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

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

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.
 

Very simply we can say that

 OEE = (Fully Productive Time) / (Planned Operating Time)

Where Fully Productive Time is equivalent to (No. of Good Parts) x (Standard Time to produce 1 Part)
and Planned Operating Time is equivalent to (Total Time) – (All Planned Downtime)

Expressed as a percentage this simple calculation gives us our headline OEE value.

Simple 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.

Therefore our Fully Productive Time = (11,000) x (0.05) = 550mins

Over the course of the shift there are some Planned Downtimes: 2 x 15minute tea breaks and 1 x 30minute lunch break.

Thus the Planned Operating Time = (12hrs) – (2 x 15mins + 1 x 30mins) = 660mins

Giving us an OEE = (550) / (660) = 0.833 = 83.3%

Note we have lost out on 110minutes of possible productive time.

Now we know how effective we were but we have no idea where we lost time. Typically at this point various stake holders might suggest that the losses are due to one thing or another. These suggested losses are generally not based on any objective data. Before investing in improvement activity we need more definition on the causes of our 110minute loss.

The beauty of the OEE system is that it is much more than a simple measure of overall equipment effectivity. It also provides a framework for measuring and categorising losses in a manner which simplifies the analysis of the losses. This categorised framework helps greatly to identify the major losses and sets us on the path to finding and eliminating the root causes.

3 basic categories of losses

The OEE system considers that there are 3 basic categories of losses (OEE Level 1 Losses);

 Availability Losses: time lost due to Breakdowns and Changeovers
 Performance Losses: time lost due to Short Stops and Speed Loss (Slow Running)
 Quality Losses: time lost producing defect parts

In our previous post (Modern Production Metrics – OEE) we have explained that this categorisation of losses leads to 3 further KPIs

•  Availability: a measure of Uptime versus Planned Operating Time
    = ((Planned Operating Time) – (All Availability Losses)) / (Planned Operating Time)
    = (Actual Operating Time) / (Planned Operating Time)
   
•  Performance: a measure of the actual machine Cycle Time versus the Standard(ideal) Time
    = ((Actual Operating Time) – (All Performance Losses)) / (Actual Operating Time)
    = (Net Operating Time) / (Actual Operating Time)
   
•  Quality: a measure of the time spent producing Good Parts versus the time making all parts, Good and Defect.
    = ((Net Operating Time) – (All Quality Loss)) / (Net Operating Time)
    = (Fully Productive Time) / (Net Operating Time)

Note: We use the term OEE Time interchangeably with Fully Productive Time.

Multiplying these three KPIs together, by cancelling above and below the line, results in the following fraction;

 Availability x Performance x Quality = (Fully Productive Time) / (Planned Operating Time)

Which we already stated at the top of this post is OEE.

So these three KPIs when multiplied together produce the OEE value. We have seen at the top of this post how to calculate OEE from the Good Parts count and the Planned Operating Time. Now we see that we can also calculate OEE if we know the Availability Losses, Performance Losses and Quality Losses.

In our next post we will describe how to calculate OEE from these losses.
 

The current economic crisis has hit manufacturing industry extremely hard. Even companies which were performing well six months ago, now face an uncertain future. The global downturn has heightened the pressure on every one of us to reduce costs and increase productivity.

Most manufacturers are facing into a period where there is a reduction in demand and a knock on tightening of margins. The key to beating the recession is to eliminate unnecessary costs as soon as possible and to tune our production equipment so that we are up and running before our competitors when the inevitable economic up-turn arrives.

In the commercial world decisions regarding investment in manufacturing assets are made on the basis of productivity (i.e. how well are the asset under our disposal being employed).In manufacturing our productivity is closely related to how well, or effectively, we operate our production equipment. The pre-eminent benchmark used to measure this is OEE or Overall Equipment Effectivity (Effectiveness).

A company operating at 100% OEE would have 0 Defects, 0 Breakdowns and no Speed Losses. 100% OEE is practically impossible to achieve and even World Class Manufacturing Companies would struggle to exceed 85% OEE.

The point is not what our ultimate OEE Target might be but that we continuously strive to improve our current OEE levels.

There are two fundamentals required to improve our equipment effectivity,

1) accurate, up-to-date, impartial data on production performance and

2) trained staff with the capability to interpret and act on the data. Companies that possess this combination of data and trained staff are empowered to identify losses and to engineer out the causes of these losses.

PROVIDEAM KEY BENEFITS

There are many reasons why Provideam is the ideal choice for companies wishing to take on the challenge of reducing costs by tackling losses and ‘continuously improving’ the effectivity of production equipment.

