Welcome to today's deep dive. I am so incredibly
thrilled to have you here with us. Today, our
mission is to take a single comprehensive Wikipedia
article and extract the absolute best nuggets
of knowledge so you can walk away with a fresh
perspective on how the world operates. Right.
We're tackling something that. on the surface
might sound a bit dry yeah we are decoding a
concept that honestly sounds like the driest
most corporate business jargon imaginable we're
talking about lead time it sounds so technical
it does but wait before your eyes glaze over
i promise you this is actually the invisible
force governing practically everything in your
life absolutely everything it dictates how long
it takes to get that shiny new car you just ordered
it determines whether or not you survive a boss
fight in your favorite video game And it is literally
the underlying math of detecting life -threatening
diseases. By the end of this deep dive, you're
going to see the hidden mechanics of the waiting
game everywhere you look. It really is the architectural
framework of waiting. It's so easy for us to
just assume that time is a fixed objective thing.
You know, a day is a day, an hour is an hour.
When you dive into the mechanics of how things
are actually made and shipped and completed,
you quickly realize that time is entirely relative.
It depends on exactly where you are standing
in a process. Understanding this completely shifts
how you view almost every system around you,
from the factory floor to your own daily schedule.
Okay, let's unpack this. Broadly speaking, a
lead time is the latency or the delay between
the initiation and the completion of a process.
Let's start with a really relatable example.
Say you are buying a new car. The lead time between
you placing that order and the manufacturer actually
delivering the car into your driveway could be
anywhere from two weeks to six months. Exactly.
And that massive variance depends on a whole
host of invisible processes happening behind
the scenes. To really understand those particularities,
you have to look at it from a manufacturing perspective,
which operates on two distinct approaches. Okay,
what's the first one? The first is make -to -order.
If you are buying a highly customized car, you
want a specific leather interior, a custom paint
job, the upgraded sound system, that's make to
order. In that scenario, the lead time is the
entire duration from the moment your order is
released to the actual production and shipment
that fulfills your highly specific request. Got
it. But let's say I don't care about custom leather.
I just need a car today. That brings us to the
second approach, which is make to stock. If it
is a standard model that the manufacturer just
wants ready on the dealership lot, the lead time
is just the time taken from releasing the order
to the production floor until that standard car
is received into the finished goods inventory.
So it's not just about how long it takes to bolt
the doors onto the frame. It's about the entire
lifespan of the request. The Chartered Institute
of Procurement and Supply actually defines this
as total lead time. And they argue it's really
a combination of two completely different ticking
clocks. Internal clock. And an external clock.
Yeah, that internal clock is everything happening
inside the buying organization. It's the time
required to progress from simply identifying
that a need exists to actually issuing the purchase
order. Okay, so just the paperwork part, basically.
Exactly. And only once that internal process
is finished does the external lead time take
over. That's the time required for the supplying
organization to do their part. The development,
the actual manufacturing, the dispatch, and the
final delivery. Sweet. Think about every... time
you, the listener, click buy on a website. As
a consumer, does your lead time clock start the
second you hit buy? Or is there a delay you aren't
seeing? That is the illusion of modern convenience.
You think it's just about the warehouse packing
the box and handing it to a delivery driver.
But there is an invisible delay running alongside
all of that. What's fascinating here is that
we often only think about the physical movement
of goods. We picture conveyor belts and delivery
trucks. But researchers who study supply chain
performance, like Mason Jones and Toll, point
out a massive bottleneck called information lead
time. If a company wants to actually secure improvements
and get things to you faster, they cannot just...
shrink the material flow lead time they must
equally reduce the information flow lead time
I see where you're going because if the physical
parts can move at lightning speed but the email
telling the factory floor to actually move them
takes three days to get routed to the right person
you are still stuck waiting you're only as fast
as your slowest piece of data Building information
-enriched supply chains is critical. If the data
doesn't move as fast as the delivery trucks,
the whole system grinds to a halt no matter how
efficient the physical manufacturing is. I never
really thought about how disconnected the information
and the physical items could be. And that brings
us to something that completely shifted my perspective
on this. Lead time is not an objective fact.
