October 28, 2025 • 12 mins
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
SPEAKER_01 (00:00):
Welcome back to the deep dive.
Okay.
Today we're tackling somethingreally fundamental for well, any
business really.
How do you get a real handle onoperational efficiency and
productivity fast?
Aaron Powell Exactly.
SPEAKER_00 (00:12):
And we're not talking about your personal
to-do list apps here.
SPEAKER_01 (00:16):
No, no.
This is the Siri stuff.
We're driving into the scienceof work study.
It's like industrialengineering.
We're really focused on howlabor and processes actually
work or sometimes don't work.
Trevor Burrus, Jr.
SPEAKER_00 (00:26):
Right.
And the sources we've looked at,they really detail the modern
approaches.
You see this a lot in sectorslike retail, hospitality, call
centers, logistics, places withhigh volume, lots of moving
parts.
So the mission today is It'sabout uncovering how time is
really being spent, not howpeople think it's spent or how
the procedures say it should bespent, but the reality on the
(00:48):
ground.
We want to pull out theactionable insights from that
data.
SPEAKER_01 (00:51):
Aaron Powell And just as a little teaser, one of
the big things that jumps out isthat productivity problems,
they're often not about lazyworkers.
They're baked into thestructure.
SPEAKER_00 (01:00):
Oh, absolutely.
And specifically, we found datashowing many managers,
especially outside those bigflagship locations, spend, I
mean, shockingly little timeactually managing people.
SPEAKER_01 (01:11):
Aaron Powell We'll definitely get into that
leadership paradox.
That sounds crucial, especiallyfor keeping good people.
SPEAKER_00 (01:17):
It really is.
Big retention issue often.
But first, I think we need tounderstand how they figure all
this out, the methodology.
Trevor Burrus, Jr.
SPEAKER_01 (01:25):
Right.
You mentioned it's structured.
SPEAKER_00 (01:27):
Yeah, it usually breaks down into three levels,
kind of like zooming in, youstart broad, then get more and
more granular.
SPEAKER_01 (01:32):
Okay.
Let's start broad then.
Level one, what's the bigpicture it gives a manager?
SPEAKER_00 (01:36):
Aaron Powell So level one is what we call the
team-wide efficiency study.
Think of it as the top-linediagnostic.
It's usually done over a decentperiod, like a full seven-hour
shift or workday.
SPEAKER_01 (01:46):
Aaron Powell And it's looking at the whole team.
SPEAKER_00 (01:48):
The whole team, yeah.
SPEAKER_01 (01:49):
Yeah.
SPEAKER_00 (01:49):
It's capturing what activities are happening when
customer demand hits, basicallycreating a baseline map of where
resources are going.
SPEAKER_01 (01:56):
Aaron Powell So it's more than just clocking in and
out.
What kind of picture does itpaint?
SPEAKER_00 (02:00):
Aaron Powell Well, yeah, it's not just hours
worked.
Critically, it splits that timeout.
How much time is spent genuinelyinteracting with customers?
How much on essential backoffice or operational tasks?
SPEAKER_01 (02:12):
Aaron Powell And the third bit.
SPEAKER_00 (02:14):
And this is often the eye-opener.
How much time is non-value addor NVA?
Basically, downtime, lost time.
SPEAKER_01 (02:21):
Ah, okay.
So that straightaway shows youwhere there might be Slack or
maybe where people are justwaiting around.
SPEAKER_00 (02:27):
Aaron Powell Precisely.
It gives you that internalpicture first, but then comes
the benchmarking.
SPEAKER_01 (02:31):
Aaron Powell Right, comparing it to others.
That must be powerful.
SPEAKER_00 (02:34):
Aaron Powell Hugely.
The L1 data gets comparedagainst this massive database
from similar businesses, similarsectors.
Suddenly you're not justguessing.
Aaron Powell So you can answerquestions like, how much
downtime do we have compared tothe average or the best in
class?
And crucially, how much stretchare our teams under?
Are they constantly rushed offtheir feet?
