沃顿商学院全套笔记-七-
沃顿商学院全套笔记（七）
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P113：11_决策标准.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
Welcome back to Corporate Finance。 Last time we applied our forecast drivers to。
our free cash flow formula to forecast free cash flows for our tablet project。
Today I want to take those free cash flows and apply our different decision。
criteria to come up with decisions regarding our project。 Let's get started。
Hi everyone welcome back to Corporate Finance。 Today we're going to talk about。
decision criteria but before doing so let's recap our previous lecture in which。
we forecasted free cash flows。 Specifically we took our forecast drivers our。
assumptions about what would happen in the future and applied them to generate。
dollar forecasts of all of the components of the free cash flow formula which we。
then built up aggregated into free cash flow forecasts。 Today we're going to turn。
to what to do with those forecasts by looking at different decision criteria。 So let's get started。
So what do we do with our cash flows from last time？ One。
thing we can do is we can compute the NPV and I'm going to assume a discount。
rate of 12% that is r equals 12% and if we do that and apply it to our free。
cash flows that we computed in the last lecture what we're going to see is that。
this project this tablet project has an NPV of 708。42 million dollars。 Not bad。
What that means is that firm value debt plus equity is going to increase by 708。42。
million dollars in expectation if the project is undertaken。 So from a decision。
making standpoint undertake the project。 That's what the NPV rule tells us。 It says。
accept all projects with a positive NPV， reject all projects with a negative NPV。
and while this has boiled it down to one number I want to be careful especially。
when we start doing our sensitivity analysis to recognize that we don't want。
to pin all our hopes to that one number。 Now another thing we can do is we can。
compute the internal rate of return。 The internal rate of return of a project。
recall is the one discount rate such that the net present value of the。
project's free cash flow is equal zero。 We've actually already seen this when we。
were talking about yields。 Remember the yield is the one discount rate such that。
when you discount the cash flows by the yield you get the price but the NPV is。
nothing more than the price minus the present value of all the future cash， flows。
So IRR and yield are really one and the same。 So what's the IRR for this， project？
Well we write our NPV formula we set our NPV equal to zero and then we。
solve for the one discount rate such that when we discount all of our free cash。
flows we get an NPV of zero。 If we do that we find that the IRR on this project is， 43。7 percent。
Well is that good？ Is it bad？ Before getting there I just want to。
mention typically we're going to need to solve this numerically unless you have。
figured out some amazing way to solve higher order polynomials。 You can use。
the IRR function in Excel I think you can use goal seek in Excel you can try trial。
and error though that's really inefficient if you're using another software program。
or a financial calculator you can do this as well。 So what is what do we do with， this 43。
7 percent IRR？ Well we're going to compare it to our cost of capital our。
hurdle rate and what we're going to do is we're going to undertake the project。
because the IRR is greater than the hurdle rate。 Intuitively it makes sense。
and this is one of those cases where intuition actually works。 It costs us。
12% to raise money in the capital markets to fund our investments to create value。
If this project generates a return of 43。7 percent that's substantially larger。
than what it costs us to raise the funds。 That sounds good that makes sense and。
so what the IRR rule says is accept all projects whose IRR is greater than R。
and reject all projects whose IRR is less than R where R is our hurdle rate。
Hurdle rate cost of capital our discount rate。 Now I do want to mention the IRR rules informative it's also somewhat intuitive。
and appeals a lot to investors who tend to think in terms of returns but it's。
got a number of shortcomings that we're going to explore in greater detail and。
topic four return on investment。 Now one picture I'd like to show you is the。
following。 I've plotted the cost of capital on the horizontal axis and the。
project NPV on the vertical axis and what the blue line shows is it shows how the。
NPV of the project varies as I vary the cost of capital。 Two points are worth， noting。
First is this point right here which is the 12% cost of capital of the， project。
You'll notice that generates an NPV as we saw earlier of a little bit， over 700 million dollars。
The second point I want to point out is this point。 The， point where the graph crosses the x-axis。
That's the point at which the NPV is， zero which as we know from our definition earlier is just the IRR。
That's 43。7%。 Now I think this graph is let me clear this up a little bit。 This graph is useful。
because from a sensitivity analysis or a robustness perspective。 Look this R is。
an estimate and to be honest with you it's typically a noisy one。 What I see here。
is I see a really wide gap between my estimated cost of capital and the point。
at which this project just breaks even。 So even if we disagree on the cost of。
capital and you're taking a more conservative view and you think it's up。
or you know our real cost of capital is 20% that's okay。 This project is still。
NPV positive it's still value-accretive and so what this gap here shows is it。
shows that I've got a lot of room for error at least on the discount rate， dimension。
The third thing we can do with our cash flows our free cash flows is。
compute a payback period which is the duration or the time until the cumulative。
free cash flows turn positive。 So let's look at our project here are free cash， flows。
I'm going to accumulate them year over year so this 510 - 510。4 is just， the 376。
8 in year 0 plus the -133。16 year 1 and on and on for years 2 through 5。
And then I'm going to look at the cumulative free cash flows and ask when， do they turn positive？
Well they turn positive right here in year 3 so our， payback period is year 3。
We turn cash flow positive in year 3。 Some people might。
say it takes three years to recover your investment。 Is that good？ Is it bad？ How do。
we know if 3 is good？ How does that help us in our decision of whether or not to。
undertake the project？ Well what we do is we compare it to some threshold and so。
the payback period rule is says accept all projects with a payback period less。
than the threshold reject all projects with payback periods greater than that， threshold。
But it should be immediately clear that the payback rule or the payback。
period rule has several shortcomings the first of which is it's ignoring the。
time value of money and risk of cash flows the first sin that we learned way。
back at the start of the course。 But fortunately that's actually quite easy， to deal with。
We can compute the discounted payback period by discounting， the free cash flows right？
The discounted payback period of a project is just the。
duration until the cumulative discounted free cash flows turn positive。 And so on。
this slide I computed those discounted free cash flows using our cost of capital。
of 12% and then I accumulate them and wait or count until they turn positive。
which is right here in year four。 So our discounted payback period is four which。
is greater than our payback period of three。 But even using the discounted payback。
period this has this rule has a number of shortcomings。 For example it ignores。
cash flows after the cutoff and that's going to lead to myopic decision making。
Let me go back a slide。 What if this cash flow in year five was 20， billion dollars？
Well 20 billion 114 million dollars。 It'd be a shame you know。
to ignore that and the implication for that of that cash flow。 So by ignoring。
those cash flows you get myopic decision making。 Number two it's not。
telling us the value implications of our decision。 Right it's not helping us。
quantify the effects of any decision that we make。 It's also not helpful in。
choosing among projects with similar payback periods。 So I've got three。
projects they all have payback periods of four which one do I choose if I can。
only choose one。 Alright so let's bring this all back together。 Let's bring it。
into a full circle。 So there's several decision criteria NPV is unambiguously。
the best and should always be used。 But I want to emphasize that others such as。
the internal rate of return and payback period or it's discounted cousin that。
they're all informative and the key is to understand the shortcomings of these。
alternative decision criteria to avoid any mistakes that feed into the ultimate， decision。
So what I want to turn to in our next class is sensitivity analysis。
Which is an integral component of any DCF。 Thanks again and I look forward to， seeing you。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P114：12_敏感性分析.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
Welcome back to Corporate Finance。
Last time， we applied our different decision criteria， to our forecasted free cash flows。
to come up with a decision as to what to do with our tablet project。 This time。
I want to push those assumptions around， the forecast drivers and other assumptions。
to see how robust our decision is in a process called sensitivity analysis。 Let's get started。
Hey everyone， welcome back to Corporate Finance。 Today we're going to be talking about decision criteria。
but as always， let's start with a recap of our last lecture。
So last time we talked about decision criteria。 In particular， we asked。
what do we do with our free cash flow？ And there were several things we could do。
We could compute an NPV， we could compute an IRR， or we could compute a payback period。
And we talked about how to implement those decision criteria， and what decision to make。
and we also alluded to some of the shortcomings， of those decision criteria。 Today。
I want to turn to sensitivity analysis。 In other words， having set up our DCF。
and having completed all the inputs， now let's push it around a little bit。
and just see how robust and sensitive our valuation is。
so we can make the most informed decision possible。 So let's get started。
And we're going to start with something called break even analysis。
Break even analysis finds the parameter value that sets the NPV， of the project equal to zero。
holding fixed all other parameters。 So let's look at our cost of capital and an initial investment forecast drivers。
And I've got base up here to represent a base case。
We're going to look at alternative cases in just a minute。
You can see our base case assumption for the cost of capital was 12， just a little over 12%。
And our initial investment was $227。7 million。 The corresponding NPVs for those assumptions were 708。
42。 It's the same， it's the base case。 That's what we computed in our previous lectures。
The break even values， in other words， the value for the cost of capital。
such that the NPV right here is zero， is 43。72。 But that should look familiar because the break even point。
for the cost of capital is nothing more than the IRR。
And as we discussed in the last lecture on decision criteria。
you can see there's a lot of room here before this project looks bad。
at least on the cost of capital dimension。 For the initial investment。
our initial investment would have to be closer to a billion dollars。
before this project started to turn negative。 Now how did I get these？ Well， I just used goal seek。
What I did is I set the NPV cell， for example this cell， to zero。
and I would change the input parameter。 And goal seek goes around by iterating it to find the zero value。
Now I did this for all of our inputs， and I can only show you a subset。
and otherwise things get ridiculously small。 It doesn't fit well on the screen。
But what I like to do when looking at the break even estimates is gauge， how much room I have。
at least in a partial equilibrium notion。 Because remember。
I'm only changing one parameter at a time， and I'm going to emphasize that again in a second。
I'm going to look at how much breathing room I have until the project turns south。
or becomes negative NPV。 And so you can see these purple arrows identify parameters。
for which there seems to be a fair amount of scope for error， a large margin for error。
So take a look at the PPE liquidation value here。 I'm assuming I'm getting 50 cents on the dollar when I liquidate all of my plant。
and equipment at the end of the five years。 It would take a loss of 2。
