Productivity insights with Simon & Sue - FAQ

Sue & Simon talk about the latest productivity trends. They discuss:

• The most frequently asked questions from clients

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Transcript

Productivity Insights and Frequently Asked Questions

Speaker 1 0:00

Welcome to the Productivity Podcast . I'm back with Sue and we're going to do some Productivity Insights and on this episode we're going to focus on frequently asked questions . So lots of questions we get asked by new clients , common clients , so we'll work through a few of them and explore where we get through . Do you want to kick off , susan ?

Speaker 2 0:24

Yes , so the first question that we get asked a lot is do people change their behaviours when they're being studied ?

Speaker 1 0:31

I think the answer is potentially for the first hour or so , but I also think that by using pace rating , that's normalised out of the data . So I think we've talked about pace rating before on a different episode , but do you want to give people a reminder of what pace rating is ?

Speaker 2 0:50

Pace rating is where analysts are trained to be able to assess the effectiveness that somebody is working at versus a British standard where the British standard is 100 . And kind of we call it pace rating . But it's more than that . It's also about how effective they're being . So , for example , if I'm walking at a good pace but I'm carrying something and spilling a load of it , then I'd be downrated because from an effective point of view I might be going quickly but I'm not doing a good job of it . So pace is then used to normalise things .

Speaker 2 1:24

So I think sometimes people have concerns that . Do people perhaps slow down when they're being timed to make things look like they take longer ? Well , again , the pace rating would pick that up , because if somebody's working at 80 , then actually when we then do the analysis , it's then normalised back as if it was 100 . The only people that you perhaps wouldn't want to study in that way would be if they're going slower because they're new in the training . So really we should only be studying qualified operatives , so that people that are competent at the role . So if you've got somebody that's brand new and in training and they're slow because of that , then they aren't a good subject to be studying .

Speaker 1 2:03

So that answers the question kind of what if you go slow , speed up , which is good One's , I kind of get . So how many stores , locations should I study ? What's the sample size ?

Speaker 2 2:17

It's always a tricky one , isn't it ? Because it depends . So the more variability there is in whatever you're measuring , then the bigger sample size you need . So if I've got 10 restaurants but each of them is a different format , I'll need to spend longer in each one than if they were all the same . So it can be variability in terms of kind of the outlet type that you're measuring . It can also be variability in the processes .

Speaker 2 2:50

So if a process is always the same , so a production line might be kind of an obvious example where it's a standard one , then you don't need to measure it very often because it's always the same . But things that are more variable so it might be different menu items in a restaurant , anywhere where people and conversations are involved , always has a lot of variability in it . So , for example , customers in a shop they might be chatty , they might be , not all that sort of thing . So it depends on the variability . I think often we say it kind of in 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 you wouldn't be looking to do 10% . So it varies . It's not just the number of stores , it's also how many days you spend .

Speaker 1 3:40

There's no magic formula . I don't think it's on a case by case basis , along with , as you say , that bit around the variability is a key bit , and I think that that's probably a casing point around the number of days that you say . So there's some organisations out there that are kind of hooked on there , measuring all the time and measuring everything . We're not massive advocates of that . I think every time you measure it changes the number . So you've then got a bigger data set , clearly more robust data , but you've got to explain the variance . So kind of leads me on to how often should you re-measure ?

Speaker 2 4:19

Again , it's that it depends question . So if you're in a business that doesn't change at all , why would you bother re-measuring ? But the reality is that businesses do change all the time . So if you assume that somebody's done a sort of a big , wholesale measure of most of the processes , you can then just follow it up . So if you change one part of your operation , you can just follow it up by measuring just that one part , and that can be a very good way to see how change is working and identifying other ways to optimise it . Generally , most people would want to rebase their numbers , a maximum of kind of three to four years , because things do just change . Customers and people change , if nothing else .

Speaker 1 5:07

Yeah , and if you think what happened pre and post pandemic , there's a massive change . There wasn't there so interesting . So more than just times again I speak to people and they say I got handed this spreadsheet or I got given this pie chart . That's great . There must be more to it . And my answer is always absolutely there is . So just talk to us a bit about insight around some of the studies .

Speaker 2 5:33

So we always like to do more than just give over the data to people . So , yes , people can have the raw data and go through it as kind of anybody else would provide to them . What we then try to do is then say , well , from that data , this is what it's telling you , this is why this is the data that supports that , and , as a result of that , here are the quantified opportunities of things that you could do , and here are some ideas that you might like to look at . So we'll always try and take it further . Partly . There's lots of richness in the data , so there's the different study types , but all of them have got a degree of richness in that we can get to partly through benchmarking , because we've got some great data sets that we can benchmark against , but we also like to combine it with the observations that our analysts make on site . You know they're all trained observers and they spot things that perhaps you wouldn't show up on the data .

Speaker 1 6:26

And I think it's like kind of some of the data that you may get presented . It's like having the book in the chapters but then no words in each chapter . The insight gives you the richness and all the detail underneath .

Speaker 2 6:37

Yeah , unless it's made actionable for you , then actually it's always quite a challenge and although we deal with this sort of data all the time , the majority of people don't . You know it's something that's different in you , and anything that we can do to help people get the most out of it is a positive thing .

Speaker 1 6:56

Brilliant One that's cropping up more and more , I think , is people are struggling with economics , shrink wage inflation . Why do I need a workload model ? Why do I need to know how long things take and then build from the bottom up to kind of suppress from the top down to meet the financial demands ? Why shouldn't I just go back to Costa Cell ?

