SELLEST OSAST
Cutting labour when demand dips can look like smart cost control. But if workers respond by finding more reliable work elsewhere, today’s saving can become tomorrow’s churn, hiring cost and capacity problem.
My guest is James Terry of Indeed Flex, who has spent more than 15 years in staffing and workforce management. We look at what happens when labour planning is treated as an HR exercise rather than an operating decision, and why that can affect fulfilment, throughput, customer service and the bottom line.
We unpack the trade-off between cost, quality and speed, challenge the assumption that workforce consistency requires the same people on every shift, and examine why HR and operations can make contradictory decisions when their data sits in separate systems. We also look at where AI can help connect those signals — and where technology alone cannot fix poor workforce design.
Listen now to understand how better labour planning can reduce expensive firefighting and improve the decisions that keep supply-chain operations moving.
If disruption hit tomorrow, would you know where your supply chain was most exposed? In 15 minutes my free scorecard helps you assess 27 resilience statements, calculate your score, and turn the result into three priorities and a 30 day action plan. You can download the scorecard free at tomraftery.com/scorecard.
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NÄITA MÄRKUSI 🔗
TRANSKRIBEERI 🔗
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And everyone is looking at their data.
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But that data's not integrated.
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And so because of that, everyone's making different decisions, and some of them are right, and some of them are wrong, and some of them are contradictory to each other.
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That disconnect is more expensive than it sounds.
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One of the more counterintuitive points in this conversation is that sending people home to save money today can actually make your workforce less reliable next week.
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Good morning, good afternoon, or good evening where everyone in the world.
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Welcome to episode 141 of Resilient Supply Chain Stories and Strategies that Keep Business Moving.
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I'm your host, Tom Raftery.
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My guest today is James Terry from Indeed Flex, who spent more than 15 years in staffing and workforce management.
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We get into labour volatility, flexibility, data, and where AI can genuinely help.
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For supply chain and operations leaders, the big takeaway is this, workforce planning isn't just an HR issue, it's an operating model decision.
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Let's dive in.
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James, welcome to the podcast.
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Would you like to introduce yourself?
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Thanks so much for having me, Tom.
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I am James Terry.
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I am lucky enough to be able to help people get jobs every day.
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I work for Indeed Flex.
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We are in the contingent labour industry and we use technology ultimately to be able to drive better outcomes for both job seekers as well as employers to find the right talent really at speed and, and hopefully allowing employers to be able to find people, quality people quickly that can drive more consistency and stronger output within their organisation, especially within the supply chain space.
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And who is it that you're doing this work for in the supply chain space, and what problems are you helping solve for them?
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Yeah, I mean, pretty much anything from warehouse distribution, last mile production.
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And so, it's really anyone who needs any type of labour, especially when it comes to peaks and troughs in demand looking to potentially outsource some of their recruitment efforts to other organisations.
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You spent over 15 years in the staffing and workspace area.
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What do you think has changed most in how companies think about temporary labour in that time?
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Yeah, there's definitely been a, a big, evolution over the course of the past decade or so, and I'd say even more protracted or more, acute actually in, in the last probably four or five years.
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Previously, I would find that a lot of the times you'd have situations where the operational teams would really drive a lot of the workforce staffing initiatives and requirements everything from the skillset requirements all the way down to kind of the scheduling and performance management.
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And I, I've noticed that in the past couple of years, I feel like HR has started to take much more of a proactive role in that and started to play a little bit more of a front seat role in being able to help these organisations operate more efficiently.
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So, whereas again, before it was the operations team who would determine everything, now you're noticing that HR, because they have a lot more access to data insights and information, they're able to go to the operational team and make suggestions as to how these ops teams should probably be running their facilities more efficiently.
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And that's really the information they're getting is from the job seeker, from the labour that's being, procured because, the labour market is, fundamentally changing ever since COVID the great resignation that happened a few years ago inflationary pressures, people looking for more because these inflationary pressures, people looking for, part-time and gig type works to fill in gaps.
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We're noticing that there are a lot of changes and, and that is driving, I think, challenges, a lot of challenges for companies that are in, you know, warehousing, distribution, really the supply chain industry more broadly.
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But for people that are embracing it and trying to be forward thinking, it's actually also posing a lot of opportunities to differentiate yourself to be able to become more of an employer of choice and, and ultimately attract better labour.