253 percent before this thing turned negative NPV。 I'd have to be dealing with some kind of。
maybe nuclear waste， right？ Something like that。 But that's not the case here。 Similarly。
the initial unit price， unless we're going to price this thing at $77， just over $77 per unit。
this thing is positive， this project is positive NPV。
This is in contrast to some of the parameters for which there doesn't seem to be。
quite as much margin for error。 So if I look at my initial market size。
I'm looking to the initial kind of trade， sorry， initial market size of a million units。
But if there's not a lot of enthusiasm， say only half a million， little over half a million。
this project becomes NPV negative， again， holding all other parameters constant。
So this break-even analysis， I think， is a useful tool for gauging how much room we have。
one dimension at a time， before the project becomes value destructive。
And I want to emphasize that this is a partial equilibrium analysis。
It assumes the parameters are independent， right？ I'm changing one parameter at a time。
and in some cases that's an unreasonable assumption。
It's not to say the break-even analysis is uninformative， it's just that we have to recognize。
the limitation of that analysis。 Now let's turn to some comparative statics。
And comparative statics are going to quantify the sensitivity of evaluation to variation in a。
parameter holding fixed all other parameters。 So what I'm going to do is I want to look at how does evaluation change parameter variation。
from what I'll call a worst case to a best case scenario for each parameter。 And again。
I'm going to focus on the cost of capital in the initial investment。
And by my estimates in discussion with Treasury， right， the sort of range of estimates for my。
cost of capital， very from a low of 9。61， which is a best case scenario， right。
very inexpensive cost of capital， to a high of 15。01%。 Likewise， my ops people tell me that this。
the initial investment is likely to be $227 million， but it could go as high as $284。
It could go as low as 185， depending upon short-term variation in materials and labor， et cetera。
Okay。 What I see underneath each parameter estimate is the corresponding NPV。
So when I change the cost of capital of 15。01%， the NPV of the project is $594 million。
When I increase the cost of the initial investment from $227 to $284， right， the NPV moves from。
$708， our base case to $649。47。 So I actually like to look at this table。
One is to get some sense of the sensitivity of the valuation to specific， to changes in。
specific parameters。 But I also like to use it as a gut check， a reality check。
to make sure that the， valuation varies sensibly with variation in the parameters。
If I had found that as I increased the cost of capital that the valuation here， the numbers。
actually went up， I've got an error somewhere in my model。 Okay。 So how did I get this？
I just used an Excel data table。 So these are inputs right here。
So all you do is highlight the matrix。 The NPV of the project is here in the bottom left。
The parameters are in the top row。 And you choose a row input cell and enter the parameter cell。
That's it。 Nothing more than a data table。
And I did this for all of the parameters， though I don't show you all the parameters， here。
and it's just useful to move through them to make sure things make sense。
So if I look at initial market share， for example， right here， what I see is a worst case scenario。
of 15% or penetrations only 15% versus an initial penetration of 35% -- sorry， an initial。
penetration of 35% in a best case scenario。 And you can see that the valuations increase with our penetration。
which makes sense。 We're selling more units。 And notice they actually increased by a lot。
We're going to see that that's an important value driver。
So what is the elasticity of the valuation with respect to each parameter？
So it's similar to what we did， but it puts a little bit more structure on the changes in the parameter。
so we can compare the sensitivity of the valuation to different parameters a little bit more sensibly。
So what is the elasticity？ Well， it's just defined as the percentage change in the NPV divided by the percentage change in the parameter。
Or delta -- remember this is nothing more than change -- the change in the NPV divided by the change in the parameter。
times the ratio of the parameter to the NPV。 Let's look at an example。 Here's my cost of capital。
What I've done is I've gone computed a cost of capital that is 1% higher and 1% lower。
And I've computed the corresponding NPVs。 So to estimate the elasticity of the NPV with respect to the cost of capital。
I've simply looked at the delta NP -- sorry， yellow。 Get rid of that guy。
Delta NPV over delta parameter -- delta in the cost of capital。 So I've taken the 7 -- this guy。
703。53， minus the 713。34。 And I've divided that by the difference of 12。13 minus 11。89。
And I multiply that times the average of the two numbers。
So this is really just the average of the parameter values。 This is the average of the NPVs。
And obviously these halves cancel one another， but I wanted to put them there to emphasize that I'm just taking the average of these two points。
And the reason I'm doing this is because the elasticity will vary depending upon which direction -- the direction in which we calculate it。
Not by much， but by a little。 So this is just a useful -- this is one possibility or one approach for computing it。
And the elasticity I get is minus 。69。 So a 1% increase in this -- in the cost of capital。
a 1% relative increase I should add， in the cost of capital， is associated with a 。
69% decrease in the NPV of the project。
And I can do that for all of the parameters。 Again， I'm just showing you a few。
but you can see a couple parameters start popping out very quickly， such as the initial market size。
our assumption on the market growth rate， right？ Our initial market share。
our assumption on cost of goods sold。 These， again。
are going to correspond pretty closely to what we saw with our breakeven analysis。
and they're really identifying the key value drivers。
The things we should really be focused on when discussing this project。
But， again， like our previous analysis breakeven and our little scenario analyses -- well。
not scenario， sorry -- our comparative statics。 Comparative statics implicitly assume parameters are independent of one another。
We're varying one parameter at a time， and sometimes that just doesn't make sense。
So that's going to lead us to scenario analysis。 What scenario analysis is going to do is it's going to quantify the sensitivity of the valuation to variation along multiple dimensions。
We can vary multiple parameters。 So what I've done here。
going back to that best and worst case scenario， is I said。
what's the worst case scenario for all the parameters here？
And the best case scenario for all the parameters。 Now。
that's not necessarily the optimal thing to do， but it's illustrative。 You might want to say。
what's sort of the worst case scenario if the economy goes south？ Right？
How's that going to affect each parameter？ It may not be the worst case for each parameter individually。
but jointly it might correspond to some sort of bad economic state of the world。 Having said that。
I've computed what I've set up each of these scenarios -- worst， best， base， and best。
And I've just done a little scenario analysis。 And what I find is that in the worst case scenario。
this project is over half a billion dollars negative NPV。
The best case scenario is 33 billion positive NPV relative to our base scenario。 Now。
I wouldn't read too much into those numbers。 Again。
because I've taken the worst case scenario for each parameter independently。
and that doesn't make a lot of sense。 Again， what you want to do when you're constructing these best and worst case scenarios is think。
okay， let's think about a bad economic state of the world。
where maybe not everything goes horrible in the project， but where some things go bad。
and maybe other things are good。 So if it's a bad state of the world， maybe labor costs go down。
right？ And it becomes cheaper to build the plant。 You want to think about these things and how parameters are related and what really represents a plausible best and worst case scenario。
Now， how did I do this？ I just used scenarios in Excel。 That's one way to do it。
That's how I initially did it。 But that's actually really inefficient。
A much better way is to set up a lookup table and a toggle that you can just switch all the parameters between。
Much more efficient。 So here's a question。 What else can we do with sensitivity analysis？
We can answer some important questions that are going to come up in discussions。 Here's one。
for example。 Imagine strategy wants to reduce the price by $30 in order to increase the initial market penetration from 25% to 30% by their estimates。
Does this make sense？ Well， we can answer that question。 In this table。
what I've done is I've set up a little two-way table。 I've got quantity。
our initial market share right here， initial penetration and price here。
So these are all prices。 Here are all my market shares。
Here's our base case scenario in which we're assuming 25% initial market share at a unit price of $200。
And marketing is asking， what's going to happen if we change these？ Well， in particular。
they want to lower the price by $30 from $200 to $170。
And they argue that's going to increase our market share upwards of， say， 30%。 Well。
what does that mean from a valuation perspective？ Well， if we look in this box right here。
we see that the NPV is going to be somewhere between $776，851 million。
So what's the answer to the original question？ It's a good thing。
NPV is going to go up relative to our base。 Here was our base。 Marketing says， drop the price。
That increases penetration。 And sure enough， valuations go up。
So it sounds like a sensible thing to do。 Marketing， on the other hand。
is concerned about uncertainty surrounding the market for tablets and wants to understand if we can shed any light on that。
Can we provide some information？ So here's another two-way table。 I've got market growth rates here。
Market growth rates there。 I've got market size assumptions here， initial market size。
Here's our base case indicated by the black outline boxes。
So the initial market size was going to be a million units， and we were assuming 2。
500% growth in that next year。 But what this picture shows is it shows that as that growth varies and as that market size vary。
what happens to the valuation？ The cells inside are NPVs。
And I've color-coded them so that green is at or better than our baseline。
You can see our baseline is all along。 I've held the baseline constant along the diagonal here。
All 70842。 Yellow is positive NPV， but worse or below the base case NPV。 And red is negative NPV。
And so this provides some picture of how much slack we have。
at least along the initial market size and market growth rate assumptions。
Finally， I want to talk about simulation analysis。 Simulation analysis。
we're going to perform the valuation for a large number of scenarios。
A whole bunch of different parameter values。
And what I did is I selected 500 different scenarios。
What I did is I just randomly drew parameter values from a distribution constrained by the best and worst case scenario for each parameter。
And I did that 500 times。 And I computed the NPV for all 500 draws。
And I plotted them here in a histogram。 So I have here the NPV on the horizontal axis。
And the y-axis is the fraction of the 500 simulated scenarios corresponding to each NPV。
And this red dashed line is the zero NPV value。 And what you can see up here is 77。
78% of the simulations are positive NPV。 22。2% are negative NPV。
And so the takeaway is it looks like a pretty good bet。 This project looks like a pretty good bet。
Conditional on all of the assumptions that we've made to get there。
We've been able to push it around through a host of different scenarios。
And this thing tends to be a positive NPV project， value-accretive。 That said。
I want to emphasize that the parameters were drawn independently of one another。
And that's not ideal。 It's going to lead to some implausible outcomes。
And it's not a particularly reasonable assumption。
especially when it comes to certain parameter pairs， such as price and quantity。 That's just one。
Ideally you want to draw from some large， multi-variant distribution。
But now we're moving way outside the scope of this course into evaluation exercise。
What I want to do is instead emphasize the importance of simulation analysis。
even small-scale simulation analysis。 So you just laid out by hand 10， 15 different scenarios。
