We've talked about processes in general.
And now I want to talk about supply chain processes.
And probably the first thing we need to really focus in on
is the problems with variability,
because variability's one of the key problems we have
with any supply chain process.
And recall the variability in processes
arise from two main sources-- the inter-arrival variability
and the process variability.
And so what I'm showing in this chart here
is the coefficient of variation of CVA for the arrival process,
and CVP for the coefficient of variation of the processing
time.
And you can think of the inter-arrival variability
for a supply chain as being, for the most part, the demand
variability.
The process variability can be thought
of as the time it takes for an order, a SKU, or whatever,
to make it through the supply chain.
So this is the combination of the order lead
time, the transit time, as well as the manufacturing approach
that is being used, whether it's make-to-stock, make-to-order,
assemble-to-order, or engineer-to-order.
And these will all have different processing times.
So if you think about it, having a wide range of different SKUs,
customers, and suppliers that take different requirements
and time to process, this will naturally
lead to variability in the supply chain processing time.
And this will lead to queues and delays.
Now, we know variability from our earlier lectures
is the bane of all supply chains.
And if we look back at our old friend, the total cost
equation, we can see the specific effect.
So this is the total cost equation.
This should be tattooed somewhere on your body.
You should know this very well.
And remember, the first term is the purchasing cost.
Then I've got the ordering cost.
Then I've got the inventory holding cost
that is comprised of the cycle stock, the safety stock,
and the pipeline inventory.
And then the last term is kind of a composite
for the cost of a stock out.
And the key components are the cost of each stock out event,
the number of cycles I have in a demand period,
a year for example, and the probability of stocking out.
If you're not familiar with this,
you should check the key concept documents for SC1x.
So where does variability play a part?
Well, it changes the variability of demand over lead time, which
affects the safety stock.
It also affects the probability of stocking out.
So increased variability leads to higher safety stock
and higher probability of stock outs,
which could lead to higher stock out costs
if I'm being penalized that way.
And this gets even worse when we look
at the effect of variability in lead time,
because up here, it's just variability and demand.
We're assuming lead time is constant for that total cost
equation.
However, if the lead time is equally variable,
then we have to use the Hadley-Whitin equation, which
we discussed about in SC1x.
And the idea here is that it increases the total variability
of demand over this variable lead time,
a random sum of random numbers.
And it has an effect that's even more pronounced.
So variability drives a lot of the problems in supply chain
management, in any kind of supply chain.
So what can we do?
Well, there's really two options.
One, I can try and reduce the variability.
And two, I can try to buffer against it.
So let's look at each of these.
So the first one, reducing the variability, this kind of makes
sense.
If I can reduce it, you should do it.
Right?
And so you want to be able to identify and measure
the sources of the variability, any defects that are causing
this, whether it's late arrivals, a longer
time for manufacturing, whatever the defect is.
And we want to develop plans and courses of action
to try to minimize these.
Now, I'll talk about tools and techniques
later in this lesson.
But another one that I want to just mention now
is the whole idea of segmentation,
because segmentation is one way that we've already been taught
and that can really reduce variability.
So think of it this way.
Think of a grocery line.
And some people buy one item, some people
buy a hundred items.
They fill up a whole grocery cart.
So the processing variability, if I have one cashier,
or one type of cashier, will be huge,
because the people who might take
one minute versus the people who might take
20 minutes to get processed.
So what we can do is a simple segmentation strategy.
I set up one fast service lane where
you can have, say, 10 or less items, and the other lanes,
where you can have as many as you want.
So what we'll naturally do is we'll
segment the demand coming in.
And so the people who only have one, two, three, or a few items
will go to that fast line versus the ones
with larger, which will go to the other cashiers.
This will naturally reduce the variability
of processing time for each of the two types of cashiers.
So segmentation is one easy way that we
can use to reduce the variability,
because we treat the different types of customers, SKUs,
whatever differently.
And we'll talk much more about that a little later
in this lesson.
But let's say, what if we can't reduce it?
