Earlier on in this module I praised some work by Taylor and [foreign]. Both of them were passionate about going out to the front line to study an operation based on observation. As opposed to studying operations based on running some tensile on an excel spreadsheet sheet? Recorded, is not working, said that moving is not working. In this session we'll introduce the overall equipment effectiveness framework. I know this sounds technical at first, but I can promise you that it is probably one of the most powerful frameworks for studying productivity improvements. The overall equipment effectiveness framework, or O.E.E framework for short, Counts all the time that you have available on a resource. That might be a human person. It might be a machine. And it starts with this time, and then identifies all these things at the resource that are waste. By taking these pieces out of the available time, your left with time that the resource is really productive. This is the overall equipment effectiveness. Its a fraction of time that the resources add in value. This is an important ingredient when you're then predicting the upside of any operational productivity improvements. Consider a piece of equipment in a production process. Equipment can be expensive, especially in production settings such as semiconductors, or highly automated assembly. We want to find out how much of the time, the equipment is actually used productively. Say we study the equipment for 100 hours. So first thing that we observe is that from these 100 hours, the machine is not running all the time. The machine is what we call in down time. Down time can be driven by things such as machine break downs or change overs. Change overs we will discuss in the module on product variety. Those are times when the machine is moved from producing one type of product to another. This really then leaves us only with 55 hours that the machine is running. Even those 55 hours are reduced further. Idle time, because of line balancing issues. And reduced speed, because of poor operator training, drives us down an hour exactly to 45 hours. But it gets even worse, from those 45 hours of net operating time of producing defects and we have to ramp up production, producing scrap potentially during start up. In this example here. The total number of losses accumulate to an overall equipment effectiveness of 30%. This is simply driven by the 55 percent of the times that we have this down time. 82%, namely the ratio between 45 and 55, that we lose before because of lower speed. And 67%, 30 relative to 45, that are driven by quality losses. So we notice that we overall equivalent effectiveness is 30%. We get eighteen minutes of value out of each hour of work that we spend at the machine. Often times, what you will notice is that even with an OE yield of 30 percent the people actually operating the equipment might require that you invest in more equipment. After all, the equipment and the workers are on it seem to be busy most of the time. But, as Ono quoted, moving is not working. The OEE helps us to realize that in this case, we have almost a 3x productivity improvement potential without investing anything further in additional equipment. There's a surprising when I worked with the case of an aircraft. We can think of the equipment as an aircraft seat. The seat is only adding value if it is in the air and it has a paying customer sitting in it. What percentage of the time do you think the typical airline seat actually adds value? When I ran the analysis for the big US carriers I found the following. On the left here I started with 365 days in the year and the 24 hours that are in a day. Most of the time is lost. Because the plane is either at the gate or it's in maintenance. This is not too surprising. Most of this in fact is driven by the fact that nobody wants to fly from Philadelphia to Chicago at two in the morning and it's just not profitable for the airlines to fly at crazy hours. The other chunk here is maintenance that is required for the planes. This leaves us with the amount of time that is typically referred to as a block time. This is a time that the plane is actually moving. But moving includes taxiing and landing, not, at least from the customer's perspective, necessarily valued at. After subtracting this as a ten percent of the time to compute that an aircraft is in taxi and landing, you get the time that the seat is in the air. But not every minute that the seat is in the air is an invalue because seats often fly empty. Typical aircraft utilizations are in the low 80 percentages. And so we have to subtract another 10-20 percent to adjust for the fact that we are flying empty seats. If you combine all of these effects together and you compute the AE or EE of the aircraft seat, you typically get a number that is around 30%. You might think that, that number is low, but I can assure you it's dramatically higher than where it was some ten years ago. The OEE framework applies to equipment at wel-, as well as it does to people. So folks at MacKenzie, which is where I've picked up the OEE framework, in that case, because the OPE, the overall people effectiveness. Let me illustrate this with an example. In a research collaboration that I'm currently conducting with the VA Hospital system, I'm trying to measure