Minimal Cost of Entry: Provideam is scaleable from 1 to 100’s of machines. For a minimal investment it can be hooked up to one machine – most machines can be integrated within a day. Once we see the benefits on a pilot machine you can roll the system out to other machines at will – thus eliminating the risk of purchasing a white-elephant.

Easy Configurability: Once data begins to flow, staff begin to understand how to interpret the data. This leads to suggestions from staff as to how to improve the data quality. The Provideam configuration is very straight forward to understand and modify to each company’s requirements. Thus staff can adapt the Provideam configuration, over time, to improve their ability to analyse the data. This configurability helps to foster a sense of ownership of the data and is an enabler of the Continuous Improvement management style.

Motivational: Real-time metrics displayed on a Live Display is an excellent motivational tool. The act of collecting and displaying these metrics identifies them as being important and acts to motivate staff to exceed targets for these metrics.
 

 
Provideam Live – Typical Motivation Screen
 

Easy Access to Operational Metrics: Provideam provides easy access to operational performance metrics through a range of views and reports. However, we’ve made it even simpler.

The majority of Provideam Users rarely logon to the application – they simply rely on scheduled emailed reports. These reports typically form the basis of a daily production meeting. Schedule Shift reports are emailed within minutes of the Shift ending. All staff are presented with the same data. The data is impartial and this facilitates an open discussion on the real reasons behind losses rather then an argument as to the validity of the data.

Trend Analysis: Several years of historical data can be stored in the Provideam database and this enables us to extract trends showing how performance has improved/disimproved over the period. It can also allow a ‘before and after’ analysis to highlight the effect of changes in the process, materials etc.

Parameter Comparison: Process Engineers use Provideam to highlight the operational performance variances between periods (e.g. Day Shift and Night Shift) or between Objects (e.g. Part A and Part B or Machine A and Machine B). This is an extremely useful way of assessing the worst and best performing machines and processes.

Innovation Support: By allowing us to identify and quantify the real causes of loss, Provideam gives us the tools to establish the business case for upgrading badly engineered equipment. For example Provideam can provide an accurate picture of time lost during change-overs which could be used to build a case for process improvements as with for example the introduction of SMED (Single Minute Exchange of Dies).

Increased Profits: Provideam gives us the tools to reduce costs and to operate equipment more effectively both in the short term and on in to the future.
 

CONCLUSION

In conclusion some points to remember;

1. The downturn won’t last forever.
2. Tackle costs immediately to survive reduced product demand and tightened margins.
3. Fine tune processes now to ensure that we are ready to take advantage of the inevitable upswing before our competitors.
 

At the recent Manufacturing Technology 2008 Exhibition, Professor Dennis McKeag of the School of Electrical & Mechanical Engineering, University of Ulster, delivered a highly stimulating presentation on the practical implementation of the mangement philosophies: Kaizen, Six Sigma and Lean Manufacturing. Through case study examples, with plenty of personal anecdotes thrown in, Professor McKeag illuminated these familiar but often misunderstood management philosophies.

Further to the presentation, Professor McKeag offered a suggested introductory reading list:

Six Sigma for Everyone; George Eckes; Wiley; ISBN 0-471-28156-5

Approximately: Stg£11.00

A short, easy to read book based on 6-sigma using Kaizen principles that can be equally applied to Lean. Perhaps the best place to start.

Gemba Kaizen – A commonsense low-cost approach to management; Masaaki Imai; McGraw-Hill; ISBN 0-07-031446-2

Approximately: Stg£20.00

Based on organising and managing the workplace. The key techniques of 5S, MUDA and Visual Management are very well covered. Good for anyone wanting to manage the shop-floor and quite easy to read.

Six Sigma – The Breakthrough Strategy Revolutionizing The World’s Top Corporations; Mikel Harry and Richard Schroeder; Currency; ISBN 0-385-49437-8

Approximately: Stg£25.00

Good for Six Sigma implementation. The complete Six Sigma philosophy in an easy to read format. Best suited to repetitive activities such as those found in offices, for example sales order processing, accounts etc.

The Lean Six Sigma Pocket Toolbox;  Michael L George, David Rowlands, Mark Price, John Maxey; McGraw-Hill; ISBN 0-07-1441190-0

Approximately: Stg£12.00

As the name implies, a large number of Lean/Six-Sigma/Kaizen tools. You are likely to find your preferred techniques here. If you find a technique here there is enough information to begin to use it, but you may want to follow up with some specific and more detailed reading on the technique.

Learning to See – value stream mapping to add value and eliminate muda; Mike Rother & John Shook; The Lean Enterprise Institute; ISBN 0-9667843-0-8

Approximately: Stg£20.00

Value stream mapping of product and information flow is the best start point for any initiative and this is the best book to use for VSM.