There's nothing we can do.
Like a lot of demand variability,
we can't eliminate it.
So what you do in that case is you buffer against it.
So there are three approaches that you can use
to buffer against variability.
The first we should be very comfortable with, is inventory.
This is the traditional approach.
This is why we used the total cost equation.
Based on the variability of demand,
I will have to set my service level,
because then my safety stock will be driven by that.
So this implies that I have pretty high inventory levels,
because I'm relying on inventory to buffer any variability.
And now I can reduce this a little bit
by using flexible inventory.
And this is the idea of using modular parts,
common platforms, generic stock.
And the idea is postponement.
So remember back to an early, early example
I gave in a coffee shop.
If I'm in a coffee shop and say I
have four different size of cups for the different coffee
that I'm going to be serving for takeaway coffee,
if I have lids that are sized for each specific size of cup,
that will be dramatically more inventory
I need to stock than if I had one size cup-- or, excuse me,
one size lid that fits on all four sizes of the cups.
And so by doing this, creating flexible inventory,
reduces the total amount of inventory I need.
It's an example of pooling.
We see this over and over again.
OK, so that's the first buffering approach, inventory.
The second is with capacity, and the idea here
is to have extra room just in case.
So for transportation, this might
mean planning your builds for a truck to only fit
85% utilization of the trailer.
So you don't plan on filling it up just
in case you get some peak.
This is also for emergency facilities.
So whether it's emergency rooms, fire houses, police stations,
most cities or governments are willing to build extra
than what they typically need to handle those peaks.
Now, this naturally implies you're
going to have a low average utilization.
And one way you can minimize the amount of capacity you need
is by using flexible capacity.
And the idea here is you have capacity that can
be used for multiple purposes.
You can think of equipments and machines that
can be used for multiple SKUs.
In supply chain, you see this most common
with cross-trained personnel.
For example, in the United States,
there's a lot of less than truckload carriers
that are unionized.
And what that means is a lot of the workers
are classified to be either dock workers or drivers.
They can't do both.
And so if you cross-train them, if you
are able to work through that, then you
get someone who can peak drive, if needed, or work on the dock.
So flexibility in what the job that person can do
will increase the amount-- or the flexibility,
and therefore decrease the amount of extra capacity,
in this case, labor, that you need to keep.
OK, the last one seems a little strange, time.
But, you know, you can buffer against variability
with inventory, with extra capacity, or with time.
And what you mean here is that you're willing to wait.
And this is a form of a buffer.
And so it implies very long wait times.
Now, we saw this when we looked at Material Requirements
Planning, MRP, systems where you build
time buffers into the system.
And we can also see this following
the same kind of flexible approach
is where you dynamically allocate the waiting
times across different customer segments based
on what you're currently producing,
what you're planning on producing.
And we saw this in SC2x when we looked
at ATP, or Available To Promise, in production planning.
So all three of these ways of buffering
are usually used in some kind of form.
You know, you have inventory, you want some safety stock.
You might look at some capacity utilization, maybe
dual sourcing, things like that, where
you have extra capacity available if you need it.
And then time.
Not all customers will be serviced
in the exact same time.
So you might be able to buffer some by making people wait.
OK, so that's a quick summary of variability and kind
of trying to tie it together to things we've already seen.
And so what are we going to do for the rest of this lesson?
Well, we only have two big things to do.
The first is we're going to look at core supply chain processes.
And I'm going to divide them into two groups,
because every company has slightly different processes,
but there are some that are really common
And I'm going to break them into external-facing,
things that look at your customers and suppliers,
and internal-facing, things that are more inward-facing
and dealing with your firm itself.
Then I'm going to talk about tools and techniques.
And these tools and techniques are used for mapping a process,
understanding how a process works,
and maybe helping design a process.
And then I'll talk about process improvement tools.
And these are tools that are used
to identify where there are problems
and maybe come up with possible solutions.
OK, so this is what we're going to do for the rest of lesson.
So let's get started.