(02:55):
Or is there capacity?
It takes the emotion and opinionout of it.
SPEAKER_01 (02:59):
Okay, that makes sense.
So you've got the big picturefrom level one.
Where does level two take you?
SPEAKER_00 (03:03):
Level two gets more specific.
It's the task activity study.
Now we're zooming in on coretasks end-to-end.
SPEAKER_01 (03:09):
So like thing it takes to process an order or
check a guest in.
SPEAKER_00 (03:14):
Exactly.
But it's not just the totaltime.
The key here is breaking thattask down into its individual
steps.
The elemental level.
SPEAKER_01 (03:23):
Why break it down so small?
SPEAKER_00 (03:24):
Because that's where you find the opportunities.
You see exactly which step istaking too long, which part
could maybe be automated, or youknow, maybe a step that could be
cut out altogether.
SPEAKER_01 (03:33):
Aaron Powell Do you have an example of where that
really makes a difference?
SPEAKER_00 (03:35):
Oh, definitely.
Think about warehouses,logistics, picking items.
The studies consistently findsomething like half the time
spent picking isn't grabbing theitem.
SPEAKER_01 (03:46):
Aaron Ross Powell What is it then?
SPEAKER_00 (03:47):
It's traveling, just walking or driving the forklift
between locations.
SPEAKER_01 (03:50):
Aaron Powell Right.
So the problem isn't necessarilythe picker speed.
SPEAKER_00 (03:53):
Aaron Ross Powell Exactly.
It's the warehouse layout, maybethe route they're given, the
sequence on the pick list, thefixes in the design, not just
telling someone to hurry up.
SPEAKER_01 (04:02):
Okay.
That's level two.
Then there's level three goingeven deeper.
SPEAKER_00 (04:06):
Yep.
Level three is the deep divemovement analysis.
This is often where they use MTMmethods time measurement.
SPEAKER_01 (04:13):
Aaron Powell MTM.
Sounds technical.
SPEAKER_00 (04:15):
Aaron Powell It is.
It's like putting the processunder a microscope often uses
video analysis.
You break down the work intothese tiny, standardized human
movements like get put, easyreach, turn, really small
actions.
SPEAKER_01 (04:29):
Aaron Powell And why on earth would you need that
level of detail?
SPEAKER_00 (04:31):
Aaron Powell It's perfect for tasks that are super
short, really repetitive, donemaybe thousands of times a day.
Think fast food, assembly lines.
SPEAKER_01 (04:40):
Aaron Powell Ah, okay.
Where shaving off even a tinybit of time adds up massively.
SPEAKER_00 (04:44):
Aaron Powell Precisely.
Saving a second on a task done10,000 times a day, that's huge
savings over a year.
SPEAKER_01 (04:50):
Can you give a concrete example, something
relatable?
Aaron Powell Sure.
SPEAKER_00 (04:52):
Quickserve restaurants are classic.
NTM analysis looked at makingburgers.
They found that installing asimple hands-free sauce
dispenser saved measurable time.
Because before the worker had toput the burger box down, pick up
the sauce bottle, dispense, putthe bottle down, pick the box
back up.
That little put-down pickupcycle, it adds up.
The hands-free dispensereliminated it.
SPEAKER_01 (05:11):
Wow.
Just changing the dispenser.
SPEAKER_00 (05:13):
And the MTM data quantified the exact time saved,
which made justifying the costof the new dispensers really
easy.
It's hard data.
SPEAKER_01 (05:21):
That's fascinating.
And I guess this kind ofanalysis, levels two and three,
also flags the obvious waste,right?
The stuff we probably all see.
SPEAKER_00 (05:28):
Oh, yeah, absolutely.
That's a core output.
Things like double handlingstock, picking it up, putting it
down, picking it up again later,adds time, risks damage.
SPEAKER_01 (05:38):
What else?
SPEAKER_00 (05:39):
Or you see this everywhere.
Self-checkout tills.
They spit out receipts, andmaybe 80% of people just leave
them there.
Why are we printing them?