how doctors are spending their time. I'm trying to determine their OPE. Let's start with the total time that we have the doctor on payroll. Well, doctors are sometimes on vacation and sometimes sick themselves, which gives us the total time the doctors are in practice. Now, not every minute of the time is booked for appointments. Even though doctors in primary care in particular tend to be very busy, they still have some empty appointment slots. That leads to idle time for the doctors. This gets us to the total time that the doctor has booked for appointments. Some of the patients, however, have an appointment and then don't show up. No-shows or cancellations, thus, further reduce the OPE of the doctor. After adjusting for cancellation, we get the total time that the doctor spends with the patient. This is when things get dicey from a data perspective, more senseless things that I don't, I know, have relatively little data about what actually happens during the doctor patient encounter. In the case of the VA system, we use video cameras to document minute by minute, what goes on when the doctor speaks to the patient. It's interesting to see that a good number of the patients that are spending time with a doctor, really don't have to be seen by a medical doctor, and could be seen easily by a nurse or anther physician extendor. Moreover, if you go minute by minute, through the processing time, typically that's a twenty minute encounter, you'll find that the doctor spends many things doing that are not really requiring the knowledge of a medical doctor. Rewriting scripts for refills for medications, patient counseling and social work. This gets you the real true value of time for the doctor. Okay. Now it's your turn to compute an OEE. Consider the following example. We have a car manufacturer that operates a 3D printing lab where computer models of designs are turned into physical models. The lab is open here for twelve hours a day. Now you see that the lab spends a fair bit of time each day on things that are not directly value add. Value add time is the 70 minutes that it takes to crunch out one of these models, but lots of other things are going on. So, take a look for yourself, you might want to pause this video and ask yourselves the following two questions. How many good models are produced every day, and want is the O-E of this lab? Now to see this. Consider the following calculation. Let's start with the question of how many good models are produced each day. We know that we have 720 minutes available per day. Of that 30 minutes per day are subtracted because of the start up effects. That leaves us with 690 minutes a day. If we ask RSF how long it takes to produce one model, we have to go through seventeen minutes of production plus 30 minutes of, setup, so if you take 690 minutes divided by 100 minutes per day, and remember here, that you can only start a new job if you're gonna finish it during the time before six:00 p.m., you see that you're gonna get each day, you're gonna get six models per day. Finally of those six models, one third, i.e. Two, one third need to be scrapped and thus it leaves you with four good models per day. Now you do the calculation backwards. From these four models per day. Basically that warrants 280 minutes of productive times, per a day. You multiply this with the six days a week that the lab is opening, because there's one day of maintenance. That gives you a total productive time per week of 1,680 minutes. This is sixth. 1,680 minutes per week of value add time. However, on the other hand, you can clearly simply see that twelve hours a day, 60 minutes an hour, and seven days a week, corresponds to 5,040 available minutes per week. So if you wanna draw a little chart here. So this 5,000 is the available time. And this time is a real value at time. The OEE is simply the ratio of this number to this number, which is 33%. If you want to be more fancy in the graphics, you can now take out. For each of these losses be it the scrap, the start up effects, the sell up time or the maintenance. You can quantify their magnitude as you go from the left to the right. But as a first start I'll always encourage you to start with the very left, the available time and with the very right the value add time. Now let me end this session by reading you another quote from Frederick Taylor. Employers derive the knowledge of how much a given class of work can be done in a day, through either their own experience, which has frequently grown hazy with age. For casual and unsystemic observation of the man or events from records. Having data on what workers actually do during their time. Is very difficult. Moreover this is even worse when we are dealing with knowledge workers. If you think about observing doctors observing insurance agents or underwriters in a bank. This is really hard and doesn't fit the culture that most organizations have. The OAE framework is powerful because it elects your documents what you have learned during these observations and identify the fraction of the work time that was truly value add. The OAE analysis will also tee up. The productivity improvement study where you can ask yourself how much of a profit lift, as we've seen in our discussion of the KPI trees can I get by reducing these various forms of waste.
