ReThink Productivity Podcast

Productivity insights with Simon & Sue - FAQ

December 03, 2023 Season 12 Episode 9
ReThink Productivity Podcast
Productivity insights with Simon & Sue - FAQ
Show Notes Transcript Chapter Markers

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

  • The most frequently asked questions from clients

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Speaker 1:

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:

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

Speaker 1:

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:

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. 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:

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:

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. 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:

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:

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:

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:

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:

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:

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:

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:

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:

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:

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:

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:

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:

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:

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:

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. 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:

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.

Speaker 1:

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:

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:

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:

Thanks bye.

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