沃顿商学院全套笔记-九-
沃顿商学院全套笔记（九）
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P131：15_学习回顾和实践问题.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
Let's start today's video with some historical context about the Toyota production system。
and thus the automotive industry。 I should have said this before and I'm sure you have noticed this by now。
but I use it， to a more lean operation and to the Toyota production system interchangeably。
That doesn't mean that I tried to set you to your company， it simply points to Toyota。
success of developing a really powerful management system。 And more on that in a moment。
but for now， let me start with a question。 As a good German， I have to ask you this question。
Do you know in which country the first car was designed and built？ You guessed it。
The first vehicle was built in Germany by a gentleman with the name of Karl Benz。 In 1886。
Karl Benz， you see a picture of Karl Benz here， built the first automotive， vehicle。
the Benz-Pattent Motor Vog。 Benz was a real engineer who was passionate about the car at that time。
my hypothesis， is that he didn't care much about cycle time， idle time and line balance。
It was all about making one beautiful car。 The story about Karl Benz wouldn't be complete without talking about his wife。
Berta。 Berta was actually the first driver。 She drove from Manheim， where I went to college。
to Fort Simon back， a 66-month drive each way。 Legend has that she ran out of gas on the way and for obvious reasons。
there weren't， any gas stations to refuel。 So smart as Berta was。
she went into the local pharmacy to get enough fuel to get back home， to Manheim。
The second part of the automotive story I want to share is about Henry Ford。
While Karl Benz was a master of the product， I would say Henry Ford was a master of the， process。
He had his epiphany for how to organize car production from visiting slaughterhouses。
And slaughterhouses meet his hanging from conveyor belts and has moved from one worker。
to the other。 So good old Henry had the idea that something like that based on a moving line should work。
in an assembly operation。 So Ford really perfected in a very Tayloristic mindset the production of cars。
He managed to bring the time that it took to build a car down from 12 hours to 2。5 hours。
Every light heavily on specialization， each worker would only be doing a very small job。
like 60 seconds and less of work per car which allowed Ford to take cheap immigrant。
labor off the streets and take them to quickly build up his production capacity。
And so already 100 years ago Ford was making well over a million vehicles per year。
The most famous vehicle was a model T of which he had built 10 million units by 1924。
He was really playing a game of scale economies。 As Ford increased production he was going down the learning curve and was squeezing the cost。
and the waste out of the system that made the car affordable for the American middle class。
which led to a period of prosperity and widespread adoption of the automotive vehicle。
The Toyota story is very different。 Toyota was initially a company making automated looms。
They went into the automotive industry just prior to World War II。
In 1937 they founded Toyota Motor Company。 And as we know there was a very difficult time for world history。
We the Germans joined by the Japanese started World War II。
At the end of the war after 1945 the world lay in roots。
And just like the US through the Marshall Plan helped rebuild the German economy the US tried。
to help the Japanese economy。 And that helped included the attempt to establish an automotive industry。
So what the American citizens took tools and experts from Detroit and transplanted them。
over to Japan including to Toyota。 But they didn't go very well。
And one of the reasons why this didn't work was that post World War II Japan had little。
domestic purchasing power。 They transcended into not having the demand。
the scale that was so characteristic for the， US market before。 That spelt problems。
For example in 1950 Toyota made a grand total of 300 vehicles。 They almost went bankrupt。
Big prices， worker strikes。 It was really in that period of distress that the folks came together heavily involved。
was this gentleman we talked about earlier on Tai Chi or no。
And they started building what we now refer to as a Toyota production system。
I reviewed the Toyota production system in a moment but to give you a sense of the impact。
that the Toyota production system had on industry considered one last piece of data。
In 1986 there was a big report published out of MIT from an institute called the International。
Motor Vehicle Program。 A group of researchers went around the world benchmarking automotive plants comparing their。
productivity levels。 You see here the type of metrics that we discuss in this course。
Gross assembly hours per car for example。 I think of as labor content。
At the time a GM plant would have 40 hours of labor in a vehicle， a Toyota plant would have， 18。
Defects 130 to 45。 Most extreme inventories。 Remember how we talked about days of supply and inventory turns？
Two weeks worth of parts in a GM plant？ Two hours at Toyota。
That meant that Toyota would turn the inventory multiple times in a day。
And that was really the time when the world realized wow。
There is just a smarter way of building a car。 The lead author of this research study。
a gentleman by the name of Jim Wumec， coined， then the term lean operations。
So again lean and Toyota have since emerged as synonyms。
I have not done any automotive work in the United States but I did research with a German。
and a French car manufacturer and both of those willingly admitted that they had embraced。
the Toyota production system。 So what is the Toyota production system？
There are many ways to visualize this but I think the most common form of summarizing。
and synthesizing the Toyota production system is in the shape of a house。
There is a roof and at the top of the roof is the idea of zero waste。
The cool thing with the Toyota production system is you can impress people at cocktail。
parties with 5 or 6 words of Japanese。 So the Japanese term for waste is "muda"。
So zero waste is the top goal。 Second waste people often times refer to four other zeros。
zero defects， zero breakdowns， zero inventory and zero setup times。
Very much in the spirit of the seven sources of waste that we have discussed earlier on。
in this module。 And then the main part of the house consists of two big pieces。
There is a flowpiece and there is a quality piece。
The flowpiece is all about matching supply with demand and avoiding inventory by delivering。
just in time。 This includes things such as mixed model production。
flows piece at the rate of demand。 Remember we talked about tech time。
we talked about single units flow， we talked about push， and pull and we talked about come back。
Again lots of things that are really popping up throughout this course。
They are really all about the idea of flow。 And then there is a piece about quality。
Quality management has many elements to it。 One big idea is really making sure that you catch defects as quickly as you can。
And when we talk about quality in the quality module of this course， we'll go back to this， idea。
As we will see to your work as I empower to stop the line whenever there is a problem。
by pulling something that we will refer to as the unknown core。 Finally the house has a foundation。
Depending on which book you read or which expert you ask the foundations vary a little， bit。
I like to think about flexibility， standardization of work and work involvement。
Work and work involvement includes a famous idea of Kaizen。
I mentioned in passing earlier on in this module， the eighth source of waste， the intellect。
of the workforce should not be wasted。 And so Kaizen is all about involving the workforce in quality improvement circles and。
continuous improvement efforts。 Standardization of work is related to many of the seven sources of waste。
We make sure to standardize the one best way as opposed to having unnecessary motions。
And then really this idea of flexibility。 The ability that I can change over a machine from one type of car to another。
Or I have a worker who is trained to do multiple things。
This allows me to use a worker for multiple things， which makes it easier to balance the， line。
Okay， I know I'm out of time for this video as I did not manage to balance video duration， as well。
So let me quickly summarize the learning objectives for this module。
You see here the definitions related to the Toyota production system。
So there are really two simple formulas， the OEE and the flow time efficiency。
So let's take a moment to practice those， one practice problem for each of those two formulas。
Take a look at this first practice problem here about the cyclist and the wind tunnel。
Put me on hold until you've figured out the solution and then press play again。 Alright， here we go。
So we start by writing down the definition of the OEE。
The OEE is simply the value at time and we're going to divide this by the total time over。
the total time we pay the resource。 I suggest we're going to use a 10 day window here for reasons that will become clear in。
just a moment。 So 10 days and 10 days they are 24 hours in a day and 16 minutes in an hour。
So here in the denominator we have the total number of minutes over those 10 days。
Now we have to look at when does work not happen， right？ What do we have to take out？
From these 10 days they were only 9 days that we were actually working， right？
So 9 days when work gets done。 And then from these 120 minutes from these 2 hours 20 minutes are not adding value。
So the procedures are each 100 minute long in terms of their value at time。
And how many procedures are we doing per day？ Well we only have 5 value at procedures a day because the other two are not adding value。
their form of rework。 And then type this into your calculator and you get an OEE of 0。3125。
Alright the next problem is about the percent value at time looking now from the perspective。
of the flow unit。 As usual put me on hold first。 Here we go again。
Let's tackle this problem together。 So again this problem is about the percentage value at time。
And then that ratio is the ratio of the value at time。
And we're going to divide that value at time by the flow time， right？
How long the unit spans in the process。 Let's start with the flow time。
That is the time that it takes the country to renew the passport。 That is the 2 days。
the 4 days and then down here another 3 days， another 2 days， 10 days。
for the printing and finally another 2 days。 We add all of this up and it's going to take 23 days。
And how much value at work it's done over those 23 days？
Well we can debate a little bit about the 10 minutes that is happening at the clerk but。
it's a such a small number it would not matter。 Let's give them credit for these 10 minutes。
And then there are 2 minutes for the printing though that's a grand total of 12 minutes。
of value at time。 And you divide it by the flow time and you get 0。00 something。
So matter what it is， it is a very small number。
Alright that was a long video and a long module。 I hope I did not digress too much into the details of the automotive industry。
In the spirit of lean， then maybe a quick year in my closing comments。
Thank you for joining me in this module。 I see you soon。 [BLANK_AUDIO]。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P132：16_小法则.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
We don't have many equations in this course， and so we should create each equation with。
great joy。 Today I will introduce our first equation， which I will refer to as "Littles Law"。
To illustrate the equation and to build some intuition， I will use a hospital example。 A while ago。
one of the interventional radiologists here at Penn had asked me to look at their， patient flow。
But what do you look for when looking for patient flows？
Well imagine you and I would take two folding chairs， coffee and donuts， and make ourselves。
comfortable in front of Penn's department of interventional radiology。
We also take a notebook along and you would take detailed notes while I munch my donuts。 Ready？
So here we go。 At 7。35am the first patient shows up。 At patient goes in， doors shut。
it is always for now。 I ask you to also take a graph tracking the cumulative inflow of patients in blue and。
the cumulative outflow in red。 10 minutes later， at 7。45am， the next patient shows up。 And at 8。
10am the third patient comes。 Before the fourth patient shows up at 9。30am。
however the first patient， the guy who came， at 7。35， comes out of the department。
At 10am I ask you a test question。 How many patients are in the unit right now？ Sharp as you are。
you will respond three because four went in and one came out。 From that point onwards。
I stop asking my stupid questions and you just take your notes。
Only patient five surprises most of us as she got in and out of the department in 15 minutes。
