December 3, 2023 • 15 mins
Sue & Simon talk about the latest productivity trends. They discuss:
- The most frequently asked questions from clients
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Welcome to the Productivity Podcast.
I'm back with Sue and we'regoing to do some Productivity
Insights and on this episodewe're going to focus on
frequently asked questions.
So lots of questions we getasked by new clients, common
clients, so we'll work through afew of them and explore where
we get through.
Do you want to kick off, susan?
Speaker 2 (00:24):
Yes, so the first question that we get asked a lot
is do people change theirbehaviours when they're being
studied?
Speaker 1 (00:31):
I think the answer is potentially for the first hour
or so, but I also think that byusing pace rating, that's
normalised out of the data.
So I think we've talked aboutpace rating before on a
different episode, but do youwant to give people a reminder
of what pace rating is?
Speaker 2 (00:50):
Pace rating is where analysts are trained to be able
to assess the effectiveness thatsomebody is working at versus a
British standard where theBritish standard is 100.
And kind of we call it pacerating.
But it's more than that.
It's also about how effectivethey're being.
So, for example, if I'm walkingat a good pace but I'm carrying
(01:12):
something and spilling a loadof it, then I'd be downrated
because from an effective pointof view I might be going quickly
but I'm not doing a good job ofit.
So pace is then used tonormalise things.
So I think sometimes people haveconcerns that.
Do people perhaps slow downwhen they're being timed to make
things look like they takelonger?
Well, again, the pace ratingwould pick that up, because if
(01:35):
somebody's working at 80, thenactually when we then do the
analysis, it's then normalisedback as if it was 100.
The only people that youperhaps wouldn't want to study
in that way would be if they'regoing slower because they're new
in the training.
So really we should only bestudying qualified operatives,
so that people that arecompetent at the role.
(01:55):
So if you've got somebodythat's brand new and in training
and they're slow because ofthat, then they aren't a good
subject to be studying.
Speaker 1 (02:03):
So that answers the question kind of what if you go
slow, speed up, which is goodOne's, I kind of get.
So how many stores, locationsshould I study?
What's the sample size?
Speaker 2 (02:17):
It's always a tricky one, isn't it?
Because it depends.
So the more variability thereis in whatever you're measuring,
then the bigger sample size youneed.
So if I've got 10 restaurantsbut each of them is a different
format, I'll need to spendlonger in each one than if they
(02:38):
were all the same.
So it can be variability interms of kind of the outlet type
that you're measuring.
It can also be variability inthe processes.
So if a process is always thesame, so a production line might
be kind of an obvious examplewhere it's a standard one, then
(02:58):
you don't need to measure itvery often because it's always
the same.
But things that are morevariable so it might be
different menu items in arestaurant, anywhere where
people and conversations areinvolved, always has a lot of
variability in it.
So, for example, customers in ashop they might be chatty, they
might be, not all that sort ofthing.
So it depends on thevariability.
(03:19):
I think often we say it kind ofin a smaller number of outlets
you might be looking to do 10%of the estate, but obviously if
you've got 2,000 shops, then youwouldn't be looking to do 10%.
So it varies.
It's not just the number ofstores, it's also how many days
you spend.
Speaker 1 (03:40):
There's no magic formula.
I don't think it's on a case bycase basis, along with, as you
say, that bit around thevariability is a key bit, and I
think that that's probably acasing point around the number
of days that you say.
So there's some organisationsout there that are kind of
(04:00):
hooked on there, measuring allthe time and measuring
everything.
We're not massive advocates ofthat.
I think every time you measureit changes the number.
So you've then got a biggerdata set, clearly more robust
data, but you've got to explainthe variance.
So kind of leads me on to howoften should you re-measure?
Speaker 2 (04:19):
Again, it's that it depends question.
So if you're in a business thatdoesn't change at all, why
would you bother re-measuring?
But the reality is thatbusinesses do change all the
time.
So if you assume thatsomebody's done a sort of a big,
wholesale measure of most ofthe processes, you can then just
(04:40):
follow it up.
So if you change one part ofyour operation, you can just
follow it up by measuring justthat one part, and that can be a
very good way to see how changeis working and identifying
other ways to optimise it.
Generally, most people wouldwant to rebase their numbers, a
maximum of kind of three to fouryears, because things do just
(05:04):
change.
Customers and people change, ifnothing else.