Computing powers cheap。 Compute the NPV and just look at how those NPVs look across the different scenarios。
Alright， so let's summarize this and bring it all back together。
I really want to emphasize that no DCF is complete without a sensitivity analysis。
It's really an integral component of any valuation。
Any sort of capital budgeting or broader valuation， I should say。
It's going to help us identify where value is created or destroyed。
It's going to identify the key value drivers where we should really focus our time and effort in terms of our discussions for making the decision。
It's also going to help us quantify and assess our risk exposure。 How much can we lose？
How frequently can we lose it？ And it's going to help us understand the robustness of the profitability of the project。
So next time we're going to turn to a new topic， return on investment。
which is actually closely related to our decision criteria。
We're really going to hone in on IRR and look at some of its strengths and weaknesses。
So thanks again for listening and I look forward to seeing you in the next lecture。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P115：13_投资回报率.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
Welcome back to Corporate Finance。 Today we're going to turn to a new topic return on investment。
but before doing so， I want to briefly recap our last topic， Discounted Cash Flow Analysis。
If you'll recall， we started off with a discussion of how firms， or people more generally。
should be making， decision through different decision criteria。 Then we talked about free cash flow。
at least from a conceptual standpoint。 We then discussed forecast drivers。
or the assumptions required to forecast free cash flows into the future。 If you'll remember。
we did that in the context of a tablet。 We then applied our assumptions， our forecast drivers。
to our free cash flow formula to get forecasts of free cash flows for our tablet project。
We then brought our decision criteria back in and applied it to the forecasted free cash flows to come to a decision of whether or not to proceed with our tablet project。
We closed out the topic with a discussion of sensitivity analysis to investigate just how robust our assumptions and decisions are。
Today， I want to talk about return on investment。 In particular。
what I want to discuss is the strengths and weaknesses of the internal rate of return relative to the net present value rule。
Let's get started。 Hey everybody， welcome back to Corporate Finance。
Today we're turning to a new topic， return on investment， but before diving in。
I actually want to spend a little bit of time recapping our last topic。
discounted cash flow analysis。 What we did in our DCF topic is we began by introducing decision making。
We showed some evidence， looked at some survey evidence of what people in practice were doing。
We showed CFOs of non-financial companies， private equity managers， and investment bankers。
And we showed that they used a number of different criteria， DCF， NPV， IRR， and payback period。
Then we turned to looking at free cash flow， one of the key components of any DCF。
And we talked about the components that make up free cash flow and how to aggregate them。
Then we talked about forecast drivers， where the assumptions necessary to forecast each of the components of free cash flow。
And we did it in the context of a capital budgeting example of a tablet。
We then applied our forecast drivers to forecast out each component of free cash flow and aggregate up to get a free cash flow projection。
We then looked at different decision criteria。 That is。
we figured out what to do with those cash flows and ultimately what decision to make regarding the tablet。
Specifically， we looked at NPV， IRR， and payback period。 We talked about their strengths。
some of their weaknesses。 And then we closed out the topic with a discussion of sensitivity analysis。
an integral element of any DCF。 Now， I really want to continue on the theme set by DCF and talk about return on an investment and really emphasize IRR versus NPV。
the two most commonly used decision metrics。 So let's get started。
So recall that the internal rate of return of an asset is the one discount rate such that the NPV of the assets free cash flow is equal to zero。
So I have NPV， I have cash flow， we could have free cash flow。
don't let the convention change you confuse you IRR is much broader than just free cash flow as is cash flow。
But anyway， IRR is just that one discount rate such that the NPV is equal to zero。
The IRR decision rule as you might recall says accept all projects for which the IRR is greater than R。
reject all projects for which the IRR is less than R。
where R is our hurdle rate or opportunity cost of capital。 And the intuition was simple。
if it's costing us R% to raise money to fund investment。
that investment should return something at least as big， if not greater than R， preferably greater。
Now， I'm going to spend some time here talking about IRR。
a little bit of motivation here because rates of return are really popular measures used for making decisions。
Remember back to the survey evidence， internal rate of return was the most popular decision criteria used by CFOs。
it just edged out NPV。 And likewise for PE firms， private equity firms。
internal rate of return is by far the dominant decision criterion used for evaluating investments。
So it's really important to understand this criterion。
So let's compare it to NPV and let's do it by way of an example， shortly， I should say。 So first。
the IRR is going to lead to the same decision except to reject as the NPV rule if all negative cash flows proceed all positive cash flows。
So here's some examples of cash flow sequences， at least the sign of the cash flows where the IRR and NPV rules will coincide。
So I pay a bunch of money today and then I get nothing but positive cash flows。
I pay money for the first three years， let me lay it out， I don't like that， let's do this。
For year one， two and three and then years four， five and on are all positive。
The key is that the negatives have to come before the positives， negative before positive。
Then IRR and NPV lead to the same decision。 If that pattern of cash flow signs is violated。
not only may IRR and NPV rules not coincide， not lead to the same decision。
but you can get some pretty wacky stuff with IRR from multiple IRRs。
in which case which one do you choose。 It's not obvious to imaginary IRRs and I mean mathematically imaginary IRRs。
you'll get an IRR times some number times the square root of negative one。
which is really hard to convey to a CEO， trust me。
So let's think about whether or not we can compare projects using IRR。
Here's where I want to work in the context of a specific example。
Imagine Wharton wants to upgrade its IT system and overhaul its network infrastructure。
So it puts out a request for proposals in RFP and incomes a bid from Cisco。
The Cisco bid we evaluate to generate $60 million in cost savings over three years for an upfront cost cost to Wharton of $100 million。
Now if our cost of capital， Wharton's cost of capital is 12%， what is our assessment of this bid？
First things first， write down the cash flows。 Here they are。 We're going to get， right， bid one。
It's going to cost us $100 million today， then we're going to experience cost savings of $60 million for the next three years。
We can compute， let's clear this up， we can compute the IRR by solving this equation， right？
Just set the NPV for the project equal to zero and solve for IRR。 That gets us 36%。 Now。
because the IRR is greater than the discount rate， our hurdle rate， remember that was 12%。
and the cash flow signs are proper， that is they follow， right， negatives followed by positives。
This project looks good。 If we compute the NPV， if we just discount the cash flows。
future cash flows by the Wharton's cost of capital 12%， we're going to get an NPV of $44。11 million。
which is greater than zero。 Well， that looks good too。
So both IRR and NPV point in the same direction。 This looks like a reasonable bid。
Wharton's going to be better off by accepting it。 But then Cisco comes back and they say。
"We're going to give you the same cost savings， $60 million over three years。
but now what we're going to do instead of charging you $100 million up front。
we're going to spread the costs over time。 $20 million today and then $35 million over three years。
"， So let's try evaluating this bid。 I mean， it sounds somewhat attractive if we can avoid that huge up front bill。
So， for bid 1A， here are the costs。 It's going to cost us $20 million up front as opposed to $100 million。
but we're going to have to pay $35 million over the next three years。
We still get the same cost savings， so the net cash flows for this bid are minus 20， 25， 25 and 25。
Let's compute the IRR and NPV， IRR first。 Well， the IRR。
we set the NPV of these cash flows equal to zero， solve for the IRR， and we get， whoa， 112%。
That's impressive。 That is very impressive。 And we see that the IRR for bid 1A， this bid。
is 112% is greater， much greater。 I'm going to put much greater than the first bid from Cisco。
which had an IRR of 36%。 We can also compute the NPV by just discounting by the cost of capital。
12% -- Wharton's cost of capital。 That gets us an NPV of $40。05 million。 So。
bid 1 had an NPV of $44。11 million。 Bid 1A has a NPV of $40。05 million。
which according to the NPV rule means bid 1 is better than bid 1A。
What's going on here？ NPV says bid 1 is better。 Right？ 44。1 is bigger than the 40。05。
IRR says bid 1A is better because 112% returns better than 36% return。 What do we do？ Well。
let's take a closer look。 That's the first thing we do。 And we see， let's look。 Here's bid 1。
Cisco's costs are $100 million all up front。 For bid 1A -- sorry， Cisco's costs。
Wharton's costs are $100 million up front。 Bid 1A， Wharton's costs are only $20 million up front。
but then $35 million every year thereafter。 And so what bid 1A is doing is it's inserting a loan。
Cisco's lending us money。 In particular， they're lending us $80 million today in exchange for $35 million over the next three years。
Here's a question。 What's the interest rate on the loan？ We can figure that out。 Right？
I can figure out what the interest rate on this loan is。 How？ Just compute the IRR。 Right？
Just compute the IRR。 And if we do that， we see that the interest rate is 15%。 Right？ And again。
this is just the IRR。 I want to emphasize it。 Let me clean that up。 It doesn't look like -- right？
If I move this over here， this will be zero。 This will equal -- oops。
Having trouble equals minus 80。 Right？ Okay。 So that is just the IRR。 It's 15%。
It's also the yield to maturity， which we've discussed in the past。 Well， is the 15% higher low？
Is it good or bad？ Well， it's terrible。 Cisco is charging us a higher interest rate than our cost of capital。
In other words， we can go to the capital markets and raise the money for a loan at 12% as opposed to implicitly borrow from Cisco at 15。
See， what happened is the IRR increased because the initial investment fell more than the future cash flows。
Right？ Small payoffs on a really small investment can generate very large returns。
We're dividing by small numbers here。 Okay。 See， but the NPV rightfully fell because Cisco's lending us money at an interest rate that's greater than our cost of capital。
So the IRR spiked because we have this smaller upfront investment。 That's very misleading。
The NPV fell。 It accurately captured the bad loan Cisco was inserting into the deal。
And so the lesson here is that IRR projects can mislead when we're deciding among projects。
whereas NPV will not mislead in comparisons。 The larger the NPV， the greater the value。 Now。
let's briefly talk about a couple of additional bids， just to hammer home the point。
We also heard from Juniper and Huawei， who had costs and cost savings for us as laid out in this table。
And how would we rank these bids according to the IRR and NPV？ Well。
we know how to compute the IRR and NPV。 We just replicate or repeat what we did for Cisco or what we've done in past lectures。
And you get this set of IRRs and this set of NPVs。
And what you quickly see is that the IRR ranking is that the Huawei number three bid is bigger than the number two bid from Juniper。
which is better than the number one bid from Cisco。
The exact opposite ranking relative to what NPV tells us。 And here's a picture of what's going on。
I've got the discount rate。 We've seen a picture like this before。
I've got the discount rate on the horizontal axis and I've got NPV on the vertical axis。
And each line graphs the relation between NPV as a function of the discount rate。