We later learned that the patient refused to sign the consent form。
At the end of our day in the hospital， we have built graphs that look something like， this。 Again。
the blue graph captures the cumulative inflow and the red graph is a cumulative outflow。
These two graphs meet at the time the last patient has left the unit。
First observe that we have taken care of 11 patients。
In prior modules we refer to this number as a flow rate which I am going to now abbreviate。
with the letter R。 The flow rate measures how many patients we saw in a given period。
of time in our case one day。 There are a couple of more things this graph teaches us about the patient flow。
We can also compute the number of patients that are in the department at any given moment， of time。
This is simply the difference between the inflow curve and the outflow curve。
The number fluctuates between zero in the morning and four around 3pm。 Remember。
we defined the number of flow units in the process SE inventory which I will now。
abbreviate with the letter I。 Finally， you can also see how long a patient spent in the process。
Some patients spent a long time with patient number nine here。 Others got in and out much faster。
Recall， patient five was in and out in 15 minutes。 This was our third measure。
the flow time which I will now abbreviate with the letter T。
The flow time measures how long it took for the patient to flow through the process from。
beginning to end。 Now the reason why I'm reviewing these three definitions here is not just because they。
are important。 If you look at the figure here which summarizes all observations for the day。
you might see， your relationship。 On average， the inventory in the process is a flow rate in the process times the flow。
time。 Inventory in this case is measured in patients。
Flow rate is patients per hour and flow time is measured in hours。
In any process and at any time the average inventory is equal to the average flow rate。
times the average flow time or I equals R times T for short。
We will refer to this relationship as Little's Law。 You see a picture of Mr。 John Little here。 Dr。
Little was born in 1928 but more importantly， his law says on average in any process inventory。
is equal to flow rate times flow time。 I've been teaching this for many， many years。
In one of my classes a student had a great idea how to remember the equation。
He just stared at my slide and shouted out， "I R T。 I remember that。 I can't beat that。"。
More importantly， let's talk about the implications。
Little's law reminds us that from the three most important performance measure in any， process。
two you can potentially mess around with and adjust but once you have done this。
the third one is really given by nature。 Well， there's an easy way to get to zero inventory。
You just stop producing。 You said R equals zero and you get I equals zero。
But obviously that's a really bad idea。 So if you want to get rid of some working capital。
get rid of some inventory， the only， way you can do this is to move the flow units faster through the process。
You have to remember that flow time and inventory holding flow rate constant move proportional。
to each other。 This is also practically very helpful。 Oftentimes when you do a process analysis。
say you visit a plant， you might only be able， to see to observe two of the three performance measures。
Using little's law， you can just compute yourself the third one。
Which of the three performance measures， IR and T， do you think is the hardest one to。
compute and practice？ Well， let's think about that。 Does a company know its flow rate？
How many tables it has made？ How many loans it has under it？ Or how many patients it has seen？ Yes。
Even fairly incompetent companies measure their flow rate。 After all。
flow rate times price equal to revenue。 Do they know their inventory？ Well。
this oftentimes is tracked less carefully in the services world in manufacturing and。
retailing where you typically are dealing with physical products as flow units。
You need to track your inventory for accounting purposes。
In servers that is oftentimes not done as carefully but still hospitals tend to know how many。
patients they have。 This number is oftentimes called the census。
Banks or insurance sometimes track the customers that are waiting for a loan as well。
It is a flow time that is oftentimes forgotten or hard to measure。
How long did I have to wait in line？ How long has this customer waited for her request？
To say it with Benjamin Franklin's words， lost time is never found again。 Wise words。
so I doubt that good old band have the ideal flow time in mind。 Anyway， the good news is this。
You can always find the average flow time as long as you have the average inventory and。
the average flow rate。 In the next videos， we will put little law to work。 [BLANK_AUDIO]。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P133：17_使用小法则确定库存周转率.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
In the last video， I introduce our first equation in this course， which we refer to as little。
slow。 We learned that on average， in every process， the average inventory is equal to。
the average flow rate times the average flow time。 Today， we will apply little slow to。
the world of physical inventories。 For that， we will do something that first might appear。
really weird。 We will pick an individual dollar bill as our flow unit。 We will then think of。
a firm as a big black box in which dollar bills flow in on the one side and the dollar。
bills flows out on the other side。 We can then ask ourselves a simple question。 How long。
does the average dollar bill spend inside the company？ Well， clearly， we cannot ask the。
dollar bill， "Hey， dollar bill， how long did you spend in the company？" But we don't， have to。
We can use little slow。 More specifically， we think of the number of dollar bills flowing。
through the organization as a cost of goods sold or cogs， for sure。 The number of dollar。
bills inside the organization is the inventory。 If you have taken accounting courses， you might。
ask about life or or FIFO。 For the purpose of what we are doing， it doesn't matter。
Note further that we're using cogs as our flow rate。 I see folks in corporate finance。
sometimes use revenues， since inventory is valued in cogs' dollar， I prefer to use cogs。
to be consistent。 So let's visualize this。 We have our individual dollar bill that goes。
into the organization， the inflow， and then we have all the dollar bills coming out。 The。
dollar bill in the organization as the inventory。 And the flow of dollar bills through the organization。
is really our cogs。 And when we want to know how long does the individual dollar bill spend。
inside the organization？ There would be the flow time。 Cogs and inventory， we can get that。
from accounting。 Again， little slow is i equals r times t。 So we're solving this for t the。
flow time， and that tells us how long the dollar bill spends in the organization。 Sounds。
to abstract？ Well， let's do a specific exam。 Let's compare two legendary computer makers。
compact and Dell， of which of course only one exists by now。 And I will give you a reason。
why you probably are not watching this video on a compact computer。 So we're going to use。
little slot to find out how long the dollar bill spent in the organization。 i equals r， times t。
And we're going to solve this for t。 Let's start with Dell's numbers。 So the。
inventory at Dell in 2000 was about 391 million。 Again， I will start with old data from the。
time when compact was still around。 And we have $20 billion a year as the flow rate， the， cogs。
So that is dollars per year times t。 So t is our unknown。 We're going to solve this， for t。
And so that means 391 divided by 20，000。 And that is expressed now in years。 So the， unit is years。
If you want to convert the years into days， you have to multiply with， 365 days in the year。
And that is about seven days。 So Dell keeps their inventory for seven。
days before spitting it out again。 We refer to this number as days of supply。 So Dell has。
about seven days worth of supply。 Now this is a lot or not， well， let's compare this with， compact。
In the same year， compact had an inventory of roughly $2 billion， $2。003 billion， to be exact。
And cogs at about 25 billion。 Multiply this with a flowtime t。 Again， we're， going to solve for t。
In this case， it's 2003 divided by 25， 263。 And then again， it's multiplied， by 365 days a year。
So we see that the average dollar spends 29 days within compact。 That's。
a very sharp contrast to Dell's number。 Again， we refer to this number as a days of supply。
We can read this directly as inventory divided by cogs times 365。 Now instead of saying we're。
going to keep the inventory for seven days， I can see I'm going to turn the inventory。
every seven days。 That means a Dell I have about 52 inventory turns per year。 So the。
inventory turns as cogs divided by inventory。 And that tells us how many times I'm turning。
the inventory。 So it's one over the flowtime t。 Now this was old data。 I picked the date。
and the year so that compact and Dell were both still around。 To take Dell's numbers。
into perspective， I showed you the data here from 1990 to the very recent past。 On this。
side slide here， you see that in the early years， this was really the rise of the Dell， model。
This was a revolution in the computer industry。 Michael Dell's idea was that you。
can make your computer to order。 So rather than having the computer wait in inventory for。
the customer， the customers waited for the computer。 That went super well。 That peaked。
at around 80 inventory turns per year in the early 2000s。 The burst of the tech bubble。
then happened around 2001。 Dell did a good job at recovering the inventory turns。 But。
you notice the decline starts before the financial crisis and that has never stopped。 So this。
is really down here now。 Note what you see is not the financial crisis。 It's really the。
end of the Dell model。 And that has a lot to do with the fact that nowadays as customers。
we don't want to wait for our computers anymore。 And we don't ask for customized computers。
The variety has gone down in the industry。 And now companies like Apple， it is again。
computers waiting for customers。 It's an irony of history， if you will。 Okay。
time for a little game。 I call this game the inventory turn scripts。 I will show。
you here four retailers， the jewelry maker Tiffany， the supermarket Kroger， the apparel， store。
Kohl's and Walmart， the world world leading retailer。 Then I give you four levels。
of inventory turns and your job is to match retailer and inventory turns。 Who is who？
Put me on pause and give this some thought。 Alright， here we go again。 So here are the。
solutions to the quiz。 Tiffany is the lowest turns。 They turn the inventory very slowly。
The reason for that is they have a huge assortment。 They provide you lots of choice。 And that means。
that each of these products， each of these wedding bands or necklaces， is going to wait。
a long time for the customer。 Kroger is the other extreme。 Supermarkets turn the inventory。
very quickly。 A lot of these items are perishable。 Fresh food inventories have to turn almost。
daily。 Kohl， again fashion wants to turn quickly but not as quickly as milk。 Walmart also somewhere。
in the middle has a large grocery component to it。 Very well managed and they have pretty。
decent turns。 Next， think about why do we care about these inventory turns。 Well inventory。
turns have something to do with inventory costs。 Let me do a thought experiment。 Imagine you。
have a thousand dollar computer and you keep that computer for an entire year。 How much。
will that cost you？ Well over a year that computer gets somewhat obsolete。 You have。
to store that computer and you have to finance that computer with your weighted average cost。
of capital or WAG for short。 Note that this is not a real out of pocket expense。 Most。
of that is a ride off and an opportunity cost。 But it's an important number。 A thousand dollar。
computer I would easily say loses $300 of value in a year。 You might have to pay $100。
to store it safely and securely and you might have to pay about 10% of the value of the computer。
in terms of opportunity cost。 So those numbers add up to something pretty big。 But of course。
you're not holding the computer for an entire year。 As we saw in the case of Dell you're。
turning it multiple times。 And so let's say for sake of argument if that inventory costs。
per year number is 30% and you're turning it six times per year that means for every unit。
for every computer that you're selling you're incurring a cost of 5 percentage points。 That。
means for a $60 item in terms of cost one item that you source for $60 you would incur。
$3 per unit cost of inventory。 The final comment on inventory cost。 This graph shows a relationship。
between profit margins and inventory turns。 Now if you look at this graph you might wonder。
why I talk so much about inventory turns being good because it seems to be a negative correlation。