It's ink, paper, machineware.
SPEAKER_01 (05:50):
Good point.
Or using handwritten lists forthings.
SPEAKER_00 (05:52):
Right.
Paper fill-up lists on a shopfloor instead of using a
handheld device linked to acentral system.
These things seem small, butthey're measurable
inefficiencies, structuralwaste.
SPEAKER_01 (06:02):
That level of detail is amazing.
But okay, let's shift gears abit.
Because you said the biggestinsights often come down to the
people, especially leadership.
You called it the leadershipparadox.
Here's where it gets reallyinteresting.
SPEAKER_00 (06:13):
Yeah, this is often the most surprising finding for
businesses.
We're talking now about the rolestudy.
It's like a day-in-the-lifeobservation, but very
structured.
Tracking managers, specialists,understanding how their time is
actually spent.
SPEAKER_01 (06:28):
Compared to how the company thinks it's spent or
intends it to be spent.
SPEAKER_00 (06:31):
Exactly.
And look, everyone agrees thedifference between a good store
or a good branch and a greatone, it's usually the local
leader, the manager.
Trevor Burrus, Jr.
Sure.
SPEAKER_01 (06:39):
That makes sense.
SPEAKER_00 (06:40):
But the data shows that many businesses, completely
unintentionally, build systemsand processes that actually stop
their managers from leadingeffectively.
They get trapped.
SPEAKER_01 (06:51):
Trapped doing what?
Yeah.
What does the data actually showabout how managers spend their
time, especially in, say,smaller branches or stores?
SPEAKER_00 (06:58):
Aaron Powell Okay, brace yourself.
The numbers are pretty stark.
For managers outside the reallybig flagship locations, some
were spending as little as 1%.
SPEAKER_01 (07:06):
1% of their day.
SPEAKER_00 (07:07):
1% of their time on actual management activities,
coaching, developing people,strategic thinking, that kind of
thing.
SPEAKER_01 (07:15):
So what were they doing?
SPEAKER_00 (07:16):
Aaron Powell The average across the board was
only about 15 to 20 percent onpeople management.
The other 80-85%.
It gets sucked up by operationaltasks.
Covering brakes, stockingshelves, dealing with immediate
firefighting.
They just become an extra pairof hands.
SPEAKER_01 (07:32):
Wow.
So the most experienced,probably highest paid person on
site.
SPEAKER_00 (07:36):
Aaron Powell is often doing the most basic
operational work.
It's hugely inefficient from acost perspective.
But worse, think about the team.
They're not getting coached, notgetting developed.
It kills morale and careerprogression.
SPEAKER_01 (07:49):
Aaron Powell Yeah, I can see that.
And it's not just the hands-onstuff, right?
What about communication?
SPEAKER_00 (07:53):
Aaron Powell That's another big time sink.
We did a study on area managersand the people who look after
multiple stores, found thataround 15% of their entire week
was just spent reacting toemails, teams messages, WhatsApp
groups, just this constantbarrage of digital
communication.
SPEAKER_01 (08:07):
So they're just constantly interrupted.
SPEAKER_00 (08:09):
Constantly.
Even if the tools were meant tohelp, the sheer volume prevents
them from doing the deep work,like having proper focus
coaching sessions with theirstore managers.
They're just fighting digitalnoise.
SPEAKER_01 (08:19):
And you also mentioned something about admin
time, like Parkinson's law.
SPEAKER_00 (08:23):
Oh, a perfect example.
There was a fast food chain.
They decided managers shouldhave dedicated admin time on
Mondays.
Off the floor, focus on reports,analysis, good intention, right?
SPEAKER_01 (08:35):
Sounds sensible.
SPEAKER_00 (08:36):
Initially, maybe it took them, say, three hours, but
because Monday was blocked outfor admin, the work just
expanded.
It filled the whole day.
SPEAKER_01 (08:45):
So they spent all of Monday on admin, even if it
wasn't needed.
SPEAKER_00 (08:49):
Pretty much.
It became this entrenched habit,a whole day away from the team,
away from the operation.