Speaker 1 (05:07):
Yeah, and if you think what happened pre and post
pandemic, there's a massivechange.
There wasn't there sointeresting.
So more than just times again Ispeak to people and they say I
got handed this spreadsheet or Igot given this pie chart.
That's great.
There must be more to it.
And my answer is alwaysabsolutely there is.
So just talk to us a bit aboutinsight around some of the
(05:30):
studies.
Speaker 2 (05:33):
So we always like to do more than just give over the
data to people.
So, yes, people can have theraw data and go through it as
kind of anybody else wouldprovide to them.
What we then try to do is thensay, well, from that data, this
is what it's telling you, thisis why this is the data that
supports that, and, as a resultof that, here are the quantified
(05:55):
opportunities of things thatyou could do, and here are some
ideas that you might like tolook at.
So we'll always try and take itfurther.
Partly.
There's lots of richness in thedata, so there's the different
study types, but all of themhave got a degree of richness in
that we can get to partlythrough benchmarking, because
we've got some great data setsthat we can benchmark against,
(06:16):
but we also like to combine itwith the observations that our
analysts make on site.
You know they're all trainedobservers and they spot things
that perhaps you wouldn't showup on the data.
Speaker 1 (06:26):
And I think it's like kind of some of the data that
you may get presented.
It's like having the book inthe chapters but then no words
in each chapter.
The insight gives you therichness and all the detail
underneath.
Speaker 2 (06:37):
Yeah, unless it's made actionable for you, then
actually it's always quite achallenge and although we deal
with this sort of data all thetime, the majority of people
don't.
You know it's something that'sdifferent in you, and anything
that we can do to help peopleget the most out of it is a
positive thing.
Speaker 1 (06:56):
Brilliant One that's cropping up more and more, I
think, is people are strugglingwith economics, shrink wage
inflation.
Why do I need a workload model?
Why do I need to know how longthings take and then build from
the bottom up to kind ofsuppress from the top down to
meet the financial demands?
Why shouldn't I just go back toCosta Cell?
Speaker 2 (07:19):
Costa Cell is just such a blunt tool, isn't it?
For one time in my career I wasrunning shops that were low
productivity and it was in atough economic area, so my
average basket sale was prettylow, so the average transaction
value was low.
I'd got colleagues that werekind of in much more affluent
(07:40):
areas, that people would buymore expensive items and we'd
still have to put the samenumber of items to the shelf.
We'd have to serve the samenumber of customers through the
till, but actually the value ofthe sales that I was getting
were lower than what some of mycolleagues would be in more well
off areas.
(08:01):
So that's a good example of whyit needs to be different,
because if you just weren't witha cost to sell, then my stores
would have been under resourcedand potentially their stores
would have been over resourced.
Speaker 1 (08:12):
Well, I think again in the current economic climate
it's an interesting debatebecause there's a divergence of
cost and volume.
So I could be, if I take amillion, if I sell a million
things for a pound or one thingfor a million pounds, in a cost
to sell scenario it's the same,but actually I've got a million
(08:33):
times more workload in one thantwo.
But ultimately at the moment,with price inflation, prices are
going up, so my sales are goingup through nothing I'm doing
but volumes probably dropping.
So actually, again in a cost tosell world, you're masking the
true impact of work needed.
So the reality, probably formost people at the moment, is
(08:54):
sales are higher but there'sless work that needs doing, so I
therefore need less budget.
It can't be intuitive, I get,but actually the price increase
isn't volume, it's item price.
That's going through Any otherquestions you can think of that.
People are often asking youwhen you're presenting about
data or in conversation.
Speaker 2 (09:17):
Perhaps one thing is about how.
What's the best way to engagetheir teams when they're doing
these things?
Because obviously, what we doisn't secret.
There's a person that turns upand is observing processes
happening, so making sure thatworks well by having teams that
are expecting us know what'shappening, know there aren't any
secrets, is usually the bestway to go.
Speaker 1 (09:41):
Yeah, and it's a balance, isn't it?
Because sometimes theinitiative is around cost-saving
, which is sensitive because asconsequences, clearly depending
on the results, sometimes it'saround just actually
understanding what's happeningin that business and then making
decisions off the back of thedata.
Sometimes it's about puttingmore people in front of
customers.
Sometimes it's a mix of allthree.