So at our cost of capital here， if we go up to each line， we can see the NPVs of each of the bids。
And quite clearly， the Cisco bid on the green line is better than the Juniper bid on the red line。
which is better than the Huawei bid， which is on the red line。
And what we've seen here is that the NPV is the NPV which is the NPV which is on the blue line。
When we go over here where the lines cross the x-axis， these are the IRRs。 By definition。
the IRR is the discount rate where the NPV equals zero。 Here are the IRR -- oh， sorry about that。
Here are the IRRs。 In this case， we see that the IRR for Huawei is much bigger than the IRR for Juniper。
which is bigger than the IRR for Cisco。 So， the IRR rule， or relative to the NPV rule。
What's going on？ Well， if you look more closely at the cash flow streams。
you can see that Huawei is small upfront costs。 That increases the IRR， relatively speaking。
And Juniper has front-loaded cash flows， which is going to increase the IRR relative to the Cisco bid。
So one lesson is the IRR does not address differences in scale。
And there's no better way to emphasize this point than to ask would you rather earn 100% on a $1 investment。
or 10% on $1 million of investment？ Well， quite clearly the latter。
you're going to wind up with a lot more money。 Some more intuition。
Juniper's bid is actually like Cisco's with an embedded loan。
And you can see that here where Cisco charges $100 million， then you get the cost savings。
What Huawei is doing is it's giving you an $80 million loan and then charging you $40 every period。
as opposed to the $35 million that Cisco's 1A bid was charging you。
And if we ask what the loan is on this interest rate， right？ What the interest rate is on this loan。
we see it's 23%。 The IRR to this loan is just 23%。
which is much bigger than the 12% cost of capital that Warden faces。
Okay， let's summarize this。
What are the lessons here？ Look， internal rate of return is useful。
It's a way to convey the attractiveness of a project or an investment in a manner that investors get returns。
Okay， the problem with the IRR decision rule is that it only works or overlaps with NPV in certain situations。
when we're not comparing projects and when the cash flow slides are all negative cash flows preceding all positive cash flows。
So I think the end message here is that use IRR in your project evaluation and your investment analysis。
your capital budgeting， but be cognizant of the limitation。 No when to use it。
no when not to use it。 That's what's critical。 So thank you so much for listening and I look forward to seeing you in the next lecture in which we're going to talk about。
what are we going to talk about？ Oh， the cost of capital。 Bye bye。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P116：0_运营概论.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
Hi， my name is Christian Tevich。
I've been the professor of operations management here at the Warren School for over 20 years。
I also teach classes on operations and innovation at Perman School of Medicine。
Welcome to my course on operations management。 Before we get into the academic details of this course。
let me see a word or two about， myself。 As you can sense from my accent， I sound like a German。
that is because I am a German。 At times I might also come across a little bit like a control freak。
That is because I am a control freak。 So you might be thinking great。
I am stuck now with a German control freak teaching me operations management。 Yes。
that's exactly the deal。 The goal of my course is to help you analyze and improve the way that you or others work。
Your professional background might be in big companies or in small companies。
You might want to become a management consultant or you dream of starting your own company。
Either way， I want to help you reimagine the way that work gets done。 Now you might be saying。
wait a minute， how can this guy in the video help me improve how， I work？
I know my work better than anybody。 This is a common concern that I hear about academics and consultants。
And you know what？ I'm married to your point。 So let me be clear of what this course does and does not do。
I cannot tell you how to do your work。 Nobody knows your work better than you do。
But what I can do is teach you some tools about how to analyze what you do and how to， improve it。
The academic discipline of analyzing and improving work is called operations management。
Operations comes from the Latin word opus， which means work。
Operations management is about helping people analyze and improve the way that they work。
As you and I work together in this course， my job is to provide you with the most important。
tools of operations management。 We will talk about process maps， flows and bottlenecks。
We'll talk about waste， variability and inflexibility。
We'll talk about lean and six-sigma and process improvements and lots of other cool stuff。
The other thing you might be wondering is what does it mean when it talks about improving， my work？
Whenever consultants show up， their primary goal surely is to cut costs。 Let me be clear。
This is not a cost-cutting course。 To see what I mean with improvement。
let's take a look at an example。 Let's say you and I want to go out and grab a coffee。
You like a latte， I like a hot chocolate and so we agree to meet in a Starbucks。
What does it take for Starbucks to make us happy customers？ For us to turn into happy customers。
a couple of things have to be in place。 First of all。
we have to get to a Starbucks without too much inconvenience。 Now， for the better or for the worse。
there are 11，000 of them in the United States。 So location should not be a problem。
Then we want to order our coffee and get it without too much of a waiting time。
Let's not talk about price today， I will pay， but clearly price matters to consumers。
Once we have the cup in our hand， we want to enjoy the consumption of the coffee。
Starbucks that you offer and order your coffee in a gazillion different ways， so I'm sure。
we find something that would match your preferences。
But even when you get your medium latte with skim milk and chocolate dusting， we need a。
good coffee to begin with。 Good coffee means a good recipe。
And the employee at Starbucks needs to execute that recipe and process your order correctly。
so you don't end up with a cup of green tea。 Now let me share a somewhat personal story。
I've been commuting by bike to Penn for the years I lived here。
I also have spent 20 years of my life racing on the bike and in the iron run series。 In 2005。
I went out on a training ride。 For reasons that would never be clear to me。
the brake cable became loose and got between， the wheels and the fork。
This is what the fork looked like。 I went head first onto the pavement。
My helmet protected my skull， but my face was sliding over the pavement。 To state the obvious。
at that moment I was in need of health care。 But what does good care look like？
Let's see if our coffee example provides any insight into how we would operationalize。
the idea of good care。 How about you pause the video for a minute and think about what I needed bleeding on。
the pavement of Windwood Avenue。 What makes for good care？ Okay， pause now。 Okay， here we go again。
Here's how I think about my customer needs with some distance， no bleeding and some fake。
teeth in my mouth。 First， in an accident like this， time is critical。
And ambulance arrived in minutes and drove me to the Penn Medicine trauma center。
The trauma center was closed by by ambulance about a 15-minute drive。
Once we got to the trauma center， I did not have to wait in line， but I was in a trauma。
baby before I woke up。 Second， the doctors and the care team， there were the specialists I needed。
At that moment I needed a trauma surgeon， not a urologist or a psychiatrist。 Third。
the care team was great。 They knew what to do it and they did it。 Well。
nobody took my orders for lunch that day， but trust me， it was the least of my， worries。
I have been told later on that they even had free Wi-Fi。 Finally， though。
my insurance did pay most of the bills， this one day adventure was more。
expensive than a week in a five-star hotel。 So if we think about what we mean with good care。
we can think along exactly the same， dimensions as providing good coffee。 In fact。
I argue that any work that is about delivering a product or service would fit into， this framework。
But differently， we want to manage an operation so that it provides a customer with the product。
or services they want。 Depending on who you ask at Wharton。
different academic disciplines have different words for， this framework。 In economics。
people refer to the dimensions of a utility function。 In marketing。
folks think about the product attributes of products or services and those， are driving utility。
And in strategy， I like the term that my friend and co-author， Nicholas， Igor Koll， uses。
He refers to these as the drivers of willingness to pay。
Now you might say that increasing customers' utility and customer happiness or willingness。
to pay is easy。 We just offer airplane passengers a glass of champagne and more leg space。
We put bigger batteries into electric cars and we offer free desserts to guests eating。
out in our restaurants。 As we will discuss in the next video。
the challenge is to increase the willingness to， pay without increasing the cost of fulfillment。
So when earlier on I was saying that we improved the way people work， we now get a sense of。
what improvements look like。 We can either increase the willingness to pay hold in cost constant or we can provide。
the same willingness to pay at lower fulfillment costs。 For now， remember。
the goal is to make things better， not necessarily cheaper。 [BLANK_AUDIO]。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P117：1_在运营中权衡利弊.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
In the last video， we talked about what it takes to delight the customer。
We talked about inconvenience， which is driven by waiting times in undesirable locations。
And we talked about consumption utility driven by the sub dimensions of fit with personal。
preferences and another sub dimension related to various elements of quality。
Let me again start today's video with a personal story。
Last year I worked with a client in the Middle East。
It was my first trip to the Middle East and it was a very short trip。 It was only three days long。
Given this time pressure， I tried to combine my work during the day with at least a little。
bit of sightseeing in the evenings。 Add to this a time difference and you will understand that I was totally exhausted when。
I entered the plane taking me back from Dua to New York。
Now I was fortunate that my client was generous enough paying for a business class ticket on。
Qatar Airways。 I had a wonderful breakfast， slept for 10 hours straight and something almost as comfortable。
as my bed at home， had another meal， checked my email and landed in New York。
What an amazing experience。 For those of us with being on an easy jet or a Ryan Air flight。
we know that experience， there is very different from this。 You're squeezed into a cabin。
there's no space for your luggage and the crew certainly。
treats you differently from a luxury airline。
But I tell you， flights from England to Spain are cheaper than a dinner in a good Philly。
restaurant。 From an operational perspective， which airline is better。
the high willingness to pay airline， like Qatar or the cheap service from Ryan Air。 On this slide。
I've compiled some data from big global airlines。 Airlines in the United States are regulated and so you can get access to pretty cool operational。
data。 For simplicity here， I focus only on two variables per airline。
The yield of the airline is determined by the revenues of the airline， divided by the。
total passenger miles the airline has provided。 Please forget the term "year after this slide。"。
It has a very different meaning in the context of operations management not to mention yields。
in finance。 What matters here is that really yield captures how much a customer is willing to pay for a。
mile of travel。 I also report the operating expense of the airline。
Here I simply look at all the operating expenses related to the passenger travel and again。
divide them by the total passenger miles。 What I want you to do is to pause the video for a minute and ask yourself which of these。
airlines fit with each of these data points？ Who is who？ Okay， stop me now。 All right， welcome back。
Here are the results of your exam that you get it right。
We'll get back to the airline data in a moment。 For now， let's just observe the following。
An airline can provide great services and can compete on quality aiming for a high willingness。