between margins and inventory turns。 You see Tiffany up there was slow turns and they make。
big margins。 And you see best buy and Walmart down here and they have fast turns and low， margins。
But that's not the point。 As I said Tiffany holds a very broad product assortment。
They have a ring or wedding band for every kind of customer who comes in that store。 That。
means you have to hold the inventory a long time。 That's low turns。 But when somebody。
then one set rank oh my god will they pay。 Best buy and Walmart are much more in the fast。
turning business offering customers less choice。 Look at the competition here between the middle。
Macy's and the gap。 They're both somewhat in the same product category。 Look at the situation。
here Macy's turns its inventory three times in a year。 Gap turns inventory four times， in a year。
Let's take a pair of jeans or some other apparel and assume that there are 36%。
cost of inventory for holding the item for an entire year。 Again that is the cost of storage。
the cost of capital and the cost of fashion becoming obsolete。 So that means 36% with three。
turns for Macy that would be 12% unit cost versus at the gap return four times that is。
a 9% unit cost。 So that little difference in terms of inventory turns is worth 3% points。
in terms of profit margins。 Thus the gap with the faster turns can achieve a significant。
financial advantage even though the profit margins on the book might actually slightly。
look lower。 Today we talked about inventory turns。 This is a key concept in inventory， planning。
purchasing and supply chain management。 It really captures the costs of inventory。 These。
costs are a combination of capital holding costs。 That's an opportunity cost of capital。
cost of storage， shrinkage and theft as well as the cost of markdowns because of value depreciation。
What I like about little slow is that it reminds us that the only way we can efficiently support。
a big flow of product in the form of big sales is by turning the inventory quickly。 Some supply。
chain managers talk about turn and earn。 I like that turn and earn the more you turn。
your inventory the more efficient of a supply chain you run。 For now turn off the video you。
earned a break。 See you in the next video。 [BLANK_AUDIO]。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P134：18_使用小法则确定员工周转率.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
In the last video， we talked about inventory turns。 We use little slaw with the Flow Unit。
being a dollar bill that will flow through our organization。 Today， we will pick a different。
Flow Unit。 We will think of the flow of employees through the organization， starting when they。
first get hired， to the point that they will leave the job。 Just like with inventory， we。
can ask ourselves how long does the typical employees stay in our organization。 This will。
get us to the ten year of the employee， and it will also help us determine how many new。
employees we have to hire each year。 So let's take a look。 Here on the left of the slide。
I repeat the calculations we did in the last video。 So the， Flow Units are dollar bills。
and the inventory is the number of dollar bills in the organization。
In little slaw connects all three， we can do the same thing for employees flowing through。
the organization where new hires are the inflows， departures are the outflows， and you can see。
here how long the organization holds an employee。 What is the average ten year of the employee。
when he or she leaves the company？ So the logic is really exactly the same。 So we can。
find the employee turnover as lost employees per year， the departures， divided by the average。
number of employees， that is employee turnover。 And then we can think about the average ten。
year of an employee on the day of their departure。 So that's really their flow time。 Of birth。
that on the day of departure， i。e。 when they leave， that is one over the employee turnover。
And notice the distinction here， the average ten year of an employee is just half the average。
ten year of the day of their departure， right？ At any time some people are new to the organization。
they might be on the first day of the job， and some people will be on the last day of， the job。
holding everything else equal， the average employee is exactly in the middle。
Let's look at a specific example。 The average employee has a ten year at cost score of nine， years。
Again， that is the average ten year。 And I'm asking you what is the average ten。
year when they leave cost score？ They're ten year on the departure， right？ And remember。
we said this is basically double the average employee ten year。 And so two times nine years。
is the ten year when they leave the organization。 This is going to be equal to 18 years。 That's。
a long time that they stay at cost score， a really long time。 So when we look at the employee。
turnover， then that is just one over 18。 So when we then want to look at how many employees。
that's cost score have to hire， and for the sake of argument， assume that the workforce。
stays constant here。 So that's little slow， i equals r times t。 And so what do we know？ Well。
we want to have 160，000 employees in the organization。 So that's our inventory。
So i equals r times the flow time， right？ And we set the flow time， that time is the ten。
year when they have， when they leave the firm after 18 years， right？ And so we're solving。
for r this time。 And so r is equal to 160，000 people divided by 18 years， which gives us。
a little less than 9，000 people that we have to hire each year。 Employee turnover is an。
important concept。 Some of the big consulting firms have an average ten year on the day。
of departure of a consultant of around four years。 That means their average ten year on。
the job is about two years。 It also means that for every hundred consultants on their， payroll。
they have to hire 25 new consultants every year， not to grow， but just to say put。
As i mentioned before， little slow always holds。 It includes the case in which a firm。
is rapidly growing and thus adding employees。 In this case， however， you might have to be。
more careful in determining the average number of employees as this number is likely to vary。
over time。 Similarly， not many employees might already have left the company。 And so。
the average ten year on the day of departure is something that you have to measure very， carefully。
Either way， the basic logic of little slow still applies even now over 50 years。
after it was first proven。 You can use it to compute flow time given inventory and flow， rate。
You can compute inventory turns and inventory costs or you can look at the turnover， of employees。
In the next video， we will practice these calculations。 See you then。 [BLANK_AUDIO]。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P135：19_学习回顾和实践问题.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
In this module， we talked about little slaw。 We used it to compute flow time given inventory。
and flow rate。 We computed inventory turns and inventory costs and we looked at the turnover。
of employees。 In this last video of this module， I want to review the learning objectives。
the key definitions and the key equations。 I will then give you some opportunities to。
practice this new knowledge on a set of very specific practice problems。 Let's start with。
our module review。 So here are the learning objectives I hope I brought across in this， module。
You should now be comfortable using little slaw to compute one of the three performance， measures I。
R， and T given the other two。 You should be able to do the inventory turns。
calculation and the employee turnover calculations。 We have a bunch of new definitions。
More importantly， we also have a bunch of formulas including little slaw itself。 Alright。
first practice， problem。 So this is a little slaw question where we have 25 callers coming in per minute。
And on average， their callers spend one minute on hold and 3。5 minutes talking to a service。
representative。 Ask yourself how many callers are in the call center either on hold or talking。
to an agent。 Put me on pause， wrestle with this by yourself， and then we'll do the problem。
together。 Alright， here we go。 So there are 25 callers arriving per minute on average。
And so that is our flow rate R。 And the average caller spends one minute on hold and 3。5 minutes。
talking to the agent。 That's our flow time T。 So I equals R times T。 That's little slaw。
And so we now have R which is 25 calls per minute。 And we have the flow time of 4。5 minutes。
If we multiply those two， then the minutes cancel out and it leaves us with an inventory， of 112。
5 callers on average。 Next up we have an inventory turns problem。 You'll see the。
following financial information here。 And I want you to find out the inventory turns。
for this company。 Again， put me on pause now。 Alright， here's my shot at doing this。 Drop。
the revenues。 Focus on the cocks。 The cost of goods sold， right？ The revenues are in。
the question here to just confuse you。 I'm sorry。 The inventory turns is cocks divided。
by the inventory。 So that is 1501 dollars per year divided by the inventory which is。
expressed in dollars。 The inventory reset is 590。 And so this is not getting us 2。54 turns。
per year。 And the units are 1 over years because it's turns， not years。 It's turns。
Alright now to get to the inventory cost， we take the annual cost of inventory， the 15。25， percent。
And that's per year。 Because you would incur if you held the inventory for an， entire year。
But you're not holding it for an entire year。 You're turning it 2。5 four， times。
And so divide this by 2。54。 And now you're applying this to the $85 of the item。
So you multiply this by $85。 And this gives you roughly $5。1 per unit as a per unit inventory， cost。
Alright， for the third and final practice problem of this module， we have a company with。
a thousand five hundred employees on average。 And it recruits 500 employees per year。 And。
I want you to find the employee turnover and the average tenure of the employee。 Clos me， now。
So what do we do？ There are a thousand five hundred employees on average。 And on。
average five hundred employees leave the company per year。 So the employee turnover is 500。
employees per year divided by 1500 employees。 So that is giving us a one over three。 And。
the units for that are one over years。 Again， it's a turn one over years。 That gives us。
already a hint on the tenure on when they leave。 That is a tenure on departure。 On average。
before leaving， they're going to stay for three years。 But that wasn't the question。
I asked for the average tenure。 For the average tenure， we have to take these three years and。
divide it by two。 Some will be just starting and some will just be finishing their time。
with the company。 On average， this is going to be 1。5 years。 Alright， that's all I have。
to say for now。 If you feel you're done， just get off the video。 If you want to hear another。
little slow story， hang on for another minute or two。 A couple of years ago， I was at the。
Philadelphia airport。 Those were the days before we had TSA pre-check-in。 And so all。
passengers had to go through the same line。 And believe me， it was a long line。 So when。
one day I arrived at Gate B for my flight to San Francisco， I was greeted by a TSA officer。
who stood at the end of that long line。 The officer was friendly and professional and handed。
me a form。 This was a form。 As you can see， the form reads hello。 I'm a representative。
of the Transportation Security Administration。 TSA is a government agency responsible for。
making your travel secure。 Please take this time-stand card and help us determine how。
long it takes you to get through the line。 It was a little time-stand on the back of the， card。 You。
that would have been me， you have been selected because you're the last person， in the line。
Thank you。 And then it goes on to say， please give this card again to the。
line monitor at the beginning of the line。 And that's where we're going to time-stand。
it again。 And that allows us to compute how long you waited in line。 You get it？ They。
were measuring how long it takes me to go through security。 In other words， they measured。
my flow time。 What do you think？ Is this how you would have measured flow time？ Well。
I didn't like it。 I had put on my professorial voice that I'm sure you now have grown sick。
and tired of。 And I explained to this officer， "Look， look officer， why don't you simply。
count the number of passengers that are standing in line that gives you your inventory？ You。
know the flow rate， which is simply the number of passengers who are boarding the airplane， today。
And then you use little's law to find the flow time。" Brilliant， isn't it？ Well。
at least said this is what I thought。 The officer， I'm sure， had his own opinion on， that matter。
See you in the next module。 [BLANK_AUDIO]。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P136：20_变异如何引起质量问题.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
In some of the previous modules， we talked about variation and flow。