And even when data showed theactual admin needed way less
time, it was really hard tobreak that pattern.
The structure created theinefficiency.
SPEAKER_01 (09:03):
Aaron Powell That makes the case really strongly
for looking beyond just onerole, doesn't it?
Looking at the whole structure,the hierarchy.
SPEAKER_00 (09:09):
Absolutely.
That's where you use the datafor bigger structural decisions.
You use internal benchmarkingfirst.
Comparing the same role acrossdifferent sites, is the team
manager job in store A basicallythe same as in store B?
Or is one person carrying waymore responsibility, maybe doing
tasks that should belong to adifferent role?
It checks for consistency.
SPEAKER_01 (09:29):
Okay.
And then the really revealingpart must be the role overlap.
SPEAKER_00 (09:32):
Aaron Powell That's often where the big savings are
found.
The analysis lays bare wheredifferent roles are essentially
doing the same things,redundancy.
SPEAKER_01 (09:39):
Aaron Powell And you have actual numbers on this?
SPEAKER_00 (09:41):
We do.
For instance, data consistentlyshows that in many retail or
hospitality setups, a managerand their assistant manager
spend, get this, 68% of theirtime doing the exact same tasks.
SPEAKER_01 (09:52):
Aaron Powell 68%.
Wow.
So more than two-thirds of thetime you've got two different
management salaries paying forthe same work.
SPEAKER_00 (09:58):
Aaron Powell Effectively, yes.
You've got duplication bakedinto the structure.
And it gets even highersometimes between a team leader
and a supervisor, often around78% overlap.
That's huge.
It is.
And that's the kind of objectivedata businesses need to say,
justify flattening thestructure, maybe going from four
layers of management to three,and crucially, implementing
(10:20):
really clear role profiles soeveryone knows exactly what they
are responsible for and whatthey're not.
SPEAKER_01 (10:25):
Okay.
So let's pull this together.
We've gone from the big picturedown to tiny movements, looked
at managers' time.
What does this all mean forsomeone listening?
How do they use this?
SPEAKER_00 (10:35):
Well, I think the real power of work study done
properly is that it takes theguesswork and frankly the
politics out of decision making.
SPEAKER_01 (10:43):
It's objective.
SPEAKER_00 (10:44):
It's objective.
It gives you a solid data-drivenreason for making changes,
sometimes tough changes.
It lets you build accurateworkload models, sometimes
called rebudget systems, so youcan match your staffing levels
precisely to when your customersactually need them.
SPEAKER_01 (10:58):
So it's about efficiency but also
effectiveness.
SPEAKER_00 (11:01):
Exactly.
The goal isn't just to cut hoursor make people rush, it's about
freeing up time.
Maximizing the time your teamspends on the things that
actually make a difference tothe customer, the things that
make your business unique.
You really can't.
(11:21):
Understanding how time is used,how processes flow, it's
fundamental to stayingprofitable, staying competitive.
It's the foundation.
SPEAKER_01 (11:30):
Absolutely.
So just to recap quickly, welooked at the three levels: the
team study for the big picture,the task analysis for workflows,
and the MTM deep dive for thosehigh frequency actions.
Right.
And probably the biggesttakeaway for many organizations
is that leadership paradoxmanagers needing to be freed up
from operational tasks toactually lead.
SPEAKER_00 (11:49):
Definitely a major finding.
So maybe here's a final thoughtfor you to chew on based on that
data.
SPEAKER_01 (11:54):
Go for it.
SPEAKER_00 (11:54):
If we know that, say, a manager and assistant
manager are spending 68% oftheir time doing identical tasks
and area managers are drowningin emails, ask yourself this.
Is your biggest productivitybottleneck really about how fast
people work?
Or is it simply about a lack ofclarity?
Maybe the biggest win comes notfrom speed, but just from
clearly defining who does what.
SPEAKER_01 (12:15):
Clarifying roles and responsibilities based on data.
SPEAKER_00 (12:18):
Exactly.
Could that clarity unlock morevalue than anything else?
