了解如何提升工作效率和提高质量标准，学会分析和改善服务业或制造业商务流程。主要概念包括流程分析、瓶颈、流程速率和库存量等。成功完成本课程后，您可以运用所学技能处理现实商务挑战，这也是沃顿商学院商务基础专项课程的组成部分。
第 3 单元班次 5 视频课程
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來自 University of Pennsylvania 的課程
运营管理概论（中文版）
29 個評分
從本節課中
第 3 单元 - 生产率
您将在本模块中学习生产力的主要构成。首先，课程将介绍生产力的学术定义，然后介绍常见阻碍生产力的因素、生产力的测量方法、效益指标、改进生产力技巧和经济价值。本模块教学结束后，您将掌握如何识别、描述和测量输入和输出之间的关系，您还将能够设计策略，减少输入和提高输出，从而提高生产率。
與講師見面
Christian TerwieschAndrew M. Heller Professor at the Wharton School, Senior Fellow Leonard Davis Institute for Health Economics Co-Director, Mack Institute of Innovation Management
The Wharton School
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.