(10:02):
So treading carefully isimportant, but I think what I've
seen, being as transparent asyou can be at the initial
briefing, is also reallyimportant.
Speaker 2 (10:12):
Yeah, I think there's .
There's sort of three stepsthat we'd say is the best
practice.
So one is, if there's a phonecall, then so a phone call with
perhaps the line managers andthat sort of people, so they've
got a chance to ask anyquestions, that's led by the
(10:33):
central team, so it's their ownpeople, and then we're there to
answer any questions.
That they've got is a great wayto do it.
Follow that up with somewritten comms, because not
everybody's going to get to thatbrief, so follow it up with
some written comms.
That again sex out.
We're interesting people, notprocessors.
We don't capture people's names.
It's not secret.
We'll happily show you thetablet.
(10:53):
We want to know your thoughts,that's.
That's a good way to do it.
And then when the analystarrives, location to start
studying.
If there's a team huddle orteam briefing, it's great if our
analysts can join that sayhello to everybody and again, it
just reassures everybody.
So you know, everybody knowswhat's happening and why.
Speaker 1 (11:14):
Yeah, and the benefits of getting information
from colleagues.
So we're independent.
They can share their gripes ortheir golden nuggets with us and
we can build that into the deckas well.
Speaker 2 (11:26):
Yeah and then a final question for you is are the
things that we can't measure?
So, is there anything that youcan't measure?
Speaker 1 (11:35):
I think practically you can measure anything that
happens from a.
Can I go and watch somebodydoing it?
I think there's a couple ofthings that always stand out one
frequency.
So you could spend a lot oftime trying to capture something
that doesn't happen very oftenor is weekly or monthly, or
Dunning the dead of night,whatever.
And I think there's also abunch of stuff, and training is
(11:56):
always the one that is aconversation.
Can you measure trainingAbsolutely?
How much did you see in thestudy?
None, yeah.
So why is that?
Well, because people are busyand it's one of the first things
that's just dropped or he'sdone it Home, or just not
happening or batched up.
And I think training is a greatexample of from a workload model
point of view.
(12:17):
It's a policy decision.
So what do you want to fund perhead, per employee, per week,
month, year, for training, andthen build that into the model?
Measuring what happens probablytells you and I'd say 99.9
percent of times what you don'twant to know, that there's not
enough of it happening or noneof it.
So you've got to turn, turn iton its head for that one and say
(12:41):
, well, so how do we create thefunding to give the time and
then locally, how do people useand plan that time effectively?
Sometimes things like emails,they're tricky.
You know how many come in.
An email response could be aline, it could be ten lines.
So again, efficiency study andlooking at it proportionally
rather than in absolute decimalminutes, they're probably there.
(13:04):
The two, those admin bits andcertainly trainings are
recurring conversation of canyou measure training?
Speaker 2 (13:10):
Yeah, and I suppose actually with the range of
techniques that we've got, wecan measure everything from
things that take Small fractionsof seconds through to kind of
as long as you want to go.
I guess some of the things thatyou pass and you know even
production lines and that sortof thing, there's really easy
ways video in and then lookingat those.
(13:30):
So I think most things, unlessit was something that you know
Happened over a year orsomething like that, like you
say, low frequency things thatdon't happen very often over a
really prolonged period of timeThen they can be trickier to do.
Speaker 1 (13:42):
Yeah, I think there's some other tricky bits around
processes that.
So if you think of a salesprocess, sometimes you might
speak to the customer and I'mtalking in a in a high-end and
furniture world.
You might speak to the customerin January, they might then
decide to buy it in April andthey might then have delivery in
August.
So there's some things whereyou're not going to see
(14:05):
end-to-end of the same customerbut you can see representative
the end-to-end of the componentparts of the process because you
you would be there physicallytoo long.
Speaker 2 (14:14):
Yes, if you see every step of the process, you can
then put those bits together,even if it wasn't one single
custom.
Speaker 1 (14:19):
Yeah, and you want to see that a number of times, so
you get a nice average.
Yeah.
And back to your point.
You know things like pizzamaking, car production, that
whole MTM world then comes intoplay, that we've talked about
another podcast around Videobreaking down those human
movements to move the kind oftime.
These three I'm sure there'splenty more.
(14:40):
I think those are the key ones.
Maybe we do another one ofthese Early in 2024, but those
are the key ones.
Hope you find that helpful andthanks again, sue, for your time
.
