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。
多样性单元班次 3 视频课程
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From the course by University of Pennsylvania
运营管理概论（中文版）
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From the lesson
第 4 单元 - 质量
质量并不是运营管理唯一的重点，但是质量对于企业长期发展和成功至关重要。本模块将介绍运营中与质量的几个主要方面，导致缺陷的常见原因、发现质量问题以及保障可靠性和标准的常用实践方法。本模块教学结束后，您将了解缺陷可能发生的原因，并且能针对质量和稳定性提出合理的方法。
Meet the Instructors
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
The last session we've seen no desire to produce in large batches.
Large batches are good for capacity. Now let's go back to our earlier example
of the restaurant that is producing cheeseburgers and veggie sandwiches.
Imagine we are producing 100 cheeseburgers followed by 100 veggie sandwiches.
That is however just the supply side of the business.
From the demand side it's very unlikely that customers walk in the store from one
to two will they just order cheeseburgers and from two to three when we're making
veggie sandwiches they just all order veggie sandwiches.
More realistically you have a veggie customer come in, a cheeseburger customer,
a veggie, a cheeseburger. [inaudible].
The demand is just more realistically mixed.
So, when you're producing in these large batches what you're doing is you're
creating a mismatch between supply and demand.
And that leads to what? Exactly, it leads to inventory.
Either we will have customers waiting for their sandwiches.
While we're making cheeseburgers, the veggie customers are lining up waiting for
their veggie sandwich and their production run to start.
Or we have sandwiches waiting for customers.
Long batches leads to inventory. Now let's formalize the example of the
restaurant making cheeseburgers and hamburgers.
Look at what's happening in the kitchen. See, during this time here, we're making
cheeseburgers, followed by a long batch of veggie sandwiches.
Now what's happening to inventory as we produce?
While we are producing cheeseburger, we are serving the cheeseburger demand, but
we also have to prepare for those days when we are making veggie sandwiches, and
thus we have to accumulate cheeseburger inventory.
In contrast, veggie inventory is declining because I'm serving customers.
Who wants a veggie sandwich, we'll I'm not producing any veggie sandwiches right now
so that inventory is going down. Once I switch production and I move from
producing cheeseburger to veggie sandwiches the reverse happens.
My veggie inventory is start to build up and the cheeseburger inventory will be in
the very meaning of the word, will be eaten up.
You see that the average inventory level on the left side here is relatively high.
That's what I meant when I said long production runs and big batches lead to
lots of inventory. Now consider an alternative consider a
frequent change over from making the cheeseburgers to making the veggies to the
cheeseburgers to the veggies. Doesn't necessarily have to be a batch set
of less than one but you notice that the batches are smaller here in the restaurant
on the right. Notice how the smaller batches are leading
to less inventory here in the process. Is extreme case, we would be switching
between cheeseburger, veggie cheeseburger, veggie, one by one.
This is what's called mixed model production, all in the Toyota production
system, and we'll discuss later on in this course.
We refer to this strategy as hai junka. Now, this example does not consider the
impact that the larger batches have on lowering capacity because of the setup
times. As we'll discuss later on, on this module,
reducing the setup times is a very important enabler, of a mixed model
production strategy. Now in the previous example I defined a
batch as a collection of [inaudible] that were produced, between two setups.
This is a definition that is quite common in practice.
However, I will now argue that this definition needs some generalization to be
really useful for more interesting cases. Consider formulas relating to products A
and B, cheeseburgers and veggie sandwiches.
Demand for A is 100 units per hour and demand for B is 75 units per hour.
The production line can make 300 units per hour of either of the product and so our
processing time, P, is simply one over 300 hours per unit.
It takes 30 minutes to switch production from A to B, and so we have a setup time
of half an hour. Now, earlier on, when we defined a batch,
we were looking at a collection of product A,
Cheeseburgers. We called that a batch.
In the setting where you're producing multiple products, potentially with
different demand traits, I found it useful to take a different approach of what we
think of a batch. In this case here we're producing product
A. We're then gonna run a setup from A to B.
We're gonna produce a bunch of product B's.
We change over again, from B to A, and then we go back to producing A.