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Okay, and let's get into the operational reality space, because that's obviously where this becomes a resilience story.
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Let's talk about warehouses, manufacturers, logistics sites, if they get staffing wrong, where does that pain show up first?
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Is it in cost?
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Is it in throughput?
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Is it in service level?
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Is it in safety?
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Is it in burnout?
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Is it in something else?
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All of the above?
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Yeah, I, I mean, it's, it's probably a little bit of all of them.
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I mean, I'd say the first thing is if you, miss a bunch of times on your labour planning, you're gonna lose your clients, right?
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That's probably the first sight of it.
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For companies that do like third party logistics or, delivery typically when you have big spikes or increased needs for labour that's because one of your clients that you're processing some type of service for, it's a make or break time for them, right?
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They're running a sale or a promotion, or they've just done a huge amount of ad spend and they're getting increased demand.
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And if you're not able to deliver to that, that's not good.
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I mean, if, even if a package is delayed or an order is delayed by, just a day, that can be the difference between retaining or, or losing a customer for a lot of their clients and also the client overall more broadly.
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So I'd say that's the first thing, and obviously there's financial impacts to that.
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But the other side of it is, poor labour planning or lack of labour planning can also have a lot of downstream impacts as it comes to kind of in the short term and also in the knock on effects.
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So, what I mean by that is that if you have, let's, let's play out this example where you think you're gonna have a huge amount of demand, or you think you're gonna have a steady amount of demand, and then all of a sudden you notice that it's not coming in right?
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Like, for some reason the, the orders haven't come through.
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And so we don't have that type of demand.
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And so conventional wisdom is well, if we don't have the need for the labour, then let's send the labour home.
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And so you'll have situations where, as an example, you're sending the labour home early.
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Well, that's because you're trying to minimise cost, right?
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Like, if I have too many of a resource, I, don't utilise as much of the resource and then I don't have to pay as much cost.
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Surface level, that makes a lot of sense, right?
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That makes a lot of sense.
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But in reality, what ends up happening is when you do that, when you send that labour home early, especially in call it middle to lower pay rolls in warehouses, forklift drivers, picker packers material handlers, so on and so forth, if those people are relying on a certain type of consistency of their employment and they get sent home early, what are they gonna do?
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They're gonna go find another job.
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And so what ends up happening is that you saved money in the short term.
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That's great.
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You saved money today or tomorrow when you had that lower demand.
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Then what happens is that two weeks later or a week later, or even the next day, all of a sudden you're noticing that you're having a lot of people not showing up and there's a higher churn.
00:07:05.182 --> 00:07:06.322
And so then what do you have to do?
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You have to then go and hire a bunch more people, onboard them, upskill them, train them, weed out the people that maybe are not as good.
00:07:14.692 --> 00:07:24.233
And so you are, I would say like penny wise and pound foolish, where to be able to lower cost in the very short term, that's fine with sending some people home.
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And so it's unbelievably critical to do your labour planning effectively, but also from there it's like, okay, what decisions are we making on a daily basis?
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And what is gonna be the, not just the today's impact, but also the medium and longer term impact to an organisation to make sure that they're not cutting off their nose to spite their face.
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That's the resilience tension in one example.
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A decision that saves money today can make the workforce less reliable next week.
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So is the bigger staffing mistake filling shifts quickly rather than filling them well?
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That really depends.
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I'd say that one of the things that we really try to understand from our clients are what are their priorities?
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The way I see it in staffing, there's, three things that you want.
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There's three key areas that are really important to organisations when they're staffing low cost, quality workers, and speed.
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But the problem is, you can't have all three at the same time.
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You need to make a sacrifice.
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You can usually have two of the three, but you can't have all three.
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You can't have the best workers at speed.
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You can't have, the highest quality workers at a low cost, right?
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And so you need to be willing to make these trade offs.
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And so it, it actually depends on what are the priorities?
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What is the role that you're asking someone to do, and, what are the priorities of the overall business?
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And then that allows us to work with clients to make tweaks and changes to the way that they're doing their labour planning.
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If key for them is speed and lowering their costs, then you might need to make a sacrifice on quality.
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So in that case, hey, maybe we're doing more ad hoc gig type labour where you can bring a lot of people in for a short period of time and you can move at speed.