Something to think about.
Welcome back to the deep dive.
Okay.
Today we're tackling somethingreally fundamental for well, any
business really.
How do you get a real handle onoperational efficiency and
productivity fast?
Aaron Powell Exactly.
SPEAKER_00 (00:12):
And we're not talking about your personal
to-do list apps here.
SPEAKER_01 (00:16):
No, no.
This is the Siri stuff.
We're driving into the scienceof work study.
It's like industrialengineering.
We're really focused on howlabor and processes actually
work or sometimes don't work.
Trevor Burrus, Jr.
SPEAKER_00 (00:26):
Right.
And the sources we've looked at,they really detail the modern
approaches.
You see this a lot in sectorslike retail, hospitality, call
centers, logistics, places withhigh volume, lots of moving
parts.
So the mission today is It'sabout uncovering how time is
really being spent, not howpeople think it's spent or how
the procedures say it should bespent, but the reality on the
(00:48):
ground.
We want to pull out theactionable insights from that
data.
SPEAKER_01 (00:51):
Aaron Powell And just as a little teaser, one of
the big things that jumps out isthat productivity problems,
they're often not about lazyworkers.
They're baked into thestructure.
SPEAKER_00 (01:00):
Oh, absolutely.
And specifically, we found datashowing many managers,
especially outside those bigflagship locations, spend, I
mean, shockingly little timeactually managing people.
SPEAKER_01 (01:11):
Aaron Powell We'll definitely get into that
leadership paradox.
That sounds crucial, especiallyfor keeping good people.
SPEAKER_00 (01:17):
It really is.
Big retention issue often.
But first, I think we need tounderstand how they figure all
this out, the methodology.
Trevor Burrus, Jr.
SPEAKER_01 (01:25):
Right.
You mentioned it's structured.
SPEAKER_00 (01:27):
Yeah, it usually breaks down into three levels,
kind of like zooming in, youstart broad, then get more and
more granular.
SPEAKER_01 (01:32):
Okay.
Let's start broad then.
Level one, what's the bigpicture it gives a manager?
SPEAKER_00 (01:36):
Aaron Powell So level one is what we call the
team-wide efficiency study.
Think of it as the top-linediagnostic.
It's usually done over a decentperiod, like a full seven-hour
shift or workday.
SPEAKER_01 (01:46):
Aaron Powell And it's looking at the whole team.
SPEAKER_00 (01:48):
The whole team, yeah.
SPEAKER_01 (01:49):
Yeah.
SPEAKER_00 (01:49):
It's capturing what activities are happening when
customer demand hits, basicallycreating a baseline map of where
resources are going.
SPEAKER_01 (01:56):
Aaron Powell So it's more than just clocking in and
out.
What kind of picture does itpaint?
SPEAKER_00 (02:00):
Aaron Powell Well, yeah, it's not just hours
worked.
Critically, it splits that timeout.
How much time is spent genuinelyinteracting with customers?
How much on essential backoffice or operational tasks?
SPEAKER_01 (02:12):
Aaron Powell And the third bit.
SPEAKER_00 (02:14):
And this is often the eye-opener.
How much time is non-value addor NVA?
Basically, downtime, lost time.
SPEAKER_01 (02:21):
Ah, okay.
So that straightaway shows youwhere there might be Slack or
maybe where people are justwaiting around.
SPEAKER_00 (02:27):
Aaron Powell Precisely.
It gives you that internalpicture first, but then comes
the benchmarking.
SPEAKER_01 (02:31):
Aaron Powell Right, comparing it to others.
That must be powerful.
SPEAKER_00 (02:34):
Aaron Powell Hugely.
The L1 data gets comparedagainst this massive database
from similar businesses, similarsectors.
Suddenly you're not justguessing.
Aaron Powell So you can answerquestions like, how much
downtime do we have compared tothe average or the best in
class?
And crucially, how much stretchare our teams under?
Are they constantly rushed offtheir feet?
(02:55):
Or is there capacity?