Speaker 2 (14:51):
Thanks bye.
Welcome to the Productivity Podcast.
I'm back with Sue and we'regoing to do some Productivity
Insights and on this episodewe're going to focus on
frequently asked questions.
So lots of questions we getasked by new clients, common
clients, so we'll work through afew of them and explore where
we get through.
Do you want to kick off, susan?
Speaker 2 (00:24):
Yes, so the first question that we get asked a lot
is do people change theirbehaviours when they're being
studied?
Speaker 1 (00:31):
I think the answer is potentially for the first hour
or so, but I also think that byusing pace rating, that's
normalised out of the data.
So I think we've talked aboutpace rating before on a
different episode, but do youwant to give people a reminder
of what pace rating is?
Speaker 2 (00:50):
Pace rating is where analysts are trained to be able
to assess the effectiveness thatsomebody is working at versus a
British standard where theBritish standard is 100.
And kind of we call it pacerating.
But it's more than that.
It's also about how effectivethey're being.
So, for example, if I'm walkingat a good pace but I'm carrying
(01:12):
something and spilling a loadof it, then I'd be downrated
because from an effective pointof view I might be going quickly
but I'm not doing a good job ofit.
So pace is then used tonormalise things.
So I think sometimes people haveconcerns that.
Do people perhaps slow downwhen they're being timed to make
things look like they takelonger?
Well, again, the pace ratingwould pick that up, because if
(01:35):
somebody's working at 80, thenactually when we then do the
analysis, it's then normalisedback as if it was 100.
The only people that youperhaps wouldn't want to study
in that way would be if they'regoing slower because they're new
in the training.
So really we should only bestudying qualified operatives,
so that people that arecompetent at the role.
(01:55):
So if you've got somebodythat's brand new and in training
and they're slow because ofthat, then they aren't a good
subject to be studying.
Speaker 1 (02:03):
So that answers the question kind of what if you go
slow, speed up, which is goodOne's, I kind of get.
So how many stores, locationsshould I study?
What's the sample size?
Speaker 2 (02:17):
It's always a tricky one, isn't it?
Because it depends.
So the more variability thereis in whatever you're measuring,
then the bigger sample size youneed.
So if I've got 10 restaurantsbut each of them is a different
format, I'll need to spendlonger in each one than if they
(02:38):
were all the same.
So it can be variability interms of kind of the outlet type
that you're measuring.
It can also be variability inthe processes.
So if a process is always thesame, so a production line might
be kind of an obvious examplewhere it's a standard one, then
(02:58):
you don't need to measure itvery often because it's always
the same.
But things that are morevariable so it might be
different menu items in arestaurant, anywhere where
people and conversations areinvolved, always has a lot of
variability in it.
So, for example, customers in ashop they might be chatty, they
might be, not all that sort ofthing.
So it depends on thevariability.
(03:19):
I think often we say it kind ofin a smaller number of outlets
you might be looking to do 10%of the estate, but obviously if
you've got 2,000 shops, then youwouldn't be looking to do 10%.
So it varies.
It's not just the number ofstores, it's also how many days
you spend.
Speaker 1 (03:40):
There's no magic formula.
I don't think it's on a case bycase basis, along with, as you
say, that bit around thevariability is a key bit, and I
think that that's probably acasing point around the number
of days that you say.
So there's some organisationsout there that are kind of
(04:00):
hooked on there, measuring allthe time and measuring
everything.
We're not massive advocates ofthat.
I think every time you measureit changes the number.
So you've then got a biggerdata set, clearly more robust
data, but you've got to explainthe variance.
So kind of leads me on to howoften should you re-measure?
Speaker 2 (04:19):
Again, it's that it depends question.
So if you're in a business thatdoesn't change at all, why
would you bother re-measuring?
But the reality is thatbusinesses do change all the
time.
So if you assume thatsomebody's done a sort of a big,
wholesale measure of most ofthe processes, you can then just
(04:40):
follow it up.
So if you change one part ofyour operation, you can just
follow it up by measuring justthat one part, and that can be a
very good way to see how changeis working and identifying
other ways to optimise it.
Generally, most people wouldwant to rebase their numbers, a
maximum of kind of three to fouryears, because things do just
(05:04):
change.
Customers and people change, ifnothing else.
Speaker 1 (05:07):
Yeah, and if you think what happened pre and post
pandemic, there's a massivechange.