It completely changes depending on who was asking
the question. Let's look at how this plays out
in the real world with something called the 2
-5 -6 rule. Ah, yes, the company A and company
B scenario. This is the perfect way to illustrate
the hidden time sinks in any process. Let's walk
through a hypothetical. Imagine you're running
company A, and you need a very specific custom
part for your assembly line. Company B, your
supplier, can manufacture this part in exactly
two days once they receive your order. Okay,
two days to make it. Right. After that, it takes
three days for company A to receive the part
once it's shipped. Finally, it takes one additional
day once it arrives before the part is actually
ready to go into the manufacturing line at company
A. Okay, so to recap the math, we have two days
to make it, three days to ship it, and one day
to prep it. Now, here is how that single reality
generates three entirely different answers, which
is where all the miscommunications happen. This
is where it gets messy. Yeah. If Company A's
supply chain department calls Company B and asks
what the lead time for this part is, Company
B will confidently court them two days. Because
from the manufacturer's perspective, their job,
the actual making of the thing, takes two days.
But consider this. What happens when Company
A's manufacturing division goes to their own
supply chain division and asks for the lead time?
The quote suddenly jumps to five days. The supply
chain division knows they have to account for
the two days of manufacturing, plus the three
days it spends on a truck. And then the final
twist. Imagine you are a line worker on the factory
floor, and you ask your boss, When is this part
actually going to be in my hands so I can do
my job? The quote is now six days. Six days.
Because the boss knows they have to add that
final day of setup time once it comes off the
truck. Two days, five days, six days. All for
the exact same part from the exact same order.
You can imagine the line worker pulling their
hair out because someone in supply chain told
them it would only take two days. It is a recipe
for internal friction if you don't understand
the anatomy of the wait. You can break this entire
journey down into three phases. Pre -processing,
processing, and post -processing. Okay, break
those down for us. Pre -processing is the planning
time, the paperwork, releasing the purchase order.
Processing is the actual procuring or making
of the item. But the silent killer of efficiency,
the place where most of that friction happens,
is often post -processing. The post -processing.
I hadn't even considered what happens when the
truck actually arrives. The wait isn't over just
because it's physically in the building, is it?
Not at all. There is something called put -away
time, and it is far from trivial. Put -away time
is the duration it takes a company to unload
a product from a truck, physically inspect it
for damage, log it into their inventory system,
move it into storage, or perhaps hold it in quarantine
for safety checks. Wow. Yeah. When a company
is running on tight constraints or using just
-in -time manufacturing where parts arrive exactly
when they are needed, knowing exactly how long
your own internal put -away process takes is
just as critical as knowing the supplier's manufacturing
time. Because if you don't account for the truck
sitting on the dock for eight hours waiting to
be unloaded and inspected, your entire assembly
line might grind to a halt. It's wild how much
is hidden in the margins. Here's where it gets
really interesting. Because massive industries
don't just guess at these numbers or rely on
rough estimates. They actually model customer
behavior using four distinct mathematical flavors
of order lead time. Or OLT. Yes, the OLT matrix.
Differentiating between these definitions is
crucial for industries to model the order behavior
of their customers and manage their own revenue
streams. The first of these four flavors is OLT
requested. The calculation here is the wish date
minus the order entry date. Let's ground this
in a real -world scenario. Yeah. Let's say you're
listening to this and you decide to order custom
sneakers for an upcoming marathon. You place
the order today, so that is your order entry
date. You want them in your hands by the first
of next month so you can break them in. That
is your wish date. Exactly. So the time between
those two dates, that's your OLT request. Right.
It represents the gap between when you entered
the system and when you desire the outcome. Next,
we have OLT quote. This is the quote date minus
the order entry date. The quote date is the agreed
upon time, often stipulated in a formal supply
chain contract, that the supplier commits to
delivering the goods. So keeping with the sneakers,
the website says, based on our current queue,
we guarantee delivery by the 5th of next month.
That is the quote. Correct. Then you have OLT
actual. This is the delivery date minus the order
entry date. This is the harsh reality of when
the goods actually arrived at your door. The
moment of truth. Exactly. And finally, there
is OLT confirmed. This is the confirmed date
minus the order entry date. The confirmed date
is when the provider actually sends that final
notification confirming the exact day they will
deliver the material, which might differ from
the original quote. So you have what you wished
for, what they initially quoted you in the contract,
what they later confirmed when it actually shipped,
and what actually happened when it hit your porch.
That is a lot of data points for one pair of
shoes. There is a way to average all this out
for massive companies, right? The average OLT
based on volume or OLTV. Yes. It sounds like
dense mathematics, but the concept is straightforward.
Imagine a factory receiving thousands of shipments.