It takes the emotion and opinionout of it.
SPEAKER_01 (02:59):
Okay, that makes sense.
So you've got the big picturefrom level one.
Where does level two take you?
SPEAKER_00 (03:03):
Level two gets more specific.
It's the task activity study.
Now we're zooming in on coretasks end-to-end.
SPEAKER_01 (03:09):
So like thing it takes to process an order or
check a guest in.
SPEAKER_00 (03:14):
Exactly.
But it's not just the totaltime.
The key here is breaking thattask down into its individual
steps.
The elemental level.
SPEAKER_01 (03:23):
Why break it down so small?
SPEAKER_00 (03:24):
Because that's where you find the opportunities.
You see exactly which step istaking too long, which part
could maybe be automated, or youknow, maybe a step that could be
cut out altogether.
SPEAKER_01 (03:33):
Aaron Powell Do you have an example of where that
really makes a difference?
SPEAKER_00 (03:35):
Oh, definitely.
Think about warehouses,logistics, picking items.
The studies consistently findsomething like half the time
spent picking isn't grabbing theitem.
SPEAKER_01 (03:46):
Aaron Ross Powell What is it then?
SPEAKER_00 (03:47):
It's traveling, just walking or driving the forklift
between locations.
SPEAKER_01 (03:50):
Aaron Powell Right.
So the problem isn't necessarilythe picker speed.
SPEAKER_00 (03:53):
Aaron Ross Powell Exactly.
It's the warehouse layout, maybethe route they're given, the
sequence on the pick list, thefixes in the design, not just
telling someone to hurry up.
SPEAKER_01 (04:02):
Okay.
That's level two.
Then there's level three goingeven deeper.
SPEAKER_00 (04:06):
Yep.
Level three is the deep divemovement analysis.
This is often where they use MTMmethods time measurement.
SPEAKER_01 (04:13):
Aaron Powell MTM.
Sounds technical.
SPEAKER_00 (04:15):
Aaron Powell It is.
It's like putting the processunder a microscope often uses
video analysis.
You break down the work intothese tiny, standardized human
movements like get put, easyreach, turn, really small
actions.
SPEAKER_01 (04:29):
Aaron Powell And why on earth would you need that
level of detail?
SPEAKER_00 (04:31):
Aaron Powell It's perfect for tasks that are super
short, really repetitive, donemaybe thousands of times a day.
Think fast food, assembly lines.
SPEAKER_01 (04:40):
Aaron Powell Ah, okay.
Where shaving off even a tinybit of time adds up massively.
SPEAKER_00 (04:44):
Aaron Powell Precisely.
Saving a second on a task done10,000 times a day, that's huge
savings over a year.
SPEAKER_01 (04:50):
Can you give a concrete example, something
relatable?
Aaron Powell Sure.
SPEAKER_00 (04:52):
Quickserve restaurants are classic.
NTM analysis looked at makingburgers.
They found that installing asimple hands-free sauce
dispenser saved measurable time.
Because before the worker had toput the burger box down, pick up
the sauce bottle, dispense, putthe bottle down, pick the box
back up.
That little put-down pickupcycle, it adds up.
The hands-free dispensereliminated it.
SPEAKER_01 (05:11):
Wow.
Just changing the dispenser.
SPEAKER_00 (05:13):
And the MTM data quantified the exact time saved,
which made justifying the costof the new dispensers really
easy.
It's hard data.
SPEAKER_01 (05:21):
That's fascinating.
And I guess this kind ofanalysis, levels two and three,
also flags the obvious waste,right?
The stuff we probably all see.
SPEAKER_00 (05:28):
Oh, yeah, absolutely.
That's a core output.
Things like double handlingstock, picking it up, putting it
down, picking it up again later,adds time, risks damage.
SPEAKER_01 (05:38):
What else?
SPEAKER_00 (05:39):
Or you see this everywhere.
Self-checkout tills.
They spit out receipts, andmaybe 80% of people just leave
them there.
Why are we printing them?
It's ink, paper, machineware.