Speaker 2 7:19

Costa Cell is just such a blunt tool , isn't it ? For one time in my career I was running shops that were low productivity and it was in a tough economic area , so my average basket sale was pretty low , so the average transaction value was low . I'd got colleagues that were kind of in much more affluent areas , that people would buy more expensive items and we'd still have to put the same number of items to the shelf . We'd have to serve the same number of customers through the till , but actually the value of the sales that I was getting were lower than what some of my colleagues would be in more well off areas . So that's a good example of why it needs to be different , because if you just weren't with a cost to sell , then my stores would have been under resourced and potentially their stores would have been over resourced .

Speaker 1 8:12

Well , I think again in the current economic climate it's an interesting debate because there's a divergence of cost and volume . So I could be , if I take a million , if I sell a million things for a pound or one thing for a million pounds , in a cost to sell scenario it's the same , but actually I've got a million times more workload in one than two . But ultimately at the moment , with price inflation , prices are going up , so my sales are going up through nothing I'm doing but volumes probably dropping . So actually , again in a cost to sell world , you're masking the true impact of work needed . So the reality , probably for most people at the moment , is sales are higher but there's less 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 other questions you can think of that . People are often asking you when you're presenting about data or in conversation .

Speaker 2 9:17

Perhaps one thing is about how . What's the best way to engage their teams when they're doing these things ? Because obviously , what we do isn't secret . There's a person that turns up and is observing processes happening , so making sure that works well by having teams that are expecting us know what's happening , know there aren't any secrets , is usually the best way to go .

Speaker 1 9:41

Yeah , and it's a balance , isn't it ? Because sometimes the initiative is around cost-saving , which is sensitive because as consequences , clearly depending on the results , sometimes it's around just actually understanding what's happening in that business and then making decisions off the back of the data . Sometimes it's about putting more people in front of customers . Sometimes it's a mix of all three . So treading carefully is important , but I think what I've seen , being as transparent as you can be at the initial briefing , is also really important .

Speaker 2 10:12

Yeah , I think there's . There's sort of three steps that we'd say is the best practice . So one is , if there's a phone call , then so a phone call with perhaps the line managers and that sort of people , so they've got a chance to ask any questions , that's led by the central team , so it's their own people , and then we're there to answer any questions . That they've got is a great way to do it . Follow that up with some written comms , because not everybody's going to get to that brief , so follow it up with some written comms . That again sex out . We're interesting people , not processors . We don't capture people's names . It's not secret . We'll happily show you the tablet . We want to know your thoughts , that's . That's a good way to do it . And then when the analyst arrives , location to start studying . If there's a team huddle or team briefing , it's great if our analysts can join that say hello to everybody and again , it just reassures everybody . So you know , everybody knows what'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 or their golden nuggets with us and we can build that into the deck as 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 you can't measure ?

Speaker 1 11:35

I think practically you can measure anything that happens from a . Can I go and watch somebody doing it ? I think there's a couple of things that always stand out one frequency . So you could spend a lot of time trying to capture something that doesn't happen very often or is weekly or monthly , or Dunning the dead of night , whatever . And I think there's also a bunch of stuff , and training is always the one that is a conversation . Can you measure training Absolutely ? How much did you see in the study ? None , yeah . So why is that ? Well , because people are busy and it's one of the first things that's just dropped or he's done it Home , or just not happening or batched up .

Speaker 1 12:13

And I think training is a great example of from a workload model point of view . It's a policy decision . So what do you want to fund per head , per employee , per week , month , year , for training , and then build that into the model ? Measuring what happens probably tells you and I'd say 99.9 percent of times what you don't want to know , that there's not enough of it happening or none of it . So you've got to turn , turn it on its head for that one and say , well , so how do we create the funding to give the time and then locally , how do people use and plan that time effectively ? Sometimes things like emails , they're tricky . You know how many come in . An email response could be a line , it could be ten lines . So again , efficiency study and looking at it proportionally rather than in absolute decimal minutes , they're probably there . The two , those admin bits and certainly trainings are recurring conversation of can you measure training ?

Speaker 2 13:10

Yeah , and I suppose actually with the range of techniques that we've got , we can measure everything from things that take Small fractions of seconds through to kind of as long as you want to go . I guess some of the things that you pass and you know even production lines and that sort of thing , there's really easy ways video in and then looking at those . So I think most things , unless it was something that you know Happened over a year or something like that , like you say , low frequency things that don't happen very often over a really prolonged period of time Then they can be trickier to do

Key Components of Sales Processes

Speaker 2 13:42

.

Speaker 1 13:42

Yeah , I think there's some other tricky bits around processes that . So if you think of a sales process , sometimes you might speak to the customer and I'm talking in a in a high-end and furniture world . You might speak to the customer in January , they might then decide to buy it in April and they might then have delivery in August . So there's some things where you're not going to see end-to-end of the same customer but you can see representative the end-to-end of the component parts of the process because you you would be there physically too 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 pizza making , car production , that whole MTM world then comes into play , that we've talked about another podcast around Video breaking down those human movements to move the kind of time . These three I'm sure there's plenty more . I think those are the key ones . Maybe we do another one of these Early in 2024 , but those are the key ones . Hope you find that helpful and thanks again , sue , for your time .

Speaker 2 14:51

Thanks bye .