There wasn't there sointeresting.
So more than just times again Ispeak to people and they say I
got handed this spreadsheet or Igot given this pie chart.
That's great.
There must be more to it.
And my answer is alwaysabsolutely there is.
So just talk to us a bit aboutinsight around some of the
(05:30):
studies.
Speaker 2 (05:33):
So we always like to do more than just give over the
data to people.
So, yes, people can have theraw data and go through it as
kind of anybody else wouldprovide to them.
What we then try to do is thensay, well, from that data, this
is what it's telling you, thisis why this is the data that
supports that, and, as a resultof that, here are the quantified
(05:55):
opportunities of things thatyou could do, and here are some
ideas that you might like tolook at.
So we'll always try and take itfurther.
Partly.
There's lots of richness in thedata, so there's the different
study types, but all of themhave got a degree of richness in
that we can get to partlythrough benchmarking, because
we've got some great data setsthat we can benchmark against,
(06:16):
but we also like to combine itwith the observations that our
analysts make on site.
You know they're all trainedobservers and they spot things
that perhaps you wouldn't showup on the data.
Speaker 1 (06:26):
And I think it's like kind of some of the data that
you may get presented.
It's like having the book inthe chapters but then no words
in each chapter.
The insight gives you therichness and all the detail
underneath.
Speaker 2 (06:37):
Yeah, unless it's made actionable for you, then
actually it's always quite achallenge and although we deal
with this sort of data all thetime, the majority of people
don't.
You know it's something that'sdifferent in you, and anything
that we can do to help peopleget the most out of it is a
positive thing.
Speaker 1 (06:56):
Brilliant One that's cropping up more and more, I
think, is people are strugglingwith economics, shrink wage
inflation.
Why do I need a workload model?
Why do I need to know how longthings take and then build from
the bottom up to kind ofsuppress from the top down to
meet the financial demands?
Why shouldn't I just go back toCosta Cell?
Speaker 2 (07:19):
Costa Cell is just such a blunt tool, isn't it?
For one time in my career I wasrunning shops that were low
productivity and it was in atough economic area, so my
average basket sale was prettylow, so the average transaction
value was low.
I'd got colleagues that werekind of in much more affluent
(07:40):
areas, that people would buymore expensive items and we'd
still have to put the samenumber of items to the shelf.
We'd have to serve the samenumber of customers through the
till, but actually the value ofthe sales that I was getting
were lower than what some of mycolleagues would be in more well
off areas.
(08:01):
So that's a good example of whyit needs to be different,
because if you just weren't witha cost to sell, then my stores
would have been under resourcedand potentially their stores
would have been over resourced.
Speaker 1 (08:12):
Well, I think again in the current economic climate
it's an interesting debatebecause there's a divergence of
cost and volume.
So I could be, if I take amillion, if I sell a million
things for a pound or one thingfor a million pounds, in a cost
to sell scenario it's the same,but actually I've got a million
(08:33):
times more workload in one thantwo.
But ultimately at the moment,with price inflation, prices are
going up, so my sales are goingup through nothing I'm doing
but volumes probably dropping.
So actually, again in a cost tosell world, you're masking the
true impact of work needed.
So the reality, probably formost people at the moment, is
(08:54):
sales are higher but there'sless work that needs doing, so I
therefore need less budget.
It can't be intuitive, I get,but actually the price increase
isn't volume, it's item price.
That's going through Any otherquestions you can think of that.
People are often asking youwhen you're presenting about
data or in conversation.
Speaker 2 (09:17):
Perhaps one thing is about how.
What's the best way to engagetheir teams when they're doing
these things?
Because obviously, what we doisn't secret.
There's a person that turns upand is observing processes
happening, so making sure thatworks well by having teams that
are expecting us know what'shappening, know there aren't any
secrets, is usually the bestway to go.
Speaker 1 (09:41):
Yeah, and it's a balance, isn't it?
Because sometimes theinitiative is around cost-saving
, which is sensitive because asconsequences, clearly depending
on the results, sometimes it'saround just actually
understanding what's happeningin that business and then making
decisions off the back of thedata.
Sometimes it's about puttingmore people in front of
customers.
Sometimes it's a mix of allthree.
(10:02):
So treading carefully isimportant, but I think what I've
seen, being as transparent asyou can be at the initial
briefing, is also reallyimportant.