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If you're looking for a higher quality worker and you're also looking for that at speed, you're, you're gonna have a higher cost, right?
00:09:10.572 --> 00:09:17.740
If you're looking at, for, at, at a lower cost, you need to be willing to bring people in and then train them and upskill them to be able to get them to that point.
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And so, you really need to have that understanding of what are the, organisational goals of that, business?
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And then what are the site level goals that are really important to them?
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And, and, and by the way, Tom, sometimes those things can change, maybe during peak.
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It's gonna be different than during their steady state business throughout the rest of the year.
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But really understanding, and, and I would say for all of your listeners really thinking through like of those three things, like how do I prioritise those, and then how does that priority actually play itself out on the day to day and the way that I am leveraging my staffing and my, and, and, and engaging with my permanent workforce.
00:09:51.535 --> 00:10:03.568
I remember years ago when I was starting out in the technology space, someone telling me that when it comes to printers you can have quality, speed, or price, any two of those, but never all three.
00:10:03.568 --> 00:10:05.759
So it's very similar to what you're saying just now.
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Yeah.
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Well, I'll tell you it wasn't my idea but when I heard it the first time, I, it's, it's, it's hard to argue.
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I mean, it really does make sense.
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and it, it, it obviously goes beyond those three as well.
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What, what makes someone the right match for a shift beyond those kind of three things we were talking about?
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Are, are we talking other signals like skills, distance, reliability, ratings, preferences, prior performance, something else entirely?
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Yep.
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All of the above again?
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Yeah, so, so that's where you get into, I think.
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one of the things that has changed a lot in the last, probably five to seven years within the staffing industry.
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Back in the day, let's call it.
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and quite honestly, with a lot of what I would call more traditional staffing agencies, what happens is candidate walks into a branch, they sit down at the desk, they get recruited, they give their profile.
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what ends up happening more often than not is the candidate is placed, they're told here is where you're gonna go and you're gonna turn up on Monday or Tuesday, or whenever it might be to go and do this job.
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They're not given the choice, they're not given the control, they're not given the opportunity to make the decision.
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And what ends up happening there is that sometimes you have matches that aren't quality.
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You sometimes don't get it right.
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The recruiter doesn't always get it right.
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And I'd like to say that all the recruiters are thinking really about the best thing for the worker and the client.
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But there's this middleman, right?
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Fundamentally there's a staffing agency that is doing the placement of these people.
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And, and a lot of the times you could have misaligned incentives in that.
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What has changed in the last five or six years is the concept of a marketplace.
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And a marketplace is really the idea that if you have a lot of workers and a lot of clients in a certain area, then you can drive stronger match quality in that marketplace.
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So what that means is that as workers come in and they're recruited and skill sets are assigned to these workers through a technology, in ours, it's Indeed Flex technology.
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We assign these skill sets and then these workers can get these roles.
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They can pick up these roles that align with the skill sets that the client needs.
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Then as workers start to progress throughout their tenure and pick up more work, whether that's a consistent work pattern with one client, whether that's just picking up individual gig, call it, or ad hoc placements, or whether that's some combination there.
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And, hey, I work, the morning shift at this factory every single day.
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And then, on Saturdays I pour beer at the local stadium working for the same company, right?
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For the same, the same app.
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Indeed Flex in this case, what you're able to do is you're able to build a essentially a verified resume for the worker.
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So then what happens is we have a huge amount of data.
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We know not just how many times they've worked, but how has the client rated them?
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You're able to rate the workers on a five star scale.
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How often do they cancel their shifts and they don't show up?
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Or how often do they show up late?
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So we have a number of detractors and then additive components to our rating system.
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So then that every time a client posts a role, what we do is we stack the deck in our favour quite honestly, Tom.
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We will give the best workers the first opportunity to pick up these roles.
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And so what ends up happening is these clients are able to get people that, oh, wow, they've worked for three or four of my competitors down the street and done a great job and been a five star worker.
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I can bring them on and I can hire them.
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I can bring them on without having to do an interview or without having to worry about it.
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And so we start to play this out and really our goal at Indeed Flex is how can we over the next few years be able to completely remove the resume?
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That's kind of crazy to think about, but if you actually think about fundamentally, what is a resume, Tom?
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It's a piece of paper that you write a bunch of stuff on and it tells your experience and your history.