to pay。 If they do this， well， they can get away with somewhat higher costs。
But an airline can also focus on efficiency。 If the airline manages its costs well。
it can be profitable even if it offers super competitive， and low prices。
If you take a course on strategy or marketing， you will learn a number of tools to help you。
in the trade of between cost and quality。 In this course。
I want to take a slightly different perspective。 Every individual firm faces a trade of between costs and willingness to pay。
It's called as a cost-quality trade-off。 Consider the following exam。
The German railway system is going through some operational transformation。 And trust me。
they need it。 One of the areas they're working on is their cost center operations。
If you think about willingness to pay in a cost center， waiting time is an important， sub-dimension。
Who likes to wait listing to beat off music？ Imagine their current's performance is here。
This is their waiting time， and this is their full improvement cost measured in dollars or。
euros per call。 Now it is really easy to run a call center with short waiting times。
You just hire an extra hundred customer service representatives and the waiting time is going。
to come down。 Unfortunately， while your waiting times go down， your fulfillment costs。
your cost per， call are going up。 Note here that I've reversed the access。
Short waiting times means more responsive， which means further up on the y-axis。
Lower fulfillment costs means more efficiency， which means further to the right on the x-axis。
But as far as that， it is really easy to cut costs in a call center and just reduce a。
head count and have fewer agents in the seat that will reduce a cost per call。
But it will obviously lead to a worse service as customers will have to wait longer。 Again。
that's a trade-off。 One way to think of operations management is as a strategy implementation tool。
You tell me what position you want to be on on this curve， and I will build you an operation。
that gets this done。 But here's another way of thinking about operations management。
Imagine you and I would do some benchmarking。 We would go through the industry and for each of the call centers。
we collect information， about the efficiency and their waiting times。 Our own position is here。
We start our benchmarking with call center A。 Call center A has a shorter response time， than we do。
which we understand when we look at the cost efficiency， which is worse than， ours。 Cool。
These guys have made their cost quality trade-off differently from what we did and they hired。
a bunch of extra agents。 Then we move to call center B。 These folks have a longer wait time。
a lower response， list， which gets them down here。 Now， they're not incompetent。 Instead。
they have squeezed their costs and are running a more efficient operation。 Good for that。 Third。
we look at competitor C。 And this is where we get puzzled。
This call center is providing a better service than we are and they're more efficient as， well。
Maybe they do not have a short of a response time as competitor A and are not as cheap。
as competitor B， but compared to us， they are faster and more efficient。
The academic term for this is that competitor C parietal dominates us。
Now to stay with academic terms for a moment， we define the line of all call centers of， all firms。
of all operations that are not paraded or dominated by somebody else as the， efficiency frontier。
Yes， they exist a trade-off between cost and quality， but this is the elite set of firms。
that has nobody providing better service at lower costs。 Sadly。
our own call center is not on the efficient frontier。
We can visualize our inefficiency as a distance between us and the frontier。
Inefficiency has two consequences for us。 First， the good news。
We have reasons to believe that we can improve our quality while also cutting our costs。
We can become a better operation and a better cost center。 This will be win-win。 Second。
to move to the upper right， we have to overcome some or all of our inefficiencies。
This will require us to do some work。 We have to up our game。 We have to get better in what we do。
Okay， let's get back to our airline data。 Here's some data from the largest US carriers。
I also sprinkled in data from some of the global carriers。
Please note that this is not a careful scientific study。 It is just an illustrative example。
How do you think about this graph？ If you wanted to apply for a job。
if you wanted to make an equity investment， if you， were a management consultant。
what goes through your head as you look at this graph？ You see。
the efficiency frontiers are very useful to think about an industry。
All right， that's it for now。 In this video， we moved beyond just pleasing the customer。 Yes。
we would love to increase the willingness to pay。 But we have to do this with also achieving reasonable fulfillment costs。
And for that， we have to get rid of the inefficiencies in our operation。 In the next video。
we'll discuss how such inefficiencies look like。 The purpose of this entire course then is to fight these inefficiencies。
As I said before， we want to make things better， which means higher willingness to pay at lower。
costs of fulfillment。 See you soon。 [BLANK_AUDIO]。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P118：2_系统抑制因素.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
In the last video， I introduced the concept of the efficiency frontier， parade to dominance。
and inefficiencies。 I have to confess that my definition of inefficiencies being the gap between the firm's current。
precision and the efficiency frontier is rather academic and abstract。 In today's video。
I want to get more concrete。 Specifically， I will define inefficiencies in the form of three system inhibitors that。
I will label waste， variability， and inflexibility。
These three system inhibitors will make inefficiencies more concrete， more tangible。
It is like coming down from the 30，000 foot level view of strategy and economics to the。
specificity of real operations。 As a business owner or management consultant。
it is important to know where you stand relative， to the efficiency frontier。
But in order to make an improvement， you have to be more specific。
You have to roll up your sleeves and tackle the inefficiencies directly。 And for that。
you need to know how these inefficiencies look like。
Let's define the three system inhibitors one by one。 Let's start with waste。
Waste corresponds to all the consumption of inputs and resources that do not add value。
to the customers。 Since waste consumes inputs and resources， waste is costly。
But since it does not add value to the customer， the customer is not willing to pay for this。
We have extensive discussion of waste later on in this course， but let's start with a。
specific example。 Did you know that most of the helicopters of the German military don't fly？
Germany has 75 helicopters of type NH90。 Take a guess what percentage of them is flying right now。
Well， from the 75， only 44 are available， the others are with a manufacturer for repair。
or maintenance。 From these 44 that are with the military， the German Bundeswehr。
only 9 actually are certified， to fly right now。 And since helicopters only fly a couple of hours a day。
chances are that right now， not， a single one of them is in the air。
As these helicopters cost 70 million per unit， that is a waste of resources。
And wasting the time and availability of resources， we oftentimes see waste of input。
The FDA estimates that some 30 to 40 percent of American food supplies get wasted in the。
supply chain。 That corresponds to $160 billion， not to mention those who don't have access to healthy food。
Even worse， it is estimated that well over half of the energy used in this country is wasted。
You see this in the school visualization of researchers here at the Lawrence Livermore。
National Laboratory。 Again， we're talking about a waste that is worth hundreds of billions of dollars and。
massive impacts on the environment。 Just because of waste， as we will see in this course。
just measuring and quantifying this， waste is already a first step towards reducing it。 For now。
keep in mind that waste is a consumption of inputs and resources that do not add value。
to the customers。
Waste that's so good， variability。 Variability corresponds to changes in either demand or supply over time。
Consider variability associated with customer demand first。
Here you see the demand for air travel in the United States starting in the spring of 2019。
Given the COVID pandemic， demand for air travel plummeted。 Even short of pandemics。
consumer demand changes rapidly。 Fashion trends， new technologies， consumer confidence。
or even the weather demand for， products or services goes up and down all the time。
As an example close to home here in Philadelphia， consider the frequencies of shootings in our。
city。 Sadly， this translates into demands for our emergency rooms and trauma centers。
You see here the variation on a monthly basis。 There's also an effect of whether my friends working in Penn's ER have a very cynical view。
to this。 When the weather turns nice， people are out in the street and more people get shot。
Back to this is the extra demand coming from motorcycle accidents and you know that when。
the trauma base are filling up， if the weather is nice。
Variability also plays out on the supply side。 Take a look at this graph here of ER physicians。
All of them work in the same ER and all of them are dealing with very similar type of， patients。
However， as my colleague， I'm a song showed in this study， the way these doctors dealt with。
these patients was very different from each other。 In this study。
how many looked at how long patients were kept in the emergency room， a。
number that is known as a length of state。 You see here that some doctors kept their patients dramatically longer than others。
I'm not judging their behavior。 I'm just observing that there exists variability across providers。
Add to this that doctors or any resource might get fatigued， machines might break down， and。
you have more variability from the supply side than you like。
So variability comes from demand and from supply。 Either way， variability is mostly a bad thing。
As we saw with variability， demand can move quickly。 Unfortunately， supply does not。
Supply that means factories， lawyers， buildings， MRI machines and other resources， those resources。
cannot quickly be added only then to be taken away later。
That gets me to my third system inhibitor and that is inflexibility。
One of the reasons why the mobility company Uber has been so successful is the ability。
to adjust their driving capacity to the change in customer demand。 If demand is high。
prices go up and some more drivers join the market。
Uber calls a search pricing。 If demand is low， prices come down。 Unlike caps。
Uber drivers don't pay pricey medallions and so they can just do something else。
rather than having to compete for the same few customers in the market。
You see the results here in this graph。 Uber and the other right-hailing companies are able to flex the number of rides they provide。
Demand for rides very predictably over the course of the week。
So sooner you can predict this extra demand for rides， the better you can drum up the。
drivers that you need to respond to this。 More drivers are needed on a rainy Saturday night than on a Sunday Tuesday at noon。
And you might like Uber and Lyft or not， they do a decent job with a virtual fleet to provide。
that capacity just when just needed。 This is referred to as volume flexibility。
Another form of flexibility relates to having operations be able to serve different types。
of customers with the same process。 As you will see over the duration of this course。
I have an interest in electric cars。 Tesla's are of course made in factories that only produce electric cars。
But how about car companies that sell both combustion engine cars as well as electric， cars？
Volkswagen for example has to decided to have the factories focus on one engine type。
The German Sveka plant is entirely focused on electric mobility。 In contrast。
most of the vaults were plants are focused on combustion engines。 As to Mercedes。
Mercedes has for the better for the worse initially decided to mix the electric production with。
their combustion engine cars。 The same assembly line on the same day even at the same time as a mix of electric and non-electric。
cars。 Being able to use machines or workers for different purposes offers a second form of。
flexibility。 I call this mixed flexibility。 So in flexibility prevents our operation to effectively respond to variability and hence。
we call it our third system inhibitor。 So these are the three system inhibitors。 Waste。
