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Tuesday, July 13, 2010

Fixing Blame for the Deepwater Horizon Blowout is a Bad Idea



Much of the news coverage about the BP Deepwater Horizon oil blowout focuses on who is to blame and who is trying to avoid blame or pass it on to someone else.  There is a consensus that BP has a lot of the blame.  The Minerals Management Service (MMS) of the U. S. Department of Interior gets a lot of blame, particularly due to a scandal during the Bush Administration alleging sex and cocaine parties in which the MMS and the oil companies participated. Democrats blame Bush, because it happened on his watch. 
Interior Secretary Salazaar and President Obama are blamed because they weren’t quick enough to clean house at MMS.  Everyone, including the U. S. Coast Guard gets blamed because the capping of the well to stop the spewing oil and natural gas is taking so long.

Saturday, July 10, 2010

Why I Call It “Bringing Out the Best At Work”



In the early 1970s I was a graduate student at The Wright Institute. were I was the second student to be admitted to its Graduate School.  The first group of twelve students came together in 1970 into program was little more than a vision of the founders of the Institute.  We created the first curriculum and went across the street to the University of California Berkeley to recruit part-time faculty.
The Wright Institute Graduate School offered a Ph.D in Social-Clinical Psychology.  The idea was to integrate the two fields which did not communicate much to each other.  Graduates who became clinicians would have more awareness of the social contributors to mental health.  Some of us would become, in a sense, clinicians to organizations. 
In a clinical case seminar, a student was presenting his patient to the group and going on about various theoretical models.  The professor stopped him and asked, “What does the patient say the problem is?”
“She says her work is driving her crazy.”
“What does she do?” The class participants leaned forward to hear the answer.
“We haven’t gotten to that yet,” the blushing student therapist said.

Wednesday, July 7, 2010

OEE – A way to Speak with Data, But It Only Works If We Use It



Speaking with data is an important part of the journey to becoming a World Class organization.  It is a way to make the invisible visible, to see things that we are otherwise likely to miss. It is also a way to get the attention of people who control the purse strings, when we need resources to solve a problem. I first discussed speaking with data in my post March 15, 2010. (http://bobduckles.blogspot.com/2010/05/speak-with-data.html)
OEE, which stands for Overall Equipment Effectiveness, can be a useful tool for managing and continuously improving.  There are three components to OEE:
1.     Availability (Planned uptime minus unplanned downtime)
2.     Performance  (Planned operating time minus minor stoppages and running slow. It is calculated by dividing the number of pieces produced by the pieces that would be produced at the rated or standard speed)
3.     Quality (All pieces produced minus defective pieces produced divided by all pieces produced)

Some companies have a mistaken view of the OEE of their equipment. I have been told that a certain machine has an OEE of .90 or .95, when I can see that it is running slowly, creating scrap, or is down fairly often. There is a convention that any OEE of .85 or above means that the equipment is working at a world-class level.  

Fairly high numbers in each of the three components can lead to an OEE that is still below level of .85. I have seen estimates that OEE in American manufacturing tends to run around .60.  I have not seen studies on which this claim is based, but it feels right to me as a ballpark. I have also been in plants where most of the equipment does not even reach that level.

Let’s consider a few examples:
OEE
=
Availability
x
Performance
x
Quality

=
0.8
x
0.9
x
0.99

=
0.71





The first example does not reach and OEE of .85.  One combination that does reach the world-class level is:
OEE
=
Availability
x
Performance
x
Quality

=
0.9
x
0.95
x
0.99

=
0.85





This combination still falls short:
OEE
=
Availability
x
Performance
x
Quality

=
0.9
x
0.9
x
0.9

=
0.73





It does not make much sense to make these calculations, unless we use the data. It is also important that we not use the data to beat people up, but to help us figure out where we need to focus our attention in order to improve.

One way to do this is to calculate the OEE for a given machine daily, at the machine. If the OEE is consistently .85 or better, there are probably other pieces of equipment that need our attention more than this one.    We still might want to ask whether this is an unusual level for this machine.  To know this, we would need to have a history.  I advocate not only manually calculating but also manually plotting OEE for each piece of equipment we are monitoring.   Here is a way to present the data. 

(Click on the image to enlarge it) 




On the graph we see that on Monday, the 5th, we suddenly had a drop to .15.  We should want to know what caused this drop.  Our first question: Was it a problem of availability, of performance, or of quality? A glance at the bottom of the chart, where the three components are recorded, we can tell immediately that or biggest problem was availability.  The machine was down.  We ought to find out right away what caused the machine to go down, so that we can take action to insure that this problem never happens again.
On Friday, the 9th,  we had another drop in OEE. A quick look at the data tells us that this time we had a problem with performance.  That is where we need to dig in to find out what happened and take corrective action.
Increasingly, calculations such as OEE are made by statistical packages into which data is, in some cases,collected automatically. Interesting high level graphs and reports can be generated. When I work with clients, I insist that the operator, at the machine, create this graph manually.  If we only have a report that comes out at the end of the week we miss an opportunity to act.  When OEE suddenly drops, we want to start asking what happened right away—the same day.  OEE charts on the machines that are being monitored can a part of visual control.
To begin to use OEE, I would not suggest that we start monitoring and charting every machine. Begin with some critical operations that we need to improve. Strive for consistency, then strive for improving OEE.  Some equipment may never need to be plotted because it is reliable and has a capacity far in excess of what we need.
World class OEE, .85 or better, is a factor to consider when ordering new equipment and calculating the needed capacity. 
I have had occasion to work with teams planning a new line and the equipment to be purchased for them. They planned as if the equipment would run at its stated capacity 100% or the time.  The improvements above .85 can be pretty challenging.  While part of achieving world class is demanding excellence of ourselves, plan with a .85 OEE in mind, nothing higher.
To get operators to record the data and make the calculations, we should explain that these data will help us solve problems around availability, performance, and quality.  Then, we need to earn credibility by using the data for that purpose.  It is discouraging to engage in an activity that does not add value to the product or the process.  Too often we start data collection and quickly forget why we are doing it. Start small.  Stay with it.  Show some results.  Spread the process from the early examples.
Notes: The Lean Thinker  has recently described how to calculate the three components of OEE. I chose to focus more on what to do with the OEE calculation, once you have it.  


While for reasons that I hope are clear, I object to capturing the data and doing the calculations in a centralized system.  I do not object to the operator having templates on a screen into which we can plug the data that give us the components and overall OEE that we then plot manually.

Friday, July 2, 2010

Lean Manufacturing Requires Responsive and Reliable Maintenance



Lean operations require maintenance that is responsive and reliable.  When we have isolated operations that are adding value to pieces in a batch that eventually gets passed on to another operation, one machine going down does not present itself as a critical event.
If we have many operations organized in a cell and one piece of equipment breaks down, the entire cell goes down.  We require equipment that does not go down very often – ideally not at all – and when it does go down we need to get it up and running as quickly as possible.  This imposes requirements on the maintenance system. The maintenance system is not limited to the maintenance function.  The system includes standard procedures that are followed by the operators and equipment that does not breakdown and/or can be repaired quickly.
A good way to begin to improve the maintenance system starts with a workshop focused on a single piece of equipment that is or will be critical to a lean, one-piece-at-a-time operation.  The workshop team goes through a process of detailing the machine.