We saw that this variation was a result of variation in demand and variation within our process。
In this module， we continue our focus on variation。 However。
we shift from the process flow perspective to looking at the actual work and the quality with which it is performed。
To motivate some of the challenges associated with consistently obtaining a high quality outcome。
let us look at the story of my bike crash that I told you at the beginning of this course。
I want to use this example to illustrate the role of variability in the occurrence of events such as an accident。
a defect or medical error。 My bike crash happened on a Saturday morning。
The brake cable turned loose and got stuck between the spinning wheel and the fork。
which sliced the fork into two pieces， and said， "Me， head first over the handle box。"。
How could such an accident happen？ And what was the role of variability？ Well。
like most always in life， lots of factors， lots of variables come together。
In the case of my bike crash， I was somewhat late for my team ride。
and so I didn't check my bike as carefully as I usually do。 Also。
I didn't have the bike with the mechanic for a while， as it was early in the season。
and the mechanic had a huge backlog。 So it wouldn't have taken much。
and this whole crash would never have happened。 But it would take much either。
and the whole crash would have been a lot worse。 I got lucky on multiple fronts。 That morning。
I had an ambulance picking me up in minutes， my hammer took most of the crash。
and the pintrauma center had a relatively light morning。 Again， lots of things come together。
and they could determine the outcome。 Take a look at this figure here。
Customers and resources influence a number of input variables。
These are variables describing how the process is operated。
as well as the variables capturing human or machine behavior。 In the case of my bike ride。
the input variables are things that I controlled。 Did I leave on time？ Did I check my bike？
Did I bring the bike to the mechanic as needed？ And most importantly， did I wear a helmet？
In addition to these input variables， there exists a number of environmental variables that also impact the quality。
In contrast to input variables， environmental variables are not directly under the control of the operation。
In the case of my bike ride， the fact that there was an idle ambulance nearby was an environmental variable。
So was the quality of the road pavement and the weather。
Input and environmental variables come together， and they could determine the outcome variable of the process。
In the case of my crash， the outcome that I was discharged from the trauma center the same day。
was a little permanent damage， other than having no bunch of fake teeth。
Other outcome measures might be customer satisfaction， net promoter score。
or a defect on an assembly line。 Again， note that many input and environmental variables together。
they could determine the outcome。 Since we have variability in input and environmental variables。
we will have variability in the outcome variables。 Typically， when bad things happen。
a number of things have to be lined up against us。
This effect is often called the Swiss cheese model。 I kid you not。
The Swiss cheese model is really a technical term in the quality management literature。
The idea here is that a process is like a stack of slices of Swiss cheese。
Though it is very unlikely that you put 10 random slices of Swiss cheese on top of each other。
and still be able to see through the stack， it's possible。 Just like Swiss cheese has random holes。
processes have random variations。 And when there's a bad outcome happening。
multiple sources of randomness are typically stacked up against you。
This bad alignment of sources of variation is just like being able to see through the entire stack of Swiss cheese。
So really bad outcomes don't happen because of one deviation in the process。
They tend to be the result of many small problems。
Disaster strike when all the holes in the Swiss cheese line up。 Alright。
enough of the Swiss cheese stuff。 Let's talk a little bit more about quality。 So now。
when you're diagnosing a bad outcome， let's call this a defect。
we need to find the input or the environmental variables that cause the defect。
Those variables will be called the root cause of the defect。 Now even in the well managed operation。
input variables and environmental variables will always suffer from some randomness。
And that will yield variation and the quality of the outcome。
Not that quality problems are always variability problems。
Since we never can eliminate all variability in the process。
we want to build processes in which variability in input and environmental variables do not automatically translate into a bad outcome。
Such processes are called robust processes。 A process is robust。
If it still leads to a good outcome， even when there's variability in inputs and environmental variables。
In the remainder of the module， I want to talk about how to use this framework to improve quality。
Understanding the source of variation in the process is really the first step。
So in the next session， I'm going to talk about how to quantify variation in the process。
In particular， I will introduce the concepts of 6 Sigma and process capability。
We then move on to discuss how you monitor the current process behavior over time。
We determine if the variation that you measure in the process at a particular moment in time is just random noise or whether it should be seen as something abnormal。
We want to find defects as quickly and as early as possible so that we can intervene。
In that context， we'll talk about control charts， the detect stop alert method and the need for quick feedback。
Once we know that something bad has happened in our process， we have to take action， right？
And for this， we have to find the root cause。 To find the root cause。
we'll discuss the 5Y framework， fishbone diagrams and parabedo charts。 And then finally。
once you have found your root causes， you have to eliminate them。
You have to prevent the root causes from needing to bad outcomes again in the future。 For that。
I'll show you a couple of frameworks including the Y-How letter and food proofing a process， i。e。
making it more robust。
Well， that's a roadmap for this quality module。 I have to confess that this narrative is not always the exact way that quality problems are tackled in practice。
But I wanted to give you a big picture framework on how the set of tools that we will discuss in this module fit together。
I find all of these tools helpful， otherwise I would not have included them in the scores。
which ones you will use， the most in your work， will certainly depend on what type of work you're doing。
In my view， the qualitative non-mathematica tools in many operations courses don't really get the attention that they deserve。
Framework like the detect stop alert method， root cause problem solving and the Y-How letter。
none of them might end up on the final exam。 But in my experience。
all of them have been super useful tools to have in your operations management toolbox。
I see you in the next video。 [BLANK_AUDIO]。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P137：21_过程能力和6西格玛.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
I don't like to talk about politics in my class， but today I will make an exception。
European Union Commission Regulation， No。 1677-88， state that Q-commerce allowed a bent of 10。
millimeters per 10 centimeters of length。 Q-commerce has been more than this， do not。
qualify as class 1 or extra class Q-commerce。 Class 2 Q-commerce allowed to bend twice， as much。
If you were a cynic， you might say that resolutions like this were the。
reason why our British friends left the EU。 But allow me to backpedal just a little。
bit。 First， to my knowledge， the EU has since changed this resolution。 Second and。
maybe more importantly， the resolution speaks to a fundamental problem in， quality management。
If you want to sort floor units into good units and de facto， units。
you have to define what is good and what is bad。 The decision is based on。
what we call a specification。 The Commission acknowledges that Q-commerce come in。
different shapes and sizes reflecting the variation inherent in any process。
certainly one involving mother nature。 Now according to the EU bureaucrats， they。
exist in ideal Q-commerce shape in the form of a straight line。 And in their， view。
the more Q-commerce bends， the less desirable it is。 But how much bend is， tolerable？
Where is the cutoff？ As you wrestle with these types of quality， management questions。
you realize that as observed this rule might be， it's， actually pretty smart。
You have a specification and you measure the output， against the specification。
Now after measuring the coverage of Q-commerce for， a couple of years。
I needed a somewhat sweeter research topic so I got interested， in the production process for M&Ms。
On the labels of M&MX it says that there are， 47。9 grams of chocolate in there。
I have a couple of questions for you。 Ask， yourself how much do you think a bag of M&M actually weighs？
Moreover， what is a， standard deviation of that weight？ If I would take a thousand bags。
how many of， them would be below 47 grams and about 53 grams？ And then the last question I。
have for you， can you think of a product from M&M that has a relatively high。
degree of variation in weight？ Now arguably these are critical questions that。
deserve careful academic analysis。 Put me on pause and think about the questions。 Alright。
let's look at the last question first。 Which M&M product has the most， variation？
Did you get that one right？ Is the M&Ms was peanuts？ Why is that？ Well。
peanuts are grown maybe not organically but they are biologically produced so。
there's more environmental variation in the production process compared to， regular M&Ms。
To find the answers to the other three questions， here's what I did。
I went to the store bought all the M&Ms I could get a hold of。 Then I got my hands。
on at a high precision scale and here's what I found。 So I created myself a sample， of 125 bags。
In the sample I found an average weight of 50。0 grams。 The， standard deviation of the sample was 1。
03。 I would say from all the things that， you can eat or drink。
there's a very low standard deviation。 Making M&Ms， especially， those without peanuts。
is highly industrialized and it's probably one of， the lowest amount of variation that you can see。
Now you see a histogram over， here and you see the raw data from the sample in an Excel spreadsheet。
Ask yourself， would you bet your life on the fact that no bag of M&Ms is below 47 or about 53。
grams？ I looked at the data in my sample and I don't see a single instance of。
such outliers in the process but I would like the art such outliers。 Are they。
absolutely impossible？ As you can see the outcomes， the weights of the bags of， M&Ms。
they really follow some sort of a normal distribution。 If we agree that。
everything below 47 grams is a defect because it doesn't have enough chocolate。
in it and the customer might get upset and everything above 53 is a defect。
because customers might sue us because they get obese or if we agree to those。
specifications then we can draw a picture like this。 You see the normal。
distribution and then at the tails are the defects。 Now of course if there's。
something we could do to reduce the standard deviation to have less variation。
in the outcomes that would be good in the sense that we would have lower。
probabilities at the tails。 So you see here in this picture that in the upper。
distribution I can go three standard deviations from the mean before I'm gonna。
hit a specification limit i。e。 before my bag becomes a defect and now in the lower。
distribution I can go six standard deviations before things go wrong。 To。
capture that idea we'll introduce a new concept and that is a concept of， process capability。
We define the CP score where a C stands for capability of。
the process as a ratio between the difference of the upper specification， limit， the USL。
minus a lower specification limit， the LSL and we're going to divide。
that by six times the standard deviation and the process。 So in our M&M example。
that gets me 53 minus 47 divided by six times 1。03。 So that is a number that is。
just about one that capability tells you how likely you're gonna incur a defect。
If you have many standard deviations between the mean and the specification。
limit defects are unlikely。 In the case of a six sigma process CP is gonna be， equal to two。
In that case defects are highly highly highly unlikely。 We're， talking about two in a billion units。
If you have a three sigma process you're， gonna have three standard deviations from the mean to the defect level and so。
defects are probable but very unlikely。 And if you only have a CP score of 0。333。
you're gonna have defects 31% of the time。 So the CP scores are really good way to。
capture the amount of variation in the process relative to the width of the， specification interval。