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And so as, as we continue to build out our product, our goal is how can we live in a world where I don't need to do a resume, I don't need to see a resume, I don't need to even maybe interview the candidate because they've done this job, the same job for a competitor or someone down the street or whatever it might be.
00:14:06.364 --> 00:14:11.344
And they've been rated a five star worker and they've done hundreds and hundreds of shifts through our technology.
00:14:11.344 --> 00:14:15.423
So that information problem fundamentally is gone.
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I don't have to worry about the quality of the worker because the marketplace has done all the work for me.
00:14:20.703 --> 00:14:23.464
That's where it really gets interesting and you get into a really fun flywheel effect.
00:14:24.520 --> 00:14:28.480
And then how do you balance what the employer needs with what the worker actually wants?
00:14:59.326 --> 00:15:03.386
Yeah, so that that goes, I think back a little bit into what we've seen, in the last few years.
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You can go on your phone right now and order just about anything that exists in the world to be delivered to you within two hours.
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That's staggering to think that you couldn't do that six or seven or 10 years ago.
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But now you can.
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And, and I mention that because those consumers, those people that are doing that are also your job seekers.
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And so people, job seekers, both because of those types of new opportunities that they have, as well as quite frankly, generational changes.
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Gen Z entering the workforce, millennials becoming leaders in a lot of these organisations now.
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Because of that, people are demanding more flexibility.
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Job seekers are demanding more flexibility.
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Hey, I can order just about anything that I want, but in my work life, I am set on this really rigid schedule and structure.
00:15:53.254 --> 00:15:54.423
Like that doesn't work for me.
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That's not what I want.
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Or maybe, maybe I live in a world where I have to take, my mom or grandma to the doctors every Wednesday.
00:16:02.073 --> 00:16:05.403
And so I can't work that nine to five Monday through Friday schedule.
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I need to be able to have more flexibility.
00:16:08.043 --> 00:16:24.223
And so a lot of the times we're having conversations with clients and helping them to understand that, you know, while the operational team has determined that this particular shift, pattern schedule way of working requirements, whatever it might be, works the best for them.
00:16:24.524 --> 00:16:26.413
It doesn't always work the best for the labour.
00:16:26.923 --> 00:16:45.300
And so that's where you really need to be able to have some critical conversations, look at the data and make some decisions around, Hey, are there, compromises that we might be willing to make with regards to the way that we consume this labour in a way that's gonna be able to ultimately open up the aperture on the availability of quality labour?
00:16:45.630 --> 00:16:53.971
So this, guy or gal who can't work on Wednesday afternoons, that doesn't mean that they're not a good employee, it just means that they have a limitation.
00:16:54.331 --> 00:16:56.971
Well, previously that would mean that they would either do one of two things.
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They would either start working for you and then not show up on a bunch of Wednesdays, or have to leave early, and they would get a bunch of attendance points or whatever and end up being churned.
00:17:05.844 --> 00:17:09.744
Or they probably wouldn't have even applied for the job in the first place.
00:17:10.044 --> 00:17:14.784
And so you didn't even have access to that candidate as an opportunity, but they could be a great worker.
00:17:15.933 --> 00:17:19.264
At the end of the day, people say, well, I need consistency.
00:17:19.834 --> 00:17:21.423
I need consistency of my labour force.
00:17:21.453 --> 00:17:26.171
And that's valid, and that's true, and I completely understand and frankly agree a hundred percent with that.
00:17:26.891 --> 00:17:28.480
But what does consistency mean?
00:17:28.721 --> 00:17:31.661
Consistency doesn't mean that it's James every day, Monday through Friday.
00:17:32.441 --> 00:17:38.711
Consistency really means that both James and Tom are strong workers for this organisation.
00:17:38.801 --> 00:17:45.581
And James might work Monday, Tuesday, Friday, and Tom jumps in for him on Wednesdays and Thursdays.
00:17:45.821 --> 00:17:54.911
I still have the consistency of a worker that knows the business that is effective, that is high quality, that is gonna show up, that is not gonna be late.
00:17:55.451 --> 00:17:56.921
It doesn't matter if it's James or Tom though.
00:17:57.791 --> 00:18:01.151
That's, I think, the fundamental difference that we're trying to educate a lot of our clients on.
00:18:02.196 --> 00:18:03.996
That distinction matters.