SPEAKER_01 (05:50):
Good point.
Or using handwritten lists forthings.
SPEAKER_00 (05:52):
Right.
Paper fill-up lists on a shopfloor instead of using a
handheld device linked to acentral system.
These things seem small, butthey're measurable
inefficiencies, structuralwaste.
SPEAKER_01 (06:02):
That level of detail is amazing.
But okay, let's shift gears abit.
Because you said the biggestinsights often come down to the
people, especially leadership.
You called it the leadershipparadox.
Here's where it gets reallyinteresting.
SPEAKER_00 (06:13):
Yeah, this is often the most surprising finding for
businesses.
We're talking now about the rolestudy.
It's like a day-in-the-lifeobservation, but very
structured.
Tracking managers, specialists,understanding how their time is
actually spent.
SPEAKER_01 (06:28):
Compared to how the company thinks it's spent or
intends it to be spent.
SPEAKER_00 (06:31):
Exactly.
And look, everyone agrees thedifference between a good store
or a good branch and a greatone, it's usually the local
leader, the manager.
Trevor Burrus, Jr.
Sure.
SPEAKER_01 (06:39):
That makes sense.
SPEAKER_00 (06:40):
But the data shows that many businesses, completely
unintentionally, build systemsand processes that actually stop
their managers from leadingeffectively.
They get trapped.
SPEAKER_01 (06:51):
Trapped doing what?
Yeah.
What does the data actually showabout how managers spend their
time, especially in, say,smaller branches or stores?
SPEAKER_00 (06:58):
Aaron Powell Okay, brace yourself.
The numbers are pretty stark.
For managers outside the reallybig flagship locations, some
were spending as little as 1%.
SPEAKER_01 (07:06):
1% of their day.
SPEAKER_00 (07:07):
1% of their time on actual management activities,
coaching, developing people,strategic thinking, that kind of
thing.
SPEAKER_01 (07:15):
So what were they doing?
SPEAKER_00 (07:16):
Aaron Powell The average across the board was
only about 15 to 20 percent onpeople management.
The other 80-85%.
It gets sucked up by operationaltasks.
Covering brakes, stockingshelves, dealing with immediate
firefighting.
They just become an extra pairof hands.
SPEAKER_01 (07:32):
Wow.
So the most experienced,probably highest paid person on
site.
SPEAKER_00 (07:36):
Aaron Powell is often doing the most basic
operational work.
It's hugely inefficient from acost perspective.
But worse, think about the team.
They're not getting coached, notgetting developed.
It kills morale and careerprogression.
SPEAKER_01 (07:49):
Aaron Powell Yeah, I can see that.
And it's not just the hands-onstuff, right?
What about communication?
SPEAKER_00 (07:53):
Aaron Powell That's another big time sink.
We did a study on area managersand the people who look after
multiple stores, found thataround 15% of their entire week
was just spent reacting toemails, teams messages, WhatsApp
groups, just this constantbarrage of digital
communication.
SPEAKER_01 (08:07):
So they're just constantly interrupted.
SPEAKER_00 (08:09):
Constantly.
Even if the tools were meant tohelp, the sheer volume prevents
them from doing the deep work,like having proper focus
coaching sessions with theirstore managers.
They're just fighting digitalnoise.
SPEAKER_01 (08:19):
And you also mentioned something about admin
time, like Parkinson's law.
SPEAKER_00 (08:23):
Oh, a perfect example.
There was a fast food chain.
They decided managers shouldhave dedicated admin time on
Mondays.
Off the floor, focus on reports,analysis, good intention, right?
SPEAKER_01 (08:35):
Sounds sensible.
SPEAKER_00 (08:36):
Initially, maybe it took them, say, three hours, but
because Monday was blocked outfor admin, the work just
expanded.
It filled the whole day.
SPEAKER_01 (08:45):
So they spent all of Monday on admin, even if it
wasn't needed.
SPEAKER_00 (08:49):
Pretty much.
It became this entrenched habit,a whole day away from the team,
away from the operation.