variability and inflexibility。 The costs of these forces are beyond what you and I can imagine。
In large global industries we're talking about billions of dollars。 But you know what？ Personally。
I couldn't care less about that money。 The real costs for society are much bigger than that。
The financial numbers typically reported don't include the anxiety of a patient who。
does not have access to care。 The misopportunities that a child has because it doesn't get high quality education。
Or the damage that we do to our planet because of wasting resources in our food and energy。
supply chains。 At the risk of becoming a bit evangelical here， let me say this。
The promise of operations management is this。 We can improve the way people work by fighting the evil effects of waste。
variability and， inflexibility。 And that is always a good thing。 Now before I let you go。
for every module I want to get into the habit of defining the， learning objectives。
So you can test at the end of the module whether you accomplished what I wanted you to get。
done in the module。 So in this module we talked about the drivers of willingness to pay。
We talked about the efficiency frontier and the three system inhibitors。
A couple of key definitions but no equations yet。 But don't get used to that。 That will soon change。
See you in the next module。 [BLANK_AUDIO]。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P119：3_过程分析框架.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
Operations management is looking at the way people and machines work。
Genji Genbutsu is a concept of the famous Toyota production system。 No， I don't speak Japanese。
but it means go and see for yourself。 The literal Japanese meaning I've been told is real location and real thing。
but the， message is the same。 In order to understand the problem。
you have to observe what is happening on site， where。
the work takes place rather than sitting in an armchair or conference room。 At the 30。
000 foot levels， all companies more or less look the same。
They create products or services and hopefully I'm able to sell them for more money than。
it costs them to create those products and services。 But on the ground， at the zero foot level。
every operation is unique。 Now， there's no point in taking an academic operations management course to learn about。
the uniqueness of one particular operation。 I owe you tools and frameworks that are generalizable。
that you can apply to the many settings that， you encounter in your career。 And to be frank。
I'm not sure I would be any good in teaching you a particular trait other。
than how to be a business school professor。 So the perspective we will be taking in this course is a compromise between the 30。
000 foot， level of your strategy and the frontline perspective of a specific job。
Maybe we should call it the 5，000 foot level of view。 At this level。
I want you to think about the process with which work gets done。
Process analysis is a framework I want to use in this course to analyze and improve operations。
Let me elaborate what I mean with this。 Every operation can be thought of as a process。
In a process， their inputs and their outputs。 These are pictures from a guitar plant that PV a guitar shows on the internet。
And you can see here there's wood flowing in and guitars flowing out。
When you do the analysis of the process， you have to pick what I will call a flow unit。
A flow unit is really the atomic unit of analysis that you track as a journey through the process。
It's a guitar in a guitar plant。 It's a patient in a hospital。 It's a kind amount of motive plant。
It might be a ton of oral or a ton of steel in a big refinery or steam。
And as a flow unit's journey from being an input into being an output， it will be in one。
of two states。
It can either be with a resource。 A resource is visualized as a box and what we define in a moment as a process flow diagram。
A resource adds value。 It helps the flow unit on its journey from being an input to being an output。
Those might be machines or those might be people。 Most of the time， however。
the flow unit is just sitting around doing nothing。
You see this pile of guitars here and the symbol that we use to visualize this is a， triangle。
It's like a pile of stuff sitting around。 Arrows then capture the flow。
And so really a process flow diagram is a map。 It's a map of how a flow unit journeys from input to output。
Now in every resource， the flow unit would have to take some time。
It might take a doctor 20 minutes to do a primary care examination。
It would take an assembly line work of 37 seconds to install a review mirror。
It might take an underwrite and a bang 35 minutes to approval on。
We call those numbers the processing times。 Now rather than looking at the processing time。
I can look at one over the processing time， and I call this the capacity。 One over 20 minutes。
a 20th patient per minute in what a doctor can see in primary care。
Capacity tells us how many units can work a see per unit of time。
But let's just assume that you're having one doctor or one machine。
This generalizes to multiple say M resources and here simply the capacity of those doctors。
those underwriters those machines together as a resource pool is computed as M divided。
by the processing time。 Now the chain is only as strong as its weakest length and so somewhere along the journey。
in this process， there will be one resource that has a lowest capacity。
We will refer to this resource as the bottleneck。 We will not be able to get more flow through this process than the bottleneck has capacity。
Again， the step with the lowest capacity is called the bottleneck。
The lowest capacity is not just the capacity of that bottleneck resource but it's really。
the capacity of the entire process。 So we say the process capacity is the capacity of the bottleneck。
With this as a foundation， we can now define the three most important measures in any process。
They are flow rate， inventory and flow time。 So let me define them one by one。
Flow rate is oftentimes referred to as throughput。
I will use flow rate and throughput interchangeably。
Flow rate captures how many flow units will go through the process per unit of time。
A hundred patients per day go through the emergency room。
A thousand cars per shift go through the auto plant。
Fifty thousand tons of steel per month come out of the steel mill。
These are throughputs or flow rates。 I like the term flow rate because it reminds us that there is a flow going on。
Now the flow rate would depend on two things。 The capacity of the process which we just saw was determined by the bottleneck or the。
demand rate。 It's a smaller of those two。 If you have a process that can make a hundred thousand cars per quarter and you only have。
demand for seventy thousand cars per quarter， well， you're only selling and making seventy。
thousand cars。 So once you have the flow rate， you know how much flows through the process or flows through。
every resource。 Next you can compute the utilization。 Evaluation is flow rate divided by capacity。
How much am I producing flow rate divided by what I could be producing if I ran out？
That's capacity。 Again utilization applies to the level of the resource。
You might say station ten is utilization of seventy five percent on my assembly line right， now。
Or utilization can apply to the entire process。 You oftentimes see people say in the Wall Street Journal that our plant has a utilization。
of ninety five percent。 And by definition utilization can never exceed one hundred percent。
Depending on whether you're constrained by demand or you're constrained by capacity。
we say the process is demand constrained or capacity constrained。 If you are capacity constrained。
you're held back by your bottleneck。 If you're demand constrained。
you're really limited by your marketing。 The quality of your product。
the demand rate or your sales generation。 Capacity or demand constrained。
What is the constraint in your business right now？ Well that's flow rate。
The next measure is inventory。 Inventory is the number of flow units in the system。
Let me repeat this。 Inventory is the number of flow units in the system。
I want to do be precise on that because it's different from what inventory is called in。
the world of accounting。 Let me give you an example。 I work a lot with hospitals and in hospitals。
Patients will be the flow unit and so the number of flow units in the system is my inventory。
The number of patients that are currently in the waiting rooms and in the exam rooms。
are my inventory。 Now you look from an accounting perspective and you look at the balance sheet of the hospital。
and I promise you you will not see patients as an inventory。
That's not because I'm right and the accounts are wrong。
It's just we have different understandings of inventory。
So again inventory is the number of flow units in the system。 The third measure is flow time。
This is a measure that I find student oftentimes get most confused with。
Flow time captures how long will it take the flow unit to go from the beginning of the。
process to the end of the process。 It includes all work that is done on the flow unit but it also includes all the time the。
flow unit spans in inventory sitting around doing nothing。
Again think maybe last time that you went through a hospital process or you applied for， a mortgage。
Sometimes what you see there most of the time the process takes on the flow unit。
Most of the flow time it really spans on waiting as opposed to actual work time。
So flow rate inventory and flow time。 Again in my view these are the most important measures in any process。
Now you notice that they don't directly appear on financial statements but to state the obvious。
flow rate times price is your revenue。 The inventory has a huge impact on your working capital productivity and flow time has to。
do with this inconvenience of time that we talked about in the introduction module in。
the form of delays。 One more footnote or comment on this slide。
Oftentimes people talk about flow time and user terms such as lead times and cycle times。
I don't like these terms because they are used very differently across different industries。
and we will see that lead times is actually very different from flow times different from。
cycle time。 So I haven't introduced these concepts not yet。 For now let's just stick with flow time。
Today we have covered a lot of definitions。 We talked about flow units。
resources and process flow diagrams。 We talked about inventory， flow rate and flow time。
We talked about capacity， utilization and the concept of the bottleneck。
Of those if you ask me to pick my favorite I pick the idea of the bottleneck。
Every process is only as good as its bottleneck。 It somewhat resembles team time trials and cycling。
It doesn't matter in the total form team trials when the first rides over the finish line。
The performance of the team depends on the weaker riders。
So whenever you want to improve an operation that is capacity constrained your first job。
is finding the bottleneck。 In the next session we will do some specific calculations to practice these definitions。
I see you then。 [BLANK_AUDIO]。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P12：11_哪些公司以客户为中心.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
Now that we understand what product centricity is all about， and we've discussed some of。
the cracks in product centricity and even some of the opportunities for companies to。
escape from and maybe do better than a product centric approach， I want to start moving our。
way towards customer centricity。 But before I give you a definition and talk it through。
I'd like you to think about what customer centricity means based on your experience as。
well as what I've discussed so far。 And so in order to do that， I want to work with a。
series of examples here。 In fact on this slide you'll see the names of four very famous retailers。
Three of them operate on a global level， so Walmart， Apple， Starbucks， and sure most of。
you are familiar with them and Nordstrom's， a high end， very high touch department store。
chain here in the US。 If you're not familiar with them， not a big deal， I think you'll appreciate。
the story anyway。 What I'd like you to do is take a moment and from your experience。
with your perceptions of these firms， decide which of them would be highly customer centric。
So I want you to think about what customer centricity means in light of our discussion。
so far and decide which of these could be one， could be all， could be none， up to you would。
be above the bar in terms of customer centricity。 Think about what customer centricity means。