Now beyond analyzing chocolate the capability analysis。
comes in handy when dealing with manufacturing tolerances。 This is a。
context for which most of this quality machinery has been developed。 For example。
my friend and colleague Carl Oryk has a kick scooter company。 You see his product。
the Zooter in many urban centers around the world。 Here's a picture。 A college。
challenge for Carl has been the steers support column。 If the unit is too small， below 79。
9 millimeter the scooter starts to rattle especially if you go over a， bumpy road。
If the unit is bigger than 80 millimeters it becomes hard to， manufacture it。
You really have to squeeze it in。 So what we did is we。
collected some data from his production process and we calculated the capability， score。
Again it's a USL minus the LSL。 In this case it would be 80 millimeters minus， 79。
9 millimeters and we divide this by 6 times the empirical standard deviation。
that we found in this plant。 To my great surprise statistics worked。 Those。
measurements were normally distributed and it turned out that this CP score。
for his factory the capability of his process was almost exactly the same as the。
one for M&M's crazy world。 All right time for practice problem。 So you see this。
very creative question about the chicken egg production process here with， three sub questions。
As usual put me on hold and see how far you can get on your， own。 All right here we go again。
So the weight of the chicken eggs follows， something that looks like a normal distribution and the weight is on。
average 47 grams and there's a standard deviation which we said is 3 grams and。
then there's a specification limits。 A lower specification limit LSL which is。
44 and an upper specification limit USL which is 50 and then the CP score as we。
defined is really the USL minus the LSL divided by 6 times the standard。
deviation。 So in our problem here that is simply 50 minus 44 divided by 6 times 3。
So that is 1/3 or 60 divided by 18。 So 0。33 is the CP score and with that we can。
look into the table that we have on the previous slide。 The CP score of 1/3 means。
that you have a defect probability so that you're outside the specification， limits of 0。317。
But the question I asked was for inside the specification limit。
Within the specification limit it is just 1 minus its stupid probability right。 So。
that is 1 minus 0。317 and that gets us just around 68 69 percent。 Next this farmer。
or the chicken of this farmer want to become more consistent right。 To find。
the required standard deviation we have to set we have to equate that CP score， to 2/3。
Well this is now an equation with one unknown。 The unknown is really the。
required standard deviation and so that is 6 divided by the 6 times sigma that we。
had before and that ratio is to be equal to 2/3。 So we're gonna solve now for。
sigma this is a linear equation and sigma so it's really not that hard to。
solve and we get a sigma of 1。5 grams。 Quality problems result from variability。
If you always get it wrong you would not be in business for long。 If you always。
get it right we would need a module on quality。 This is a little like my serve， in tennis。
My first serve is too short and goes into the net and the second serve。
is too long and it's called out but on average my serve is just fine。 So once。
again we're stuck talking about variability。 In the next video I want to。
introduce some definitions of variability that help us monitor process over time。
so that we rapidly detect when there exists some change in outcome。 I see you， then。 >> Okay。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P138：22_快速反馈的力量.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
When I was a student in Business and Computer Science at the University of Manheim， all。
my grades from my diploma， almost five years of studies， were determined within a couple， of weeks。
I would take my courses in computer network design， econometrics or production planning。
but I would basically get no feedback that I completed all courses and it would enroll。
in the final examination process。 The fact that these were four。
five hour long exams and followed by oral examinations that。
were held in public didn't make this any more fun。
Contrast this with the approach of frequent testing and examination that we now have in。
most business schools and colleges。 It doesn't matter if you want to improve your tennis serve or your operations management。
skills。 Learning and improvement is based on feedback and the sooner you can get that feedback。
the faster world you'll learn。 In today's video I want to talk about quick feedback。 Sakichi Toyota。
the founder of the Toyota company， had invented the power loom at the， end of the 19th century。
It was the first automated loom。 Now that the loom was automated， it don't need an operator。
But then what do you do when things go wrong？ Now in his brilliance Mr。
Toyota had the idea that the loom itself should detect if it was。
running out of threat or something else was broken。
And in that case it should stop and alert an operator to come。 Detect， stop alert。
Whenever there's a problem in a process it is critical that we quickly recognize there's， a problem。
We then need to stop what we're doing and we need to alert management or our coworkers。
to fix the problem and to think about preventing that problem in the future。 Why is it a good idea？
An analogy from coaching might again be helpful。 If you're coaching somebody how to play tennis。
how to swim or how to sing， providing feedback， is critical and feedback is the most useful really when it is given quickly。
So really in the remainder of the video we'll talk about detecting when something abnormal。
happens after all defects hopefully are something abnormal in the operation。
And we want to do that detection as quickly as possible。
Now I'm by no means a school of the Japanese language but the term jidoka is a simple Wikipedia。
search reveals， stands for automation and some degree of self-consciousness or self-awareness。
And so the loom in the Toyota plant is realizing by itself that it is broken hence this idea。
of jidoka， detect， stop alert。 Now when Toyota moved into the assembly line world for the automotive production they basically。
embodied the principle of jidoka into something that is known as the unknown court。
The unknown court is a literally a court that runs adjacent to the assembly line， the workers。
on the line are empowered to pull the court when they detect a problem， when that happens。
the line is stopped and through the end and board， which is a board that you see here。
on the picture， the supervisor is alerted of the problem。 Detect， stop alert。
There's alert then to address some form of problem solving intervention。
So the end and court supports a quick feedback to the operators and to the foreman。
A huge hindrance or a huge delay when it comes to feedback has to do with inventory。
Imagine you have a process flow and you have two consecutive resources with the buffer， in between。
Safe for sake of argument the processing times at each of the resources are one minute per， unit。
Now imagine we are making these circuits here one by one but at some point the first。
resource makes a mistake。 Instead of producing a circuit say it produces a square。
Ask yourself how long will it take the second resource to find out about the mistake？
Well if there's a lot of inventory for example if there are six or seven units of inventory。
in between the stations and the flow is at one minute per flow unit in that case it would。
take six or seven minutes until the second resource realizes that there's a mistake。
But what happens in the meantime？ Well chances are that the first resource since it didn't get any feedback keeps on making。
the same stupid mistake。 Now in contrast imagine we would reduce the inventory level and we would only have one。
or two units in the buffer between the two resources。
Again what happens when the first resource makes a mistake？ Now the feedback comes in much quicker。
We thereby learn faster and avoid that we make further mistakes。
So you notice that a really inventory is delaying the feedback。 Inventory is covering up defect。
This is why it's oftentimes referred to as one of the most evil sources of waste。
And often use visualization of this effect that inventory is covering up defects and problems。
is in the form of this picture。 A boat on the river。
Imagine you're operating a canal or lake and in that lake you have a bunch of rocks。
You're using these boats here with an expensive cargo and so you really want to avoid that。
the boat hits the rock。 So what do you do？ Well on the one hand it's very tempting to increase the water level。
That makes it just less likely that the boat ever hits the rock。 What's wrong with that？
Well now that the water is so high you'll actually don't see the rocks and you'll always。
have them underneath the surface。 The opposing argument is that rather than increasing the water level you should actually。
decrease it。 Yes you will face a short term problem in the sense that you might bump into a rock but。
it's the only way that makes you sure that the rock ultimately is removed。
And once you've removed the rock what you do instead of comfortably sailing you're then。
going to reduce the water level again。 You get ready for removing the next rock。
The same can be said about buffer and inventory。 Rather than buffering every problem， setups。
transports， pull line balancing or defects， away the idea of the Toyota production system is to reduce your inventory so that those。
problems get exposed。 Only once the problems are exposed will you see the problem and have the pressure to solve。
them。 You don't rest on your laurels but you reduce the inventory further。
And now we talked about command cards in the lean module。
Command cards come in as a way for management to intentionally to purposefully reduce the。
inventory level in the process。 Remember from the lean module that you can never have more inventory than you authorize。
through the command cards。 So exposing problems instead of hiding them is a key to G-doker。
Another way to provide feedback to those operating the process is to use a statistics。
This is the idea of statistical process control or control charts。
What you're doing is you're tracking an outcome variable such as a weight of M&M， such as。
a curvature of a cucumber or the height of a steersupport column and you're tracking that。
number over time。 You're mapping that out in a graph that includes two limits known as a control limit。
So basically the 95th percentile confidence intervals of pass data。
So when the current observed unit falls out of that interval you know something abnormal。
most likely something wrong has happened。 Now please don't confuse these control limits which are based on the 95th percentile interval。
of pass data that you observed in the process。 Please don't confuse those with specification limits that are introduced in the video of。
Six Sigma。 Specification limits come from the customer。
Control limits come from pass process performance。
Again such data provide you feedback to what extent the process is currently deviating from。
pass performance。 Now from the automated looms of Saki。
Chitoyoda to monitoring a process continuously using。
statistics the principle of G-doker is in my view and evergreen。
And something abnormal happens I want to somehow detect it as quickly as possible and。
I as a manage of the process want to be alerted sooner rather than later。 Detect stop alert。
One more thought about the stopping part of Chitoyoda。
Why do you think it is important to stop the line？
One reason clearly is that it makes little sense to keep on running a machine that is， broken。
It's just like trying to run a job through a jam printer。
But there's another beauty in the stop part of Chitoyoda。
By stopping normal operation in the case of a plant by stopping the production line we。
send a very clear signal that something is wrong。 We focus attention on fixing the problem。
That doesn't sharp contrast to what I oftentimes see in healthcare。
My former colleague Anita Takah now at Boston University has done a number of studies on。
what nurses do when they encounter a problem。 What do they do？ They work around the problem。
They improvise。 They find a solution so that they can go on with their work and help the patient。
Now notice that such behavior is first and foremost something that we should have brought。
And maybe this is a good moment for us to say thank you to all the nurses and caregivers。
for the great work and service they provide to society。
But even if the nurse files a report about that problem afterwards more often than not。
nothing happens。 The organization never develops a sense of urgency。
So all management knows that whenever the next problem pops up the nurse will once again。
improvise and find a solution。 So stopping the process is a big deal。
It puts long term quality over short term revenue goals。