Overall Equipment Effectivity:

OEE, Overall Equipment Effectivity, is the most popular KPI, Key Performance Indicator, in manufacturing productivity philosophies such as WCM, World Class Manufacturing, TPM, Total Productive Maintenance etc.

OEE is a measure of a machine’s effectiveness based on six general loss classifications, i.e. Breakdowns, Changeovers, Short Stops, Slow Running(Speed Losses), Yield Losses and Start-up Losses. These losses are grouped under the general headings; Availability Losses, Performance Losses and Quality Losses as described below.

Put simply, OEE is a measure of the amount of time a machine is actually producing good product compared to the time it should be producing good product (the total planned production time for that machine). Any difference between these two measures of time is considered a loss.

Figure 1 shows an OEE Level 1 Pie Chart Report which divides the loss into three groups, Availability, Performance and Quality.

Fig. 1 OEE Level 1 Pie Chart Report (click image to see full size).

The Six Losses are listed and categorized as follows:   

• Availability Losses.
  Availability Loss is a general term for any loss which causes a machine to be unavailable to produce good products.
  1. Breakdowns:
     When a machine has broken down it is unavailable for production. In some OEE systems a machine is only considered to be ‘broken down’ if a technician is required to restart the machine. However this would require manual classification of each loss and in automatic OEE Production Monitoring Systems manual classification is considered cumbersome. Consequently the compromise is generally made that if the downtime duration is longer than, say, 5minutes the downtime is automatically classified as a Breakdown, otherwise the downtime is classified as a Short Stop.
      
  2. Changeovers:
     When a machine has broken down it is unavailable for production. In some OEE systems a machine is only considered to be ‘broken down’ if a technician is required to restart the machine. However this would require manual classification of each loss and in automatic OEE Production Monitoring Systems manual classification is considered cumbersome. Consequently the compromise is generally made that if the downtime duration is longer than, say, 5minutes the downtime is automatically classified as a Breakdown, otherwise the downtime is classified as a Short Stop. 

• Performance Loss.
  Performance Loss is a general term for a loss occurring during production which reduces the performance of the machine. Performance Loss is sometimes also referred to as Speed Loss.
  1. Short Stops:
     When a machine is in production and it stops for a short period of time for a minor fault that the operator can correct in a few seconds, this is called a Short Stop. If an operator is operating more than one machine these Short Stops are highly significant. It may only take a few seconds to fix the machine, but if the operator is busy with another machine or taking a break, then the machine will be stopped for a significant amount of time. If these minor faults occur frequently, then over the course of a day the amount of time lost can accumulate quite rapidly. This is especially true where an operator is running a large number of machines as it will not be possible to keep them all running if there are a lot of minor faults happening simultaneously.
      
  2. Speed Loss(Slow Running):
     This loss is not usually apparent by simply looking at a machine. Generally when a machine is running and producing good parts, it may appear that all is well. However there may still be a loss occurring if the machine is operating below its designed speed. That is, if the machine is designed to produce 1000 parts per hour but for some reason is actually only producing 750 units per hour then it is only running at 75% of its capability and there is a 25% loss due to Slow Running.

•  Quality Losses.
  Quality Loss is the general term for the time lost producing bad or reject parts.
  1. Yield Loss:
     When a machine produces a defect not only is the material used in producing the defect lost or in need of rework but the time and other resources used producing the piece are also wasted. A machine with ten defects per hundred is in effect only achieving a yield of ninety per cent of its capability.
      
  2. Start-up Loss:
     If a machine needs to be set up by doing some trial production then the material used is wasted. For example setting up a machine at the start of a shift could involve producing one or two test pieces and then making adjustments until the set up is perfect. The material lost and the time spent producing it are both wasted and again this is a problem. 

Key Implications:

• Classifying the losses in terms of Availability, Performance and Quality helps to identify the nature of the most significant losses effecting production.
   
• From knowing the nature of the most significant losses it becomes easier to identify the root causes and to implement strategies to eliminate the losses.
   
• It’s only when you multiply the 3 elements, Availability, Performance Rate and Quality Rate together that you see the compound effect.
   
• This compound effect often highlights a surprisingly aggressive erosion of OEE! (Remember that two fractions multiplied together will always result in a smaller fraction!)

Calculating OEE:

OEE may be expressed as a time value or as a percentage (rate). Below it is shown how to calculate the OEE value as either a time or a percentage.

Expressing OEE as a Time Value:

The figure below shows the losses occurring on a machine over a given time frame.