and which of these firms qualify in that regard。 Think about that for a second and then I want。
to talk through all four of them。 In my book， none of these firms are truly customer centric。
Now I want to be careful about this。 I have great admiration for all these firms。 I'm。
a big customer of all of them。 I really like what they do。 But all of them for different。
reasons fail to be truly customer centric， nearly as much as perhaps some of you thought。
when deciding which of these firms are， aren't customer centric。 So let me just take a few。
moments to talk through each one of them and then finally we'll bring up our definitions。
of customer centricity。 First there's Walmart。 Now again， Walmart is a terrific firm but。
Walmart knows surprisingly little about any one of its customers。 Unlike Harrah's， unlike， Tesco。
unlike so many other retailers out there， Walmart does not have a loyalty program。
Walmart has made very little effort to date to try to figure out exactly what each customer。
is doing and how they can influence each customer's behavior。 So while Walmart might not make。
a lot of efforts to understand what any one customer is going to buy， they make great。
efforts to understand the customers as a whole。 They understand regional differences。 They。
understand when certain kinds of events occur， for instance， when a hurricane is about to。
hit the southeastern US， they need to fill the stores with water and batteries and so on。
So they understand the customer in a generic way but they make very little effort to understand。
the customers in a very specific， granular way as a direct marketer would suggest。 And。
you know what？ That doesn't bother me because Walmart isn't intending to be a direct marketer。
If you think about the Walmart business model， it's about selling in great volumes。 It's about。
bringing the cost way down。 So in many ways， Walmart is a prototypical and a wonderfully。
successful product centric firm。 Let's come up with products that we can sell a whole。
lot of that's going to let us bring our costs down。 And let's figure out ways to extend our。
product goodness and all the aspects that I mentioned for product centricity apply to， Walmart。
So in many ways， I excuse them。 I allow them to focus on product centricity。
because they're so good at it。 There are very few firms in the world that can operate in。
an operationally excellent manner as well as Walmart can。 It's a similar but different。
story for Apple because Apple， again， is the classic performance superiority firm。 They。
don't spend a whole lot of time doing market research to figure out exactly what the customer。
wants。 They don't spend a whole lot of time focusing on segmentation and real granular。
analyses to try to predict what any one customer is going to do over time。 What Apple focuses。
on is leveraging its product expertise， is taking the kinds of products that they've。
already developed and figuring out what are the next ones that they should develop。 So， again。
a classic example of product centricity and they do it better than most other companies。
on the planet and they can get away with it。 Now， while Walmart and Apple， for the most。
part of focusing on doing product centric things， operational excellence for Walmart。
performance superiority for Apple， they are doing some smart things at the margin to。
understand their customers better。 For instance， Walmart is spending a little bit more time。
developing technology that's not only going to help them learn about their customers， but。
even more operationally efficient than they were before。 So for instance here in the， US。
they have a new program they call Scan and Go， a mobile app that lets people scan products。
as they move around the store so as they check out， the whole scanning process happens much， faster。
It's a brilliant idea that lets them be more operationally excellent but also lets。
them start tagging individual customers and tracking them over time。 So they're starting。
to take on some more customer centric initiatives without sacrificing the operational excellence。
And Apple is also starting to do a number of things。 Again， small initiatives not driving。
the business that are letting them understand their customers a little bit better。 Whether。
it's tracking people's music preferences through iTunes or some of the activities that they。
do in the Apple retail stores， slowly but surely they're starting to develop a better。
understanding of their customers at a more granular level。 And who knows， one day， if。
and when competition catches up and Apple can no longer be the product leader that they， are。
they could probably turn around and start to be a great customer centric firm as well。
But today it's not quite as mission critical as it is for other firms。
The third company on our list， Starbucks， is a very interesting contradiction。 At a local， level。
Starbucks or any coffee retailer is very， very customer centric。 The barista。
the person on the other side of the counter， the person who makes your coffee knows a lot。
about you if you're a regular customer。 Not only does he or she understand your coffee。
preferences and what other items you might buy in that store but just through the casual。
conversations you have with them， they might know what movies you like， what kind of clothing。
you like to buy， something about your job， your family and they'll often make recommendations。
to you that are going to make your life better even if Starbucks itself isn't making a penny。
off of those recommendations。 That is customer centricity。 Being a trusted advisor to the really。
good customers， finding ways to lock that customer in and so on。 So the paradox is while。
Starbucks is very customer centric at a local level， they're not that customer centric at。
a national level。 You take your Starbucks loyalty card and you bring it to a Starbucks。
in another city or another country and show it to them and say， "I'd like the usual please。"。
They have no idea who you are。 It's not only can they not meet your immediate needs but。
it's hard for them to be a trusted advisor and to make other recommendations to you when。
they have no idea about anything about your history。 So to me that's a really key point。
It's not enough for a company to be customer centric some of the time when they know who。
you are but a truly customer centric company will identify you and will be able to value。
you and make recommendations no matter what kinds of interactions you have with them。 Whether。
you go from store to store， whether you go online or offline， that's what customer centricity。
is all about。 Now Starbucks to their credit recognizes this and they're coming up with。
all kinds of interesting technologies that are going to let them collect and integrate。
your data across stores and across other touch points you have with them。 They recognize。
that the opportunities and the necessity for customer centricity is at least as important。
as it is to come up with the next great coffee flavor。 So again it's that balance between。
focusing on the product and focusing on the customer that so many companies are now struggling。
with。 And finally there's Nordstrom's。 Now while that might be the least familiar company。
on the list especially to those of you outside the US it might be the most interesting example。
to help us understand what customer centricity really is and isn't。 But whether you've shopped。
it in Nordstrom store or not you might be familiar with the story that makes Nordstrom。
so supposedly customer centric or not。 And here's the way it goes。 Nordstrom's a high。
end department store they sell clothing shoes and so on。 One thing they don't sell is tires。
Yet one day someone walked into a Nordstrom store supposedly in Fairbanks， Alaska and。
wanted to return a set of tires that obviously they could not have bought at Nordstrom's。 Perhaps。
there was a tire store at that location before Nordstrom's open shop。 And Nordstrom's being。
so incredibly customer centric gave them the money back for tires that they didn't buy。
at Nordstrom's。 Now is that customer centricity or what？ I like to say or what？ If you think。
about it for a minute is that really customer centric or is it actually kind of stupid？ Does。
it make sense to give someone money back for a product that they couldn't possibly have。
bought from you？ For me I say most of the time it's probably a bad idea to do that。 And the。
question is under what circumstances would it be a good idea to do that？ Think about。
that for a second。 When would it make sense to give someone money back for a product that。
they couldn't have possibly bought from you？ When would it make sense？ And here's the answer。
If that customer is incredibly valuable to you and I'm talking about future value， I'm。
talking about the fact that we expect this customer to be buying so much from us in the。
future that if we don't give them money back for the tires that they thought they bought， from us。
if we don't give them money back today we're going to lose that value。 If that's。
the case we'll happily give you the money back for the tires that you didn't buy。 Maybe we'll。
double the money back。 Who knows？ So it all depends on the value of the customer， the， future value。
the lifetime value of the customer。 If that's sufficiently high then we'll have。
a roll out the red carpet for you。 And if it's not and for most customers it wouldn't be。
then we would politely decline。 We might still be nice to you of course but we're not going。
to give you money back if we don't see the value in it。 And that's the problem with Nordstroms。
Nordstroms offer such wonderful service。 They treat everybody so incredibly well regardless。
of the value of that customer。 And that's the problem with Nordstroms is that because。
they fail to focus on figuring out the future value of each and every customer they're just。
going to treat everybody really well。 And there's a lot to be said for that。 It's a wonderful。
company。 I like knowing that when I go in there I'm going to be treated really well。
But I think that they're missing some opportunities by picking and choosing a little bit more。
In the old days it was impossible to do that。 But today Nordstroms like every other retailer。
has the capability to collect the data and use technology to do a little bit more targeting。
and a little bit more selection to figure out who is worth the extra special treatment。
and who doesn't necessarily deserve it。 So to me the Nordstrom example is a great example。
of where product and customer centricity collide。 And what I want to do now is to start focusing。
more on what customer centricity really means。 And that's what we're going to do next。 [Music]。
(buzzing)。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P120：4_绘制过程流程图.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
As we discussed in the last video， the framework that we will use to look at the world in this。
course is process analysis。 There sounds a little dry and technical， but don't worry。
this is really not brain surgery。 In this video I want to practice these definitions。
Though most of my work relates to service operations， a number of years ago I did some。
work on an iron-on processing plant。 Though the headquarters of the company we worked with was in Cleveland。
Ohio， the plant itself， was entrinodent。 Here is a brief cut of iron ore produced by the plant。
Let's see how it is made。 When you're landing entrinodent you actually see the plant from the airplane。
This is a plant。 It takes iron ore finds as input and it spits out these lovely brickheads as output。
It looks like a big monster。 The monster is a highly sophisticated plant。
a real masterpiece of engineering。 The plant is the size of several football fields。
I spare you the technical details。 Here you see the engineering drawings of the plant。
It looks a bit like a blueprint when you're building a house。
You see a number of large machines that will be the resources in all processes。
Now in this course we look at a plant not as an engineering drawing but as a process flow， diagram。
Remember in a process flow diagram there are boxes， arrows and triangles。
This like the brickheading machine shown in this photo capture resources。
Resources are adding value to the flow unit。 They help advance the flow unit on its journey。
As we discussed in the last video resources have capacities。
At any given moment of time some flow units are sitting in these resources。
For example tons of iron ore will be in the reactors of the plant。 But most of the iron ore。
the real inventory， sits at the beginning of the plant between。
resources or at the end of the process。 This is visualized with the triangles。
Often times we refer to those triangles as buffers。