With that being said it is time for me to stop and I see you in the next video。 [BLANK_AUDIO]。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P139：23_5次为什么框架-找到问题的根本原因.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
Once you have detected your process has a problem， once you have stopped it and you've。
alerted the operator， you need to find the root cause of the problem。 Again。
an example from healthcare might help。 So you get alerted to the following problem。
A doctor has ordered a lab， but that lab report is not in the medical record of the patient。
I want you to spend two minutes thinking about why your doctor was requested a particular。
lab test to be done by not see the results of the lab test in the patient's medical record。
Pause the video， take two minutes by yourself， starting now。
So what possible reasons did you come up with？ Well， let me ask you a slightly different question。
How did you go about the task？ Here's what I did。 I thought about the process from the doctor requesting the lab to the doctor seeing the。
labs in the chart。 In my imagination， that process looks something like that。
The doctor puts in the lab order。 The patient has to go to the lab。
The lab has to do the test and upload the test results。
And then the office has to update the chart with the lab results。 So clearly。
lots of things can go wrong along the way。 I try to visualize this on this chart。
All of these are reasons why the doctor cannot find the lab results in the chart。
Some of these reasons are specific to the office， some to the doctor， some to the lab。
and some to the patient。 Lots of things that could go wrong。 Let's visualize this。
This graph is known as a fishbone diagram。 The reason for that， I hope。
is pretty self-explanatory when you look at the picture。 For less obvious reason。
it's also known as an Ishikawa diagram。 Kawoo Ishikawa was a Japanese quality guru。
And I guess it's what happens when you get famous。 They start naming a graph after you。
Once you have this fishbone or Ishikawa diagram on the table， you get a team of people together。
and the team can now brain some of why the lab reports are often missing from the charts。 Really。
the fishbone diagram gives this brain-storming some structure。
It's better than just a long laundry list。 Some experts propose to use the following labels as the main bones of your fish。
Machines， methods， materials， manpower， and measurement。 That gives you five M's。 Personally。
I couldn't care less on how you label the bones of your fish。
Just label them in whatever way that works for you， but you get the sense here。
There are multiple potential root causes。 Now， rather than talking about the five M's。
I won't talk with you about the five Y's。 Sorry， I know that today I'm not sounding academically all that vigorous。
but this is pretty helpful stuff。 The five Y's are really a famous quality tool that once again go back to Taichiyono。
Rather than just accepting the defect that you just got alerted to， you should ask。
"Why did this defect happen？"， For example， we can ask， "Why did the lab not receive the request？"。
Well， maybe because it was still faxed to them and now 90% of the requests come in electronically。
Next we ask， "Well， why are they still faxing the request？"， Well。
maybe that is because at one practice doesn't have the resources to migrate everything to electronic data transfer。
But why？ So again， you grow the chart and step-by-step you identify additional potential root causes。
Now， it is important to know that the fishbone diagram is entirely a mental picture of what could have caused the problem。
It is really not based on data。 So here's where our second tool for today comes in and that is the per-rato chart。
The fishbone diagram lists the potential root causes of the problem。
The per-rato chart lists each of these potential causes and then counts the frequency of their occurrence。
How many times did that root cause contribute to the failure in the outcome variable？
In other words， here's what you do。 So you look at the last 100 or so charts with missing labs and you investigate why the labs were missing。
You simply count the frequencies of the root causes and then the per-rato chart sorts the root causes。
starting with the most relevant， the most common root cause。
And then what you do next is you can capture a line that counts cumulative percentages across all those root causes。
And the common empirical pattern then is that the vast majority of the problems can be explained by a very small set of root causes。
This pattern is oftentimes known as the per-rato principle or the 80/20 rule。
That means that really 20% of the root causes explain 80% of the failures。 Now。
that insight is going to be super helpful when you're going eventually going to improve the process。
In that sense， it somewhat resembles the value driver and the KPI trees。
What I like about the per-rato analysis is that it turns problems solving into an empirical exercise。
I'm sure we all have spent counting our sitting and meeting rooms and talked about problems and how to fix them。
The Fishborne diagram gives such a discussion and helpful structure。
but it's really the per-rato chart that forces us to collect data。
I also like the philosophy of the 5-Y framework。 More often than not。
the root cause is not at the level of the operator or even at the level of the process。
You notice that our idea of the root cause of the problem is shifting as we continue to ask why。
Initially， we want to blame the nurse。 Now we're blaming the hospital management for not updating the IT system。
As much as I like this framework and the next video， I want to talk about some of its limitations。
[BLANK_AUDIO]。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P14：13_生活在以客户为中心的世界.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
So now that we've laid out the definition of customer centricity， we've spoken a little。
bit about some of the challenges that it requires companies to meet， changing incentive structures。
and so on。 What I'd like to do is just to step back and review all the aspects of living in a customer。
centric world。 What does that mean and then we'll spend a little bit more time talking about some of。
the other aspects of it。 So first， if you live in a customer centric world。
let me ask you this question。 What's the overarching objective for the commercial enterprise？
You remember I asked that and we discussed it before for the product centric enterprise。
but what is it for the customer centric enterprise？ So usually when I ask this question。
people give me a lot of customer oriented answers， building loyalty， creating satisfaction。
getting people to buy things。 Yeah， that's all nice。 That's all terrific。
We want to do all that and we hope that customer centricity will help us do those things but。
that's not the single overarching objective。 The overarching objective is the same as it was before。
To maximize shareholder value。 To maximize the profits of the company in the short run and the long run。
recognizing， the time value of money。 And even though that point seems kind of silly。
it's the same thing， that's why I want to， emphasize it。 But in the end。
the overall objective of any commercial enterprise is to make as much money， as possible。
The problem is this。 There's too many people who think that the money making thing is uniquely associated with。
product centricity but it's not。 There's lots of different paths that we can follow and while customer centricity is quite。
different in many ways from product centricity， it's a path that's not a path that's not a。
path that actually might help you get there faster and better。
So if I want to emphasize that point that we're trying to achieve the same overarching。
goal but in a very different way。 So let's talk about how we achieve it。 Again。
going back to product centricity。 For most firms， the performance superior ones and the operationally excellent ones。
it， was all about blockbuster idea。 Let's produce a lot of it。 Let's produce it efficiently。
Let's think about the next thing to produce。 And again。
that formula has worked for so many companies， still works today。
So what is it in the customer centric world？ What is it that we celebrate in the customer centric world？
Going back to the Harras and the Tesco and the IBM。 Well。
this at this point might be more subtle but it's very， very important。
What we celebrate in the customer centric world is customer heterogeneity。
The idea that not all customers are created equal。
The idea that some customers are just inherently much more valuable， much more profitable than。
other customers。 See， in the old days， companies didn't know the profitability of customers。
They didn't understand how customers are different from each other。 And again。
they didn't care because they were so intent on just pushing products out， there。
Once they started realizing the customers are different from each other， at first it。
was a nuisance。 Oh my gosh， different customers。 We're going to have to talk to them in different ways and develop different products for them。
It was a nuisance。 It added to our costs。 It was a hassle。
And the more we learn about our customers， the more we realize they're really， really different。
from each other。 And it's always true。 We can't avoid that。
So unless we're going to paint ourselves into a corner and only work with one kind of， customer。
we need to acknowledge and celebrate that heterogeneity。
We need to find a way to say that these differences across the customers not only exist。
but they're， a good thing。 Let's find the kinds of customers who can be very valuable to us。
Let's make them valuable and let's find others like them。 And at the same time。
let's find ways of dealing with the other customers in a reasonably， profitable manner。
So we celebrate heterogeneity。 One point I want to emphasize along the way。
and I say this over and over and over again， but it's important to make it explicit。
is that when we're focusing on heterogeneity and， we're focusing on the profitability of our customers。
we're talking about future profitability。 Notice that I'm always pointing over here to the future。
It's great to look at past profitability in many cases that will be a guide towards future。
profitability， but it's not a perfect one-to-one match。 So we need to use our data。
We need to use models and technology in order to project the future value of our customers。
So the celebration of heterogeneity isn't only what the customers have been worth and which。
customers have been the most valuable， but it's which ones we think will be most valuable。
Going back to my example about the MBA students and the airlines， most of the value is what。
we're going to create and extract in the future。 And that's the really pivotal role of this idea of customer lifetime value。
Now here's a tough question for you。 Okay， we're going to want to measure CLV。
we're going to want to manage around it。 How do we do that？
So when we look at a company as it starts changing from being product-centric to customer-centric。
what kinds of tactics change？ So one point that I want to emphasize right now。
but we're going to go into much greater， depth in module three are those three tactics that lie at the heart of customer centricity。
that are the tactics that make it possible for companies to potentially make more money。
being customer centric than product centric。 And you see those words right here。
it's all about customer acquisition， customer retention， customer development。
And a lot of you might be looking at those words and saying， well that's not new。
Companies have been acquiring customers forever。 Companies have been thinking about retention and development。
making customers more valuable。 These ideas are not new。 And you know what， they're not。
that's true。 But in many cases， these ideas are treated at a fairly low level within the marketing。
organization。 Because the marketing organization is often there just to support the product-centric blockbuster。
mentality。 So it's all about how can we get as much stuff out there as quickly as possible。
It's all about coming up with the message。 Very often branding might be associated with products-centricity。
Not always， but in many cases it is。 And so instead。
as we start to think about how customers different from each other， we're。
going to want to ask questions about which kinds of customers should we be acquiring？
How much should we be willing to spend to acquire them？ On the retention side。
should we try to keep everybody？ Should we roll out the red carpet for everyone？
Or should we be a little bit more selective？ And when it comes to customer development。
are there some customers who we can make into， better customers than others？
And how do those tactics tie in with the acquisition and the retention？
So I'm going to spend much more time talking about it later on。
But my point here is that these three tactics need to be elevated。
The people who are going to be working on them need to be higher in the organization。
