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。

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En provenance du cours de University of Pennsylvania

运营管理概论（中文版）

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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。

À partir de la leçon

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。

- 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 implication of product variety goes beyond to product settings and beyond

simple set up times. Product variety will also impact the

distribution system of all operations. The more we will segment demand into

smaller and smaller segments the harder will it be to accurately predict the

amount. Consider the following example; how many

shirts in blue of size L was a gap here in Philadelphia sell tomorrow morning.

One. Two, three, four, or five.

I'd be surprised if we would get this forecast right, even within 50 to 100%.

Now ask yourself, how many shirts across all colors, sizes.

Stores will pick up sales in all of the next quarter.

Probably we can get that forecast within ten or twenty% of the real number.

The reason for that is the more we added it to forecast, we added uncertainty, and

the uncertainty starts behaving following the laws of statistics, then it becomes

easier to plan. This will be the focus of this session.

To make distribution decisions, we typically need a forecast of demand.

The problem with demand forecast is, they're not always right.

We face what is called demand uncertainty. When we describe demand uncertainty, we

think of demand as a distribution, drawn from some underlying distribution.

Let's think of a running shoe company. The running shoe company has two models.

Model one and model two. It makes forecasts for model one and model

two but thinking about the mean or the expected amount of shoes that they going

to sell and it's just common in operations and statistics to use a Greek symbol Mu to

capture this and the standard deviation of that demand which we're gonna call sigma.

Now we might think about sigma as the amount of uncertainty that the firm faces.

However, sigma or loan the standard deviation of loan the standard deviation

of loan is not a good proxy for the amount of variability or uncertainty in demand.

A thousand running shoes standard deviation, is it a big number or small

number? That really depends on the mu on the mean.

If I'm having a 1,000 standard deviation for an expected demand of 2,000, we would

call this probably a lot of statistical variation.

However, if it's a 1,000 standard deviation for a million shoes, that would

be relatively little. With this in mind, we define the

coefficient of variation, also CV, coefficient of variation as a ratio

between the standard deviation. And, the mean.

Now consider a competitor, of our running shoe business.

It has some how managed to combine shoes one and two in to one model.

Think of this a second, shoes for men and shoes for female runners.

Where as this company has like just one common shoe for everybody.

Let's assume for sake of argument that the demand for the female running shoes and

the demand for the male version are independent of each other.

Moreover let's assume that the market sizes are roughly similar and the market

uncertainty is also roughly similar. So, in other words the Mu1 is equal to the

Mu2 and the Mu, the Sigma one is equal to the Sigma two.

Now, what's gonna be the demand for company two over on the right?

The expectation is simply gonna be mu one + mu two which is = two, assuming that

they are the same. Which is = to two mu.

How about the standard deviation. To find the standard deviation of the

combined demand, we have to look at the square root of sigma one squared plus

sigma two squared plus two times the covariance between demand one.

And demand two. We assumed independence, and so this

fellow here is gonna be equals to zero. And we also assumed that sigma one is

equals to sigma two, which leaves us here with the square root of two times, sigma

one or sigma two squared. I can simplify this and write, this is the

square root of two, times sigma. Now, how about a coefficient of variation?

Well, again, I have already computed now the standard deviation.

And so the standard deviation / the mean, which is two mu is gonna be = to one

over the square root of two sigma / mu. So you notice that the firm here to the

right is facing a lower variability of demand, then the firm here on the left.

So we see that by combining demands I'm able to reduce the demand variability as

measured by the co efficient of variation or put differently is in combine and

demand the standard deviation by demand goes up slow than the underlying mean.

This effect is called demand pooling. Pooling demand or aggregating demand is a

way for others to reduce uncertainty. That would make it much easier for us to

get the right amount of orders in the right place.

Notice that pooling does not always require independence.

It works on nicely mathematically if the two demands are independent.

But even if there's a correlation between the demand of product one and product two

pooling still offers tremendous benefits. In this session, we went from the left to

the right, and asked ourselves what would happen if I could combine the two

products. Now just put this argument on its head.

Ask yourself, what would happen if I'm a company offering one running shoe and I'm

thinking about customizing it now and offer more variety, offer a product for

the male runners and to female runners. You'll notice that as we're fragmenting

the demand, so if pooling goes this way. Here we are fragmenting demand.

As we're fragmenting demand, and increasing the amount of demand on

certainty. Now let's go back to our comparison of

McDonalds and Subway that we started in the module on Process and Analysis.

We said that McDonalds followed some make-to-stock strategy.

Make to stop means that they make the burgers before having the orders.

In contrast we said that Subway produces made to order, why then.

Let's think about this. At Mcdonalds, the choice of burgers is

limited You can have the cheeseburger. You can have the hamburger, the big mac,

but you cannot have the sandwich customized your way.

This limited set of offering keeps the demand variability relatively low and

let's McDonald's come up with reasonably good forecast of demand.

At Subway and subway customization strategy this does not work.

There's so many versions in which you can have your Subway sandwich made that it

would be impossible for Subway to hold one in inventory for every possible offering.

Notice that we observe the same. Two strategies in the computer industry.

We have the dell model that is basically playing the subway strategy.

We are taking the orders of customers and through the web site and makes the

computer to order. Apple on the other hand plays the

McDonald's strategy. They offer a handful of variants.

These are so popular that customization is typically not necessary, that allows them

to reduce demand variability and get some edge to supply, between supply and demand

reasonably correct. In this session, I introduce the concept

of demand pooling. By pooling the demand across multiple

items in a product line or multiple locations in a distribution network, I can

decrease the demand uncertainty. In other words, I can tame the uncertain

demand. This is a direct consequence of the laws

of statistics. By reducing the demand to uncertainty, I

also reduce the supply/demand mismatches. I have fewer customers that are

disappointed because they couldn't get the shirt and size and model that they wanted.

And vice versa, I have fewer shirts that nobody wanted to buy.

Pulling is probably one of the most powerful inside in concept in operation

management. We will see it again and again in the

remainder of this course.

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