00:18:04.806 --> 00:18:08.886
A resilient workforce doesn't have to mean the same people every day.
00:18:09.516 --> 00:18:12.936
It can mean the same capability is reliably available.
00:18:13.740 --> 00:18:17.341
So where does human judgement still beat the algorithm?
00:18:17.778 --> 00:18:23.278
I mean, at the end of the day, we are a technology that helps workers get faster access to work.
00:18:23.278 --> 00:18:25.602
But, our product is not the technology.
00:18:25.602 --> 00:18:26.682
Our product is the labour.
00:18:26.682 --> 00:18:27.882
Our product is the people.
00:18:27.932 --> 00:18:30.961
We are fundamentally a human based business.
00:18:31.012 --> 00:18:35.022
And that's where the feedback loop becomes really critical from clients of, rating workers.
00:18:35.022 --> 00:18:49.272
And so, like the fundamental feedback loop of like, the qualitative feedback loop is really critical and very important because again, this is, this is, we can do our best job of recruiting and placing candidates, but at the end of the day, it's the work that they're doing on the warehouse floor.
00:18:49.272 --> 00:18:53.892
And we need to get that feedback to be able to refine on the offers that we're making out in the future.
00:18:54.989 --> 00:18:59.184
And what metrics tell you whether a flexible staffing model is actually working?
00:19:00.052 --> 00:19:01.252
That's a really good question.
00:19:01.352 --> 00:19:06.005
I would say one of the main things that we look at obviously is there's actually a couple of them.
00:19:06.285 --> 00:19:08.285
There is fulfilment rate, right?
00:19:08.285 --> 00:19:11.339
What is the fulfilment rate for this workforce.
00:19:11.369 --> 00:19:16.469
Industry-wide, the fulfilment in, in the contingent labour industry is anywhere between 75 and 85%.
00:19:17.069 --> 00:19:21.119
So if I need, 10 people on average, you're only gonna get eight that are gonna show up.
00:19:21.548 --> 00:19:23.439
That's really tough to deal with.
00:19:23.798 --> 00:19:35.778
The other side of it that's interesting about how we're able to start to drive a little bit more efficiency in, in this process is actually sounds like it's, contrary to what you're saying.
00:19:35.778 --> 00:19:40.489
So you're talking about flexibility, but one of the things that we also look at is repeat worker rate.
00:19:40.999 --> 00:19:45.009
So a lot of the times in, staffing, you look at what's the turnover rate, right?
00:19:45.219 --> 00:19:46.719
How many people do I have?
00:19:46.719 --> 00:19:48.519
And then how many people leave?
00:19:48.699 --> 00:19:50.820
What's the likelihood of someone leaving?
00:19:51.369 --> 00:20:01.389
When you're looking at flexible staffing though, or some type of model that employs flexible staffing, actually looking at the repeat worker rate is really important because it doesn't matter quite frankly.
00:20:01.389 --> 00:20:07.729
Like if, if, if I'm the consistent person and then Tom, you're the person that, does a little bit of that more ad hoc gig work.
00:20:07.909 --> 00:20:11.689
If you don't work for a week or two, that doesn't mean you're a churned worker, it means you were doing something else.
00:20:11.689 --> 00:20:14.449
But then you can step in and quickly pick up that role again.
00:20:15.019 --> 00:20:22.788
That repetition, that repeat worker shows the consistency shows that hey, Tom has the experience, he's done it before and so he can do it again.
00:20:23.224 --> 00:20:24.859
And that drives a lot of, benefits.
00:20:24.865 --> 00:20:36.185
And so we actually have a lot of clients where, in staffing, typically what happens is you hit a certain number of hours of staffing and then you can convert over to a full-time worker at no cost.
00:20:36.185 --> 00:20:37.775
That's kind of the industry standard.
00:20:38.675 --> 00:20:46.235
We actually have a significant number of workers that say, you know what, I actually don't wanna convert over to full-time.
00:20:47.405 --> 00:20:50.345
Indeed Flex gives me benefits and gives me these opportunities.
00:20:50.345 --> 00:20:57.845
And so, you know what, like, and also I can work four days a week or three days a week or six days a week based on what's going on in my own life.
00:20:58.115 --> 00:20:59.855
And so we have a lot of people that say, you know what?