The people who are running the marketing function have to be at least as painfully aware as acquisition。
retention and development as they are around some of the branding ideas that Barbara spoke， about。
And so we're going to get back into that， but I just want to plant that seed right now。
One point that I've mentioned from time to time， but I want to make a little bit more。
explicit here， would be the challenges for the organization itself。 Again。
instead of having an organization that's organized purely around the different kinds。
of products and services， we want to have a customer centric organizational structure。 Ideally。
the whole org chart would be built around the different kinds of customers we。
have and then below them the different ways that we're going to create and extract the。
profits from them。
I'm going to give you a nice example of a company that's seriously exploring different。
organizational approaches towards customer centricity。
It's a company that today focuses quite a bit on developing and distributing blockbuster， products。
But more and more， they're realizing that they actually need to be or could be a direct， marketer。
The company is Procter & Gamble。 What does Procter & Gamble know about you or me？ Actually not much。
Today， Procter & Gamble's customer would be the retailer， the Walmart's or other grocery。
chains who they sell their products to。 But Procter & Gamble recognizes that with this shift towards customer centricity。
with， this shift towards direct marketing that eventually their customer will be me and you。
And they want to start to understand who the really valuable customers are， how we can。
sort them out from other customers， and what are things that we can do to create more value。
for those customers。 So here's an example of a really nice initiative。
one of many that Procter & Gamble is trying， out。 It's called "My Black is Beautiful" and it's aimed at African American women。
And P&G has determined that this is a really valuable customer segment for us。 We need to be there。
We want to be seen as a trusted advisor。 So take a look at the slide in front of you here。
There's a number of very unusual aspects about it compared to traditional Procter & Gamble。
or package goods advertising。 First， look at the bottom of the slide。
You see a number of different P&G brands being advertised together。
It's pretty unusual for a company like P&G or again other package goods manufacturers。
to use that kind of umbrella branding， going back to some of Barbara's content。
And if you look higher up on the slide， you notice that they're also talking about recipes。
and music and all kinds of things that P&G isn't involved with。
But this would be an example of Procter & Gamble trying to position itself as a trusted advisor。
that they're offering all kinds of products and services to this valuable customer segment。
that they don't necessarily make any money on， but they want them to see P&G as someone。
who has their best interest in mind。 And if you look at the bottom of the slide。
you'll notice something fairly unusual。 You see here just the mention of a line of cosmetics called Covergirl Queen。
Covergirl is a big line of cosmetics under Procter & Gamble， but Queen refers to Queen， Latifa。
the popular actress， and so they developed a whole line of cosmetics specifically for。
African American women。 This goes back to our definition of customer centricity。
According to the R&D people in saying， "You know， instead of coming up with a blockbuster。
product that everybody's going to buy， here's a valuable customer segment， we want to come。
up with something for them that they're going to find very valuable。 Others might buy it too。
and that would be great。 But this idea of leveraging R&D around a focal segment。
that's starting to show us what。
customer centricity is all about。 Now， I have no idea if this initiative by Procter & Gamble will be successful。
I don't even know if this is the right segment to go after。 I'm not going to comment on that。
But given that they are going after this segment， this is the right way to do it。
This is customer centricity。 And you have to believe that in the Procter & Gamble Organizational Chart。
there are some， people who are responsible for "My Black is Beautiful。"。
And they're going to bring whatever resources they can， whatever products within the P&G。
family or outside of it in order to make this customer group as valuable as possible。
That's what I like to see for customer centricity， and I want to see more companies developing。
these kinds of organizational and marketing structures around it。
The bottom line for customer centricity is this idea of relationship expertise。
If you remember earlier， the key to product centricity was product expertise。
We're really good at developing and delivering a certain kind of product。
We're always steps ahead of everybody else。 But as we discussed。
the cracks in product centricity are shortening some of those steps。
It's much harder to stay ahead when it comes to product expertise。
But when it comes to relationship expertise， I believe that they're a meaningful， sustainable。
long-run advantages。 And I'm not just talking about soft， squishy。
understand your customers in some generic， way。 I'm talking about data。 I'm talking about models。
I'm talking about forecasts。 I'm talking about truly understanding your customer， your customers。
celebrating the， heterogeneity。 One of the beautiful things about it is that when you collect the data and you develop。
these kinds of forecasts， nobody can ever take it away from you。 It will never become commoditized。
And so I believe that if your customers are assets， and I think they are， that investing。
in the data， in the knowledge， in the heterogeneity can actually lead to better outcomes to companies。
than pure product centricity。 There's one more point I want to raise to really help us understand the contrast between。
product and customer centricity。 You might remember a chart that I showed you earlier that showed a lot of the characteristics。
of a product centric firm。 And I really focused on the idea of the divergent thinking。
We have this product goodness。 What do we do with it？ Well。
here's the complete chart now where it shows you the contrast between the product。
centric firm and the customer centric firm。 I hope that you'll see that many of them are entirely consistent with our discussion so。
far。 Let's focus on the valuable customers instead of the blockbuster products， the different。
kinds of metrics， customer retention， lifetime value。
We're going to be saying more about those as we go on。 Again。
I just want to call your attention towards the bottom of the slide。 Instead of divergent thinking。
what do we do with this product？ It's convergent thinking。
How do we bring more value to this customer？ What products and services can we develop？
What information can we provide？ What can we do in the relationship to create and extract more value for these really valuable。
customers？ Again， moving from the product centric world to the customer centric world is very difficult。
Going from divergent to convergent thinking doesn't happen overnight。
It requires all kinds of different incentives。 It requires different kinds of people。
A totally different mindset。 That's one of the challenges associated with customer centricity。
I want to talk about a few more。 [MUSIC]。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P140：24_如何使用为什么-如何梯度来定义问题.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
In the last video， we talked about the five wire framework。
We kept on asking why until we got deeper into the problem。 And once we found the root cause。
we wanted to eliminate it so that future problems don't occur。 You also noticed that by asking why。
we shifted from looking at the problem as a bad outcome。
to looking at the problem as a poor process， to ultimately looking at the bigger structure。
Outcomes are determined by processes and processes are designed in the bigger structure。
This nesting of structure， process and outcome is pretty common across industries。 In healthcare。
it is known as a Donabedian model of quality。 It is named after Evades， Donabedian。
a physician and formerly a professor at the University of Michigan。
Moving from outcome to process and from process to structure is something I do like about the five wire framework。
In today's video， I want to talk about some limitations of the tool however。
I also want to introduce you to a related tool that I found very effective for this type of work。
So here we go。 Consider the case of the spread of the COVID-19 pandemic and the various measures that a community like our university or a business can take to limit the number of infections。
As we know from epidemiology， the spread of the virus can be slowed or even entirely stopped by appearing to standards of hygiene and social distancing。
In the world of operations management we would refer to new infections as bad outcomes and thus defects。
Every day there are thousands of interactions between members of the community and some of them knowingly or not are infected。
We have countless opportunities for infection and thus defects。
Now the vast majority of these interactions cause no infection but take some of them due。
With that said， again a variation in outcome。 If that variation consisted only of randomness。
we would refer to this as a common cause variation。 If however。
as it has been documented in numerous studies， some term missions or submissions occur because of poor adherence to good hygiene standards and social distancing will speak of a signable cause variation。
Next let's look at the usefulness of the five-wife framework。
Let's think about a student here at Warden who got infected early on in the pandemic。
say spring of 2020。 Why did the student get infected？
Well because he or she interacted with other students not wearing a face mask。
Well why did the student not wear a face mask？ Well because at least in April of 2020 it was hard to get good face masks here in Philadelphia。
Well why was it hard to get a face mask in Philadelphia in April 2020？
Well because the US face a massive shortage of personal protective equipment。
Why did the US face a massive shortage of personal protective equipment？
You see where I'm going with this。 We've started out with a very concrete problem of infection control here on campus and before we notice we're not facing a problem that is deeply involved in the world of politics。
If you're interested in the PPE supply chain topic。
let me know and I'll share with you an editorial that's the CEO of Penn Medicine。
a colleague Kevin Wop and I wrote。 I wrote about the limitations of just in time supply chains。
But to emphasize my point this is not a problem that can be solved next week。
So let me articulate a different process of defining the problem。
I would actually allegedly say if I were given one hour to save the planet I would spend 59 minutes defining the problem and one minute to solve it。
Whether this is true or not can be debated but clearly a good problem definition is critical。
You begin the process of defining the process by stating the problem。
I like to state problems setting with the words how might we or in what ways might we。
Because that language is open ended and really focuses on the problem rather than already baking in a solution。
For example， we frame a quality project preventing COVID as how might we increase the adherence to facial mask wearing here on campus。
Write the statement down against that with how might we or in what ways。 Next。
let's apply the logic of the five y's。 Ask yourself why？ Why would we want to do that？
What would be good about that？ In our case the answer might be well because it would increase compliance with good hygiene standards。
Why？ What would be good about that？ Well it would reduce the spread of COVID infections。
And again ask yourself why？ What would be good about that？
We might say well that would lead to a healthy and safer campus environment。
Now nothing wrong with that but do you really want to have your team work on the problem how might we create a healthy and safe campus environment？
Again I think you get my point。 As we keep on asking why we're at risk of making the problem too broad。
So how about doing the opposite rather than asking why let's simply ask how。
One way in which we might be able to address a problem how might we increase the adherence to facial mask wearing on campus is by reminding people to wear a mask。
Another approach would be to make masks more widely available。
So we might now think of the problem how might we increase the availability of face masks here on campus。
But we could focus our problem even further。 For example we might ask how might we conveniently locate face masks。
At some point however you really run the risk that you make the problem too specific to narrow。
That creates such a narrow problem definition that you're really ruling out many effective solution approaches that might prevent the spread of the virus with less effort and at lower cost。
