5mins

This tells us that in this example downtimes are the most significant type of loss.

And now look at the individual losses in a similar table

Speed Loss (Slow Running) 41.5
ChangeOver     25.0
No Caps in Hopper 15.0
No Air 10.0
No Cap (Defect Time) 12.5
Underfilled(Defect Time) 5.0
Short Stops 1.0

Here we see that Speed Loss is in fact the biggest individual loss. In the absence of a downtime monitoring system Speed Losses are often missed as it can appear that the machine is running perfectly well when in fact it is producing much less then it should.

When generated on a shift by shift basis these tables are helpful in the day to day operational management of the machine. However when calculated over longer periods of time you can build up a very insightful picture as to the real causes of loss – as opposed to your presumptions – which may or may not in fact be correct.

In our next post we will show how Provideam can help to organise the data you have collected manually into a database which can be analysed in many different ways. The beauty of a database over a spreadsheet, like Excel, is that the data can easily be grouped and filtered by all sorts of interesting criteria in a rapid and flexible manner.