Speaker 2 (10:12):
Yeah, I think there's .
There's sort of three stepsthat we'd say is the best
practice.
So one is, if there's a phonecall, then so a phone call with
perhaps the line managers andthat sort of people, so they've
got a chance to ask anyquestions, that's led by the
(10:33):
central team, so it's their ownpeople, and then we're there to
answer any questions.
That they've got is a great wayto do it.
Follow that up with somewritten comms, because not
everybody's going to get to thatbrief, so follow it up with
some written comms.
That again sex out.
We're interesting people, notprocessors.
We don't capture people's names.
It's not secret.
We'll happily show you thetablet.
(10:53):
We want to know your thoughts,that's.
That's a good way to do it.
And then when the analystarrives, location to start
studying.
If there's a team huddle orteam briefing, it's great if our
analysts can join that sayhello to everybody and again, it
just reassures everybody.
So you know, everybody knowswhat's happening and why.
Speaker 1 (11:14):
Yeah, and the benefits of getting information
from colleagues.
So we're independent.
They can share their gripes ortheir golden nuggets with us and
we can build that into the deckas well.
Speaker 2 (11:26):
Yeah and then a final question for you is are the
things that we can't measure?
So, is there anything that youcan't measure?
Speaker 1 (11:35):
I think practically you can measure anything that
happens from a.
Can I go and watch somebodydoing it?
I think there's a couple ofthings that always stand out one
frequency.
So you could spend a lot oftime trying to capture something
that doesn't happen very oftenor is weekly or monthly, or
Dunning the dead of night,whatever.
And I think there's also abunch of stuff, and training is
(11:56):
always the one that is aconversation.
Can you measure trainingAbsolutely?
How much did you see in thestudy?
None, yeah.
So why is that?
Well, because people are busyand it's one of the first things
that's just dropped or he'sdone it Home, or just not
happening or batched up.
And I think training is a greatexample of from a workload model
point of view.
(12:17):
It's a policy decision.
So what do you want to fund perhead, per employee, per week,
month, year, for training, andthen build that into the model?
Measuring what happens probablytells you and I'd say 99.9
percent of times what you don'twant to know, that there's not
enough of it happening or noneof it.
So you've got to turn, turn iton its head for that one and say
(12:41):
, well, so how do we create thefunding to give the time and
then locally, how do people useand plan that time effectively?
Sometimes things like emails,they're tricky.
You know how many come in.
An email response could be aline, it could be ten lines.
So again, efficiency study andlooking at it proportionally
rather than in absolute decimalminutes, they're probably there.
(13:04):
The two, those admin bits andcertainly trainings are
recurring conversation of canyou measure training?
Speaker 2 (13:10):
Yeah, and I suppose actually with the range of
techniques that we've got, wecan measure everything from
things that take Small fractionsof seconds through to kind of
as long as you want to go.
I guess some of the things thatyou pass and you know even
production lines and that sortof thing, there's really easy
ways video in and then lookingat those.
(13:30):
So I think most things, unlessit was something that you know
Happened over a year orsomething like that, like you
say, low frequency things thatdon't happen very often over a
really prolonged period of timeThen they can be trickier to do.
Speaker 1 (13:42):
Yeah, I think there's some other tricky bits around
processes that.
So if you think of a salesprocess, sometimes you might
speak to the customer and I'mtalking in a in a high-end and
furniture world.
You might speak to the customerin January, they might then
decide to buy it in April andthey might then have delivery in
August.
So there's some things whereyou're not going to see
(14:05):
end-to-end of the same customerbut you can see representative
the end-to-end of the componentparts of the process because you
you would be there physicallytoo long.
Speaker 2 (14:14):
Yes, if you see every step of the process, you can
then put those bits together,even if it wasn't one single
custom.
Speaker 1 (14:19):
Yeah, and you want to see that a number of times, so
you get a nice average.
Yeah.
And back to your point.
You know things like pizzamaking, car production, that
whole MTM world then comes intoplay, that we've talked about
another podcast around Videobreaking down those human
movements to move the kind oftime.
These three I'm sure there'splenty more.
(14:40):
I think those are the key ones.
Maybe we do another one ofthese Early in 2024, but those
are the key ones.
Hope you find that helpful andthanks again, sue, for your time
.
Speaker 2 (14:51):
Thanks bye.
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