To understand their true lead time, they multiply
the volume of the product delivered by its specific
OLT, add all of those together for a specific
facility, and then divide by the total quantity
delivered in that period. So it's volume weighted.
Meaning an order of 10 ,000 widgets taking five
days to arrive matters a whole lot more to the
factory's average efficiency than a tiny order
of 10 widgets taking five days. Precisely. But
why do companies go through all this trouble?
Why track the wish dates against the actual dates
so meticulously? Because this data is the foundation
of revenue management. First, it gives a company
a deep understanding of market behavior. If they
know customers consistently have an OLT requested
of two weeks, but their OLT quote is four weeks,
they know they are losing business and need to
redesign their schema to fit customer needs.
That makes sense. Second, it creates transparency.
You can improve customer relations immensely
just by communicating the gap between the quote
date and the confirmed date. And there's a third
reason, which is really the corporate enforcer
part of the equation. Tracking this increases
a company's ability to detect and penalize contract
breaches. If they have a massive supplier, And
they can prove with data that the supplier is
consistently missing the OLT, quote, they can
take action. That's the power of the matrix.
If OLT actual is consistently longer than OLT,
quote, you have quantifiable, undeniable proof
that the supplier is failing. It's the data -driven
way to manage accountability, levy fines, or
renegotiate contracts. So we know how to measure
the waiting game down to the absolute minute.
But how do companies actually shrink these times?
Nobody wants a longer lead time. There are several
specific methodologies for shortening lead time
across manufacturing, production, and delivery.
The overarching goal is improving each processing
step's efficiency by minimizing waste and quickly
resolving any bottlenecks. One of the primary
concepts used is called hijumka. I've heard that
term in lean manufacturing before. It means production
leveling, right? But what does that actually
look like in practice? Imagine a factory that
receives orders very sporadically. On Monday,
they get an order for 100 units and everyone
works a frantic 14 -hour shift. On Tuesday, they
get an order for 10 units and the machines sit
idle half the day. That sounds exhausting. It
is. And that feast or famine approach creates
massive, unpredictable lead times. Hajunka is
the practice of leveling out the type and quantity
of production over a fixed period of time. You
smooth out the schedule so you are producing
at a steady, predictable pace, which ironically
allows you to deliver faster overall because
you aren't constantly starting and stopping.
It's amazing how much time is lost just waiting
around. And what's fascinating is that reducing
the length of the idle or waiting process stages
is often the absolute easiest place to tackle
first. Don't try to make the complex assembly
machine run 10 % faster if the finished part
is just sitting on a forklift for two days doing
nothing. Cut the idle time first. Exactly. Find
the waiting and eliminate it. Now, this exact
same struggle to cut out idle time isn't just
a headache for factory managers. It's actually
the foundational puzzle for project managers,
too. Yes. In project management, lead time takes
on a slightly different nuance. The Project Management
Institute's PMBOK, their seventh edition, defines
it as the time between a customer request and
the actual delivery. It is considered a fundamental
deliverable metric. Right. So if you are picturing
a giant to -do list for a software development
team, lead time shows the amount of elapsed time
from a chunk of work, what they call a story
entering the backlog, all the way to the end
of the iteration or release. The clock starts
the second the idea goes on the board and stops
when the software is in the user's hands. And
naturally, a smaller lead time in this context
means the process is more effective and the project
team is highly productive. But there is a secondary
definition in project management that is quite
clever. Lead can also refer to the saved time
achieved by starting an activity before its predecessor
is completely finished. I love this trick. It's
called advancing a successor activity with respect
to a predecessor activity. Think about it like
cooking dinner. Oh, that's a perfect analogy.
Let's say making the pasta takes 15 minutes and
chopping and cooking the vegetables takes 10
minutes. If you wait for the pasta to be completely
done before you even start chopping, dinner takes
25 minutes. But if you give the vegetable task
a lead of 10 minutes, meaning you start chopping
while the water is still boiling, both components
finish at the exact same time. It is a brilliant
scheduling hack to overlap work and compress
the timeline. It requires meticulous planning
and a deep understanding of dependencies, but
it is a highly effective way to shrink the overall
duration of a project's critical path. Okay,
so we've covered factories, we've covered project
managers trying to launch software, but the final
piece of this puzzle takes the concept of lead
time into some completely unexpected arenas.