Fig. 2 OEE Time (click image to see full size).

• • Time Frame
    The period over which the OEE is to be calculated.
     
  • Planned Operating Time
    The amount of time for which it is planned that the machine should be producing good parts. It is the Time Frame less any planned downtime such as preventative maintenance or operator training etc.
     
  • Availability Loss
    The total amount of time for which the machine has been broken down or on changeover. 
     
  • Actual Operating Time
    The Planned Operating Time less the Availability Loss.
     
  • Performance Loss
    The combination of Short Stops and Slow Running.
     
  • Net Operating Time
    The Actual Operating Time less the Performance Loss.
     
  • Quality Loss
    The amount of time lost producing bad or reject parts. It is calculated by multiplying the number of reject parts by the optimum (design) machine cycle time.
     
  • OEE Time (Fully Productive Time)
    The total amount of time that the machine was operating at its optimum or designed rate. It is the Planned Operating Time less the Availability Loss, less the Performance Loss and less the Quality Loss. The OEE Time is also known as the Fully Productive Time.

Expressing OEE as a Percentage:

Which in graphical format can be represented by figure 3 below.

Fig. 3 OEE Time as Percentage (click image to see full size).

• • Availability (Rate)
    The percentage of time the machine is actually available to produce good parts. In other words, the Actual Operating Time (Planned Operating Time – Availability Loss) compared to the Planned Operating Time.

    
     

  • Performance (Rate)
    The Net Operating Time (Actual Operating Time – Performance Loss) compared to the Actual Operating Time.

    
     

  • Quality (Rate)
    The OEE Time (Net Operating Time – Quality Loss) compared to the Net Operating Time. The Quality Rate is equivalent to the Yield.

    
     

Interpreting OEE Values:

As you will see from figure 2, Availability, Performance and Quality Losses eat into the Planned Operating Time for the machine. The greater the losses the less output from the machine. The aim therefore is to keep these losses to a minimum.

This is equivalent to saying that the Availability, Performance and Quality Rate must be kept as high as possible.

If, for example, the Availability (Rate) is 0%, this means that the machine was either broken down or on changeover for the whole time period. Whereas if the Availability is 100% it means there were no breakdowns or changeover losses during the time period.

The key value is the percentage OEE. This indicates how well the machine is performing. The higher the value the better the machine is performing. An OEE of 100% indicates that there were no losses during the time period and that the machine was running at its optimum rate.

The OEE value that can be achieved for a particular machine will depend on a wide variety of factors but in general companies tend to aim for an OEE value of between 70 – 90%.

The purpose of separating losses into different categories is to help focus on the reasons and effects of the different types of losses. The reasons for breakdown losses are often for totally different reasons then reject losses.

Availability Losses will generally be related to poor machine reliability, bad maintenance or overloading the machine causing failure.

Performance Losses can often relate to material problems requiring the machine speed to be reduced, poor design requiring regular operator intervention for minor stoppages, poor operation due to insufficient operator training.

Quality Losses can be related to faulty raw material, or machine problems.

OEE Loss Levels:

There are three levels in the hierarchical OEE model of grouping losses.

Level 1 Losses are Availability, Performance and Quality losses.

Figure 4 shows a Pie chart type OEE Level 1 Loss Report. Each slice represents the fraction of Total Loss which is attributed to each category.

  Fig. 4: OEE Level 1 Pie Chart Report (click image to see full size).

Level 2 Losses are simply Level 1 Losses sub-divided by the six major losses defined above, Breakdowns, Changeover’s, Short Stops, Slow Running, Yield Losses and Start-up Losses.

Figure 5 shows a Pie chart type OEE Level 2 Loss Report. Again each slice represents the fraction of Total Loss which is attributed to each category.

  Fig. 5: OEE Level 2 Pie Chart Report (click image to see full size).

Level 3 Losses are the individual reasons for each Level 2 Loss. An example of a Level 3 Loss might be “jam on station 1” where this is a specific reason for the machine to stop – causing a loss.

Figure 6 shows OEE Level 3 Pie Chart Loss Report. Here each slice represents the fraction of Total Loss which is attributed to each individual loss reason.

  Fig. 6: OEE Level 3 Pie Chart Report (click image to see full size).

Using Provideam to identify and prioritise improvement activity.

Provideam provides the tools to analyse your production data to identify the real reasons for loss.  Thus you can focus improvement activities on the eliminating these ‘real’ losses.  Thereby ensuring that you do not waste time eliminating ‘perceived’ losses which are not actually significant in the overall productivity equation.

If you have any queries or would like further information, please do not hesitate to contact paul.mitchell@provideam.com

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