Instead of the previously, somewhat more narrow view of the batch, I now want you
to think of a batch as all of these units of A and of B that recede before repeating
the pattern of production. With that definition in mind, you notice
that we're gonna have two setups that are happening in this batch or another term
for this is, is a production run. Both of them now in half an hour long, so
that means really the total is one hour of setup.
Now, how are we gonna choose the batch size here?
Well, we know that we're gonna have. 170 units per hour to stay on top of
demand. This is simply our target flows.
Now, the capacity formula that we introduced early on in this modulus says
its a floor, the capacity, that we can get out of this system was set up is driven by
the batch size, divided by the set up time plus the batch size times the processing
time p. In our example here, that is batch size,
divided by one hour, plus said batch size times one over 300.
This gives me a sample equation. 175 has to be equal to, the batch size
divided by one plus the batch size. One over 300 which is a linear equation in
B, I can simply cross multiply with one plus B divided by 300.
And I can solve for B, to get B equals to 420.
Now that means that this, the batch size, it is the total number of parts that are
produced before repeating the pattern again.
So those 420 they are bunch of A's in a bunch of B.
Now, we notice that I have to make more A's than B's.
And so, for that reason I can take 420 and simply make sure that the ratio from A to
total is reflecting this so I multiplied it simply with 100 of A divided by 175
which is a total and that gives me 240 of product A.
And then I know that since I have 420 in the veg total, 240,
That means that there's 180 for B. That would simply be 420 times 75 divided
by 175. And so we would now refer to a [inaudible]
as 240 of type A followed by set up followed by 180 of type B followed by a
set up, And that completes the production run.
Now let's continue the example of the previous slide, and imagine the case that
our marketing folks are adding a third product to the mix.
This might be a situation where our old product A is simply offered in two
versions. A1 and A2 total demand we assume for now
is staying the same. So product A1 is offering 50 units.
50 units of A2, this is our old demand of a 100 units per hour of A.
And then B, 75 units per hour. And that's all old, 175 units per hour.
The setup times are still as before, takes a half hour to switch from any one product
to another. And it is taking us P = 1/300, and that
would be, again, hours per unit. Now the production run that we're looking
for is going to look something like this. We're gonna make some a1.
So we're gonna change over, make some A2's.
We change over, we make some B's. We change over, and then we start again.
So the production run would look like this, and the batch is really a collection
of A1's, A2's and B's. The question here is, of course, how do we
choose the batch size? Well, again, we have to produce at 175
units per hour. We know that this is equal to the batch
size divided by the set-up time plus the batch size times p.
You notice now, however, that there are three set-ups in a, production run.
And for that reason, S is now no longer one but 1.5, everything
else really stays the same and just as we did before we can solve for B and I'll
leave that to you as a homework assignment.
You'll find that B = 630 units. So 630 units is set up before the whole
pattern repeats itself. From the 630 units.
630 times 50, divided by 175, equals to 180 .
Are gonna be produced on A1. The same, 630, 50 to 175.
180 will be a2. And b's will be 630 75 / 175 = to 270,
will be of product B. Now the words of production run will look
something like, like the following. 180 A1's set up, 180 of A2 set up.
And then, 270 of product b. What you see here, is its productions runs
got longer compared to the previous example.
The total amount stays the same, but by adding a third product to the mix, I have
increased the length of the production run and I'm now I am working in larger
batches. Note further that product B which really
has not been affected in terms of its demand, is also produced now in larger,
batches than before. Instead of making 180 of part B, I'm
making 270. So what we're seeing here is that more
setups force us to spend more capacity on setup.
That means we have to do bigger production runs to keep up with the total demand
rate, and that will lead to more inventory.
So, once again, you notice how variety leads to more setups, which leads to more
inventory. This is one of the biggest cost of
offering variety. Did I just squeeze it to mismatch between
supply and demand. But I just have that's a root cause for
inventory. In this session we will see how a firm
that increases its variety by, for example adding a third product to it's product
line, is going to be forced to add more inventory.
The reason for this was holding the overall flow constant.
The extra product required extra set-ups which we can only afford if we run longer
batches. The dream of every plant manager is to
produce at exactly the rate and the mix of demand.
This is what is called [inaudible]. Mixed model production.
As we will see later on in this module. This however requires that we get these
setup times reduced further and further. This is done by a technique that we will
refer to as SMET.
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