00:20:59.855 --> 00:21:02.855
I have the opportunity to get this consistency, but I actually don't want that.
00:21:02.855 --> 00:21:04.355
I wanna be able to select my schedule.
00:21:04.355 --> 00:21:05.015
But you know what.
00:21:05.711 --> 00:21:12.431
These people, when you actually press the Enter key, they're still working 30, 35 hours a week on average, but they're just not working on that consistent shift pattern.
00:21:13.235 --> 00:21:13.445
Yeah.
00:21:13.445 --> 00:21:18.125
And what would you say has been harder than expected in changing how companies approach flexible labour?
00:21:20.047 --> 00:21:28.027
Really exactly what, what we're talking about right now, which is that a lot of the time, I mean, these, these concepts are different.
00:21:28.086 --> 00:21:29.647
They're sometimes scary.
00:21:29.707 --> 00:21:35.227
You can present these things and you know the responses, but we've always done it this other way.
00:21:35.747 --> 00:21:37.367
This isn't the way that it works.
00:21:37.677 --> 00:21:41.667
And so you have some apprehension of employing these types of models.
00:21:41.667 --> 00:21:43.527
And so what you have to do is you have to start small.
00:21:44.147 --> 00:21:52.764
You have to really be thoughtful and, really surgical about your approach at the beginning to make sure that you're employing these types of strategies that aren't gonna have major, major impacts.
00:21:52.821 --> 00:21:55.611
You really think about like, what location could we try this out?
00:21:55.611 --> 00:21:57.921
Which has the most variable demand requirements?
00:21:58.371 --> 00:22:02.271
What location has really strong leadership that might be more tech forward, right?
00:22:02.271 --> 00:22:04.761
Because you do need to be willing to rate the workers.
00:22:04.761 --> 00:22:06.561
You need to be able to give us the feedback loop.
00:22:07.031 --> 00:22:10.691
Which locations are we gonna be able to try this out, like on one shift pattern?
00:22:10.691 --> 00:22:14.501
And you just see how it works and build a pool of workers to allow them to flex in and flex out.
00:22:15.071 --> 00:22:20.217
But, Tom, don't get me wrong, I don't think the answer is everyone, working gig shifts for the rest of their life.
00:22:20.217 --> 00:22:21.777
Like there's a balance there, right?
00:22:21.777 --> 00:22:29.584
There's, there's a balance in, having that consistent, 80% of your workforce being consistent, but you always know you're gonna need that 80% and that's fine.
00:22:29.913 --> 00:22:38.014
It's that extra 20% or whatever percentage that is, that flexes up and flexes down where you wanna be able to quickly access those people and access the quality people.
00:22:38.326 --> 00:22:40.787
You know, you're not gonna need them for a very long period of time.
00:22:41.050 --> 00:22:45.017
That's where there becomes a lot of opportunity to leverage these different types of labour programmes.
00:22:45.394 --> 00:22:59.947
Then also the benefit of that is if you're employing it for that 20% of the variability, then when you do have schedule issues with that 80% of the core, you're able to plug it in a lot easier because you've almost, you've got the reserves, right?
00:22:59.947 --> 00:23:04.117
You've got, the people that are waiting on deck to come in, and it's really easy to plug those gaps.
00:23:05.451 --> 00:23:05.921
Okay.
00:23:06.461 --> 00:23:13.556
And, looking ahead, what's going to separate companies that handle labour volatility well from those that keep firefighting?
00:23:15.275 --> 00:23:20.335
It's the companies where you have a really strong partnership between the operational team and the HR team.
00:23:20.665 --> 00:23:28.825
And that's really where you can get a lot of benefits is if, the HR team can take all of the data from a, how easy or hard is it to hire?
00:23:28.945 --> 00:23:30.071
How long does it take?
00:23:30.115 --> 00:23:33.071
What's our first, month or first week retention rate?
00:23:33.415 --> 00:23:45.715
All these different types of metrics and be able to take them and then make recommendations to the HR teams around, Hey, we think that we need to make changes to the way that we're operating as a business on the ground in these different areas.
00:23:45.925 --> 00:23:52.945
That partnership where operations is willing to take on some of this feedback and make changes can actually drive a lot better quality.
00:23:52.945 --> 00:23:54.894
And, you know, again, it, it's not easy.