And even when data showed theactual admin needed way less
time, it was really hard tobreak that pattern.
The structure created theinefficiency.
SPEAKER_01 (09:03):
Aaron Powell That makes the case really strongly
for looking beyond just onerole, doesn't it?
Looking at the whole structure,the hierarchy.
SPEAKER_00 (09:09):
Absolutely.
That's where you use the datafor bigger structural decisions.
You use internal benchmarkingfirst.
Comparing the same role acrossdifferent sites, is the team
manager job in store A basicallythe same as in store B?
Or is one person carrying waymore responsibility, maybe doing
tasks that should belong to adifferent role?
It checks for consistency.
SPEAKER_01 (09:29):
Okay.
And then the really revealingpart must be the role overlap.
SPEAKER_00 (09:32):
Aaron Powell That's often where the big savings are
found.
The analysis lays bare wheredifferent roles are essentially
doing the same things,redundancy.
SPEAKER_01 (09:39):
Aaron Powell And you have actual numbers on this?
SPEAKER_00 (09:41):
We do.
For instance, data consistentlyshows that in many retail or
hospitality setups, a managerand their assistant manager
spend, get this, 68% of theirtime doing the exact same tasks.
SPEAKER_01 (09:52):
Aaron Powell 68%.
Wow.
So more than two-thirds of thetime you've got two different
management salaries paying forthe same work.
SPEAKER_00 (09:58):
Aaron Powell Effectively, yes.
You've got duplication bakedinto the structure.
And it gets even highersometimes between a team leader
and a supervisor, often around78% overlap.
That's huge.
It is.
And that's the kind of objectivedata businesses need to say,
justify flattening thestructure, maybe going from four
layers of management to three,and crucially, implementing
(10:20):
really clear role profiles soeveryone knows exactly what they
are responsible for and whatthey're not.
SPEAKER_01 (10:25):
Okay.
So let's pull this together.
We've gone from the big picturedown to tiny movements, looked
at managers' time.
What does this all mean forsomeone listening?
How do they use this?
SPEAKER_00 (10:35):
Well, I think the real power of work study done
properly is that it takes theguesswork and frankly the
politics out of decision making.
SPEAKER_01 (10:43):
It's objective.
SPEAKER_00 (10:44):
It's objective.
It gives you a solid data-drivenreason for making changes,
sometimes tough changes.
It lets you build accurateworkload models, sometimes
called rebudget systems, so youcan match your staffing levels
precisely to when your customersactually need them.
SPEAKER_01 (10:58):
So it's about efficiency but also
effectiveness.
SPEAKER_00 (11:01):
Exactly.
The goal isn't just to cut hoursor make people rush, it's about
freeing up time.
Maximizing the time your teamspends on the things that
actually make a difference tothe customer, the things that
make your business unique.
You really can't.
(11:21):
Understanding how time is used,how processes flow, it's
fundamental to stayingprofitable, staying competitive.
It's the foundation.
SPEAKER_01 (11:30):
Absolutely.
So just to recap quickly, welooked at the three levels: the
team study for the big picture,the task analysis for workflows,
and the MTM deep dive for thosehigh frequency actions.
Right.
And probably the biggesttakeaway for many organizations
is that leadership paradoxmanagers needing to be freed up
from operational tasks toactually lead.
SPEAKER_00 (11:49):
Definitely a major finding.
So maybe here's a final thoughtfor you to chew on based on that
data.
SPEAKER_01 (11:54):
Go for it.
SPEAKER_00 (11:54):
If we know that, say, a manager and assistant
manager are spending 68% oftheir time doing identical tasks
and area managers are drowningin emails, ask yourself this.
Is your biggest productivitybottleneck really about how fast
people work?
Or is it simply about a lack ofclarity?
Maybe the biggest win comes notfrom speed, but just from
clearly defining who does what.
SPEAKER_01 (12:15):
Clarifying roles and responsibilities based on data.
SPEAKER_00 (12:18):
Exactly.
Could that clarity unlock morevalue than anything else?
Something to think about.
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