The key distinction setting buffers apart from resources is that they do not add value。
The flow unit does not have to spend time at a buffer and it still can come out as a good， unit。
You can be a happily completed flow unit and have never spent a minute in a buffer。
So here is what the process flow diagram looks like。
What I do here is I overlay the process flow diagram with the engineering drawings。
You don't have to do this but I thought it would be kind of cool from a pedagogical， perspective。
So that explains the arrangements of the boxes。 You see the input and the output。
The pile of iron ore finds are the input and the piles of briquettes are the output。
And then you see the main processing steps。 Starting with a preheater all the way to the briqueting machine。
Seven steps， seven boxes。 What we want to do is to find the bottleneck。
How much can this plan produce？ What is the process capacity？
To answer these questions we have to find the bottleneck。
So when looking for the bottleneck we have to look at the resources and their capacity， levels。
In this example we have the preheater， the lock hoppers， the first reactor， the second， reactor。
the flesh eater， the discharge and finally that briqueting machine。
Note that the triangles are inventories and they're not in the race for being the bottleneck。
We have to look for the resource with the lowest capacity。
That clearly is the second reactor with the capacity of 100 tons per hour。
So that is the bottleneck。 And that's the process capacity is 100 tons per hour。
The chain is only as strong as its weakest link。 To find the flow rate remember the flow rate is the minimum between demand and capacity。
Assuming there's enough iron ore to be processed and enough demand to sell this stuff our flow。
rate would be equal to the process capacity at 100 tons per hour。 My father is an engineer。
my brother is an engineer， I'm not。 And believe me they have reminded me many times。
But in order to understand and improve an operation even something as complex as an iron。
ore plant you don't necessarily need an engineering degree。 All these technical details， heaters。
different types of reactors， heat exchangers， yes somebody， better understands how this stuff works。
But that somebody doesn't have to be you。 Notice that at the end of the day few of these details actually matter。
In fact for most business related calculations the single most important number is the capacity。
at the bottleneck。 This plant produces 100 tons per hour。 That is a crucial information。
The bottleneck is a constraint on the system。
And as I said before the bottleneck is really the key concept in this course。
So when you look at an operation always ask yourself where is the bottleneck。
See you in the next video。 [BLANK_AUDIO]。
沃顿商学院《商务基础》｜Business Foundations Specialization｜（中英字幕） - P121：5_把工作看作是一个过程.zh_en - GPT中英字幕课程资源 - BV1R34y1c74c
In this video， I want to tie up a couple of loose things related to process analysis。
I first want to explain how process analysis and the process flow diagram help us reconcile。
two perspectives through an operation。 The perspective of the flow unit， which likely。
will be handled by multiple research as it's journeying through the process。
And the perspective of a resource， which likely will see many flow units over time。
And I want to give you the opportunity to practice the definition we introduced so far。
with a little practice problem。 Okay， here we go。 First imagine you work in an auto plant。
and you're the person who puts in the seat into the car。 Really every minute or two。
a new car arrives on the assembly line and you repeat your operation。 You put in that seat。
You do this over and over and over again。 Similarly。
you might be working in the radiology department。 Every 15 minutes or so。
an image of a buddy part comes in。 You diagnose， you read the image， you put together a report。
and then the next patient comes。 This is a view of a resource in a process。
So that's simply called it the resources perspective。 As a resource， you have a capacity。
you have a utilization， and you might even be the bottleneck。
Diageoño is known as the father of the Toyota production system。
and is really one of the most influential thinkers in operations management。
He would famously draw a chalk circle on a piece of the factory floor。
and his students would have to stand in this chalk circle。
and they will have to observe the process。 Ono would check in on them and ask them to report their observations。
If they are not seen the waste and the inefficiencies that all wanted them to see。
they will just keep on standing in that chalk circle。
I hope you very much appreciate that in my course， all you have to do is watch some videos。
rather than spending your day in a chalk circle。 Contrast the resources perspective with the floor units perspective。
As a patient， you very likely have experienced the floor unit perspective。
when you went through an episode of care， such as an radiology appointment。
To show you the example of the floor units perspective and in order more of assembly process。
take a look at the video from Tesla that I found on YouTube。
Really what you're doing is you're attaching yourself to the floor unit， and then you flow along。
you flow through the process。 You see one resource after the other。
and by making your journey through the assembly line。
you start really in this case as a piece of metal， and then you go through the process。
and step by step the floor goes on。 You spend your floor time and the amount of time it takes you to go through the process here。
with the resources and at the end of the process you complete the process。
and you have turned into a wonderful car。 There you go。
Now the beauty of process analysis is that it combines both the perspective of the floor unit。
and the perspective of the resource， all in one diagram。
Here's a simplified process flow diagram from a Tesla plant。
You can read that process flow diagram with the eyes on the floor units。
and on floor time the journey through the plant， or you can take the perspective of a particular resource。
like the paint shop， and you just focus on that capacity。 Whatever perspective you're taking。
you will have to deal with inventory。 Remember from before。
inventory is a number of floor units in the system。
Inventory pass up whenever demand and supply don't match。 Since demand is moving quickly。
or a supplier is simply slow to adjust， you either have too much demand or you have too much supply。
Now as a risk of creating some confusion， inventory in the car example can be a pile of cars。
waiting for demand， and historically for most car companies that has been the default。
most car companies carry about 60 to 80 days of inventory in their lot。
But in my framework it can also be a number of customers waiting for a car。
Tesla for example had hundreds of thousands of customers waiting for the Model 3， I was one of them。
and later on they had hundreds of thousands of people waiting for the Cybertron。
If we define the customer as a floor unit， the number of customers waiting for their cars is simply inventory。
Again， I know it's a little bit confusing because it's different from accounting。
Not that operations is right and accounting is wrong。
it's a different way of looking at the world including inventory。
In making physical stuff you can have inventory reflecting waiting stuff， widgets， cars， iPhones。
whatever you're producing， or you can have waiting customers。
When you're dealing with service operations however， you can only have one scenario。
For all practical purposes it's impossible to produce a hard surgery into inventory。
You also cannot have an investment discussion with your clients before the client actually shows up。
So in a service operation inventory always really relates to waiting customers。
Given that we are at the end of a module， it's a good time to review what we have done so far in this module。
We work through a ton of definitions from floor unit over processing times to capacity。
all the way to my favorite three， floor rate， flow time， and inventory。 More importantly。
I want you to at this point be capable， competent and confident to do the following。
You should be able to map out an operation as a process flow。
find the capacity of an individual resource， and then find the bottleneck in a process。
You should also be able to compute the flow rate as a minimum of demand and capacity。
and find the utilization of a resource。 For that these three definitions and equations at the bottom of the slide will come in handy。
as we are going to see in the first practice problem that we are about to tackle together in a couple of seconds。
Okay， for this module I've done enough of the talking。 It is your time to do some work。
What I want you to do is I'm going to show you a practice problem and I give you a time to wrestle with it。
You simply read the problem， stop the video， see if you can find the solutions to the questions。
And then you press play again to see me solve the problem。
This practice problem here describes the patient flow in a dental practice in Philadelphia。
See if you can answer the following questions。 Put me on pause now。
So let's take a look at this together。 Let's do the process flow diagram first。
We start out by drawing a little triangle。 That's inventory of patients who need to self-check in at the kiosk。
From there we have a bunch of other steps， versus another triangle。
where the patients are waiting for the receptionist。
Right now we have no clue where the bottleneck might be。 So let's put a buffer here just in case。
Then a box， another triangle just before the dental assistant。 I hope you get the basic idea。
Last triangle before the dentist， then comes the dentist。
And I don't know what you do after seeing the dentist， but I typically go home。
So no triangle of completed patients。 But if for whatever reason we want to have people still recovering from their pain。
we could have another triangle or box as a recovery area。
That's the end of the process flow diagram。 Okay， next is what is the capacity of the dentist？
The dentist is a resource， and the capacity of the resource is M， the number of dentists。
divided by the processing time。 So we have 10 dentists。
and each of them takes 30 minutes to see one patient。 So just 10 divided by 30。
and that is expressed in patients per minute， one third of a patient per minute。
Or if you want to translate this into hours， you multiply this by 60 minutes in an hour。
the minutes cancel out， and we add 20 patients per hour in terms of the capacity。 All right。
what's the bottleneck in the process？ The bottleneck is the resource with the lowest capacity。
And so we have to look at the minimum of these capacities。 The first capacity is one half。
one over two at the kiosk。 One resource M equals one divided by two minutes。 And it's two over five。
And then we get three over 15。 And then finally， as we just computed， 10 over 30 for the dentist。
And the lowest one of those will point us to the location of the bottleneck。
And that is three over 15。 Right？ Three over 15 is one over five。 Again。
this is expressed in patients per minute。 So this corresponds to 12 patients per hour as a bottleneck capacity。
And that means that's the capacity of the entire process。 Flow rate， remember。
is the minimum of demand and capacity。 So minimum of demand and capacity here。
meaning there are plenty of patients who want our service。 If demand is unlimited， of course。
unlimited is a really big number。 And then the minimum， thereby， is 12 patients per hour。
which was our process capacity。 Finally， what's the utilization at the resource of the receptionist？
Remember， in general， when we think about utilization。
we think about the ratio between the flow rate， how much is the process producing。
and the capacity of how much could the process be computing， if it went all out。
So the utilization is flow rate divided by capacity。 And in this case。
you see that it's 12 patients per hour， which was our flow rate determined by the bottleneck。
divided by the capacity of the receptionist， which is 2/5 patients per minute。
or 24 patients per hour。 That's the utilization of the receptionist。 It's 50%。 By now。
I'm sure you're getting comfortable with the idea of capacities。
utilization， bottlenecks， and my three favorite measures， inventory， flow rate， and flow time。
This is really the foundation for any good process analysis。 In the next set of videos。
I will start building on this foundation。 We will add a number of diagnostic performance measures。
and from there， we'll start thinking about the first set of operational improvements。
See you in the next module。 [BLANK_AUDIO]。