00:23:54.894 --> 00:23:59.755
It's not an easy ask that I'm, saying here of asking the operations teams to make changes.
00:24:00.115 --> 00:24:31.455
But if you can do it with data and you can do it in a pilot based setting and you can partner with HR who knows the business fundamentally and is probably pretty close to the actual labour and the has relationships with them, a lot of the times that feedback can help create, like one plus one in this scenario a lot of times can equal three and you can get much better output from your labour force and much better retention rates just by kind of listening to what's actually happening on the street as opposed to just saying, well, we have these 2, 3, 2 shift patterns, like two on three off, two on three off, or whatever that might be.
00:24:31.455 --> 00:24:40.485
And like, no one wants to work that, like no one wants to, to have a different shift pattern every single day, but they do it because it's employment.
00:24:40.485 --> 00:24:56.415
But if you could lower your turnover rate by 10% and increase the quality of your workers and the ramp up time significantly, all of a sudden now we're not talking about a HR good feeling decision.
00:24:56.715 --> 00:25:00.134
You're actually talking about a cost and a revenue based decision.
00:25:00.644 --> 00:25:06.644
There is a fundamental bottom line impact if you can reduce your turnover rate by 10% and you can drive more quality.
00:25:06.975 --> 00:25:13.875
I mean, we have conversations with clients where it's like, okay, how can we get an extra four days on average?
00:25:13.875 --> 00:25:26.055
Like if you look at the entire cohort of workers, how can we get four more days of productivity before people turn over, like in, in a warehouse environment, that four days of productivity is astounding how much that impacts the bottom line.
00:25:26.144 --> 00:25:31.215
And just making some of these changes can actually achieve that and really have strong impacts.
00:25:31.215 --> 00:25:35.475
But you need to be able to partner and be willing to partner with the HR team around some of these changes.
00:25:36.104 --> 00:25:37.153
That's the bigger point.
00:25:37.514 --> 00:25:44.804
Retention, scheduling and throughput aren't separate HR metrics, they're operating economics.
00:25:45.630 --> 00:25:54.460
And over the next 3, 5, 10 years, how do you see workforce planning change for supply chain and manufacturing leaders?
00:25:55.451 --> 00:26:05.022
What I have found is that finance runs on Excel, warehouses, run on a WMS warehouse management system.
00:26:05.922 --> 00:26:13.302
HR runs on probably spreadsheets, probably an ATS system, probably some type of workforce management system, right?
00:26:13.302 --> 00:26:15.732
A another WMS, but a different type of WMS.
00:26:16.602 --> 00:26:18.342
And everyone is looking at their data.
00:26:19.192 --> 00:26:20.272
But that data's not integrated.
00:26:21.052 --> 00:26:29.572
And so because of that, everyone's making different decisions, and some of them are right, and some of them are wrong, and some of them are contradictory to each other.
00:26:30.502 --> 00:26:43.232
So where I think things are gonna change is as you start bringing AI into the equation, we're noticing a lot of our clients that are the most forward thinking are doing this, which is how can you connect all these systems to be able to give you, 'cause the data's there, right?
00:26:43.232 --> 00:26:45.849
But the, symphony of the data's not there, right?
00:26:45.849 --> 00:26:48.249
It's not all working in concert with each other.
00:26:48.429 --> 00:26:59.986
And so being able to take this data, integrate it, and then have the different correlations be brought to light, Hey, did you know that when you send workers home early, three days later, you're gonna have higher turnover?
00:26:59.986 --> 00:27:05.626
Well, sending workers home early and, and whatnot, that comes from your warehouse management system.
00:27:05.806 --> 00:27:08.865
Turnover rate comes from your workforce management system.
00:27:08.865 --> 00:27:10.066
So never the two shall meet.
00:27:10.066 --> 00:27:14.986
Well, now you have the opportunity to be able to pull that data out that allows you to make better decisions.
00:27:15.796 --> 00:27:21.826
In addition to that, I think that looking at your vendors and being able to share some of this information with them is really critical.
00:27:21.826 --> 00:27:25.916
So we have some clients where they would send us labour orders.
00:27:25.916 --> 00:27:29.333
Here's how many people we need, here's how many new people we need on these days.
00:27:29.336 --> 00:27:34.256
And you start to ask the questions and say, well, where, where is this information coming from?