So my point is this。 Quality problems can be stated at various levels of specificity。
Write them down from the specific to the general。 The Wai-Hau letter is really a very helpful tool that helps you in this exercise。
Eventually I want you to pick a problem formulation that you feel comfortable with。
And in case of doubt I suggest you air on the side of making the problem a little broader than you first envisioned it。
Let me illustrate the power of the Wai-Hau letter as an approach towards defining problems using an example of the rental car company Hertz。
Hertz a number of years ago oftentimes faces a problem of long lines and has long grading times at the airport rental car stations which is arguably a really bad quality outcome。
Now a natural way to frame the problem that is to how might we shorten the time in line。
When you do this you really take the current process as a given and most likely you end up with improvements such as better staffing or faster computers。
Now don't get me wrong these are exactly the type of solutions we talked about in much of the earlier parts of this course。
So I really like that。 But how about this？ Let's ask why。
Why would we want to shorten the line or what would be good about a shorter line by doing so we broaden the problem。
So why shorten the line？ Well we really want customers to get out on their way to the destinations faster。
Isn't that interesting？ Now we could tackle the problem how might we get our customer to their way to the destination faster。
That is a related but it's a different problem。 We just broaden the problem。
This allows us really to think outside the box。 Maybe we don't need a line at all。
How about customers go right to the car？ How about the car waits for them at the gate？
Ultimately the idea of her school was that frequent flyers， frequent customers offer us。
which is go right to their cars without standing in line at all。
That however was not a solution that you would have come up with。
If you only work on the initial problem how might we have shortened the line。
Alright I think I digress a little bit here。 We're in the quality module and your professor is talking about waiting lines。
So back to quality。 My idea behind the Y-Haul letter is that you now have a framework that can help you define any type of problem。
And of course it includes quality problems。 But really quality problems are somewhat special as you relate to deviations from standards and specifications。
So are there any specific tools， any standard tricks that we can play when dealing with quality problems？
Thank you for asking。 If you want to avoid bad outcomes you have to reduce variation in the process and we have to avoid that these variations lead to bad outcome。
That was really the whole idea behind this was cheese model。
So how can we avoid such deviations and how can we avoid such deviations that still exists don't lead to that bad outcome。
For that we have to build processes that are fail safe。
Expert oftentimes talk about foolproofing a process。
The term might be perceived as somewhat disrespectful and offensive so jumping into the Japanese language once again might be helpful。
Let me define Polka Yoga as preventing mistakes from happening。
Now strictly speaking with Polka Yoga mistakes should not happen inadvertently。
Grows negligence or really evil intent by the operator could still trigger a bad outcome。
Baka Yoga in contrast means literally foolproofing an operation。
And Baka Yoga can thus be thought of as really an extreme form of Polka Yoga。
Now the effort required to make a mistake with Baka Yoga would go up dramatically。
You really have to try hard to make a defect。 Now these two terms capture the idea of a robust process that I mentioned in the first video of this module。
A process is robust when random variations in input and environmental variables。
which includes accidental mistakes are avoided。 And it's really robust when not even a fool can trigger a bad outcome。
As a difference between these definitions of Baka Yoga and Polka Yoga suggest。
they are really multiple levels of error prevention。 Take the example of Baka safety。
a topic that you know by now is really close to my heart。
The most basic protection comes in the form of traffic law。
Traffic law in Pennsylvania states that drivers have to pass me a cyclist with a four feet distance。
Now most drivers don't know that law but it's good to know that the law is on my side when I get hit by the truck。
The next level more fancy is to put a sign near the road。 I hate those signs。
They say share the road or watch for cyclists。 The reason why I share those signs is they have little impact on the drivers。
They have you sharing the road is simply different from mine。
But at least it's better than doing nothing。 The next level more fancy would be not just to have a sign but clear markings on the road。
In Philly we fortunately have a number of bike lanes。
They work pretty well except for more recently they have really turned the bike lanes into a over drop of end pickup lines。
The fourth level of making a process more robust is often referred to as a multi-sensory。
Now an undesirable action is really hard on the operator。 Look at these little barriers here。
If that SUV driver wants to kill me he or she can still do so but now they really need to make an effort。
Drivers who accidentally drift over into the bike lane they get a very strong feedback in the form of their car starting to rattle。
The ultimate foolproof way to keep our cyclists safe is to give us our own road network。
You see this really a lot in Holland and throughout Europe。
And to be fair Philadelphia really has a great number of such bike paths。
Now even our stupid and evil driver will not be able to hit the cyclists。
The five wise and the why how letter are great tools that can help you think about a problem at different levels of analysis。
These two some provide you with answers but they do steer you towards asking the right questions。
When it comes to quality problems most of the action is making workers adherent to some standards and some specifications。
The five levels of prevention and foolproofing are not giving you an easy answer but they force you to think about alternative ways to look for solutions。
So I hope you found them useful as well。 That's all I got for today。 I'll see you next time。
[ Silence ]。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P141：25_PDCA循环.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
There is one last piece of content that I wanted to share with you as part of this module。
on quality。 Just like the Y-How letter， it is really a broader tool and it doesn't。
squarely fit into any of the boxes that I had outlined at the beginning of the module。
But I should not teach a module on quality without talking about this concept which。
is known as the PDCA cycle。 Today， it gave me a few minutes to talk about the PDCA cycle。
and then I will wrap up the module。 So here we go。 PDCA stands for Plan， Do， Check and Act。
With really everything that we discussed in this module， we are now well equipped to plan。
some process changes。 Plan n consists in a large part of analyzing past performance， data。
It should include things such as the Fishbone diagram， the Pareto charts， but it。
might also include other elements that we have discussed in this course such as process flow。
diagrams or OE charts。 The do piece is then about trying out a change。 For example， you。
just thought about a way of preventing a major root cause from happening again。 Maybe you。
found a way to offload the bottleneck。 Sounds like a good idea， but will it work？ You really。
cannot plan your way to success。 At some point， you have to just try stuff out。 Taking action。
is at the do part in the D of PDCA。 Check then stands for the evaluation of your action。
Whether or not that change works， that's really an empirical question。 Just like hypothesis。
in other sciences that either supported or rejected by data， our process improvement idea。
is a speculative hypothesis as long as it hasn't been evaluated in practice。 And then， finally， act。
Act stands for Acting on what you have learned in the experiment。 If the。
experiment confirmed that the process change was a good one， you want to make it part of。
the standard of your work going forward。 The famous or somewhat cheesy thing in the process。
improvement community is that the road to quality has no end。 I know again， somewhat， cheesy。
but there's really， there's something in here。 There's always something that you。
can do to improve the process further。 That's what I like about the PDCA cycle。 The cycle。
has no beginning and no end。 You keep it on cycling through PDCA and hopefully along the， way。
your operations are going to improve。 The PDCA cycle is also known as a Deming cycle。
which honors the work of William Edward's Deming was a pioneer in the quality management。
and community。 Deming was instrumental in importing statistically rigorous approaches to learning。
and experimentation into the fields of operation management。 That gets me to my last point in。
this module and maybe throughout the scores and that is a combination of theory and empirical， data。
Good operations in my view， just like in any science， requires an interration between。
the real world， which tends to be messy and dirty and complex and the academic tools that。
I taught you in the scores。 All right， let me summarize。 This module was really light on， math。
only one equation here on the capability score， but very heavy on definition。 I have。
a longness of vocabulary here for you to learn， but maybe more importantly， allow me to switch。
over and review the big picture of the narrative of quality management。 We started this module。
with measuring variation。 It was the idea behind capability analysis， whether we managed cucumbers。
steering support parts or M&M bags。 There are capabilities scores which capture the likelihood。
that the real-life observation is outside the specifications。 Once we understood the variables。
in the process， we could conduct a conformance analysis to see whether or not our process。
is behaving in line with past behavior。 There's something abnormal going on， either statistically。
detected or detected by an operator， we want to do detect， stop alert。 Once we are stopping。
we want to understand the root cause of why we're stopping。 Why did we have the problem。
in the first place？ That got us to the five Y-framer， the fishbone diagram and the parator， chart。
And then finally， we talked about process improvement， in particular the concept of the。
Y-howl letter。 How might we go about improving the process？ And how can we make the process。
more robust so that future variations in input or environmental variables will not again。
lead to a bad outcome。 That together creates a damning cycle。 It is really a loop of problem。
solving， a loop of quality improvement that never ends。 Alright， are you ready for the。
one practice problem of the module？ Here it comes。 I'm not sure it's all that creative。
but for one last time， put me on hold and try it by yourself。 Alright， so let's look。
at this together。 So we have these weights of the beef pads and in the following， I didn't。
say this explicitly， but let's just assume they follow some sort of a normal distribution。
The main we said is 250。 The standard deviation is 4 grams。 And the specification limits are。
240 and 260。 So that's the interval。 And so then when we compute a CP score， we look at。
CP as 260 minus 240， upper specification limit minus lower specification limit。 And we're。
going to divide this by 6 times standard deviation。 And that is， in this case here， 20 divided。
by 24。 So if you want to make sure that this is a 6-sigma process， CP score has to be equal。
to 2 now。 We're not going to use a generic， not 4， but a generic standard deviation。 And。
so we said 2 is equal to 20 divided by 6 times sigma， where the 20 is really the 260 minus， the 240。
We solve this equation for sigma。 We do so we cross multiply。 And then we want。
to get that sigma is equal to 20 divided by 12。 With that standard deviation， it's a 6-sigma。
process。 That concludes the module on quality。 As I said before。
this module was length mathematical。
and maybe even at times somewhat philosophical。 But for the better or for the worse， the complexity。
of real operations cannot always be squeezed into a set of elegant formulas。 We're better。
off simply admitting that。 And then we can iterate， we can prototype， we can experiment。
Our models are still useful。 We're not just randomly trying process improvement ideas， out。
Good theory in the form of good models will guide us where to look next。 These models。
will help us formulate hypotheses and they will guide us in their testing。 With that， approach。
operations is as much as a science or physics or psychology。 Thank you for your， time。
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