Let's talk about journalism first. In publishing
and journalism, lead time describes the production
period of a particular publication. It is the
amount of time a journalist has between receiving
a writing assignment and submitting the completed
piece before the issue date is released to the
public. Depending on the medium, this varies
wildly. Right. A daily newspaper or a breaking
news website have a lead time of hours, maybe
even minutes. But a glossy monthly magazine might
have a lead time of three to six months. They
are finalizing their Christmas issue in July.
But it gets even wilder than journalism. Video
games. Lead time is actually a crucial mechanic
in high -twitch action games. This is a fascinating
application of the concept because it uses latency
as a deliberate obstacle. In video games, lead
time refers to the built -in delay required to
successfully complete certain special or important
actions, like using a health -recovering item
or casting a powerful spell. Think about the
last time you played an intense game. You're
in a massive boss fight, the monster is charging
at you, your health bar is flashing red, and
you hit the button to drink a healing potion.
Your character doesn't just instantly heal. They
stand there for two agonizing seconds, uncorking
the bottle and drinking it totally vulnerable
while the boss is bearing down on you. That is
intentional lead time. It is entirely intentional.
Game developers build this latency in to prevent
players from abusing helpful abilities or items.
By making them a little more difficult to use
safely, the game forces the player to employ
strategy, calculate risk, and exercise caution.
You have to earn the health by finding a safe
window of time to endure the lead time. They're
using latency as a tool to create tension. It's
brilliant. But from the virtual world to the
very real world, the final example we have is
without a doubt the most profound. If we connect
this to the bigger picture, medicine provides
the most consequential definition of lead time.
When referring to a disease, lead time is the
length of time between the detection of a disease
through screening and the moment in time where
it would have normally presented with symptoms
and led to a diagnosis. It's the window between
catching something early because you went in
for a test and when you would have naturally
felt sick and gone to the doctor anyway. Yes.
A common example is breast cancer population
screening. Women who are completely asymptomatic
might receive a positive test result through
a routine mammography. That underlying disease
might have taken many more years to physically
manifest and show noticeable symptoms. That gap
in time, that specific lead time, is the critical
window where early intervention is possible.
In that context, lead time isn't just a metric
for factory efficiency, and it isn't a game mechanic
to make a boss fight harder. It is literally
the window for saving a life. The longer the
lead time you can gain over the disease through
early screening, the better your chances of survival.
It really puts the whole concept of waiting in
time into a staggering perspective. It certainly
does. It transitions the concept from a measure
of business latency into a measure of human survival.
Time is the most valuable resource we have, and
in medicine, lead time is the act of buying more
of it. So what does this all mean? We started
with custom cars and purchase orders, moved through
the hidden days, lost unloading trucks on a dock,
mapped out the equations for tracking our wishes
against reality, learned how to overlap our schedules
by chopping vegetables while the water boils,
and finally saw how this same exact principle
governs the tension in our video games and the
life -saving potential of medical screenings.
Whether you are waiting for a package, managing
a team, or popping a health potion, lead time
is the invisible framework. dictating your reality.
It really is the unifying theory of latency.
And there is one final concept connected to this
that I think serves as a perfect concluding thought.
When you look at the broader study of lead time,
it inevitably links to two other fascinating
disciplines. Queuing theory, which is the actual
complex mathematical study of waiting lines,
and the concept of safety stock. Safety stock.
Is that just the extra stuff a company keeps
around in a warehouse just in case? Essentially,
yes. Safety stock is extra inventory kept strictly
to mitigate the risks of unpredictable lead times.
If you cannot trust the latency of your suppliers,
you build a physical buffer to protect yourself.
And this raises an important question, not just
for supply chain managers, but for everyone listening
right now. I like where this is going. Look around
your own life, your own schedule, your own relationships.
Where are you subconsciously keeping your own
safety stock? Whether that is padding your schedule
with extra time, hoarding extra resources, or
keeping emotional buffers. You do it to protect
yourself from the hidden, unpredictable lead
times of others. Wow. Are you leaving 20 minutes
early for every single meeting because you're
buffering the lead time of traffic? Are you keeping
a safe... safety stock of patients because you
know the pre -processing time of your co -workers
is going to drive you crazy. That is something
to seriously mull over today. Thank you so much
for joining us on this deep dive into the mechanics
of waiting. We hope you view the systems around
you just a little bit differently today. Keep
questioning, keep looking for those hidden frameworks,
and most importantly, stay insanely curious.
We will catch you next time.