00:27:34.256 --> 00:27:35.426
Oh, it's coming from this system.
00:27:35.426 --> 00:27:37.346
Well, how can we get access to that system?
00:27:37.406 --> 00:27:40.196
Well, it's proprietary, that's fine, but can you at least give us signals?
00:27:41.216 --> 00:27:46.376
Because if you can give us signals more quickly, then we can act more quickly and we can help you make better decisions.
00:27:47.115 --> 00:27:57.456
And so that's where I think taking all the data, putting it together, and using AI to be able to drive better visibility, more information, and better decision making, I think that's really where things are gonna start to evolve.
00:27:57.974 --> 00:28:01.874
The practical takeaway from all of that is to start small.
00:28:02.504 --> 00:28:16.844
Pick one variable demand site, connect the HR and operations data you already have, and test whether more flexible scheduling improves fulfilment, retention, and throughput.
00:28:17.534 --> 00:28:19.814
Then scale what works.
00:28:20.744 --> 00:28:22.839
And with that a left field question.
00:28:23.668 --> 00:28:35.393
If you could have any person or character, alive or dead, real or fictional as a champion for resilient staffing, who would it be and why?
00:28:36.896 --> 00:28:44.096
Ooh, I'm gonna go with for some reason this one came, was the first, the first one that came to my mind was Mother Theresa.
00:28:45.089 --> 00:28:46.139
and here is why.
00:28:46.199 --> 00:28:47.339
She was a great communicator.
00:28:47.339 --> 00:28:48.449
She was a great leader.
00:28:48.749 --> 00:28:58.739
I don't think she gets enough credit for that, but I think the big thing is that so often in staffing, the priority for staffing organisations is the client, right?
00:28:58.949 --> 00:29:01.559
Whatever the client wants, never say no to the client.
00:29:01.619 --> 00:29:08.159
If they wanna add a bunch of layers of complexity or steps to the process, staffing companies have a hard time saying, no.
00:29:09.569 --> 00:29:11.099
We fundamentally have flipped that.
00:29:11.559 --> 00:29:13.359
And the client obviously matters, right?
00:29:13.479 --> 00:29:17.679
They're the ones that pay the bills, but actually who matters most is the worker.
00:29:19.179 --> 00:29:33.939
If you can give the worker what they want, if you can allow them to have the type of work life they want, the type of job that they want in the location for the pay rate, with the skill that they want to be utilising, actually that's gonna make the client more happy.
00:29:34.749 --> 00:29:39.639
And so, I'd, I'd have Mother Theresa because she was always thinking about how to empower and, help people out.
00:29:39.639 --> 00:29:41.319
And that's really fundamentally what we do.
00:29:41.319 --> 00:29:43.479
Our motto is, we help people get jobs instantly.
00:29:43.479 --> 00:29:51.788
And so the more we can help people get better jobs more quickly, more easily, then you're gonna have more satisfaction on the client side as well.
00:29:52.748 --> 00:29:55.118
So you mentioned Indeed flex.com already.
00:29:55.358 --> 00:30:02.858
Apart from that, if people would like to know more about yourself or any things that we discussed on the podcast today, where would you have me direct them?
00:30:03.191 --> 00:30:05.091
Yeah, you can, you can look me up on LinkedIn.
00:30:05.091 --> 00:30:05.631
James Terry.
00:30:05.631 --> 00:30:06.651
I work for Indeed Flex.
00:30:06.681 --> 00:30:08.230
You can also send me an email, James@indeedflex.com.
00:30:09.471 --> 00:30:11.961
I'm always, I am a, a constant learner.
00:30:11.961 --> 00:30:14.391
I'm always thirsty to learn and know more.
00:30:14.391 --> 00:30:21.471
That's the reason that I love the job that I'm in, is because I have an opportunity to talk to businesses across many, many different sectors every day.
00:30:21.471 --> 00:30:24.321
And I am always asking a lot of questions to learn.
00:30:24.321 --> 00:30:29.206
So, I would love to learn about you and your business and also see if, we might be able to help you out.
00:30:30.978 --> 00:30:31.398
Fantastic.
00:30:31.668 --> 00:30:32.868
James, that's been really interesting.
00:30:32.928 --> 00:30:34.518
Thanks a million for coming on the podcast today.
00:30:34.625 --> 00:30:35.165
Thanks Tom.